Merge branch 'dev' into fix/sheets-write-not-working-picker

.github/workflows/claude.yml

@@ -44,12 +44,6 @@ jobs:
         with:
           fetch-depth: 1
 
-      - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.1
-        with:
-          large-packages: false  # slow
-          docker-images: false  # limited benefit
-
       # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
       - name: Set up Python
         uses: actions/setup-python@v5

.github/workflows/platform-frontend-ci.yml

@@ -12,10 +12,6 @@ on:
       - "autogpt_platform/frontend/**"
   merge_group:
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
   run:
     shell: bash

.github/workflows/platform-fullstack-ci.yml

@@ -12,10 +12,6 @@ on:
       - "autogpt_platform/**"
   merge_group:
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
   run:
     shell: bash

.github/workflows/repo-close-stale-issues.yml

@@ -11,7 +11,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v10
+      - uses: actions/stale@v9
         with:
           # operations-per-run: 5000
           stale-issue-message: >

.github/workflows/repo-pr-label.yml

@@ -61,6 +61,6 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@v6
+      - uses: actions/labeler@v5
         with:
           sync-labels: true

autogpt_platform/Makefile

@@ -1,4 +1,4 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
 
 # Run just Supabase + Redis + RabbitMQ
 start-core:
@@ -42,10 +42,7 @@ run-frontend:
 
 test-data:
 	cd backend && poetry run python test/test_data_creator.py
-
-load-store-agents:
-	cd backend && poetry run load-store-agents
-
+	
 help:
 	@echo "Usage: make <target>"
 	@echo "Targets:"
@@ -57,5 +54,4 @@ help:
 	@echo "  migrate - Run backend database migrations"
 	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
-	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  test-data - Run the test data creator"

autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py

@@ -57,9 +57,6 @@ class APIKeySmith:
 
     def hash_key(self, raw_key: str) -> tuple[str, str]:
         """Migrate a legacy hash to secure hash format."""
-        if not raw_key.startswith(self.PREFIX):
-            raise ValueError("Key without 'agpt_' prefix would fail validation")
-
         salt = self._generate_salt()
         hash = self._hash_key_with_salt(raw_key, salt)
         return hash, salt.hex()

autogpt_platform/autogpt_libs/autogpt_libs/auth/helpers.py

@@ -1,25 +1,29 @@
 from fastapi import FastAPI
+from fastapi.openapi.utils import get_openapi
 
 from .jwt_utils import bearer_jwt_auth
 
 
 def add_auth_responses_to_openapi(app: FastAPI) -> None:
     """
-    Patch a FastAPI instance's `openapi()` method to add 401 responses
+    Set up custom OpenAPI schema generation that adds 401 responses
     to all authenticated endpoints.
 
     This is needed when using HTTPBearer with auto_error=False to get proper
     401 responses instead of 403, but FastAPI only automatically adds security
     responses when auto_error=True.
     """
-    # Wrap current method to allow stacking OpenAPI schema modifiers like this
-    wrapped_openapi = app.openapi
 
     def custom_openapi():
         if app.openapi_schema:
             return app.openapi_schema
 
-        openapi_schema = wrapped_openapi()
+        openapi_schema = get_openapi(
+            title=app.title,
+            version=app.version,
+            description=app.description,
+            routes=app.routes,
+        )
 
         # Add 401 response to all endpoints that have security requirements
         for path, methods in openapi_schema["paths"].items():

autogpt_platform/backend/TESTING.md

@@ -108,7 +108,7 @@ import fastapi.testclient
 import pytest
 from pytest_snapshot.plugin import Snapshot
 
-from backend.api.features.myroute import router
+from backend.server.v2.myroute import router
 
 app = fastapi.FastAPI()
 app.include_router(router)
@@ -149,7 +149,7 @@ These provide the easiest way to set up authentication mocking in test modules:
 import fastapi
 import fastapi.testclient
 import pytest
-from backend.api.features.myroute import router
+from backend.server.v2.myroute import router
 
 app = fastapi.FastAPI()
 app.include_router(router)

autogpt_platform/backend/agents/StoreAgent_rows.csv

@@ -1,242 +0,0 @@
-listing_id,storeListingVersionId,slug,agent_name,agent_video,agent_image,featured,sub_heading,description,categories,useForOnboarding,is_available
-6e60a900-9d7d-490e-9af2-a194827ed632,d85882b8-633f-44ce-a315-c20a8c123d19,flux-ai-image-generator,Flux AI Image Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg""]",false,Transform ideas into breathtaking images,"Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!","[""creative""]",false,true
-f11fc6e9-6166-4676-ac5d-f07127b270c1,c775f60d-b99f-418b-8fe0-53172258c3ce,youtube-transcription-scraper,YouTube Transcription Scraper,https://youtu.be/H8S3pU68lGE,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg""]",false,Fetch the transcriptions from the most popular YouTube videos in your chosen topic,"Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content.","[""writing""]",false,true
-17908889-b599-4010-8e4f-bed19b8f3446,6e16e65a-ad34-4108-b4fd-4a23fced5ea2,business-ownerceo-finder,Decision Maker Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg""]",false,Contact CEOs today,"Find the key decision-makers you need, fast.
-
-This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses you’re looking for and where, and it will:
-
-* Search the area and gather public information
-* Return names, roles, and contact details when available
-* Provide smart Google search suggestions if details aren’t found
-
-Perfect for:
-
-* B2B sales teams seeking verified leads
-* Recruiters sourcing local talent
-* Researchers looking to connect with business leaders
-
-Save hours of manual searching and get straight to the people who matter most.","[""business""]",true,true
-72beca1d-45ea-4403-a7ce-e2af168ee428,415b7352-0dc6-4214-9d87-0ad3751b711d,smart-meeting-brief,Smart Meeting Prep,https://youtu.be/9ydZR2hkxaY,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png""]",true,Business meeting briefings delivered daily,"Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls.
-
-How It Works
-1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day.
-2. It reviews recent email threads with each participant to surface key relationship history and communication context.
-3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data.
-4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points.","[""personal""]",true,true
-9fa5697a-617b-4fae-aea0-7dbbed279976,b8ceb480-a7a2-4c90-8513-181a49f7071f,automated-support-ai,Automated Support Agent,https://youtu.be/nBMfu_5sgDA,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png""]",true,Automate up to 80 percent of inbound support emails,"Overview:
-Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution.
-
-How it Works:
-New support emails are routed to the agent.
-The agent checks internal documentation for answers.
-It measures confidence in the answer found and either replies directly or escalates to a human.
-
-Business Value:
-Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times.","[""business""]",false,true
-2bdac92b-a12c-4131-bb46-0e3b89f61413,31daf49d-31d3-476b-aa4c-099abc59b458,unspirational-poster-maker,Unspirational Poster Maker,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg""]",false,Because adulting is hard,"This witty AI agent generates hilariously relatable ""motivational"" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to ""get it together."" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.","[""creative""]",false,true
-9adf005e-2854-4cc7-98cf-f7103b92a7b7,a03b0d8c-4751-43d6-a54e-c3b7856ba4e3,ai-shortform-video-generator-create-viral-ready-content,AI Video Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png""]",false,Create Viral-Ready Shorts Content in Seconds,"OVERVIEW
-Transform any trending headline or broad topic into a polished, vertical short-form video in a single run.  
-The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags.
-
-HOW IT WORKS
-1. Input a topic or an exact news headline.  
-2. The agent fetches live search results and selects the most engaging related story.  
-3. Key facts are summarised into concise research notes.  
-4. Claude writes a 30–35 second script with visual cues, a three-second hook, tension loops, and a call-to-action.  
-5. GPT-4o generates an eye-catching title and one or two discoverability hashtags.  
-6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”).  
-   – All voice, style and resolution settings can be adjusted in the Builder before you press ""Run"".  
-7. Output delivered: Title, Script, Hashtags, Video URL.
-
-KEY USE CASES
-- Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”).  
-- Real-time newsjacking with a specific breaking headline.  
-- Product-launch spotlights and quick event recaps while interest is high.  
-
-BUSINESS VALUE
-- One-click speed: from idea to finished video in minutes.  
-- Consistent brand look: Revid presets keep voice, style and aspect ratio on spec.  
-- No-code workflow: marketers create social video without design or development queues.  
-- Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys.  
-  Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once.
-
-IMPORTANT NOTES
-- The agent outputs exactly one video per execution. Run it again for additional shorts.  
-- Video rendering time varies; AI-generated footage may take several minutes.","[""writing""]",false,true
-864e48ef-fee5-42c1-b6a4-2ae139db9fc1,55d40473-0f31-4ada-9e40-d3a7139fcbd4,automated-blog-writer,Automated SEO Blog Writer,https://youtu.be/nKcDCbDVobs,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg""]",true,"Automate research, writing, and publishing for high-ranking blog posts","Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility.
-
-How it works:
-
-1. Share your pitch, website, and values.
-2. The agent studies your site and uncovers proven SEO opportunities.
-3. It spends two hours researching and drafting each post.
-4. You set the cadence—publishing runs on autopilot.
-
-Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most.
-
-Use cases:
-• Founders: Keep your blog active with no time drain.
-• Agencies: Deliver scalable SEO content for clients.
-• Strategists: Automate execution, focus on strategy.
-• Marketers: Drive steady organic growth.
-• Local businesses: Capture nearby search traffic.","[""writing""]",false,true
-6046f42e-eb84-406f-bae0-8e052064a4fa,a548e507-09a7-4b30-909c-f63fcda10fff,lead-finder-local-businesses,Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp""]",false,Auto-Prospect Like a Pro,"Turbo-charge your local lead generation with the AutoGPT Marketplace’s top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching.
-
-**WHAT IT DOES**
-• Searches Google Maps via the official API (no scraping)
-• Prompts like “dentists in Chicago” or “coffee shops near me”
-• Returns: Name, Website, Rating, Reviews, **Phone & Address**
-• Exports instantly to your CRM, sheet, or outreach workflow
-
-**WHY YOU’LL LOVE IT**
-✓ Hyper-targeted leads in minutes
-✓ Unlimited searches & locations
-✓ Zero CAPTCHAs or IP blocks
-✓ Works on AutoGPT Cloud or self-hosted (with your API key)
-✓ Cut prospecting time by 90%
-
-**PERFECT FOR**
-— Marketers & PPC agencies
-— SEO consultants & designers
-— SaaS founders & sales teams
-
-Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit.
-
-→ Click *Add to Library* and own your market today.","[""business""]",true,true
-f623c862-24e9-44fc-8ce8-d8282bb51ad2,eafa21d3-bf14-4f63-a97f-a5ee41df83b3,linkedin-post-generator,LinkedIn Post Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png""]",false,Auto‑craft LinkedIn gold,"Create research‑driven, high‑impact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed.  
-
-FEATURES  
-• Automated YouTube research – discovers and analyses top‑ranked videos so you don’t have to  
-• AI‑curated synthesis – combines multiple transcripts into one authoritative narrative  
-• Full creative control – adjust style, tone, objective, opinion, clarity, target word count and number of videos  
-• LinkedIn‑optimised output – hook, 2‑3 key points, CTA, strategic line breaks, 3‑5 hashtags, no markdown  
-• One‑click publish – returns a ready‑to‑post text block (≤1 300 characters)  
-
-HOW IT WORKS  
-1. Enter a topic and your preferred writing parameters.  
-2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs.  
-3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet.  
-4. The model writes a concise, engaging post designed for maximum LinkedIn engagement.  
-
-USE CASES  
-• Thought‑leadership updates backed by fresh video research  
-• Rapid industry summaries after major events, webinars, or conferences  
-• Consistent LinkedIn content for busy founders, marketers, and creators  
-
-WHY YOU’LL LOVE IT  
-Save hours of manual research, avoid surface‑level hot‑takes, and publish posts that showcase real expertise—without the heavy lift.","[""writing""]",true,true
-7d4120ad-b6b3-4419-8bdb-7dd7d350ef32,e7bb29a1-23c7-4fee-aa3b-5426174b8c52,youtube-to-linkedin-post-converter,YouTube to LinkedIn Post Converter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png""]",false,Transform Your YouTube Videos into Engaging LinkedIn Posts with AI,"WHAT IT DOES: 
-This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn.
-
-HOW IT WORKS:
-- You provide the URL to the YouTube video (required)
-- You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.)
-- You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.)
-- The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model
-- The models extract key insights, memorable quotes, and the main points from the video
-- You’ll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement
-
-INPUTS:
-- Source YouTube Video – Provide the URL to the YouTube video
-- Structure – Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.)
-- Content – Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.)
-- Tone – Select the tone for the post (e.g., Conversational, Inspirational, etc.)
-
-OUTPUT:
-- LinkedIn Post – A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences
-
-Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding.","[""writing""]",false,true
-c61d6a83-ea48-4df8-b447-3da2d9fe5814,00fdd42c-a14c-4d19-a567-65374ea0e87f,personalized-morning-coffee-newsletter,Personal Newsletter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png""]",false,Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood.,"This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained.
-
-
-How It Works
-1. Enter your favorite topics, industries, or areas of interest.
-2. Choose your tone—professional, casual, or humorous.
-3. Set your preferred delivery cadence: daily or weekly.
-4. The agent scans top sources and compiles 3–5 engaging stories, insights, and fun facts into a conversational newsletter.
-
-Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day.
-
-
-Use Cases
-• Executives: Get a daily digest of market updates and leadership insights.
-• Marketers: Receive curated creative trends and campaign inspiration.
-• Entrepreneurs: Stay updated on your industry without information overload.","[""research""]",true,true
-e2e49cfc-4a39-4d62-a6b3-c095f6d025ff,fc2c9976-0962-4625-a27b-d316573a9e7f,email-address-finder,Email Scout - Contact Finder Assistant,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg""]",false,Find contact details from name and location using AI search,"Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information.
-
-Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like ""Tim Cook, USA"" or ""Sarah Smith, London"" and let the AI assistant do the work of finding potential contact details.
-
-Key Features:
-- Quick search from just name and location
-- Scans multiple public sources
-- Automated AI-powered search process
-- Easy to use with simple inputs
-
-Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact.
-
-Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible.","[""""]",false,true
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-
-FEATURES
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-• CONTENT ANALYSIS  
-  Extracts key points from the video while preserving your message and intent.
-
-• CUSTOMIZABLE OUTPUT  
-  Select a tone that fits your audience: casual, professional, educational, or formal.
-
-• SEO OPTIMIZATION  
-  Automatically creates engaging titles and structured subheadings for better search visibility.
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-• USER-FRIENDLY  
-  Repurpose your videos into written content to expand your reach and improve accessibility.
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-- Effortless Function Generation: convert natural‑language specs into complete functions.  
-- Customizable Inputs: define the parameters that matter to you.  
-- Versatile Use Cases: simulate data, automate tasks, prototype ideas.  
-- Seamless Integration: add the generated function directly to your codebase.  
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-[  
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autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json

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-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
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-          "claude-haiku-4-5-20251001": "anthropic",
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-          "llama3": "ollama",
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-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
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-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
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-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
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-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
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-    },
-    "required": [
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-    "title": "AIFunctionCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json

@@ -1,403 +0,0 @@
-{
-  "id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-  "version": 12,
-  "is_active": true,
-  "name": "Flux AI Image Generator",
-  "description": "Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "7482c59d-725f-4686-82b9-0dfdc4e92316",
-      "block_id": "cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
-      "input_default": {
-        "text": "Press the \"Advanced\" toggle and input your replicate API key.\n\nYou can get one here:\nhttps://replicate.com/account/api-tokens\n"
-      },
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-      "webhook": null
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-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Generated Image"
-      },
-      "metadata": {
-        "position": {
-          "x": 1453.6844137728922,
-          "y": 963.2466395125115
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-          "sink_name": "value",
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-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Image Subject",
-        "value": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks.",
-        "description": "The subject of the image"
-      },
-      "metadata": {
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-      "input_links": [],
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-          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "block_id": "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-      "input_default": {
-        "prompt": "dog",
-        "output_format": "png",
-        "replicate_model_name": "Flux Pro 1.1"
-      },
-      "metadata": {
-        "position": {
-          "x": 873.0119949791526,
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-      "input_links": [
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-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
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-      "id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o-mini",
-        "prompt": "Generate an incredibly detailed, photorealistic image prompt about {{TOPIC}}, describing the camera it's taken with and prompting the diffusion model to use all the best quality techniques.\n\nOutput only the prompt with no additional commentary.",
-        "prompt_values": {}
-      },
-      "metadata": {
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-          "y": 962.8382498113764
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-      "input_links": [
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-          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
-        }
-      ],
-      "output_links": [
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-          "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
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-      "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
-      "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-      "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_TOPIC",
-      "is_static": true
-    },
-    {
-      "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-      "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
-      "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T18:46:11.492Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Image Subject": {
-        "advanced": false,
-        "secret": false,
-        "title": "Image Subject",
-        "description": "The subject of the image",
-        "default": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks."
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Generated Image": {
-        "advanced": false,
-        "secret": false,
-        "title": "Generated Image"
-      }
-    },
-    "required": [
-      "Generated Image"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "replicate_api_key_credentials": {
-        "credentials_provider": [
-          "replicate"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "replicate",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.REPLICATE: 'replicate'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
-          "google/gemini-2.0-flash-lite-001": "open_router",
-          "google/gemini-2.5-flash": "open_router",
-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
-          "google/gemini-2.5-pro-preview-03-25": "open_router",
-          "google/gemini-3-pro-preview": "open_router",
-          "gpt-3.5-turbo": "openai",
-          "gpt-4-turbo": "openai",
-          "gpt-4.1-2025-04-14": "openai",
-          "gpt-4.1-mini-2025-04-14": "openai",
-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "gpt-4o-mini"
-        ]
-      }
-    },
-    "required": [
-      "replicate_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "FluxAIImageGeneratorCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json

@@ -1,505 +0,0 @@
-{
-  "id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-  "version": 12,
-  "is_active": true,
-  "name": "AI Webpage Copy Improver",
-  "description": "Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver.",
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autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json

@@ -1,615 +0,0 @@
-{
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-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
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-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "claude-sonnet-4-5-20250929"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "anthropic_api_key_credentials"
-    ],
-    "title": "EmailAddressFinderCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/backend/api/external/middleware.py

@@ -1,107 +0,0 @@
-from fastapi import HTTPException, Security, status
-from fastapi.security import APIKeyHeader, HTTPAuthorizationCredentials, HTTPBearer
-from prisma.enums import APIKeyPermission
-
-from backend.data.auth.api_key import APIKeyInfo, validate_api_key
-from backend.data.auth.base import APIAuthorizationInfo
-from backend.data.auth.oauth import (
-    InvalidClientError,
-    InvalidTokenError,
-    OAuthAccessTokenInfo,
-    validate_access_token,
-)
-
-api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
-bearer_auth = HTTPBearer(auto_error=False)
-
-
-async def require_api_key(api_key: str | None = Security(api_key_header)) -> APIKeyInfo:
-    """Middleware for API key authentication only"""
-    if api_key is None:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing API key"
-        )
-
-    api_key_obj = await validate_api_key(api_key)
-
-    if not api_key_obj:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key"
-        )
-
-    return api_key_obj
-
-
-async def require_access_token(
-    bearer: HTTPAuthorizationCredentials | None = Security(bearer_auth),
-) -> OAuthAccessTokenInfo:
-    """Middleware for OAuth access token authentication only"""
-    if bearer is None:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Missing Authorization header",
-        )
-
-    try:
-        token_info, _ = await validate_access_token(bearer.credentials)
-    except (InvalidClientError, InvalidTokenError) as e:
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=str(e))
-
-    return token_info
-
-
-async def require_auth(
-    api_key: str | None = Security(api_key_header),
-    bearer: HTTPAuthorizationCredentials | None = Security(bearer_auth),
-) -> APIAuthorizationInfo:
-    """
-    Unified authentication middleware supporting both API keys and OAuth tokens.
-
-    Supports two authentication methods, which are checked in order:
-    1. X-API-Key header (existing API key authentication)
-    2. Authorization: Bearer <token> header (OAuth access token)
-
-    Returns:
-        APIAuthorizationInfo: base class of both APIKeyInfo and OAuthAccessTokenInfo.
-    """
-    # Try API key first
-    if api_key is not None:
-        api_key_info = await validate_api_key(api_key)
-        if api_key_info:
-            return api_key_info
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key"
-        )
-
-    # Try OAuth bearer token
-    if bearer is not None:
-        try:
-            token_info, _ = await validate_access_token(bearer.credentials)
-            return token_info
-        except (InvalidClientError, InvalidTokenError) as e:
-            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=str(e))
-
-    # No credentials provided
-    raise HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Missing authentication. Provide API key or access token.",
-    )
-
-
-def require_permission(permission: APIKeyPermission):
-    """
-    Dependency function for checking specific permissions
-    (works with API keys and OAuth tokens)
-    """
-
-    async def check_permission(
-        auth: APIAuthorizationInfo = Security(require_auth),
-    ) -> APIAuthorizationInfo:
-        if permission not in auth.scopes:
-            raise HTTPException(
-                status_code=status.HTTP_403_FORBIDDEN,
-                detail=f"Missing required permission: {permission.value}",
-            )
-        return auth
-
-    return check_permission

autogpt_platform/backend/backend/api/external/v1/integrations.py

@@ -1,655 +0,0 @@
-"""
-External API endpoints for integrations and credentials.
-
-This module provides endpoints for external applications (like Autopilot) to:
-- Initiate OAuth flows with custom callback URLs
-- Complete OAuth flows by exchanging authorization codes
-- Create API key, user/password, and host-scoped credentials
-- List and manage user credentials
-"""
-
-import logging
-from typing import TYPE_CHECKING, Annotated, Any, Literal, Optional, Union
-from urllib.parse import urlparse
-
-from fastapi import APIRouter, Body, HTTPException, Path, Security, status
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel, Field, SecretStr
-
-from backend.api.external.middleware import require_permission
-from backend.api.features.integrations.models import get_all_provider_names
-from backend.data.auth.base import APIAuthorizationInfo
-from backend.data.model import (
-    APIKeyCredentials,
-    Credentials,
-    CredentialsType,
-    HostScopedCredentials,
-    OAuth2Credentials,
-    UserPasswordCredentials,
-)
-from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.oauth import CREDENTIALS_BY_PROVIDER, HANDLERS_BY_NAME
-from backend.integrations.providers import ProviderName
-from backend.util.settings import Settings
-
-if TYPE_CHECKING:
-    from backend.integrations.oauth import BaseOAuthHandler
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-creds_manager = IntegrationCredentialsManager()
-
-integrations_router = APIRouter(prefix="/integrations", tags=["integrations"])
-
-
-# ==================== Request/Response Models ==================== #
-
-
-class OAuthInitiateRequest(BaseModel):
-    """Request model for initiating an OAuth flow."""
-
-    callback_url: str = Field(
-        ..., description="The external app's callback URL for OAuth redirect"
-    )
-    scopes: list[str] = Field(
-        default_factory=list, description="OAuth scopes to request"
-    )
-    state_metadata: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Arbitrary metadata to echo back on completion",
-    )
-
-
-class OAuthInitiateResponse(BaseModel):
-    """Response model for OAuth initiation."""
-
-    login_url: str = Field(..., description="URL to redirect user for OAuth consent")
-    state_token: str = Field(..., description="State token for CSRF protection")
-    expires_at: int = Field(
-        ..., description="Unix timestamp when the state token expires"
-    )
-
-
-class OAuthCompleteRequest(BaseModel):
-    """Request model for completing an OAuth flow."""
-
-    code: str = Field(..., description="Authorization code from OAuth provider")
-    state_token: str = Field(..., description="State token from initiate request")
-
-
-class OAuthCompleteResponse(BaseModel):
-    """Response model for OAuth completion."""
-
-    credentials_id: str = Field(..., description="ID of the stored credentials")
-    provider: str = Field(..., description="Provider name")
-    type: str = Field(..., description="Credential type (oauth2)")
-    title: Optional[str] = Field(None, description="Credential title")
-    scopes: list[str] = Field(default_factory=list, description="Granted scopes")
-    username: Optional[str] = Field(None, description="Username from provider")
-    state_metadata: dict[str, Any] = Field(
-        default_factory=dict, description="Echoed metadata from initiate request"
-    )
-
-
-class CredentialSummary(BaseModel):
-    """Summary of a credential without sensitive data."""
-
-    id: str
-    provider: str
-    type: CredentialsType
-    title: Optional[str] = None
-    scopes: Optional[list[str]] = None
-    username: Optional[str] = None
-    host: Optional[str] = None
-
-
-class ProviderInfo(BaseModel):
-    """Information about an integration provider."""
-
-    name: str
-    supports_oauth: bool = False
-    supports_api_key: bool = False
-    supports_user_password: bool = False
-    supports_host_scoped: bool = False
-    default_scopes: list[str] = Field(default_factory=list)
-
-
-# ==================== Credential Creation Models ==================== #
-
-
-class CreateAPIKeyCredentialRequest(BaseModel):
-    """Request model for creating API key credentials."""
-
-    type: Literal["api_key"] = "api_key"
-    api_key: str = Field(..., description="The API key")
-    title: str = Field(..., description="A name for this credential")
-    expires_at: Optional[int] = Field(
-        None, description="Unix timestamp when the API key expires"
-    )
-
-
-class CreateUserPasswordCredentialRequest(BaseModel):
-    """Request model for creating username/password credentials."""
-
-    type: Literal["user_password"] = "user_password"
-    username: str = Field(..., description="Username")
-    password: str = Field(..., description="Password")
-    title: str = Field(..., description="A name for this credential")
-
-
-class CreateHostScopedCredentialRequest(BaseModel):
-    """Request model for creating host-scoped credentials."""
-
-    type: Literal["host_scoped"] = "host_scoped"
-    host: str = Field(..., description="Host/domain pattern to match")
-    headers: dict[str, str] = Field(..., description="Headers to include in requests")
-    title: str = Field(..., description="A name for this credential")
-
-
-# Union type for credential creation
-CreateCredentialRequest = Annotated[
-    CreateAPIKeyCredentialRequest
-    | CreateUserPasswordCredentialRequest
-    | CreateHostScopedCredentialRequest,
-    Field(discriminator="type"),
-]
-
-
-class CreateCredentialResponse(BaseModel):
-    """Response model for credential creation."""
-
-    id: str
-    provider: str
-    type: CredentialsType
-    title: Optional[str] = None
-
-
-# ==================== Helper Functions ==================== #
-
-
-def validate_callback_url(callback_url: str) -> bool:
-    """Validate that the callback URL is from an allowed origin."""
-    allowed_origins = settings.config.external_oauth_callback_origins
-
-    try:
-        parsed = urlparse(callback_url)
-        callback_origin = f"{parsed.scheme}://{parsed.netloc}"
-
-        for allowed in allowed_origins:
-            # Simple origin matching
-            if callback_origin == allowed:
-                return True
-
-        # Allow localhost with any port in development (proper hostname check)
-        if parsed.hostname == "localhost":
-            for allowed in allowed_origins:
-                allowed_parsed = urlparse(allowed)
-                if allowed_parsed.hostname == "localhost":
-                    return True
-
-        return False
-    except Exception:
-        return False
-
-
-def _get_oauth_handler_for_external(
-    provider_name: str, redirect_uri: str
-) -> "BaseOAuthHandler":
-    """Get an OAuth handler configured with an external redirect URI."""
-    # Ensure blocks are loaded so SDK providers are available
-    try:
-        from backend.blocks import load_all_blocks
-
-        load_all_blocks()
-    except Exception as e:
-        logger.warning(f"Failed to load blocks: {e}")
-
-    if provider_name not in HANDLERS_BY_NAME:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Provider '{provider_name}' does not support OAuth",
-        )
-
-    # Check if this provider has custom OAuth credentials
-    oauth_credentials = CREDENTIALS_BY_PROVIDER.get(provider_name)
-
-    if oauth_credentials and not oauth_credentials.use_secrets:
-        import os
-
-        client_id = (
-            os.getenv(oauth_credentials.client_id_env_var)
-            if oauth_credentials.client_id_env_var
-            else None
-        )
-        client_secret = (
-            os.getenv(oauth_credentials.client_secret_env_var)
-            if oauth_credentials.client_secret_env_var
-            else None
-        )
-    else:
-        client_id = getattr(settings.secrets, f"{provider_name}_client_id", None)
-        client_secret = getattr(
-            settings.secrets, f"{provider_name}_client_secret", None
-        )
-
-    if not (client_id and client_secret):
-        logger.error(f"Attempt to use unconfigured {provider_name} OAuth integration")
-        raise HTTPException(
-            status_code=status.HTTP_501_NOT_IMPLEMENTED,
-            detail={
-                "message": f"Integration with provider '{provider_name}' is not configured.",
-                "hint": "Set client ID and secret in the application's deployment environment",
-            },
-        )
-
-    handler_class = HANDLERS_BY_NAME[provider_name]
-    return handler_class(
-        client_id=client_id,
-        client_secret=client_secret,
-        redirect_uri=redirect_uri,
-    )
-
-
-# ==================== Endpoints ==================== #
-
-
-@integrations_router.get("/providers", response_model=list[ProviderInfo])
-async def list_providers(
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[ProviderInfo]:
-    """
-    List all available integration providers.
-
-    Returns a list of all providers with their supported credential types.
-    Most providers support API key credentials, and some also support OAuth.
-    """
-    # Ensure blocks are loaded
-    try:
-        from backend.blocks import load_all_blocks
-
-        load_all_blocks()
-    except Exception as e:
-        logger.warning(f"Failed to load blocks: {e}")
-
-    from backend.sdk.registry import AutoRegistry
-
-    providers = []
-    for name in get_all_provider_names():
-        supports_oauth = name in HANDLERS_BY_NAME
-        handler_class = HANDLERS_BY_NAME.get(name)
-        default_scopes = (
-            getattr(handler_class, "DEFAULT_SCOPES", []) if handler_class else []
-        )
-
-        # Check if provider has specific auth types from SDK registration
-        sdk_provider = AutoRegistry.get_provider(name)
-        if sdk_provider and sdk_provider.supported_auth_types:
-            supports_api_key = "api_key" in sdk_provider.supported_auth_types
-            supports_user_password = (
-                "user_password" in sdk_provider.supported_auth_types
-            )
-            supports_host_scoped = "host_scoped" in sdk_provider.supported_auth_types
-        else:
-            # Fallback for legacy providers
-            supports_api_key = True  # All providers can accept API keys
-            supports_user_password = name in ("smtp",)
-            supports_host_scoped = name == "http"
-
-        providers.append(
-            ProviderInfo(
-                name=name,
-                supports_oauth=supports_oauth,
-                supports_api_key=supports_api_key,
-                supports_user_password=supports_user_password,
-                supports_host_scoped=supports_host_scoped,
-                default_scopes=default_scopes,
-            )
-        )
-
-    return providers
-
-
-@integrations_router.post(
-    "/{provider}/oauth/initiate",
-    response_model=OAuthInitiateResponse,
-    summary="Initiate OAuth flow",
-)
-async def initiate_oauth(
-    provider: Annotated[str, Path(title="The OAuth provider")],
-    request: OAuthInitiateRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> OAuthInitiateResponse:
-    """
-    Initiate an OAuth flow for an external application.
-
-    This endpoint allows external apps to start an OAuth flow with a custom
-    callback URL. The callback URL must be from an allowed origin configured
-    in the platform settings.
-
-    Returns a login URL to redirect the user to, along with a state token
-    for CSRF protection.
-    """
-    # Validate callback URL
-    if not validate_callback_url(request.callback_url):
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                f"Callback URL origin is not allowed. "
-                f"Allowed origins: {settings.config.external_oauth_callback_origins}",
-            ),
-        )
-
-    # Validate provider
-    try:
-        provider_name = ProviderName(provider)
-    except ValueError:
-        # Check if it's a dynamically registered provider
-        if provider not in HANDLERS_BY_NAME:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"Provider '{provider}' not found",
-            )
-        provider_name = provider
-
-    # Get OAuth handler with external callback URL
-    handler = _get_oauth_handler_for_external(
-        provider if isinstance(provider_name, str) else provider_name.value,
-        request.callback_url,
-    )
-
-    # Store state token with external flow metadata
-    # Note: initiated_by_api_key_id is only available for API key auth, not OAuth
-    api_key_id = getattr(auth, "id", None) if auth.type == "api_key" else None
-    state_token, code_challenge = await creds_manager.store.store_state_token(
-        user_id=auth.user_id,
-        provider=provider if isinstance(provider_name, str) else provider_name.value,
-        scopes=request.scopes,
-        callback_url=request.callback_url,
-        state_metadata=request.state_metadata,
-        initiated_by_api_key_id=api_key_id,
-    )
-
-    # Build login URL
-    login_url = handler.get_login_url(
-        request.scopes, state_token, code_challenge=code_challenge
-    )
-
-    # Calculate expiration (10 minutes from now)
-    from datetime import datetime, timedelta, timezone
-
-    expires_at = int((datetime.now(timezone.utc) + timedelta(minutes=10)).timestamp())
-
-    return OAuthInitiateResponse(
-        login_url=login_url,
-        state_token=state_token,
-        expires_at=expires_at,
-    )
-
-
-@integrations_router.post(
-    "/{provider}/oauth/complete",
-    response_model=OAuthCompleteResponse,
-    summary="Complete OAuth flow",
-)
-async def complete_oauth(
-    provider: Annotated[str, Path(title="The OAuth provider")],
-    request: OAuthCompleteRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> OAuthCompleteResponse:
-    """
-    Complete an OAuth flow by exchanging the authorization code for tokens.
-
-    This endpoint should be called after the user has authorized the application
-    and been redirected back to the external app's callback URL with an
-    authorization code.
-    """
-    # Verify state token
-    valid_state = await creds_manager.store.verify_state_token(
-        auth.user_id, request.state_token, provider
-    )
-
-    if not valid_state:
-        logger.warning(f"Invalid or expired state token for provider {provider}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Invalid or expired state token",
-        )
-
-    # Verify this is an external flow (callback_url must be set)
-    if not valid_state.callback_url:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="State token was not created for external OAuth flow",
-        )
-
-    # Get OAuth handler with the original callback URL
-    handler = _get_oauth_handler_for_external(provider, valid_state.callback_url)
-
-    try:
-        scopes = valid_state.scopes
-        scopes = handler.handle_default_scopes(scopes)
-
-        credentials = await handler.exchange_code_for_tokens(
-            request.code, scopes, valid_state.code_verifier
-        )
-
-        # Handle Linear's space-separated scopes
-        if len(credentials.scopes) == 1 and " " in credentials.scopes[0]:
-            credentials.scopes = credentials.scopes[0].split(" ")
-
-        # Check scope mismatch
-        if not set(scopes).issubset(set(credentials.scopes)):
-            logger.warning(
-                f"Granted scopes {credentials.scopes} for provider {provider} "
-                f"do not include all requested scopes {scopes}"
-            )
-
-    except Exception as e:
-        logger.error(f"OAuth2 Code->Token exchange failed for provider {provider}: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"OAuth2 callback failed to exchange code for tokens: {str(e)}",
-        )
-
-    # Store credentials
-    await creds_manager.create(auth.user_id, credentials)
-
-    logger.info(f"Successfully completed external OAuth for provider {provider}")
-
-    return OAuthCompleteResponse(
-        credentials_id=credentials.id,
-        provider=credentials.provider,
-        type=credentials.type,
-        title=credentials.title,
-        scopes=credentials.scopes,
-        username=credentials.username,
-        state_metadata=valid_state.state_metadata,
-    )
-
-
-@integrations_router.get("/credentials", response_model=list[CredentialSummary])
-async def list_credentials(
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[CredentialSummary]:
-    """
-    List all credentials for the authenticated user.
-
-    Returns metadata about each credential without exposing sensitive tokens.
-    """
-    credentials = await creds_manager.store.get_all_creds(auth.user_id)
-    return [
-        CredentialSummary(
-            id=cred.id,
-            provider=cred.provider,
-            type=cred.type,
-            title=cred.title,
-            scopes=cred.scopes if isinstance(cred, OAuth2Credentials) else None,
-            username=cred.username if isinstance(cred, OAuth2Credentials) else None,
-            host=cred.host if isinstance(cred, HostScopedCredentials) else None,
-        )
-        for cred in credentials
-    ]
-
-
-@integrations_router.get(
-    "/{provider}/credentials", response_model=list[CredentialSummary]
-)
-async def list_credentials_by_provider(
-    provider: Annotated[str, Path(title="The provider to list credentials for")],
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.READ_INTEGRATIONS)
-    ),
-) -> list[CredentialSummary]:
-    """
-    List credentials for a specific provider.
-    """
-    credentials = await creds_manager.store.get_creds_by_provider(
-        auth.user_id, provider
-    )
-    return [
-        CredentialSummary(
-            id=cred.id,
-            provider=cred.provider,
-            type=cred.type,
-            title=cred.title,
-            scopes=cred.scopes if isinstance(cred, OAuth2Credentials) else None,
-            username=cred.username if isinstance(cred, OAuth2Credentials) else None,
-            host=cred.host if isinstance(cred, HostScopedCredentials) else None,
-        )
-        for cred in credentials
-    ]
-
-
-@integrations_router.post(
-    "/{provider}/credentials",
-    response_model=CreateCredentialResponse,
-    status_code=status.HTTP_201_CREATED,
-    summary="Create credentials",
-)
-async def create_credential(
-    provider: Annotated[str, Path(title="The provider to create credentials for")],
-    request: Union[
-        CreateAPIKeyCredentialRequest,
-        CreateUserPasswordCredentialRequest,
-        CreateHostScopedCredentialRequest,
-    ] = Body(..., discriminator="type"),
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.MANAGE_INTEGRATIONS)
-    ),
-) -> CreateCredentialResponse:
-    """
-    Create non-OAuth credentials for a provider.
-
-    Supports creating:
-    - API key credentials (type: "api_key")
-    - Username/password credentials (type: "user_password")
-    - Host-scoped credentials (type: "host_scoped")
-
-    For OAuth credentials, use the OAuth initiate/complete flow instead.
-    """
-    # Validate provider exists
-    all_providers = get_all_provider_names()
-    if provider not in all_providers:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Provider '{provider}' not found",
-        )
-
-    # Create the appropriate credential type
-    credentials: Credentials
-    if request.type == "api_key":
-        credentials = APIKeyCredentials(
-            provider=provider,
-            api_key=SecretStr(request.api_key),
-            title=request.title,
-            expires_at=request.expires_at,
-        )
-    elif request.type == "user_password":
-        credentials = UserPasswordCredentials(
-            provider=provider,
-            username=SecretStr(request.username),
-            password=SecretStr(request.password),
-            title=request.title,
-        )
-    elif request.type == "host_scoped":
-        # Convert string headers to SecretStr
-        secret_headers = {k: SecretStr(v) for k, v in request.headers.items()}
-        credentials = HostScopedCredentials(
-            provider=provider,
-            host=request.host,
-            headers=secret_headers,
-            title=request.title,
-        )
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Unsupported credential type: {request.type}",
-        )
-
-    # Store credentials
-    try:
-        await creds_manager.create(auth.user_id, credentials)
-    except Exception as e:
-        logger.error(f"Failed to store credentials: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Failed to store credentials: {str(e)}",
-        )
-
-    logger.info(f"Created {request.type} credentials for provider {provider}")
-
-    return CreateCredentialResponse(
-        id=credentials.id,
-        provider=provider,
-        type=credentials.type,
-        title=credentials.title,
-    )
-
-
-class DeleteCredentialResponse(BaseModel):
-    """Response model for deleting a credential."""
-
-    deleted: bool = Field(..., description="Whether the credential was deleted")
-    credentials_id: str = Field(..., description="ID of the deleted credential")
-
-
-@integrations_router.delete(
-    "/{provider}/credentials/{cred_id}",
-    response_model=DeleteCredentialResponse,
-)
-async def delete_credential(
-    provider: Annotated[str, Path(title="The provider")],
-    cred_id: Annotated[str, Path(title="The credential ID to delete")],
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.DELETE_INTEGRATIONS)
-    ),
-) -> DeleteCredentialResponse:
-    """
-    Delete a credential.
-
-    Note: This does not revoke the tokens with the provider. For full cleanup,
-    use the main API's delete endpoint which handles webhook cleanup and
-    token revocation.
-    """
-    creds = await creds_manager.store.get_creds_by_id(auth.user_id, cred_id)
-    if not creds:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND, detail="Credentials not found"
-        )
-    if creds.provider != provider:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Credentials do not match the specified provider",
-        )
-
-    await creds_manager.delete(auth.user_id, cred_id)
-
-    return DeleteCredentialResponse(deleted=True, credentials_id=cred_id)

autogpt_platform/backend/backend/api/external/v1/tools.py

@@ -1,152 +0,0 @@
-"""External API routes for chat tools - stateless HTTP endpoints.
-
-Note: These endpoints use ephemeral sessions that are not persisted to Redis.
-As a result, session-based rate limiting (max_agent_runs, max_agent_schedules)
-is not enforced for external API calls. Each request creates a fresh session
-with zeroed counters. Rate limiting for external API consumers should be
-handled separately (e.g., via API key quotas).
-"""
-
-import logging
-from typing import Any
-
-from fastapi import APIRouter, Security
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel, Field
-
-from backend.api.external.middleware import require_permission
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools import find_agent_tool, run_agent_tool
-from backend.api.features.chat.tools.models import ToolResponseBase
-from backend.data.auth.base import APIAuthorizationInfo
-
-logger = logging.getLogger(__name__)
-
-tools_router = APIRouter(prefix="/tools", tags=["tools"])
-
-# Note: We use Security() as a function parameter dependency (auth: APIAuthorizationInfo = Security(...))
-# rather than in the decorator's dependencies= list. This avoids duplicate permission checks
-# while still enforcing auth AND giving us access to auth for extracting user_id.
-
-
-# Request models
-class FindAgentRequest(BaseModel):
-    query: str = Field(..., description="Search query for finding agents")
-
-
-class RunAgentRequest(BaseModel):
-    """Request to run or schedule an agent.
-
-    The tool automatically handles the setup flow:
-    - First call returns available inputs so user can decide what values to use
-    - Returns missing credentials if user needs to configure them
-    - Executes when inputs are provided OR use_defaults=true
-    - Schedules execution if schedule_name and cron are provided
-    """
-
-    username_agent_slug: str = Field(
-        ...,
-        description="The marketplace agent slug (e.g., 'username/agent-name')",
-    )
-    inputs: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Dictionary of input values for the agent",
-    )
-    use_defaults: bool = Field(
-        default=False,
-        description="Set to true to run with default values (user must confirm)",
-    )
-    schedule_name: str | None = Field(
-        None,
-        description="Name for scheduled execution (triggers scheduling mode)",
-    )
-    cron: str | None = Field(
-        None,
-        description="Cron expression (5 fields: minute hour day month weekday)",
-    )
-    timezone: str = Field(
-        default="UTC",
-        description="IANA timezone (e.g., 'America/New_York', 'UTC')",
-    )
-
-
-def _create_ephemeral_session(user_id: str | None) -> ChatSession:
-    """Create an ephemeral session for stateless API requests."""
-    return ChatSession.new(user_id)
-
-
-@tools_router.post(
-    path="/find-agent",
-)
-async def find_agent(
-    request: FindAgentRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.USE_TOOLS)
-    ),
-) -> dict[str, Any]:
-    """
-    Search for agents in the marketplace based on capabilities and user needs.
-
-    Args:
-        request: Search query for finding agents
-
-    Returns:
-        List of matching agents or no results response
-    """
-    session = _create_ephemeral_session(auth.user_id)
-    result = await find_agent_tool._execute(
-        user_id=auth.user_id,
-        session=session,
-        query=request.query,
-    )
-    return _response_to_dict(result)
-
-
-@tools_router.post(
-    path="/run-agent",
-)
-async def run_agent(
-    request: RunAgentRequest,
-    auth: APIAuthorizationInfo = Security(
-        require_permission(APIKeyPermission.USE_TOOLS)
-    ),
-) -> dict[str, Any]:
-    """
-    Run or schedule an agent from the marketplace.
-
-    The endpoint automatically handles the setup flow:
-    - Returns missing inputs if required fields are not provided
-    - Returns missing credentials if user needs to configure them
-    - Executes immediately if all requirements are met
-    - Schedules execution if schedule_name and cron are provided
-
-    For scheduled execution:
-    - Cron format: "minute hour day month weekday"
-    - Examples: "0 9 * * 1-5" (9am weekdays), "0 0 * * *" (daily at midnight)
-    - Timezone: Use IANA timezone names like "America/New_York"
-
-    Args:
-        request: Agent slug, inputs, and optional schedule config
-
-    Returns:
-        - setup_requirements: If inputs or credentials are missing
-        - execution_started: If agent was run or scheduled successfully
-        - error: If something went wrong
-    """
-    session = _create_ephemeral_session(auth.user_id)
-    result = await run_agent_tool._execute(
-        user_id=auth.user_id,
-        session=session,
-        username_agent_slug=request.username_agent_slug,
-        inputs=request.inputs,
-        use_defaults=request.use_defaults,
-        schedule_name=request.schedule_name or "",
-        cron=request.cron or "",
-        timezone=request.timezone,
-    )
-    return _response_to_dict(result)
-
-
-def _response_to_dict(result: ToolResponseBase) -> dict[str, Any]:
-    """Convert a tool response to a dictionary for JSON serialization."""
-    return result.model_dump()

autogpt_platform/backend/backend/api/features/analytics_test.py

@@ -1,340 +0,0 @@
-"""Tests for analytics API endpoints."""
-
-import json
-from unittest.mock import AsyncMock, Mock
-
-import fastapi
-import fastapi.testclient
-import pytest
-import pytest_mock
-from pytest_snapshot.plugin import Snapshot
-
-from .analytics import router as analytics_router
-
-app = fastapi.FastAPI()
-app.include_router(analytics_router)
-
-client = fastapi.testclient.TestClient(app)
-
-
-@pytest.fixture(autouse=True)
-def setup_app_auth(mock_jwt_user):
-    """Setup auth overrides for all tests in this module."""
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
-    yield
-    app.dependency_overrides.clear()
-
-
-# =============================================================================
-# /log_raw_metric endpoint tests
-# =============================================================================
-
-
-def test_log_raw_metric_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    test_user_id: str,
-) -> None:
-    """Test successful raw metric logging."""
-    mock_result = Mock(id="metric-123-uuid")
-    mock_log_metric = mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "metric_name": "page_load_time",
-        "metric_value": 2.5,
-        "data_string": "/dashboard",
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 200, f"Unexpected response: {response.text}"
-    assert response.json() == "metric-123-uuid"
-
-    mock_log_metric.assert_called_once_with(
-        user_id=test_user_id,
-        metric_name="page_load_time",
-        metric_value=2.5,
-        data_string="/dashboard",
-    )
-
-    configured_snapshot.assert_match(
-        json.dumps({"metric_id": response.json()}, indent=2, sort_keys=True),
-        "analytics_log_metric_success",
-    )
-
-
-@pytest.mark.parametrize(
-    "metric_value,metric_name,data_string,test_id",
-    [
-        (100, "api_calls_count", "external_api", "integer_value"),
-        (0, "error_count", "no_errors", "zero_value"),
-        (-5.2, "temperature_delta", "cooling", "negative_value"),
-        (1.23456789, "precision_test", "float_precision", "float_precision"),
-        (999999999, "large_number", "max_value", "large_number"),
-        (0.0000001, "tiny_number", "min_value", "tiny_number"),
-    ],
-)
-def test_log_raw_metric_various_values(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    metric_value: float,
-    metric_name: str,
-    data_string: str,
-    test_id: str,
-) -> None:
-    """Test raw metric logging with various metric values."""
-    mock_result = Mock(id=f"metric-{test_id}-uuid")
-    mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "metric_name": metric_name,
-        "metric_value": metric_value,
-        "data_string": data_string,
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 200, f"Failed for {test_id}: {response.text}"
-
-    configured_snapshot.assert_match(
-        json.dumps(
-            {"metric_id": response.json(), "test_case": test_id},
-            indent=2,
-            sort_keys=True,
-        ),
-        f"analytics_metric_{test_id}",
-    )
-
-
-@pytest.mark.parametrize(
-    "invalid_data,expected_error",
-    [
-        ({}, "Field required"),
-        ({"metric_name": "test"}, "Field required"),
-        (
-            {"metric_name": "test", "metric_value": "not_a_number", "data_string": "x"},
-            "Input should be a valid number",
-        ),
-        (
-            {"metric_name": "", "metric_value": 1.0, "data_string": "test"},
-            "String should have at least 1 character",
-        ),
-        (
-            {"metric_name": "test", "metric_value": 1.0, "data_string": ""},
-            "String should have at least 1 character",
-        ),
-    ],
-    ids=[
-        "empty_request",
-        "missing_metric_value_and_data_string",
-        "invalid_metric_value_type",
-        "empty_metric_name",
-        "empty_data_string",
-    ],
-)
-def test_log_raw_metric_validation_errors(
-    invalid_data: dict,
-    expected_error: str,
-) -> None:
-    """Test validation errors for invalid metric requests."""
-    response = client.post("/log_raw_metric", json=invalid_data)
-
-    assert response.status_code == 422
-    error_detail = response.json()
-    assert "detail" in error_detail, f"Missing 'detail' in error: {error_detail}"
-
-    error_text = json.dumps(error_detail)
-    assert (
-        expected_error in error_text
-    ), f"Expected '{expected_error}' in error response: {error_text}"
-
-
-def test_log_raw_metric_service_error(
-    mocker: pytest_mock.MockFixture,
-    test_user_id: str,
-) -> None:
-    """Test error handling when analytics service fails."""
-    mocker.patch(
-        "backend.data.analytics.log_raw_metric",
-        new_callable=AsyncMock,
-        side_effect=Exception("Database connection failed"),
-    )
-
-    request_data = {
-        "metric_name": "test_metric",
-        "metric_value": 1.0,
-        "data_string": "test",
-    }
-
-    response = client.post("/log_raw_metric", json=request_data)
-
-    assert response.status_code == 500
-    error_detail = response.json()["detail"]
-    assert "Database connection failed" in error_detail["message"]
-    assert "hint" in error_detail
-
-
-# =============================================================================
-# /log_raw_analytics endpoint tests
-# =============================================================================
-
-
-def test_log_raw_analytics_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-    test_user_id: str,
-) -> None:
-    """Test successful raw analytics logging."""
-    mock_result = Mock(id="analytics-789-uuid")
-    mock_log_analytics = mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "type": "user_action",
-        "data": {
-            "action": "button_click",
-            "button_id": "submit_form",
-            "timestamp": "2023-01-01T00:00:00Z",
-            "metadata": {"form_type": "registration", "fields_filled": 5},
-        },
-        "data_index": "button_click_submit_form",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 200, f"Unexpected response: {response.text}"
-    assert response.json() == "analytics-789-uuid"
-
-    mock_log_analytics.assert_called_once_with(
-        test_user_id,
-        "user_action",
-        request_data["data"],
-        "button_click_submit_form",
-    )
-
-    configured_snapshot.assert_match(
-        json.dumps({"analytics_id": response.json()}, indent=2, sort_keys=True),
-        "analytics_log_analytics_success",
-    )
-
-
-def test_log_raw_analytics_complex_data(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test raw analytics logging with complex nested data structures."""
-    mock_result = Mock(id="analytics-complex-uuid")
-    mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        return_value=mock_result,
-    )
-
-    request_data = {
-        "type": "agent_execution",
-        "data": {
-            "agent_id": "agent_123",
-            "execution_id": "exec_456",
-            "status": "completed",
-            "duration_ms": 3500,
-            "nodes_executed": 15,
-            "blocks_used": [
-                {"block_id": "llm_block", "count": 3},
-                {"block_id": "http_block", "count": 5},
-                {"block_id": "code_block", "count": 2},
-            ],
-            "errors": [],
-            "metadata": {
-                "trigger": "manual",
-                "user_tier": "premium",
-                "environment": "production",
-            },
-        },
-        "data_index": "agent_123_exec_456",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 200
-
-    configured_snapshot.assert_match(
-        json.dumps(
-            {"analytics_id": response.json(), "logged_data": request_data["data"]},
-            indent=2,
-            sort_keys=True,
-        ),
-        "analytics_log_analytics_complex_data",
-    )
-
-
-@pytest.mark.parametrize(
-    "invalid_data,expected_error",
-    [
-        ({}, "Field required"),
-        ({"type": "test"}, "Field required"),
-        (
-            {"type": "test", "data": "not_a_dict", "data_index": "test"},
-            "Input should be a valid dictionary",
-        ),
-        ({"type": "test", "data": {"key": "value"}}, "Field required"),
-    ],
-    ids=[
-        "empty_request",
-        "missing_data_and_data_index",
-        "invalid_data_type",
-        "missing_data_index",
-    ],
-)
-def test_log_raw_analytics_validation_errors(
-    invalid_data: dict,
-    expected_error: str,
-) -> None:
-    """Test validation errors for invalid analytics requests."""
-    response = client.post("/log_raw_analytics", json=invalid_data)
-
-    assert response.status_code == 422
-    error_detail = response.json()
-    assert "detail" in error_detail, f"Missing 'detail' in error: {error_detail}"
-
-    error_text = json.dumps(error_detail)
-    assert (
-        expected_error in error_text
-    ), f"Expected '{expected_error}' in error response: {error_text}"
-
-
-def test_log_raw_analytics_service_error(
-    mocker: pytest_mock.MockFixture,
-    test_user_id: str,
-) -> None:
-    """Test error handling when analytics service fails."""
-    mocker.patch(
-        "backend.data.analytics.log_raw_analytics",
-        new_callable=AsyncMock,
-        side_effect=Exception("Analytics DB unreachable"),
-    )
-
-    request_data = {
-        "type": "test_event",
-        "data": {"key": "value"},
-        "data_index": "test_index",
-    }
-
-    response = client.post("/log_raw_analytics", json=request_data)
-
-    assert response.status_code == 500
-    error_detail = response.json()["detail"]
-    assert "Analytics DB unreachable" in error_detail["message"]
-    assert "hint" in error_detail

autogpt_platform/backend/backend/api/features/builder/db.py

@@ -1,689 +0,0 @@
-import logging
-from dataclasses import dataclass
-from datetime import datetime, timedelta, timezone
-from difflib import SequenceMatcher
-from typing import Sequence
-
-import prisma
-
-import backend.api.features.library.db as library_db
-import backend.api.features.library.model as library_model
-import backend.api.features.store.db as store_db
-import backend.api.features.store.model as store_model
-import backend.data.block
-from backend.blocks import load_all_blocks
-from backend.blocks.llm import LlmModel
-from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
-from backend.data.db import query_raw_with_schema
-from backend.integrations.providers import ProviderName
-from backend.util.cache import cached
-from backend.util.models import Pagination
-
-from .model import (
-    BlockCategoryResponse,
-    BlockResponse,
-    BlockType,
-    CountResponse,
-    FilterType,
-    Provider,
-    ProviderResponse,
-    SearchEntry,
-)
-
-logger = logging.getLogger(__name__)
-llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
-
-MAX_LIBRARY_AGENT_RESULTS = 100
-MAX_MARKETPLACE_AGENT_RESULTS = 100
-MIN_SCORE_FOR_FILTERED_RESULTS = 10.0
-
-SearchResultItem = BlockInfo | library_model.LibraryAgent | store_model.StoreAgent
-
-
-@dataclass
-class _ScoredItem:
-    item: SearchResultItem
-    filter_type: FilterType
-    score: float
-    sort_key: str
-
-
-@dataclass
-class _SearchCacheEntry:
-    items: list[SearchResultItem]
-    total_items: dict[FilterType, int]
-
-
-def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse]:
-    categories: dict[BlockCategory, BlockCategoryResponse] = {}
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't have categories (all should have at least one)
-        if not block.categories:
-            continue
-
-        # Add block to the categories
-        for category in block.categories:
-            if category not in categories:
-                categories[category] = BlockCategoryResponse(
-                    name=category.name.lower(),
-                    total_blocks=0,
-                    blocks=[],
-                )
-
-            categories[category].total_blocks += 1
-
-            # Append if the category has less than the specified number of blocks
-            if len(categories[category].blocks) < category_blocks:
-                categories[category].blocks.append(block.get_info())
-
-    # Sort categories by name
-    return sorted(categories.values(), key=lambda x: x.name)
-
-
-def get_blocks(
-    *,
-    category: str | None = None,
-    type: BlockType | None = None,
-    provider: ProviderName | None = None,
-    page: int = 1,
-    page_size: int = 50,
-) -> BlockResponse:
-    """
-    Get blocks based on either category, type or provider.
-    Providing nothing fetches all block types.
-    """
-    # Only one of category, type, or provider can be specified
-    if (category and type) or (category and provider) or (type and provider):
-        raise ValueError("Only one of category, type, or provider can be specified")
-
-    blocks: list[AnyBlockSchema] = []
-    skip = (page - 1) * page_size
-    take = page_size
-    total = 0
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't match the category
-        if category and category not in {c.name.lower() for c in block.categories}:
-            continue
-        # Skip blocks that don't match the type
-        if (
-            (type == "input" and block.block_type.value != "Input")
-            or (type == "output" and block.block_type.value != "Output")
-            or (type == "action" and block.block_type.value in ("Input", "Output"))
-        ):
-            continue
-        # Skip blocks that don't match the provider
-        if provider:
-            credentials_info = block.input_schema.get_credentials_fields_info().values()
-            if not any(provider in info.provider for info in credentials_info):
-                continue
-
-        total += 1
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            blocks.append(block)
-
-    return BlockResponse(
-        blocks=[b.get_info() for b in blocks],
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-def get_block_by_id(block_id: str) -> BlockInfo | None:
-    """
-    Get a specific block by its ID.
-    """
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.id == block_id:
-            return block.get_info()
-    return None
-
-
-async def update_search(user_id: str, search: SearchEntry) -> str:
-    """
-    Upsert a search request for the user and return the search ID.
-    """
-    if search.search_id:
-        # Update existing search
-        await prisma.models.BuilderSearchHistory.prisma().update(
-            where={
-                "id": search.search_id,
-            },
-            data={
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            },
-        )
-        return search.search_id
-    else:
-        # Create new search
-        new_search = await prisma.models.BuilderSearchHistory.prisma().create(
-            data={
-                "userId": user_id,
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            }
-        )
-        return new_search.id
-
-
-async def get_recent_searches(user_id: str, limit: int = 5) -> list[SearchEntry]:
-    """
-    Get the user's most recent search requests.
-    """
-    searches = await prisma.models.BuilderSearchHistory.prisma().find_many(
-        where={
-            "userId": user_id,
-        },
-        order={
-            "updatedAt": "desc",
-        },
-        take=limit,
-    )
-    return [
-        SearchEntry(
-            search_query=s.searchQuery,
-            filter=s.filter,  # type: ignore
-            by_creator=s.byCreator,
-            search_id=s.id,
-        )
-        for s in searches
-    ]
-
-
-async def get_sorted_search_results(
-    *,
-    user_id: str,
-    search_query: str | None,
-    filters: Sequence[FilterType],
-    by_creator: Sequence[str] | None = None,
-) -> _SearchCacheEntry:
-    normalized_filters: tuple[FilterType, ...] = tuple(sorted(set(filters or [])))
-    normalized_creators: tuple[str, ...] = tuple(sorted(set(by_creator or [])))
-    return await _build_cached_search_results(
-        user_id=user_id,
-        search_query=search_query or "",
-        filters=normalized_filters,
-        by_creator=normalized_creators,
-    )
-
-
-@cached(ttl_seconds=300, shared_cache=True)
-async def _build_cached_search_results(
-    user_id: str,
-    search_query: str,
-    filters: tuple[FilterType, ...],
-    by_creator: tuple[str, ...],
-) -> _SearchCacheEntry:
-    normalized_query = (search_query or "").strip().lower()
-
-    include_blocks = "blocks" in filters
-    include_integrations = "integrations" in filters
-    include_library_agents = "my_agents" in filters
-    include_marketplace_agents = "marketplace_agents" in filters
-
-    scored_items: list[_ScoredItem] = []
-    total_items: dict[FilterType, int] = {
-        "blocks": 0,
-        "integrations": 0,
-        "marketplace_agents": 0,
-        "my_agents": 0,
-    }
-
-    block_results, block_total, integration_total = _collect_block_results(
-        normalized_query=normalized_query,
-        include_blocks=include_blocks,
-        include_integrations=include_integrations,
-    )
-    scored_items.extend(block_results)
-    total_items["blocks"] = block_total
-    total_items["integrations"] = integration_total
-
-    if include_library_agents:
-        library_response = await library_db.list_library_agents(
-            user_id=user_id,
-            search_term=search_query or None,
-            page=1,
-            page_size=MAX_LIBRARY_AGENT_RESULTS,
-        )
-        total_items["my_agents"] = library_response.pagination.total_items
-        scored_items.extend(
-            _build_library_items(
-                agents=library_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    if include_marketplace_agents:
-        marketplace_response = await store_db.get_store_agents(
-            creators=list(by_creator) or None,
-            search_query=search_query or None,
-            page=1,
-            page_size=MAX_MARKETPLACE_AGENT_RESULTS,
-        )
-        total_items["marketplace_agents"] = marketplace_response.pagination.total_items
-        scored_items.extend(
-            _build_marketplace_items(
-                agents=marketplace_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    sorted_items = sorted(
-        scored_items,
-        key=lambda entry: (-entry.score, entry.sort_key, entry.filter_type),
-    )
-
-    return _SearchCacheEntry(
-        items=[entry.item for entry in sorted_items],
-        total_items=total_items,
-    )
-
-
-def _collect_block_results(
-    *,
-    normalized_query: str,
-    include_blocks: bool,
-    include_integrations: bool,
-) -> tuple[list[_ScoredItem], int, int]:
-    results: list[_ScoredItem] = []
-    block_count = 0
-    integration_count = 0
-
-    if not include_blocks and not include_integrations:
-        return results, block_count, integration_count
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        block_info = block.get_info()
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        is_integration = len(credentials) > 0
-
-        if is_integration and not include_integrations:
-            continue
-        if not is_integration and not include_blocks:
-            continue
-
-        score = _score_block(block, block_info, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        filter_type: FilterType = "integrations" if is_integration else "blocks"
-        if is_integration:
-            integration_count += 1
-        else:
-            block_count += 1
-
-        results.append(
-            _ScoredItem(
-                item=block_info,
-                filter_type=filter_type,
-                score=score,
-                sort_key=_get_item_name(block_info),
-            )
-        )
-
-    return results, block_count, integration_count
-
-
-def _build_library_items(
-    *,
-    agents: list[library_model.LibraryAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_library_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="my_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
-
-
-def _build_marketplace_items(
-    *,
-    agents: list[store_model.StoreAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_store_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="marketplace_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
-
-
-def get_providers(
-    query: str = "",
-    page: int = 1,
-    page_size: int = 50,
-) -> ProviderResponse:
-    providers = []
-    query = query.lower()
-
-    skip = (page - 1) * page_size
-    take = page_size
-
-    all_providers = _get_all_providers()
-
-    for provider in all_providers.values():
-        if (
-            query not in provider.name.value.lower()
-            and query not in provider.description.lower()
-        ):
-            continue
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            providers.append(provider)
-
-    total = len(all_providers)
-
-    return ProviderResponse(
-        providers=providers,
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-async def get_counts(user_id: str) -> CountResponse:
-    my_agents = await prisma.models.LibraryAgent.prisma().count(
-        where={
-            "userId": user_id,
-            "isDeleted": False,
-            "isArchived": False,
-        }
-    )
-    counts = await _get_static_counts()
-    return CountResponse(
-        my_agents=my_agents,
-        **counts,
-    )
-
-
-@cached(ttl_seconds=3600)
-async def _get_static_counts():
-    """
-    Get counts of blocks, integrations, and marketplace agents.
-    This is cached to avoid unnecessary database queries and calculations.
-    """
-    all_blocks = 0
-    input_blocks = 0
-    action_blocks = 0
-    output_blocks = 0
-    integrations = 0
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        all_blocks += 1
-
-        if block.block_type.value == "Input":
-            input_blocks += 1
-        elif block.block_type.value == "Output":
-            output_blocks += 1
-        else:
-            action_blocks += 1
-
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        if len(credentials) > 0:
-            integrations += 1
-
-    marketplace_agents = await prisma.models.StoreAgent.prisma().count()
-
-    return {
-        "all_blocks": all_blocks,
-        "input_blocks": input_blocks,
-        "action_blocks": action_blocks,
-        "output_blocks": output_blocks,
-        "integrations": integrations,
-        "marketplace_agents": marketplace_agents,
-    }
-
-
-def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
-    for field in schema_cls.model_fields.values():
-        if field.annotation == LlmModel:
-            # Check if query matches any value in llm_models
-            if any(query in name for name in llm_models):
-                return True
-    return False
-
-
-def _score_block(
-    block: AnyBlockSchema,
-    block_info: BlockInfo,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = block_info.name.lower()
-    description = block_info.description.lower()
-    score = _score_primary_fields(name, description, normalized_query)
-
-    category_text = " ".join(
-        category.get("category", "").lower() for category in block_info.categories
-    )
-    score += _score_additional_field(category_text, normalized_query, 12, 6)
-
-    credentials_info = block.input_schema.get_credentials_fields_info().values()
-    provider_names = [
-        provider.value.lower()
-        for info in credentials_info
-        for provider in info.provider
-    ]
-    provider_text = " ".join(provider_names)
-    score += _score_additional_field(provider_text, normalized_query, 15, 6)
-
-    if _matches_llm_model(block.input_schema, normalized_query):
-        score += 20
-
-    return score
-
-
-def _score_library_agent(
-    agent: library_model.LibraryAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.name.lower()
-    description = (agent.description or "").lower()
-    instructions = (agent.instructions or "").lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(instructions, normalized_query, 15, 6)
-    score += _score_additional_field(
-        agent.creator_name.lower(), normalized_query, 10, 5
-    )
-
-    return score
-
-
-def _score_store_agent(
-    agent: store_model.StoreAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.agent_name.lower()
-    description = agent.description.lower()
-    sub_heading = agent.sub_heading.lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(sub_heading, normalized_query, 12, 6)
-    score += _score_additional_field(agent.creator.lower(), normalized_query, 10, 5)
-
-    return score
-
-
-def _score_primary_fields(name: str, description: str, query: str) -> float:
-    score = 0.0
-    if name == query:
-        score += 120
-    elif name.startswith(query):
-        score += 90
-    elif query in name:
-        score += 60
-
-    score += SequenceMatcher(None, name, query).ratio() * 50
-    if description:
-        if query in description:
-            score += 30
-        score += SequenceMatcher(None, description, query).ratio() * 25
-    return score
-
-
-def _score_additional_field(
-    value: str,
-    query: str,
-    contains_weight: float,
-    similarity_weight: float,
-) -> float:
-    if not value or not query:
-        return 0.0
-
-    score = 0.0
-    if query in value:
-        score += contains_weight
-    score += SequenceMatcher(None, value, query).ratio() * similarity_weight
-    return score
-
-
-def _should_include_item(score: float, normalized_query: str) -> bool:
-    if not normalized_query:
-        return True
-    return score >= MIN_SCORE_FOR_FILTERED_RESULTS
-
-
-def _get_item_name(item: SearchResultItem) -> str:
-    if isinstance(item, BlockInfo):
-        return item.name.lower()
-    if isinstance(item, library_model.LibraryAgent):
-        return item.name.lower()
-    return item.agent_name.lower()
-
-
-@cached(ttl_seconds=3600)
-def _get_all_providers() -> dict[ProviderName, Provider]:
-    providers: dict[ProviderName, Provider] = {}
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled:
-            continue
-
-        credentials_info = block.input_schema.get_credentials_fields_info().values()
-        for info in credentials_info:
-            for provider in info.provider:  # provider is a ProviderName enum member
-                if provider in providers:
-                    providers[provider].integration_count += 1
-                else:
-                    providers[provider] = Provider(
-                        name=provider, description="", integration_count=1
-                    )
-    return providers
-
-
-@cached(ttl_seconds=3600)
-async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
-    suggested_blocks = []
-    # Sum the number of executions for each block type
-    # Prisma cannot group by nested relations, so we do a raw query
-    # Calculate the cutoff timestamp
-    timestamp_threshold = datetime.now(timezone.utc) - timedelta(days=30)
-
-    results = await query_raw_with_schema(
-        """
-        SELECT
-            agent_node."agentBlockId" AS block_id,
-            COUNT(execution.id) AS execution_count
-        FROM {schema_prefix}"AgentNodeExecution" execution
-        JOIN {schema_prefix}"AgentNode" agent_node ON execution."agentNodeId" = agent_node.id
-        WHERE execution."endedTime" >= $1::timestamp
-        GROUP BY agent_node."agentBlockId"
-        ORDER BY execution_count DESC;
-        """,
-        timestamp_threshold,
-    )
-
-    # Get the top blocks based on execution count
-    # But ignore Input and Output blocks
-    blocks: list[tuple[BlockInfo, int]] = []
-
-    for block_type in load_all_blocks().values():
-        block: AnyBlockSchema = block_type()
-        if block.disabled or block.block_type in (
-            backend.data.block.BlockType.INPUT,
-            backend.data.block.BlockType.OUTPUT,
-            backend.data.block.BlockType.AGENT,
-        ):
-            continue
-        # Find the execution count for this block
-        execution_count = next(
-            (row["execution_count"] for row in results if row["block_id"] == block.id),
-            0,
-        )
-        blocks.append((block.get_info(), execution_count))
-    # Sort blocks by execution count
-    blocks.sort(key=lambda x: x[1], reverse=True)
-
-    suggested_blocks = [block[0] for block in blocks]
-
-    # Return the top blocks
-    return suggested_blocks[:count]

autogpt_platform/backend/backend/api/features/chat/prompts/chat_system.md

@@ -1,104 +0,0 @@
-You are Otto, an AI Co-Pilot and Forward Deployed Engineer for AutoGPT, an AI Business Automation tool. Your mission is to help users quickly find and set up AutoGPT agents to solve their business problems.
-
-Here are the functions available to you:
-
-<functions>
-1. **find_agent** - Search for agents that solve the user's problem
-2. **run_agent** - Run or schedule an agent (automatically handles setup)
-</functions>
-
-## HOW run_agent WORKS
-
-The `run_agent` tool automatically handles the entire setup flow:
-
-1. **First call** (no inputs) → Returns available inputs so user can decide what values to use
-2. **Credentials check** → If missing, UI automatically prompts user to add them (you don't need to mention this)
-3. **Execution** → Runs when you provide `inputs` OR set `use_defaults=true`
-
-Parameters:
-- `username_agent_slug` (required): Agent identifier like "creator/agent-name"
-- `inputs`: Object with input values for the agent
-- `use_defaults`: Set to `true` to run with default values (only after user confirms)
-- `schedule_name` + `cron`: For scheduled execution
-
-## WORKFLOW
-
-1. **find_agent** - Search for agents that solve the user's problem
-2. **run_agent** (first call, no inputs) - Get available inputs for the agent
-3. **Ask user** what values they want to use OR if they want to use defaults
-4. **run_agent** (second call) - Either with `inputs={...}` or `use_defaults=true`
-
-## YOUR APPROACH
-
-**Step 1: Understand the Problem**
-- Ask maximum 1-2 targeted questions
-- Focus on: What business problem are they solving?
-- Move quickly to searching for solutions
-
-**Step 2: Find Agents**
-- Use `find_agent` immediately with relevant keywords
-- Suggest the best option from search results
-- Explain briefly how it solves their problem
-
-**Step 3: Get Agent Inputs**
-- Call `run_agent(username_agent_slug="creator/agent-name")` without inputs
-- This returns the available inputs (required and optional)
-- Present these to the user and ask what values they want
-
-**Step 4: Run with User's Choice**
-- If user provides values: `run_agent(username_agent_slug="...", inputs={...})`
-- If user says "use defaults": `run_agent(username_agent_slug="...", use_defaults=true)`
-- On success, share the agent link with the user
-
-**For Scheduled Execution:**
-- Add `schedule_name` and `cron` parameters
-- Example: `run_agent(username_agent_slug="...", inputs={...}, schedule_name="Daily Report", cron="0 9 * * *")`
-
-## FUNCTION CALL FORMAT
-
-To call a function, use this exact format:
-`<function_call>function_name(parameter="value")</function_call>`
-
-Examples:
-- `<function_call>find_agent(query="social media automation")</function_call>`
-- `<function_call>run_agent(username_agent_slug="creator/agent-name")</function_call>` (get inputs)
-- `<function_call>run_agent(username_agent_slug="creator/agent-name", inputs={"topic": "AI news"})</function_call>`
-- `<function_call>run_agent(username_agent_slug="creator/agent-name", use_defaults=true)</function_call>`
-
-## KEY RULES
-
-**What You DON'T Do:**
-- Don't help with login (frontend handles this)
-- Don't mention or explain credentials to the user (frontend handles this automatically)
-- Don't run agents without first showing available inputs to the user
-- Don't use `use_defaults=true` without user explicitly confirming
-- Don't write responses longer than 3 sentences
-
-**What You DO:**
-- Always call run_agent first without inputs to see what's available
-- Ask user what values they want OR if they want to use defaults
-- Keep all responses to maximum 3 sentences
-- Include the agent link in your response after successful execution
-
-**Error Handling:**
-- Authentication needed → "Please sign in via the interface"
-- Credentials missing → The UI handles this automatically. Focus on asking the user about input values instead.
-
-## RESPONSE STRUCTURE
-
-Before responding, wrap your analysis in <thinking> tags to systematically plan your approach:
-- Extract the key business problem or request from the user's message
-- Determine what function call (if any) you need to make next
-- Plan your response to stay under the 3-sentence maximum
-
-Example interaction:
-```
-User: "Run the AI news agent for me"
-Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news")</function_call>
-[Tool returns: Agent accepts inputs - Required: topic. Optional: num_articles (default: 5)]
-Otto: The AI News agent needs a topic. What topic would you like news about, or should I use the defaults?
-User: "Use defaults"
-Otto: <function_call>run_agent(username_agent_slug="autogpt/ai-news", use_defaults=true)</function_call>
-```
-
-KEEP ANSWERS TO 3 SENTENCES

autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py

@@ -1,501 +0,0 @@
-"""Unified tool for agent operations with automatic state detection."""
-
-import logging
-from typing import Any
-
-from pydantic import BaseModel, Field, field_validator
-
-from backend.api.features.chat.config import ChatConfig
-from backend.api.features.chat.model import ChatSession
-from backend.data.graph import GraphModel
-from backend.data.model import CredentialsMetaInput
-from backend.data.user import get_user_by_id
-from backend.executor import utils as execution_utils
-from backend.util.clients import get_scheduler_client
-from backend.util.exceptions import DatabaseError, NotFoundError
-from backend.util.timezone_utils import (
-    convert_utc_time_to_user_timezone,
-    get_user_timezone_or_utc,
-)
-
-from .base import BaseTool
-from .models import (
-    AgentDetails,
-    AgentDetailsResponse,
-    ErrorResponse,
-    ExecutionOptions,
-    ExecutionStartedResponse,
-    SetupInfo,
-    SetupRequirementsResponse,
-    ToolResponseBase,
-    UserReadiness,
-)
-from .utils import (
-    check_user_has_required_credentials,
-    extract_credentials_from_schema,
-    fetch_graph_from_store_slug,
-    get_or_create_library_agent,
-    match_user_credentials_to_graph,
-)
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Constants for response messages
-MSG_DO_NOT_RUN_AGAIN = "Do not run again unless explicitly requested."
-MSG_DO_NOT_SCHEDULE_AGAIN = "Do not schedule again unless explicitly requested."
-MSG_ASK_USER_FOR_VALUES = (
-    "Ask the user what values to use, or call again with use_defaults=true "
-    "to run with default values."
-)
-MSG_WHAT_VALUES_TO_USE = (
-    "What values would you like to use, or would you like to run with defaults?"
-)
-
-
-class RunAgentInput(BaseModel):
-    """Input parameters for the run_agent tool."""
-
-    username_agent_slug: str = ""
-    inputs: dict[str, Any] = Field(default_factory=dict)
-    use_defaults: bool = False
-    schedule_name: str = ""
-    cron: str = ""
-    timezone: str = "UTC"
-
-    @field_validator(
-        "username_agent_slug", "schedule_name", "cron", "timezone", mode="before"
-    )
-    @classmethod
-    def strip_strings(cls, v: Any) -> Any:
-        """Strip whitespace from string fields."""
-        return v.strip() if isinstance(v, str) else v
-
-
-class RunAgentTool(BaseTool):
-    """Unified tool for agent operations with automatic state detection.
-
-    The tool automatically determines what to do based on provided parameters:
-    1. Fetches agent details (always, silently)
-    2. Checks if required inputs are provided
-    3. Checks if user has required credentials
-    4. Runs immediately OR schedules (if cron is provided)
-
-    The response tells the caller what's missing or confirms execution.
-    """
-
-    @property
-    def name(self) -> str:
-        return "run_agent"
-
-    @property
-    def description(self) -> str:
-        return """Run or schedule an agent from the marketplace.
-
-        The tool automatically handles the setup flow:
-        - Returns missing inputs if required fields are not provided
-        - Returns missing credentials if user needs to configure them
-        - Executes immediately if all requirements are met
-        - Schedules execution if cron expression is provided
-
-        For scheduled execution, provide: schedule_name, cron, and optionally timezone."""
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "username_agent_slug": {
-                    "type": "string",
-                    "description": "Agent identifier in format 'username/agent-name'",
-                },
-                "inputs": {
-                    "type": "object",
-                    "description": "Input values for the agent",
-                    "additionalProperties": True,
-                },
-                "use_defaults": {
-                    "type": "boolean",
-                    "description": "Set to true to run with default values (user must confirm)",
-                },
-                "schedule_name": {
-                    "type": "string",
-                    "description": "Name for scheduled execution (triggers scheduling mode)",
-                },
-                "cron": {
-                    "type": "string",
-                    "description": "Cron expression (5 fields: min hour day month weekday)",
-                },
-                "timezone": {
-                    "type": "string",
-                    "description": "IANA timezone for schedule (default: UTC)",
-                },
-            },
-            "required": ["username_agent_slug"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        """All operations require authentication."""
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the tool with automatic state detection."""
-        params = RunAgentInput(**kwargs)
-        session_id = session.session_id
-
-        # Validate agent slug format
-        if not params.username_agent_slug or "/" not in params.username_agent_slug:
-            return ErrorResponse(
-                message="Please provide an agent slug in format 'username/agent-name'",
-                session_id=session_id,
-            )
-
-        # Auth is required
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required. Please sign in to use this tool.",
-                session_id=session_id,
-            )
-
-        # Determine if this is a schedule request
-        is_schedule = bool(params.schedule_name or params.cron)
-
-        try:
-            # Step 1: Fetch agent details (always happens first)
-            username, agent_name = params.username_agent_slug.split("/", 1)
-            graph, store_agent = await fetch_graph_from_store_slug(username, agent_name)
-
-            if not graph:
-                return ErrorResponse(
-                    message=f"Agent '{params.username_agent_slug}' not found in marketplace",
-                    session_id=session_id,
-                )
-
-            # Step 2: Check credentials
-            graph_credentials, missing_creds = await match_user_credentials_to_graph(
-                user_id, graph
-            )
-
-            if missing_creds:
-                # Return credentials needed response with input data info
-                # The UI handles credential setup automatically, so the message
-                # focuses on asking about input data
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
-                )
-                missing_creds_check = await check_user_has_required_credentials(
-                    user_id, credentials
-                )
-                missing_credentials_dict = {
-                    c.id: c.model_dump() for c in missing_creds_check
-                }
-
-                return SetupRequirementsResponse(
-                    message=self._build_inputs_message(graph, MSG_WHAT_VALUES_TO_USE),
-                    session_id=session_id,
-                    setup_info=SetupInfo(
-                        agent_id=graph.id,
-                        agent_name=graph.name,
-                        user_readiness=UserReadiness(
-                            has_all_credentials=False,
-                            missing_credentials=missing_credentials_dict,
-                            ready_to_run=False,
-                        ),
-                        requirements={
-                            "credentials": [c.model_dump() for c in credentials],
-                            "inputs": self._get_inputs_list(graph.input_schema),
-                            "execution_modes": self._get_execution_modes(graph),
-                        },
-                    ),
-                    graph_id=graph.id,
-                    graph_version=graph.version,
-                )
-
-            # Step 3: Check inputs
-            # Get all available input fields from schema
-            input_properties = graph.input_schema.get("properties", {})
-            required_fields = set(graph.input_schema.get("required", []))
-            provided_inputs = set(params.inputs.keys())
-
-            # If agent has inputs but none were provided AND use_defaults is not set,
-            # always show what's available first so user can decide
-            if input_properties and not provided_inputs and not params.use_defaults:
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
-                )
-                return AgentDetailsResponse(
-                    message=self._build_inputs_message(graph, MSG_ASK_USER_FOR_VALUES),
-                    session_id=session_id,
-                    agent=self._build_agent_details(graph, credentials),
-                    user_authenticated=True,
-                    graph_id=graph.id,
-                    graph_version=graph.version,
-                )
-
-            # Check if required inputs are missing (and not using defaults)
-            missing_inputs = required_fields - provided_inputs
-
-            if missing_inputs and not params.use_defaults:
-                # Return agent details with missing inputs info
-                credentials = extract_credentials_from_schema(
-                    graph.credentials_input_schema
-                )
-                return AgentDetailsResponse(
-                    message=(
-                        f"Agent '{graph.name}' is missing required inputs: "
-                        f"{', '.join(missing_inputs)}. "
-                        "Please provide these values to run the agent."
-                    ),
-                    session_id=session_id,
-                    agent=self._build_agent_details(graph, credentials),
-                    user_authenticated=True,
-                    graph_id=graph.id,
-                    graph_version=graph.version,
-                )
-
-            # Step 4: Execute or Schedule
-            if is_schedule:
-                return await self._schedule_agent(
-                    user_id=user_id,
-                    session=session,
-                    graph=graph,
-                    graph_credentials=graph_credentials,
-                    inputs=params.inputs,
-                    schedule_name=params.schedule_name,
-                    cron=params.cron,
-                    timezone=params.timezone,
-                )
-            else:
-                return await self._run_agent(
-                    user_id=user_id,
-                    session=session,
-                    graph=graph,
-                    graph_credentials=graph_credentials,
-                    inputs=params.inputs,
-                )
-
-        except NotFoundError as e:
-            return ErrorResponse(
-                message=f"Agent '{params.username_agent_slug}' not found",
-                error=str(e) if str(e) else "not_found",
-                session_id=session_id,
-            )
-        except DatabaseError as e:
-            logger.error(f"Database error: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to process request: {e!s}",
-                error=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error processing agent request: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to process request: {e!s}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
-        """Extract inputs list from schema."""
-        inputs_list = []
-        if isinstance(input_schema, dict) and "properties" in input_schema:
-            for field_name, field_schema in input_schema["properties"].items():
-                inputs_list.append(
-                    {
-                        "name": field_name,
-                        "title": field_schema.get("title", field_name),
-                        "type": field_schema.get("type", "string"),
-                        "description": field_schema.get("description", ""),
-                        "required": field_name in input_schema.get("required", []),
-                    }
-                )
-        return inputs_list
-
-    def _get_execution_modes(self, graph: GraphModel) -> list[str]:
-        """Get available execution modes for the graph."""
-        trigger_info = graph.trigger_setup_info
-        if trigger_info is None:
-            return ["manual", "scheduled"]
-        return ["webhook"]
-
-    def _build_inputs_message(
-        self,
-        graph: GraphModel,
-        suffix: str,
-    ) -> str:
-        """Build a message describing available inputs for an agent."""
-        inputs_list = self._get_inputs_list(graph.input_schema)
-        required_names = [i["name"] for i in inputs_list if i["required"]]
-        optional_names = [i["name"] for i in inputs_list if not i["required"]]
-
-        message_parts = [f"Agent '{graph.name}' accepts the following inputs:"]
-        if required_names:
-            message_parts.append(f"Required: {', '.join(required_names)}.")
-        if optional_names:
-            message_parts.append(
-                f"Optional (have defaults): {', '.join(optional_names)}."
-            )
-        if not inputs_list:
-            message_parts = [f"Agent '{graph.name}' has no required inputs."]
-        message_parts.append(suffix)
-
-        return " ".join(message_parts)
-
-    def _build_agent_details(
-        self,
-        graph: GraphModel,
-        credentials: list[CredentialsMetaInput],
-    ) -> AgentDetails:
-        """Build AgentDetails from a graph."""
-        trigger_info = (
-            graph.trigger_setup_info.model_dump() if graph.trigger_setup_info else None
-        )
-        return AgentDetails(
-            id=graph.id,
-            name=graph.name,
-            description=graph.description,
-            inputs=graph.input_schema,
-            credentials=credentials,
-            execution_options=ExecutionOptions(
-                manual=trigger_info is None,
-                scheduled=trigger_info is None,
-                webhook=trigger_info is not None,
-            ),
-            trigger_info=trigger_info,
-        )
-
-    async def _run_agent(
-        self,
-        user_id: str,
-        session: ChatSession,
-        graph: GraphModel,
-        graph_credentials: dict[str, CredentialsMetaInput],
-        inputs: dict[str, Any],
-    ) -> ToolResponseBase:
-        """Execute an agent immediately."""
-        session_id = session.session_id
-
-        # Check rate limits
-        if session.successful_agent_runs.get(graph.id, 0) >= config.max_agent_runs:
-            return ErrorResponse(
-                message="Maximum agent runs reached for this session. Please try again later.",
-                session_id=session_id,
-            )
-
-        # Get or create library agent
-        library_agent = await get_or_create_library_agent(graph, user_id)
-
-        # Execute
-        execution = await execution_utils.add_graph_execution(
-            graph_id=library_agent.graph_id,
-            user_id=user_id,
-            inputs=inputs,
-            graph_credentials_inputs=graph_credentials,
-        )
-
-        # Track successful run
-        session.successful_agent_runs[library_agent.graph_id] = (
-            session.successful_agent_runs.get(library_agent.graph_id, 0) + 1
-        )
-
-        library_agent_link = f"/library/agents/{library_agent.id}"
-        return ExecutionStartedResponse(
-            message=(
-                f"Agent '{library_agent.name}' execution started successfully. "
-                f"View at {library_agent_link}. "
-                f"{MSG_DO_NOT_RUN_AGAIN}"
-            ),
-            session_id=session_id,
-            execution_id=execution.id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            library_agent_id=library_agent.id,
-            library_agent_link=library_agent_link,
-        )
-
-    async def _schedule_agent(
-        self,
-        user_id: str,
-        session: ChatSession,
-        graph: GraphModel,
-        graph_credentials: dict[str, CredentialsMetaInput],
-        inputs: dict[str, Any],
-        schedule_name: str,
-        cron: str,
-        timezone: str,
-    ) -> ToolResponseBase:
-        """Set up scheduled execution for an agent."""
-        session_id = session.session_id
-
-        # Validate schedule params
-        if not schedule_name:
-            return ErrorResponse(
-                message="schedule_name is required for scheduled execution",
-                session_id=session_id,
-            )
-        if not cron:
-            return ErrorResponse(
-                message="cron expression is required for scheduled execution",
-                session_id=session_id,
-            )
-
-        # Check rate limits
-        if (
-            session.successful_agent_schedules.get(graph.id, 0)
-            >= config.max_agent_schedules
-        ):
-            return ErrorResponse(
-                message="Maximum agent schedules reached for this session.",
-                session_id=session_id,
-            )
-
-        # Get or create library agent
-        library_agent = await get_or_create_library_agent(graph, user_id)
-
-        # Get user timezone
-        user = await get_user_by_id(user_id)
-        user_timezone = get_user_timezone_or_utc(user.timezone if user else timezone)
-
-        # Create schedule
-        result = await get_scheduler_client().add_execution_schedule(
-            user_id=user_id,
-            graph_id=library_agent.graph_id,
-            graph_version=library_agent.graph_version,
-            name=schedule_name,
-            cron=cron,
-            input_data=inputs,
-            input_credentials=graph_credentials,
-            user_timezone=user_timezone,
-        )
-
-        # Convert next_run_time to user timezone for display
-        if result.next_run_time:
-            result.next_run_time = convert_utc_time_to_user_timezone(
-                result.next_run_time, user_timezone
-            )
-
-        # Track successful schedule
-        session.successful_agent_schedules[library_agent.graph_id] = (
-            session.successful_agent_schedules.get(library_agent.graph_id, 0) + 1
-        )
-
-        library_agent_link = f"/library/agents/{library_agent.id}"
-        return ExecutionStartedResponse(
-            message=(
-                f"Agent '{library_agent.name}' scheduled successfully as '{schedule_name}'. "
-                f"View at {library_agent_link}. "
-                f"{MSG_DO_NOT_SCHEDULE_AGAIN}"
-            ),
-            session_id=session_id,
-            execution_id=result.id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            library_agent_id=library_agent.id,
-            library_agent_link=library_agent_link,
-        )

autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py

@@ -1,391 +0,0 @@
-import uuid
-
-import orjson
-import pytest
-
-from ._test_data import (
-    make_session,
-    setup_firecrawl_test_data,
-    setup_llm_test_data,
-    setup_test_data,
-)
-from .run_agent import RunAgentTool
-
-# This is so the formatter doesn't remove the fixture imports
-setup_llm_test_data = setup_llm_test_data
-setup_test_data = setup_test_data
-setup_firecrawl_test_data = setup_firecrawl_test_data
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent(setup_test_data):
-    """Test that the run_agent tool successfully executes an approved agent"""
-    # Use test data from fixture
-    user = setup_test_data["user"]
-    graph = setup_test_data["graph"]
-    store_submission = setup_test_data["store_submission"]
-
-    # Create the tool instance
-    tool = RunAgentTool()
-
-    # Build the proper marketplace agent_id format: username/slug
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-
-    # Build the session
-    session = make_session(user_id=user.id)
-
-    # Execute the tool
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={"test_input": "Hello World"},
-        session=session,
-    )
-
-    # Verify the response
-    assert response is not None
-    assert hasattr(response, "result")
-    # Parse the result JSON to verify the execution started
-
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-    assert "execution_id" in result_data
-    assert "graph_id" in result_data
-    assert result_data["graph_id"] == graph.id
-    assert "graph_name" in result_data
-    assert result_data["graph_name"] == "Test Agent"
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_missing_inputs(setup_test_data):
-    """Test that the run_agent tool returns error when inputs are missing"""
-    # Use test data from fixture
-    user = setup_test_data["user"]
-    store_submission = setup_test_data["store_submission"]
-
-    # Create the tool instance
-    tool = RunAgentTool()
-
-    # Build the proper marketplace agent_id format
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-
-    # Build the session
-    session = make_session(user_id=user.id)
-
-    # Execute the tool without required inputs
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={},  # Missing required input
-        session=session,
-    )
-
-    # Verify that we get an error response
-    assert response is not None
-    assert hasattr(response, "result")
-    # The tool should return an ErrorResponse when setup info indicates not ready
-
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-    assert "message" in result_data
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_invalid_agent_id(setup_test_data):
-    """Test that the run_agent tool returns error for invalid agent ID"""
-    # Use test data from fixture
-    user = setup_test_data["user"]
-
-    # Create the tool instance
-    tool = RunAgentTool()
-
-    # Build the session
-    session = make_session(user_id=user.id)
-
-    # Execute the tool with invalid agent ID
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug="invalid/agent-id",
-        inputs={"test_input": "Hello World"},
-        session=session,
-    )
-
-    # Verify that we get an error response
-    assert response is not None
-    assert hasattr(response, "result")
-
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-    assert "message" in result_data
-    # Should get an error about failed setup or not found
-    assert any(
-        phrase in result_data["message"].lower() for phrase in ["not found", "failed"]
-    )
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_with_llm_credentials(setup_llm_test_data):
-    """Test that run_agent works with an agent requiring LLM credentials"""
-    # Use test data from fixture
-    user = setup_llm_test_data["user"]
-    graph = setup_llm_test_data["graph"]
-    store_submission = setup_llm_test_data["store_submission"]
-
-    # Create the tool instance
-    tool = RunAgentTool()
-
-    # Build the proper marketplace agent_id format
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-
-    # Build the session
-    session = make_session(user_id=user.id)
-
-    # Execute the tool with a prompt for the LLM
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={"user_prompt": "What is 2+2?"},
-        session=session,
-    )
-
-    # Verify the response
-    assert response is not None
-    assert hasattr(response, "result")
-
-    # Parse the result JSON to verify the execution started
-
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should successfully start execution since credentials are available
-    assert "execution_id" in result_data
-    assert "graph_id" in result_data
-    assert result_data["graph_id"] == graph.id
-    assert "graph_name" in result_data
-    assert result_data["graph_name"] == "LLM Test Agent"
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_shows_available_inputs_when_none_provided(setup_test_data):
-    """Test that run_agent returns available inputs when called without inputs or use_defaults."""
-    user = setup_test_data["user"]
-    store_submission = setup_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Execute without inputs and without use_defaults
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={},
-        use_defaults=False,
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should return agent_details type showing available inputs
-    assert result_data.get("type") == "agent_details"
-    assert "agent" in result_data
-    assert "message" in result_data
-    # Message should mention inputs
-    assert "inputs" in result_data["message"].lower()
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_with_use_defaults(setup_test_data):
-    """Test that run_agent executes successfully with use_defaults=True."""
-    user = setup_test_data["user"]
-    graph = setup_test_data["graph"]
-    store_submission = setup_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Execute with use_defaults=True (no explicit inputs)
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={},
-        use_defaults=True,
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should execute successfully
-    assert "execution_id" in result_data
-    assert result_data["graph_id"] == graph.id
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_missing_credentials(setup_firecrawl_test_data):
-    """Test that run_agent returns setup_requirements when credentials are missing."""
-    user = setup_firecrawl_test_data["user"]
-    store_submission = setup_firecrawl_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Execute - user doesn't have firecrawl credentials
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={"url": "https://example.com"},
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should return setup_requirements type with missing credentials
-    assert result_data.get("type") == "setup_requirements"
-    assert "setup_info" in result_data
-    setup_info = result_data["setup_info"]
-    assert "user_readiness" in setup_info
-    assert setup_info["user_readiness"]["has_all_credentials"] is False
-    assert len(setup_info["user_readiness"]["missing_credentials"]) > 0
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_invalid_slug_format(setup_test_data):
-    """Test that run_agent returns error for invalid slug format (no slash)."""
-    user = setup_test_data["user"]
-
-    tool = RunAgentTool()
-    session = make_session(user_id=user.id)
-
-    # Execute with invalid slug format
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug="no-slash-here",
-        inputs={},
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should return error
-    assert result_data.get("type") == "error"
-    assert "username/agent-name" in result_data["message"]
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_unauthenticated():
-    """Test that run_agent returns need_login for unauthenticated users."""
-    tool = RunAgentTool()
-    session = make_session(user_id=None)
-
-    # Execute without user_id
-    response = await tool.execute(
-        user_id=None,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug="test/test-agent",
-        inputs={},
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Base tool returns need_login type for unauthenticated users
-    assert result_data.get("type") == "need_login"
-    assert "sign in" in result_data["message"].lower()
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_schedule_without_cron(setup_test_data):
-    """Test that run_agent returns error when scheduling without cron expression."""
-    user = setup_test_data["user"]
-    store_submission = setup_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Try to schedule without cron
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={"test_input": "test"},
-        schedule_name="My Schedule",
-        cron="",  # Empty cron
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should return error about missing cron
-    assert result_data.get("type") == "error"
-    assert "cron" in result_data["message"].lower()
-
-
-@pytest.mark.asyncio(scope="session")
-async def test_run_agent_schedule_without_name(setup_test_data):
-    """Test that run_agent returns error when scheduling without schedule_name."""
-    user = setup_test_data["user"]
-    store_submission = setup_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Try to schedule without schedule_name
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={"test_input": "test"},
-        schedule_name="",  # Empty name
-        cron="0 9 * * *",
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "result")
-    assert isinstance(response.result, str)
-    result_data = orjson.loads(response.result)
-
-    # Should return error about missing schedule_name
-    assert result_data.get("type") == "error"
-    assert "schedule_name" in result_data["message"].lower()

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -1,288 +0,0 @@
-"""Shared utilities for chat tools."""
-
-import logging
-from typing import Any
-
-from backend.api.features.library import db as library_db
-from backend.api.features.library import model as library_model
-from backend.api.features.store import db as store_db
-from backend.data import graph as graph_db
-from backend.data.graph import GraphModel
-from backend.data.model import CredentialsMetaInput
-from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.util.exceptions import NotFoundError
-
-logger = logging.getLogger(__name__)
-
-
-async def fetch_graph_from_store_slug(
-    username: str,
-    agent_name: str,
-) -> tuple[GraphModel | None, Any | None]:
-    """
-    Fetch graph from store by username/agent_name slug.
-
-    Args:
-        username: Creator's username
-        agent_name: Agent name/slug
-
-    Returns:
-        tuple[Graph | None, StoreAgentDetails | None]: The graph and store agent details,
-        or (None, None) if not found.
-
-    Raises:
-        DatabaseError: If there's a database error during lookup.
-    """
-    try:
-        store_agent = await store_db.get_store_agent_details(username, agent_name)
-    except NotFoundError:
-        return None, None
-
-    # Get the graph from store listing version
-    graph_meta = await store_db.get_available_graph(
-        store_agent.store_listing_version_id
-    )
-    graph = await graph_db.get_graph(
-        graph_id=graph_meta.id,
-        version=graph_meta.version,
-        user_id=None,  # Public access
-        include_subgraphs=True,
-    )
-    return graph, store_agent
-
-
-def extract_credentials_from_schema(
-    credentials_input_schema: dict[str, Any] | None,
-) -> list[CredentialsMetaInput]:
-    """
-    Extract credential requirements from graph's credentials_input_schema.
-
-    This consolidates duplicated logic from get_agent_details.py and setup_agent.py.
-
-    Args:
-        credentials_input_schema: The credentials_input_schema from a Graph object
-
-    Returns:
-        List of CredentialsMetaInput with provider and type info
-    """
-    credentials: list[CredentialsMetaInput] = []
-
-    if (
-        not isinstance(credentials_input_schema, dict)
-        or "properties" not in credentials_input_schema
-    ):
-        return credentials
-
-    for cred_name, cred_schema in credentials_input_schema["properties"].items():
-        provider = _extract_provider_from_schema(cred_schema)
-        cred_type = _extract_credential_type_from_schema(cred_schema)
-
-        credentials.append(
-            CredentialsMetaInput(
-                id=cred_name,
-                title=cred_schema.get("title", cred_name),
-                provider=provider,  # type: ignore
-                type=cred_type,  # type: ignore
-            )
-        )
-
-    return credentials
-
-
-def extract_credentials_as_dict(
-    credentials_input_schema: dict[str, Any] | None,
-) -> dict[str, CredentialsMetaInput]:
-    """
-    Extract credential requirements as a dict keyed by field name.
-
-    Args:
-        credentials_input_schema: The credentials_input_schema from a Graph object
-
-    Returns:
-        Dict mapping field name to CredentialsMetaInput
-    """
-    credentials: dict[str, CredentialsMetaInput] = {}
-
-    if (
-        not isinstance(credentials_input_schema, dict)
-        or "properties" not in credentials_input_schema
-    ):
-        return credentials
-
-    for cred_name, cred_schema in credentials_input_schema["properties"].items():
-        provider = _extract_provider_from_schema(cred_schema)
-        cred_type = _extract_credential_type_from_schema(cred_schema)
-
-        credentials[cred_name] = CredentialsMetaInput(
-            id=cred_name,
-            title=cred_schema.get("title", cred_name),
-            provider=provider,  # type: ignore
-            type=cred_type,  # type: ignore
-        )
-
-    return credentials
-
-
-def _extract_provider_from_schema(cred_schema: dict[str, Any]) -> str:
-    """Extract provider from credential schema."""
-    if "credentials_provider" in cred_schema and cred_schema["credentials_provider"]:
-        return cred_schema["credentials_provider"][0]
-    if "properties" in cred_schema and "provider" in cred_schema["properties"]:
-        return cred_schema["properties"]["provider"].get("const", "unknown")
-    return "unknown"
-
-
-def _extract_credential_type_from_schema(cred_schema: dict[str, Any]) -> str:
-    """Extract credential type from credential schema."""
-    if "credentials_types" in cred_schema and cred_schema["credentials_types"]:
-        return cred_schema["credentials_types"][0]
-    if "properties" in cred_schema and "type" in cred_schema["properties"]:
-        return cred_schema["properties"]["type"].get("const", "api_key")
-    return "api_key"
-
-
-async def get_or_create_library_agent(
-    graph: GraphModel,
-    user_id: str,
-) -> library_model.LibraryAgent:
-    """
-    Get existing library agent or create new one.
-
-    This consolidates duplicated logic from run_agent.py and setup_agent.py.
-
-    Args:
-        graph: The Graph to add to library
-        user_id: The user's ID
-
-    Returns:
-        LibraryAgent instance
-    """
-    existing = await library_db.get_library_agent_by_graph_id(
-        graph_id=graph.id, user_id=user_id
-    )
-    if existing:
-        return existing
-
-    library_agents = await library_db.create_library_agent(
-        graph=graph,
-        user_id=user_id,
-        create_library_agents_for_sub_graphs=False,
-    )
-    assert len(library_agents) == 1, "Expected 1 library agent to be created"
-    return library_agents[0]
-
-
-async def match_user_credentials_to_graph(
-    user_id: str,
-    graph: GraphModel,
-) -> tuple[dict[str, CredentialsMetaInput], list[str]]:
-    """
-    Match user's available credentials against graph's required credentials.
-
-    Uses graph.aggregate_credentials_inputs() which handles credentials from
-    multiple nodes and uses frozensets for provider matching.
-
-    Args:
-        user_id: The user's ID
-        graph: The Graph with credential requirements
-
-    Returns:
-        tuple[matched_credentials dict, missing_credential_descriptions list]
-    """
-    graph_credentials_inputs: dict[str, CredentialsMetaInput] = {}
-    missing_creds: list[str] = []
-
-    # Get aggregated credentials requirements from the graph
-    aggregated_creds = graph.aggregate_credentials_inputs()
-    logger.debug(
-        f"Matching credentials for graph {graph.id}: {len(aggregated_creds)} required"
-    )
-
-    if not aggregated_creds:
-        return graph_credentials_inputs, missing_creds
-
-    # Get all available credentials for the user
-    creds_manager = IntegrationCredentialsManager()
-    available_creds = await creds_manager.store.get_all_creds(user_id)
-
-    # For each required credential field, find a matching user credential
-    # field_info.provider is a frozenset because aggregate_credentials_inputs()
-    # combines requirements from multiple nodes. A credential matches if its
-    # provider is in the set of acceptable providers.
-    for credential_field_name, (
-        credential_requirements,
-        _node_fields,
-    ) in aggregated_creds.items():
-        # Find first matching credential by provider and type
-        matching_cred = next(
-            (
-                cred
-                for cred in available_creds
-                if cred.provider in credential_requirements.provider
-                and cred.type in credential_requirements.supported_types
-            ),
-            None,
-        )
-
-        if matching_cred:
-            try:
-                graph_credentials_inputs[credential_field_name] = CredentialsMetaInput(
-                    id=matching_cred.id,
-                    provider=matching_cred.provider,  # type: ignore
-                    type=matching_cred.type,
-                    title=matching_cred.title,
-                )
-            except Exception as e:
-                logger.error(
-                    f"Failed to create CredentialsMetaInput for field '{credential_field_name}': "
-                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
-                    f"credential_id={matching_cred.id}",
-                    exc_info=True,
-                )
-                missing_creds.append(
-                    f"{credential_field_name} (validation failed: {e})"
-                )
-        else:
-            missing_creds.append(
-                f"{credential_field_name} "
-                f"(requires provider in {list(credential_requirements.provider)}, "
-                f"type in {list(credential_requirements.supported_types)})"
-            )
-
-    logger.info(
-        f"Credential matching complete: {len(graph_credentials_inputs)}/{len(aggregated_creds)} matched"
-    )
-
-    return graph_credentials_inputs, missing_creds
-
-
-async def check_user_has_required_credentials(
-    user_id: str,
-    required_credentials: list[CredentialsMetaInput],
-) -> list[CredentialsMetaInput]:
-    """
-    Check which required credentials the user is missing.
-
-    Args:
-        user_id: The user's ID
-        required_credentials: List of required credentials
-
-    Returns:
-        List of missing credentials (empty if user has all)
-    """
-    if not required_credentials:
-        return []
-
-    creds_manager = IntegrationCredentialsManager()
-    available_creds = await creds_manager.store.get_all_creds(user_id)
-
-    missing: list[CredentialsMetaInput] = []
-    for required in required_credentials:
-        has_matching = any(
-            cred.provider == required.provider and cred.type == required.type
-            for cred in available_creds
-        )
-        if not has_matching:
-            missing.append(required)
-
-    return missing

autogpt_platform/backend/backend/api/features/oauth.py

@@ -1,833 +0,0 @@
-"""
-OAuth 2.0 Provider Endpoints
-
-Implements OAuth 2.0 Authorization Code flow with PKCE support.
-
-Flow:
-1. User clicks "Login with AutoGPT" in 3rd party app
-2. App redirects user to /auth/authorize with client_id, redirect_uri, scope, state
-3. User sees consent screen (if not already logged in, redirects to login first)
-4. User approves → backend creates authorization code
-5. User redirected back to app with code
-6. App exchanges code for access/refresh tokens at /api/oauth/token
-7. App uses access token to call external API endpoints
-"""
-
-import io
-import logging
-import os
-import uuid
-from datetime import datetime
-from typing import Literal, Optional
-from urllib.parse import urlencode
-
-from autogpt_libs.auth import get_user_id
-from fastapi import APIRouter, Body, HTTPException, Security, UploadFile, status
-from gcloud.aio import storage as async_storage
-from PIL import Image
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel, Field
-
-from backend.data.auth.oauth import (
-    InvalidClientError,
-    InvalidGrantError,
-    OAuthApplicationInfo,
-    TokenIntrospectionResult,
-    consume_authorization_code,
-    create_access_token,
-    create_authorization_code,
-    create_refresh_token,
-    get_oauth_application,
-    get_oauth_application_by_id,
-    introspect_token,
-    list_user_oauth_applications,
-    refresh_tokens,
-    revoke_access_token,
-    revoke_refresh_token,
-    update_oauth_application,
-    validate_client_credentials,
-    validate_redirect_uri,
-    validate_scopes,
-)
-from backend.util.settings import Settings
-from backend.util.virus_scanner import scan_content_safe
-
-settings = Settings()
-logger = logging.getLogger(__name__)
-
-router = APIRouter()
-
-
-# ============================================================================
-# Request/Response Models
-# ============================================================================
-
-
-class TokenResponse(BaseModel):
-    """OAuth 2.0 token response"""
-
-    token_type: Literal["Bearer"] = "Bearer"
-    access_token: str
-    access_token_expires_at: datetime
-    refresh_token: str
-    refresh_token_expires_at: datetime
-    scopes: list[str]
-
-
-class ErrorResponse(BaseModel):
-    """OAuth 2.0 error response"""
-
-    error: str
-    error_description: Optional[str] = None
-
-
-class OAuthApplicationPublicInfo(BaseModel):
-    """Public information about an OAuth application (for consent screen)"""
-
-    name: str
-    description: Optional[str] = None
-    logo_url: Optional[str] = None
-    scopes: list[str]
-
-
-# ============================================================================
-# Application Info Endpoint
-# ============================================================================
-
-
-@router.get(
-    "/app/{client_id}",
-    responses={
-        404: {"description": "Application not found or disabled"},
-    },
-)
-async def get_oauth_app_info(
-    client_id: str, user_id: str = Security(get_user_id)
-) -> OAuthApplicationPublicInfo:
-    """
-    Get public information about an OAuth application.
-
-    This endpoint is used by the consent screen to display application details
-    to the user before they authorize access.
-
-    Returns:
-    - name: Application name
-    - description: Application description (if provided)
-    - scopes: List of scopes the application is allowed to request
-    """
-    app = await get_oauth_application(client_id)
-    if not app or not app.is_active:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Application not found",
-        )
-
-    return OAuthApplicationPublicInfo(
-        name=app.name,
-        description=app.description,
-        logo_url=app.logo_url,
-        scopes=[s.value for s in app.scopes],
-    )
-
-
-# ============================================================================
-# Authorization Endpoint
-# ============================================================================
-
-
-class AuthorizeRequest(BaseModel):
-    """OAuth 2.0 authorization request"""
-
-    client_id: str = Field(description="Client identifier")
-    redirect_uri: str = Field(description="Redirect URI")
-    scopes: list[str] = Field(description="List of scopes")
-    state: str = Field(description="Anti-CSRF token from client")
-    response_type: str = Field(
-        default="code", description="Must be 'code' for authorization code flow"
-    )
-    code_challenge: str = Field(description="PKCE code challenge (required)")
-    code_challenge_method: Literal["S256", "plain"] = Field(
-        default="S256", description="PKCE code challenge method (S256 recommended)"
-    )
-
-
-class AuthorizeResponse(BaseModel):
-    """OAuth 2.0 authorization response with redirect URL"""
-
-    redirect_url: str = Field(description="URL to redirect the user to")
-
-
-@router.post("/authorize")
-async def authorize(
-    request: AuthorizeRequest = Body(),
-    user_id: str = Security(get_user_id),
-) -> AuthorizeResponse:
-    """
-    OAuth 2.0 Authorization Endpoint
-
-    User must be logged in (authenticated with Supabase JWT).
-    This endpoint creates an authorization code and returns a redirect URL.
-
-    PKCE (Proof Key for Code Exchange) is REQUIRED for all authorization requests.
-
-    The frontend consent screen should call this endpoint after the user approves,
-    then redirect the user to the returned `redirect_url`.
-
-    Request Body:
-    - client_id: The OAuth application's client ID
-    - redirect_uri: Where to redirect after authorization (must match registered URI)
-    - scopes: List of permissions (e.g., "EXECUTE_GRAPH READ_GRAPH")
-    - state: Anti-CSRF token provided by client (will be returned in redirect)
-    - response_type: Must be "code" (for authorization code flow)
-    - code_challenge: PKCE code challenge (required)
-    - code_challenge_method: "S256" (recommended) or "plain"
-
-    Returns:
-    - redirect_url: The URL to redirect the user to (includes authorization code)
-
-    Error cases return a redirect_url with error parameters, or raise HTTPException
-    for critical errors (like invalid redirect_uri).
-    """
-    try:
-        # Validate response_type
-        if request.response_type != "code":
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "unsupported_response_type",
-                "Only 'code' response type is supported",
-            )
-
-        # Get application
-        app = await get_oauth_application(request.client_id)
-        if not app:
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "invalid_client",
-                "Unknown client_id",
-            )
-
-        if not app.is_active:
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "invalid_client",
-                "Application is not active",
-            )
-
-        # Validate redirect URI
-        if not validate_redirect_uri(app, request.redirect_uri):
-            # For invalid redirect_uri, we can't redirect safely
-            # Must return error instead
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=(
-                    "Invalid redirect_uri. "
-                    f"Must be one of: {', '.join(app.redirect_uris)}"
-                ),
-            )
-
-        # Parse and validate scopes
-        try:
-            requested_scopes = [APIKeyPermission(s.strip()) for s in request.scopes]
-        except ValueError as e:
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "invalid_scope",
-                f"Invalid scope: {e}",
-            )
-
-        if not requested_scopes:
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "invalid_scope",
-                "At least one scope is required",
-            )
-
-        if not validate_scopes(app, requested_scopes):
-            return _error_redirect_url(
-                request.redirect_uri,
-                request.state,
-                "invalid_scope",
-                "Application is not authorized for all requested scopes. "
-                f"Allowed: {', '.join(s.value for s in app.scopes)}",
-            )
-
-        # Create authorization code
-        auth_code = await create_authorization_code(
-            application_id=app.id,
-            user_id=user_id,
-            scopes=requested_scopes,
-            redirect_uri=request.redirect_uri,
-            code_challenge=request.code_challenge,
-            code_challenge_method=request.code_challenge_method,
-        )
-
-        # Build redirect URL with authorization code
-        params = {
-            "code": auth_code.code,
-            "state": request.state,
-        }
-        redirect_url = f"{request.redirect_uri}?{urlencode(params)}"
-
-        logger.info(
-            f"Authorization code issued for user #{user_id} "
-            f"and app {app.name} (#{app.id})"
-        )
-
-        return AuthorizeResponse(redirect_url=redirect_url)
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error in authorization endpoint: {e}", exc_info=True)
-        return _error_redirect_url(
-            request.redirect_uri,
-            request.state,
-            "server_error",
-            "An unexpected error occurred",
-        )
-
-
-def _error_redirect_url(
-    redirect_uri: str,
-    state: str,
-    error: str,
-    error_description: Optional[str] = None,
-) -> AuthorizeResponse:
-    """Helper to build redirect URL with OAuth error parameters"""
-    params = {
-        "error": error,
-        "state": state,
-    }
-    if error_description:
-        params["error_description"] = error_description
-
-    redirect_url = f"{redirect_uri}?{urlencode(params)}"
-    return AuthorizeResponse(redirect_url=redirect_url)
-
-
-# ============================================================================
-# Token Endpoint
-# ============================================================================
-
-
-class TokenRequestByCode(BaseModel):
-    grant_type: Literal["authorization_code"]
-    code: str = Field(description="Authorization code")
-    redirect_uri: str = Field(
-        description="Redirect URI (must match authorization request)"
-    )
-    client_id: str
-    client_secret: str
-    code_verifier: str = Field(description="PKCE code verifier")
-
-
-class TokenRequestByRefreshToken(BaseModel):
-    grant_type: Literal["refresh_token"]
-    refresh_token: str
-    client_id: str
-    client_secret: str
-
-
-@router.post("/token")
-async def token(
-    request: TokenRequestByCode | TokenRequestByRefreshToken = Body(),
-) -> TokenResponse:
-    """
-    OAuth 2.0 Token Endpoint
-
-    Exchanges authorization code or refresh token for access token.
-
-    Grant Types:
-    1. authorization_code: Exchange authorization code for tokens
-       - Required: grant_type, code, redirect_uri, client_id, client_secret
-       - Optional: code_verifier (required if PKCE was used)
-
-    2. refresh_token: Exchange refresh token for new access token
-       - Required: grant_type, refresh_token, client_id, client_secret
-
-    Returns:
-    - access_token: Bearer token for API access (1 hour TTL)
-    - token_type: "Bearer"
-    - expires_in: Seconds until access token expires
-    - refresh_token: Token for refreshing access (30 days TTL)
-    - scopes: List of scopes
-    """
-    # Validate client credentials
-    try:
-        app = await validate_client_credentials(
-            request.client_id, request.client_secret
-        )
-    except InvalidClientError as e:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail=str(e),
-        )
-
-    # Handle authorization_code grant
-    if request.grant_type == "authorization_code":
-        # Consume authorization code
-        try:
-            user_id, scopes = await consume_authorization_code(
-                code=request.code,
-                application_id=app.id,
-                redirect_uri=request.redirect_uri,
-                code_verifier=request.code_verifier,
-            )
-        except InvalidGrantError as e:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=str(e),
-            )
-
-        # Create access and refresh tokens
-        access_token = await create_access_token(app.id, user_id, scopes)
-        refresh_token = await create_refresh_token(app.id, user_id, scopes)
-
-        logger.info(
-            f"Access token issued for user #{user_id} and app {app.name} (#{app.id})"
-            "via authorization code"
-        )
-
-        if not access_token.token or not refresh_token.token:
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail="Failed to generate tokens",
-            )
-
-        return TokenResponse(
-            token_type="Bearer",
-            access_token=access_token.token.get_secret_value(),
-            access_token_expires_at=access_token.expires_at,
-            refresh_token=refresh_token.token.get_secret_value(),
-            refresh_token_expires_at=refresh_token.expires_at,
-            scopes=list(s.value for s in scopes),
-        )
-
-    # Handle refresh_token grant
-    elif request.grant_type == "refresh_token":
-        # Refresh access token
-        try:
-            new_access_token, new_refresh_token = await refresh_tokens(
-                request.refresh_token, app.id
-            )
-        except InvalidGrantError as e:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=str(e),
-            )
-
-        logger.info(
-            f"Tokens refreshed for user #{new_access_token.user_id} "
-            f"by app {app.name} (#{app.id})"
-        )
-
-        if not new_access_token.token or not new_refresh_token.token:
-            raise HTTPException(
-                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                detail="Failed to generate tokens",
-            )
-
-        return TokenResponse(
-            token_type="Bearer",
-            access_token=new_access_token.token.get_secret_value(),
-            access_token_expires_at=new_access_token.expires_at,
-            refresh_token=new_refresh_token.token.get_secret_value(),
-            refresh_token_expires_at=new_refresh_token.expires_at,
-            scopes=list(s.value for s in new_access_token.scopes),
-        )
-
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Unsupported grant_type: {request.grant_type}. "
-            "Must be 'authorization_code' or 'refresh_token'",
-        )
-
-
-# ============================================================================
-# Token Introspection Endpoint
-# ============================================================================
-
-
-@router.post("/introspect")
-async def introspect(
-    token: str = Body(description="Token to introspect"),
-    token_type_hint: Optional[Literal["access_token", "refresh_token"]] = Body(
-        None, description="Hint about token type ('access_token' or 'refresh_token')"
-    ),
-    client_id: str = Body(description="Client identifier"),
-    client_secret: str = Body(description="Client secret"),
-) -> TokenIntrospectionResult:
-    """
-    OAuth 2.0 Token Introspection Endpoint (RFC 7662)
-
-    Allows clients to check if a token is valid and get its metadata.
-
-    Returns:
-    - active: Whether the token is currently active
-    - scopes: List of authorized scopes (if active)
-    - client_id: The client the token was issued to (if active)
-    - user_id: The user the token represents (if active)
-    - exp: Expiration timestamp (if active)
-    - token_type: "access_token" or "refresh_token" (if active)
-    """
-    # Validate client credentials
-    try:
-        await validate_client_credentials(client_id, client_secret)
-    except InvalidClientError as e:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail=str(e),
-        )
-
-    # Introspect the token
-    return await introspect_token(token, token_type_hint)
-
-
-# ============================================================================
-# Token Revocation Endpoint
-# ============================================================================
-
-
-@router.post("/revoke")
-async def revoke(
-    token: str = Body(description="Token to revoke"),
-    token_type_hint: Optional[Literal["access_token", "refresh_token"]] = Body(
-        None, description="Hint about token type ('access_token' or 'refresh_token')"
-    ),
-    client_id: str = Body(description="Client identifier"),
-    client_secret: str = Body(description="Client secret"),
-):
-    """
-    OAuth 2.0 Token Revocation Endpoint (RFC 7009)
-
-    Allows clients to revoke an access or refresh token.
-
-    Note: Revoking a refresh token does NOT revoke associated access tokens.
-    Revoking an access token does NOT revoke the associated refresh token.
-    """
-    # Validate client credentials
-    try:
-        app = await validate_client_credentials(client_id, client_secret)
-    except InvalidClientError as e:
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail=str(e),
-        )
-
-    # Try to revoke as access token first
-    # Note: We pass app.id to ensure the token belongs to the authenticated app
-    if token_type_hint != "refresh_token":
-        revoked = await revoke_access_token(token, app.id)
-        if revoked:
-            logger.info(
-                f"Access token revoked for app {app.name} (#{app.id}); "
-                f"user #{revoked.user_id}"
-            )
-            return {"status": "ok"}
-
-    # Try to revoke as refresh token
-    revoked = await revoke_refresh_token(token, app.id)
-    if revoked:
-        logger.info(
-            f"Refresh token revoked for app {app.name} (#{app.id}); "
-            f"user #{revoked.user_id}"
-        )
-        return {"status": "ok"}
-
-    # Per RFC 7009, revocation endpoint returns 200 even if token not found
-    # or if token belongs to a different application.
-    # This prevents token scanning attacks.
-    logger.warning(f"Unsuccessful token revocation attempt by app {app.name} #{app.id}")
-    return {"status": "ok"}
-
-
-# ============================================================================
-# Application Management Endpoints (for app owners)
-# ============================================================================
-
-
-@router.get("/apps/mine")
-async def list_my_oauth_apps(
-    user_id: str = Security(get_user_id),
-) -> list[OAuthApplicationInfo]:
-    """
-    List all OAuth applications owned by the current user.
-
-    Returns a list of OAuth applications with their details including:
-    - id, name, description, logo_url
-    - client_id (public identifier)
-    - redirect_uris, grant_types, scopes
-    - is_active status
-    - created_at, updated_at timestamps
-
-    Note: client_secret is never returned for security reasons.
-    """
-    return await list_user_oauth_applications(user_id)
-
-
-@router.patch("/apps/{app_id}/status")
-async def update_app_status(
-    app_id: str,
-    user_id: str = Security(get_user_id),
-    is_active: bool = Body(description="Whether the app should be active", embed=True),
-) -> OAuthApplicationInfo:
-    """
-    Enable or disable an OAuth application.
-
-    Only the application owner can update the status.
-    When disabled, the application cannot be used for new authorizations
-    and existing access tokens will fail validation.
-
-    Returns the updated application info.
-    """
-    updated_app = await update_oauth_application(
-        app_id=app_id,
-        owner_id=user_id,
-        is_active=is_active,
-    )
-
-    if not updated_app:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Application not found or you don't have permission to update it",
-        )
-
-    action = "enabled" if is_active else "disabled"
-    logger.info(f"OAuth app {updated_app.name} (#{app_id}) {action} by user #{user_id}")
-
-    return updated_app
-
-
-class UpdateAppLogoRequest(BaseModel):
-    logo_url: str = Field(description="URL of the uploaded logo image")
-
-
-@router.patch("/apps/{app_id}/logo")
-async def update_app_logo(
-    app_id: str,
-    request: UpdateAppLogoRequest = Body(),
-    user_id: str = Security(get_user_id),
-) -> OAuthApplicationInfo:
-    """
-    Update the logo URL for an OAuth application.
-
-    Only the application owner can update the logo.
-    The logo should be uploaded first using the media upload endpoint,
-    then this endpoint is called with the resulting URL.
-
-    Logo requirements:
-    - Must be square (1:1 aspect ratio)
-    - Minimum 512x512 pixels
-    - Maximum 2048x2048 pixels
-
-    Returns the updated application info.
-    """
-    if (
-        not (app := await get_oauth_application_by_id(app_id))
-        or app.owner_id != user_id
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="OAuth App not found",
-        )
-
-    # Delete the current app logo file (if any and it's in our cloud storage)
-    await _delete_app_current_logo_file(app)
-
-    updated_app = await update_oauth_application(
-        app_id=app_id,
-        owner_id=user_id,
-        logo_url=request.logo_url,
-    )
-
-    if not updated_app:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Application not found or you don't have permission to update it",
-        )
-
-    logger.info(
-        f"OAuth app {updated_app.name} (#{app_id}) logo updated by user #{user_id}"
-    )
-
-    return updated_app
-
-
-# Logo upload constraints
-LOGO_MIN_SIZE = 512
-LOGO_MAX_SIZE = 2048
-LOGO_ALLOWED_TYPES = {"image/jpeg", "image/png", "image/webp"}
-LOGO_MAX_FILE_SIZE = 3 * 1024 * 1024  # 3MB
-
-
-@router.post("/apps/{app_id}/logo/upload")
-async def upload_app_logo(
-    app_id: str,
-    file: UploadFile,
-    user_id: str = Security(get_user_id),
-) -> OAuthApplicationInfo:
-    """
-    Upload a logo image for an OAuth application.
-
-    Requirements:
-    - Image must be square (1:1 aspect ratio)
-    - Minimum 512x512 pixels
-    - Maximum 2048x2048 pixels
-    - Allowed formats: JPEG, PNG, WebP
-    - Maximum file size: 3MB
-
-    The image is uploaded to cloud storage and the app's logoUrl is updated.
-    Returns the updated application info.
-    """
-    # Verify ownership to reduce vulnerability to DoS(torage) or DoM(oney) attacks
-    if (
-        not (app := await get_oauth_application_by_id(app_id))
-        or app.owner_id != user_id
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="OAuth App not found",
-        )
-
-    # Check GCS configuration
-    if not settings.config.media_gcs_bucket_name:
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail="Media storage is not configured",
-        )
-
-    # Validate content type
-    content_type = file.content_type
-    if content_type not in LOGO_ALLOWED_TYPES:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=f"Invalid file type. Allowed: JPEG, PNG, WebP. Got: {content_type}",
-        )
-
-    # Read file content
-    try:
-        file_bytes = await file.read()
-    except Exception as e:
-        logger.error(f"Error reading logo file: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Failed to read uploaded file",
-        )
-
-    # Check file size
-    if len(file_bytes) > LOGO_MAX_FILE_SIZE:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=(
-                "File too large. "
-                f"Maximum size is {LOGO_MAX_FILE_SIZE // 1024 // 1024}MB"
-            ),
-        )
-
-    # Validate image dimensions
-    try:
-        image = Image.open(io.BytesIO(file_bytes))
-        width, height = image.size
-
-        if width != height:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Logo must be square. Got {width}x{height}",
-            )
-
-        if width < LOGO_MIN_SIZE:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Logo too small. Minimum {LOGO_MIN_SIZE}x{LOGO_MIN_SIZE}. "
-                f"Got {width}x{height}",
-            )
-
-        if width > LOGO_MAX_SIZE:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=f"Logo too large. Maximum {LOGO_MAX_SIZE}x{LOGO_MAX_SIZE}. "
-                f"Got {width}x{height}",
-            )
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error validating logo image: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Invalid image file",
-        )
-
-    # Scan for viruses
-    filename = file.filename or "logo"
-    await scan_content_safe(file_bytes, filename=filename)
-
-    # Generate unique filename
-    file_ext = os.path.splitext(filename)[1].lower() or ".png"
-    unique_filename = f"{uuid.uuid4()}{file_ext}"
-    storage_path = f"oauth-apps/{app_id}/logo/{unique_filename}"
-
-    # Upload to GCS
-    try:
-        async with async_storage.Storage() as async_client:
-            bucket_name = settings.config.media_gcs_bucket_name
-
-            await async_client.upload(
-                bucket_name, storage_path, file_bytes, content_type=content_type
-            )
-
-            logo_url = f"https://storage.googleapis.com/{bucket_name}/{storage_path}"
-    except Exception as e:
-        logger.error(f"Error uploading logo to GCS: {e}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail="Failed to upload logo",
-        )
-
-    # Delete the current app logo file (if any and it's in our cloud storage)
-    await _delete_app_current_logo_file(app)
-
-    # Update the app with the new logo URL
-    updated_app = await update_oauth_application(
-        app_id=app_id,
-        owner_id=user_id,
-        logo_url=logo_url,
-    )
-
-    if not updated_app:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Application not found or you don't have permission to update it",
-        )
-
-    logger.info(
-        f"OAuth app {updated_app.name} (#{app_id}) logo uploaded by user #{user_id}"
-    )
-
-    return updated_app
-
-
-async def _delete_app_current_logo_file(app: OAuthApplicationInfo):
-    """
-    Delete the current logo file for the given app, if there is one in our cloud storage
-    """
-    bucket_name = settings.config.media_gcs_bucket_name
-    storage_base_url = f"https://storage.googleapis.com/{bucket_name}/"
-
-    if app.logo_url and app.logo_url.startswith(storage_base_url):
-        # Parse blob path from URL: https://storage.googleapis.com/{bucket}/{path}
-        old_path = app.logo_url.replace(storage_base_url, "")
-        try:
-            async with async_storage.Storage() as async_client:
-                await async_client.delete(bucket_name, old_path)
-            logger.info(f"Deleted old logo for OAuth app #{app.id}: {old_path}")
-        except Exception as e:
-            # Log but don't fail - the new logo was uploaded successfully
-            logger.warning(
-                f"Failed to delete old logo for OAuth app #{app.id}: {e}", exc_info=e
-            )

autogpt_platform/backend/backend/api/utils/openapi.py

@@ -1,41 +0,0 @@
-from fastapi import FastAPI
-
-
-def sort_openapi(app: FastAPI) -> None:
-    """
-    Patch a FastAPI instance's `openapi()` method to sort the endpoints,
-    schemas, and responses.
-    """
-    wrapped_openapi = app.openapi
-
-    def custom_openapi():
-        if app.openapi_schema:
-            return app.openapi_schema
-
-        openapi_schema = wrapped_openapi()
-
-        # Sort endpoints
-        openapi_schema["paths"] = dict(sorted(openapi_schema["paths"].items()))
-
-        # Sort endpoints -> methods
-        for p in openapi_schema["paths"].keys():
-            openapi_schema["paths"][p] = dict(
-                sorted(openapi_schema["paths"][p].items())
-            )
-
-            # Sort endpoints -> methods -> responses
-            for m in openapi_schema["paths"][p].keys():
-                openapi_schema["paths"][p][m]["responses"] = dict(
-                    sorted(openapi_schema["paths"][p][m]["responses"].items())
-                )
-
-        # Sort schemas and responses as well
-        for k in openapi_schema["components"].keys():
-            openapi_schema["components"][k] = dict(
-                sorted(openapi_schema["components"][k].items())
-            )
-
-        app.openapi_schema = openapi_schema
-        return openapi_schema
-
-    app.openapi = custom_openapi

autogpt_platform/backend/backend/app.py

@@ -36,10 +36,10 @@ def main(**kwargs):
     Run all the processes required for the AutoGPT-server (REST and WebSocket APIs).
     """
 
-    from backend.api.rest_api import AgentServer
-    from backend.api.ws_api import WebsocketServer
     from backend.executor import DatabaseManager, ExecutionManager, Scheduler
     from backend.notifications import NotificationManager
+    from backend.server.rest_api import AgentServer
+    from backend.server.ws_api import WebsocketServer
 
     run_processes(
         DatabaseManager().set_log_level("warning"),

autogpt_platform/backend/backend/blocks/agent.py

@@ -11,7 +11,7 @@ from backend.data.block import (
     BlockType,
     get_block,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
+from backend.data.execution import ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
@@ -72,9 +72,9 @@ class AgentExecutorBlock(Block):
         input_data: Input,
         *,
         graph_exec_id: str,
-        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
+
         from backend.executor import utils as execution_utils
 
         graph_exec = await execution_utils.add_graph_execution(
@@ -83,9 +83,8 @@ class AgentExecutorBlock(Block):
             user_id=input_data.user_id,
             inputs=input_data.inputs,
             nodes_input_masks=input_data.nodes_input_masks,
-            execution_context=execution_context.model_copy(
-                update={"parent_execution_id": graph_exec_id},
-            ),
+            parent_graph_exec_id=graph_exec_id,
+            is_sub_graph=True,  # AgentExecutorBlock executions are always sub-graphs
         )
 
         logger = execution_utils.LogMetadata(

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,7 +1,6 @@
 from typing import Any
 
 from backend.blocks.llm import (
-    DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
     AIBlockBase,
@@ -50,7 +49,7 @@ class AIConditionBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for evaluating the condition.",
             advanced=False,
         )
@@ -82,7 +81,7 @@ class AIConditionBlock(AIBlockBase):
                 "condition": "the input is an email address",
                 "yes_value": "Valid email",
                 "no_value": "Not an email",
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -20,7 +20,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.exceptions import BlockExecutionError
 from backend.util.request import Requests
 
 TEST_CREDENTIALS = APIKeyCredentials(
@@ -247,11 +246,7 @@ class AIShortformVideoCreatorBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(
@@ -427,11 +422,7 @@ class AIAdMakerVideoCreatorBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(
@@ -608,11 +599,7 @@ class AIScreenshotToVideoAdBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/airtable/_api.py

@@ -1371,7 +1371,7 @@ async def create_base(
     if tables:
         params["tables"] = tables
 
-    logger.debug(f"Creating Airtable base with params: {params}")
+    print(params)
 
     response = await Requests().post(
         "https://api.airtable.com/v0/meta/bases",

autogpt_platform/backend/backend/blocks/branching.py

@@ -106,10 +106,7 @@ class ConditionBlock(Block):
             ComparisonOperator.LESS_THAN_OR_EQUAL: lambda a, b: a <= b,
         }
 
-        try:
-            result = comparison_funcs[operator](value1, value2)
-        except Exception as e:
-            raise ValueError(f"Comparison failed: {e}") from e
+        result = comparison_funcs[operator](value1, value2)
 
         yield "result", result
 

autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py

@@ -182,10 +182,13 @@ class DataForSeoRelatedKeywordsBlock(Block):
             if results and len(results) > 0:
                 # results is a list, get the first element
                 first_result = results[0] if isinstance(results, list) else results
-                # Handle missing key, null value, or valid list value
-                if isinstance(first_result, dict):
-                    items = first_result.get("items") or []
-                else:
+                items = (
+                    first_result.get("items", [])
+                    if isinstance(first_result, dict)
+                    else []
+                )
+                # Ensure items is never None
+                if items is None:
                     items = []
                 for item in items:
                     # Extract keyword_data from the item

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -1,9 +1,8 @@
 import base64
 import io
 import mimetypes
-from enum import Enum
 from pathlib import Path
-from typing import Any, Literal, cast
+from typing import Any
 
 import discord
 from pydantic import SecretStr
@@ -34,19 +33,6 @@ TEST_CREDENTIALS = TEST_BOT_CREDENTIALS
 TEST_CREDENTIALS_INPUT = TEST_BOT_CREDENTIALS_INPUT
 
 
-class ThreadArchiveDuration(str, Enum):
-    """Discord thread auto-archive duration options"""
-
-    ONE_HOUR = "60"
-    ONE_DAY = "1440"
-    THREE_DAYS = "4320"
-    ONE_WEEK = "10080"
-
-    def to_minutes(self) -> int:
-        """Convert the duration string to minutes for Discord API"""
-        return int(self.value)
-
-
 class ReadDiscordMessagesBlock(Block):
     class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
@@ -1180,211 +1166,3 @@ class DiscordChannelInfoBlock(Block):
             raise ValueError(f"Login error occurred: {login_err}")
         except Exception as e:
             raise ValueError(f"An error occurred: {e}")
-
-
-class CreateDiscordThreadBlock(Block):
-    class Input(BlockSchemaInput):
-        credentials: DiscordCredentials = DiscordCredentialsField()
-        channel_name: str = SchemaField(
-            description="Channel ID or channel name to create the thread in"
-        )
-        server_name: str = SchemaField(
-            description="Server name (only needed if using channel name)",
-            advanced=True,
-            default="",
-        )
-        thread_name: str = SchemaField(description="The name of the thread to create")
-        is_private: bool = SchemaField(
-            description="Whether to create a private thread (requires Boost Level 2+) or public thread",
-            default=False,
-        )
-        auto_archive_duration: ThreadArchiveDuration = SchemaField(
-            description="Duration before the thread is automatically archived",
-            advanced=True,
-            default=ThreadArchiveDuration.ONE_WEEK,
-        )
-        message_content: str = SchemaField(
-            description="Optional initial message to send in the thread",
-            advanced=True,
-            default="",
-        )
-
-    class Output(BlockSchemaOutput):
-        status: str = SchemaField(description="Operation status")
-        thread_id: str = SchemaField(description="ID of the created thread")
-        thread_name: str = SchemaField(description="Name of the created thread")
-
-    def __init__(self):
-        super().__init__(
-            id="e8f3c9a2-7b5d-4f1e-9c6a-3d8e2b4f7a1c",
-            input_schema=CreateDiscordThreadBlock.Input,
-            output_schema=CreateDiscordThreadBlock.Output,
-            description="Creates a new thread in a Discord channel.",
-            categories={BlockCategory.SOCIAL},
-            test_input={
-                "channel_name": "general",
-                "thread_name": "Test Thread",
-                "is_private": False,
-                "auto_archive_duration": ThreadArchiveDuration.ONE_HOUR,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("status", "Thread created successfully"),
-                ("thread_id", "123456789012345678"),
-                ("thread_name", "Test Thread"),
-            ],
-            test_mock={
-                "create_thread": lambda *args, **kwargs: {
-                    "status": "Thread created successfully",
-                    "thread_id": "123456789012345678",
-                    "thread_name": "Test Thread",
-                }
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def create_thread(
-        self,
-        token: str,
-        channel_name: str,
-        server_name: str | None,
-        thread_name: str,
-        is_private: bool,
-        auto_archive_duration: ThreadArchiveDuration,
-        message_content: str,
-    ) -> dict:
-        intents = discord.Intents.default()
-        intents.guilds = True
-        intents.message_content = True  # Required for sending messages in threads
-        client = discord.Client(intents=intents)
-
-        result = {}
-
-        @client.event
-        async def on_ready():
-            channel = None
-
-            # Try to parse as channel ID first
-            try:
-                channel_id = int(channel_name)
-                try:
-                    channel = await client.fetch_channel(channel_id)
-                except discord.errors.NotFound:
-                    result["status"] = f"Channel with ID {channel_id} not found"
-                    await client.close()
-                    return
-                except discord.errors.Forbidden:
-                    result["status"] = (
-                        f"Bot does not have permission to view channel {channel_id}"
-                    )
-                    await client.close()
-                    return
-            except ValueError:
-                # Not an ID, treat as channel name
-                # Collect all matching channels to detect duplicates
-                matching_channels = []
-                for guild in client.guilds:
-                    # Skip guilds if server_name is provided and doesn't match
-                    if (
-                        server_name
-                        and server_name.strip()
-                        and guild.name != server_name
-                    ):
-                        continue
-                    for ch in guild.text_channels:
-                        if ch.name == channel_name:
-                            matching_channels.append(ch)
-
-                if not matching_channels:
-                    result["status"] = f"Channel not found: {channel_name}"
-                    await client.close()
-                    return
-                elif len(matching_channels) > 1:
-                    result["status"] = (
-                        f"Multiple channels named '{channel_name}' found. "
-                        "Please specify server_name to disambiguate."
-                    )
-                    await client.close()
-                    return
-                else:
-                    channel = matching_channels[0]
-
-            if not channel:
-                result["status"] = "Failed to resolve channel"
-                await client.close()
-                return
-
-            # Type check - ensure it's a text channel that can create threads
-            if not hasattr(channel, "create_thread"):
-                result["status"] = (
-                    f"Channel {channel_name} cannot create threads (not a text channel)"
-                )
-                await client.close()
-                return
-
-            # After the hasattr check, we know channel is a TextChannel
-            channel = cast(discord.TextChannel, channel)
-
-            try:
-                # Create the thread using discord.py 2.0+ API
-                thread_type = (
-                    discord.ChannelType.private_thread
-                    if is_private
-                    else discord.ChannelType.public_thread
-                )
-
-                # Cast to the specific Literal type that discord.py expects
-                duration_minutes = cast(
-                    Literal[60, 1440, 4320, 10080], auto_archive_duration.to_minutes()
-                )
-
-                # The 'type' parameter exists in discord.py 2.0+ but isn't in type stubs yet
-                # pyright: ignore[reportCallIssue]
-                thread = await channel.create_thread(
-                    name=thread_name,
-                    type=thread_type,
-                    auto_archive_duration=duration_minutes,
-                )
-
-                # Send initial message if provided
-                if message_content:
-                    await thread.send(message_content)
-
-                result["status"] = "Thread created successfully"
-                result["thread_id"] = str(thread.id)
-                result["thread_name"] = thread.name
-
-            except discord.errors.Forbidden as e:
-                result["status"] = (
-                    f"Bot does not have permission to create threads in this channel. {str(e)}"
-                )
-            except Exception as e:
-                result["status"] = f"Error creating thread: {str(e)}"
-            finally:
-                await client.close()
-
-        await client.start(token)
-        return result
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            result = await self.create_thread(
-                token=credentials.api_key.get_secret_value(),
-                channel_name=input_data.channel_name,
-                server_name=input_data.server_name or None,
-                thread_name=input_data.thread_name,
-                is_private=input_data.is_private,
-                auto_archive_duration=input_data.auto_archive_duration,
-                message_content=input_data.message_content,
-            )
-
-            yield "status", result.get("status", "Unknown error")
-            if "thread_id" in result:
-                yield "thread_id", result["thread_id"]
-            if "thread_name" in result:
-                yield "thread_name", result["thread_name"]
-
-        except discord.errors.LoginFailure as login_err:
-            raise ValueError(f"Login error occurred: {login_err}")

autogpt_platform/backend/backend/blocks/firecrawl/extract.py

@@ -15,7 +15,6 @@ from backend.sdk import (
     SchemaField,
     cost,
 )
-from backend.util.exceptions import BlockExecutionError
 
 from ._config import firecrawl
 
@@ -60,18 +59,11 @@ class FirecrawlExtractBlock(Block):
     ) -> BlockOutput:
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
-        try:
-            extract_result = app.extract(
-                urls=input_data.urls,
-                prompt=input_data.prompt,
-                schema=input_data.output_schema,
-                enable_web_search=input_data.enable_web_search,
-            )
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Extract failed: {e}",
-                block_name=self.name,
-                block_id=self.id,
-            ) from e
+        extract_result = app.extract(
+            urls=input_data.urls,
+            prompt=input_data.prompt,
+            schema=input_data.output_schema,
+            enable_web_search=input_data.enable_web_search,
+        )
 
         yield "data", extract_result.data

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -19,7 +19,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.exceptions import ModerationError
 from backend.util.file import MediaFileType, store_media_file
 
 TEST_CREDENTIALS = APIKeyCredentials(
@@ -154,8 +153,6 @@ class AIImageEditorBlock(Block):
             ),
             aspect_ratio=input_data.aspect_ratio.value,
             seed=input_data.seed,
-            user_id=user_id,
-            graph_exec_id=graph_exec_id,
         )
         yield "output_image", result
 
@@ -167,8 +164,6 @@ class AIImageEditorBlock(Block):
         input_image_b64: Optional[str],
         aspect_ratio: str,
         seed: Optional[int],
-        user_id: str,
-        graph_exec_id: str,
     ) -> MediaFileType:
         client = ReplicateClient(api_token=api_key.get_secret_value())
         input_params = {
@@ -178,21 +173,11 @@ class AIImageEditorBlock(Block):
             **({"seed": seed} if seed is not None else {}),
         }
 
-        try:
-            output: FileOutput | list[FileOutput] = await client.async_run(  # type: ignore
-                model_name,
-                input=input_params,
-                wait=False,
-            )
-        except Exception as e:
-            if "flagged as sensitive" in str(e).lower():
-                raise ModerationError(
-                    message="Content was flagged as sensitive by the model provider",
-                    user_id=user_id,
-                    graph_exec_id=graph_exec_id,
-                    moderation_type="model_provider",
-                )
-            raise ValueError(f"Model execution failed: {e}") from e
+        output: FileOutput | list[FileOutput] = await client.async_run(  # type: ignore
+            model_name,
+            input=input_params,
+            wait=False,
+        )
 
         if isinstance(output, list) and output:
             output = output[0]

autogpt_platform/backend/backend/blocks/github/example_payloads/discussion.created.json

@@ -1,108 +0,0 @@
-{
-  "action": "created",
-  "discussion": {
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "category": {
-      "id": 12345678,
-      "node_id": "DIC_kwDOJKSTjM4CXXXX",
-      "repository_id": 614765452,
-      "emoji": ":pray:",
-      "name": "Q&A",
-      "description": "Ask the community for help",
-      "created_at": "2023-03-16T09:21:07Z",
-      "updated_at": "2023-03-16T09:21:07Z",
-      "slug": "q-a",
-      "is_answerable": true
-    },
-    "answer_html_url": null,
-    "answer_chosen_at": null,
-    "answer_chosen_by": null,
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/discussions/9999",
-    "id": 5000000001,
-    "node_id": "D_kwDOJKSTjM4AYYYY",
-    "number": 9999,
-    "title": "How do I configure custom blocks?",
-    "user": {
-      "login": "curious-user",
-      "id": 22222222,
-      "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-      "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-      "url": "https://api.github.com/users/curious-user",
-      "html_url": "https://github.com/curious-user",
-      "type": "User",
-      "site_admin": false
-    },
-    "state": "open",
-    "state_reason": null,
-    "locked": false,
-    "comments": 0,
-    "created_at": "2024-12-01T17:00:00Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Question\n\nI'm trying to create a custom block for my specific use case. I've read the documentation but I'm not sure how to:\n\n1. Define the input/output schema\n2. Handle authentication\n3. Test my block locally\n\nCan someone point me to examples or provide guidance?\n\n## Environment\n\n- AutoGPT Platform version: latest\n- Python: 3.11",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/timeline"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "has_discussions": true,
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "curious-user",
-    "id": 22222222,
-    "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/curious-user",
-    "html_url": "https://github.com/curious-user",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/issues.opened.json

@@ -1,112 +0,0 @@
-{
-  "action": "opened",
-  "issue": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345",
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "labels_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/labels{/name}",
-    "comments_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/comments",
-    "events_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/events",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/issues/12345",
-    "id": 2000000001,
-    "node_id": "I_kwDOJKSTjM5wXXXX",
-    "number": 12345,
-    "title": "Bug: Application crashes when processing large files",
-    "user": {
-      "login": "bug-reporter",
-      "id": 11111111,
-      "node_id": "MDQ6VXNlcjExMTExMTEx",
-      "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-      "url": "https://api.github.com/users/bug-reporter",
-      "html_url": "https://github.com/bug-reporter",
-      "type": "User",
-      "site_admin": false
-    },
-    "labels": [
-      {
-        "id": 5272676214,
-        "node_id": "LA_kwDOJKSTjM8AAAABOkandg",
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/labels/bug",
-        "name": "bug",
-        "color": "d73a4a",
-        "default": true,
-        "description": "Something isn't working"
-      }
-    ],
-    "state": "open",
-    "locked": false,
-    "assignee": null,
-    "assignees": [],
-    "milestone": null,
-    "comments": 0,
-    "created_at": "2024-12-01T16:00:00Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "closed_at": null,
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Description\n\nWhen I try to process a file larger than 100MB, the application crashes with an out of memory error.\n\n## Steps to Reproduce\n\n1. Open the application\n2. Select a file larger than 100MB\n3. Click 'Process'\n4. Application crashes\n\n## Expected Behavior\n\nThe application should handle large files gracefully.\n\n## Environment\n\n- OS: Ubuntu 22.04\n- Python: 3.11\n- AutoGPT Version: 1.0.0",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/timeline",
-    "state_reason": null
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "open_issues_count": 190,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "bug-reporter",
-    "id": 11111111,
-    "node_id": "MDQ6VXNlcjExMTExMTEx",
-    "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/bug-reporter",
-    "html_url": "https://github.com/bug-reporter",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/release.published.json

@@ -1,97 +0,0 @@
-{
-  "action": "published",
-  "release": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789",
-    "assets_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets",
-    "upload_url": "https://uploads.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets{?name,label}",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/tag/v1.0.0",
-    "id": 123456789,
-    "author": {
-      "login": "ntindle",
-      "id": 12345678,
-      "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-      "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-      "gravatar_id": "",
-      "url": "https://api.github.com/users/ntindle",
-      "html_url": "https://github.com/ntindle",
-      "type": "User",
-      "site_admin": false
-    },
-    "node_id": "RE_kwDOJKSTjM4HWwAA",
-    "tag_name": "v1.0.0",
-    "target_commitish": "master",
-    "name": "AutoGPT Platform v1.0.0",
-    "draft": false,
-    "prerelease": false,
-    "created_at": "2024-12-01T10:00:00Z",
-    "published_at": "2024-12-01T12:00:00Z",
-    "assets": [
-      {
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/assets/987654321",
-        "id": 987654321,
-        "node_id": "RA_kwDOJKSTjM4HWwBB",
-        "name": "autogpt-v1.0.0.zip",
-        "label": "Release Package",
-        "content_type": "application/zip",
-        "state": "uploaded",
-        "size": 52428800,
-        "download_count": 0,
-        "created_at": "2024-12-01T11:30:00Z",
-        "updated_at": "2024-12-01T11:35:00Z",
-        "browser_download_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/download/v1.0.0/autogpt-v1.0.0.zip"
-      }
-    ],
-    "tarball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/tarball/v1.0.0",
-    "zipball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/zipball/v1.0.0",
-    "body": "## What's New\n\n- Feature 1: Amazing new capability\n- Feature 2: Performance improvements\n- Bug fixes and stability improvements\n\n## Breaking Changes\n\nNone\n\n## Contributors\n\nThanks to all our contributors!"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T12:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "ntindle",
-    "id": 12345678,
-    "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-    "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/ntindle",
-    "html_url": "https://github.com/ntindle",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/star.created.json

@@ -1,53 +0,0 @@
-{
-  "action": "created",
-  "starred_at": "2024-12-01T15:30:00Z",
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T15:30:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170001,
-    "watchers_count": 170001,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "awesome-contributor",
-    "id": 98765432,
-    "node_id": "MDQ6VXNlcjk4NzY1NDMy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/98765432?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/awesome-contributor",
-    "html_url": "https://github.com/awesome-contributor",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -159,391 +159,3 @@ class GithubPullRequestTriggerBlock(GitHubTriggerBase, Block):
 
 
 # --8<-- [end:GithubTriggerExample]
-
-
-class GithubStarTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub star events - useful for milestone celebrations."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "star.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#star
-            """
-
-            created: bool = False
-            deleted: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The star events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The star event that triggered the webhook ('created' or 'deleted')"
-        )
-        starred_at: str = SchemaField(
-            description="ISO timestamp when the repo was starred (empty if deleted)"
-        )
-        stargazers_count: int = SchemaField(
-            description="Current number of stars on the repository"
-        )
-        repository_name: str = SchemaField(
-            description="Full name of the repository (owner/repo)"
-        )
-        repository_url: str = SchemaField(description="URL to the repository")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="551e0a35-100b-49b7-89b8-3031322239b6",
-            description="This block triggers on GitHub star events. "
-            "Useful for celebrating milestones (e.g., 1k, 10k stars) or tracking engagement.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubStarTriggerBlock.Input,
-            output_schema=GithubStarTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="star.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("starred_at", example_payload.get("starred_at", "")),
-                ("stargazers_count", example_payload["repository"]["stargazers_count"]),
-                ("repository_name", example_payload["repository"]["full_name"]),
-                ("repository_url", example_payload["repository"]["html_url"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        yield "event", input_data.payload["action"]
-        yield "starred_at", input_data.payload.get("starred_at", "")
-        yield "stargazers_count", input_data.payload["repository"]["stargazers_count"]
-        yield "repository_name", input_data.payload["repository"]["full_name"]
-        yield "repository_url", input_data.payload["repository"]["html_url"]
-
-
-class GithubReleaseTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub release events - ideal for announcing new versions."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "release.published.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#release
-            """
-
-            published: bool = False
-            unpublished: bool = False
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            prereleased: bool = False
-            released: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The release events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The release event that triggered the webhook (e.g., 'published')"
-        )
-        release: dict = SchemaField(description="The full release object")
-        release_url: str = SchemaField(description="URL to the release page")
-        tag_name: str = SchemaField(description="The release tag name (e.g., 'v1.0.0')")
-        release_name: str = SchemaField(description="Human-readable release name")
-        body: str = SchemaField(description="Release notes/description")
-        prerelease: bool = SchemaField(description="Whether this is a prerelease")
-        draft: bool = SchemaField(description="Whether this is a draft release")
-        assets: list = SchemaField(description="List of release assets/files")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="2052dd1b-74e1-46ac-9c87-c7a0e057b60b",
-            description="This block triggers on GitHub release events. "
-            "Perfect for automating announcements to Discord, Twitter, or other platforms.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubReleaseTriggerBlock.Input,
-            output_schema=GithubReleaseTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="release.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"published": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("release", example_payload["release"]),
-                ("release_url", example_payload["release"]["html_url"]),
-                ("tag_name", example_payload["release"]["tag_name"]),
-                ("release_name", example_payload["release"]["name"]),
-                ("body", example_payload["release"]["body"]),
-                ("prerelease", example_payload["release"]["prerelease"]),
-                ("draft", example_payload["release"]["draft"]),
-                ("assets", example_payload["release"]["assets"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        release = input_data.payload["release"]
-        yield "event", input_data.payload["action"]
-        yield "release", release
-        yield "release_url", release["html_url"]
-        yield "tag_name", release["tag_name"]
-        yield "release_name", release.get("name", "")
-        yield "body", release.get("body", "")
-        yield "prerelease", release["prerelease"]
-        yield "draft", release["draft"]
-        yield "assets", release["assets"]
-
-
-class GithubIssuesTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub issues events - great for triage and notifications."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "issues.opened.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#issues
-            """
-
-            opened: bool = False
-            edited: bool = False
-            deleted: bool = False
-            closed: bool = False
-            reopened: bool = False
-            assigned: bool = False
-            unassigned: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            transferred: bool = False
-            milestoned: bool = False
-            demilestoned: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The issue events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The issue event that triggered the webhook (e.g., 'opened')"
-        )
-        number: int = SchemaField(description="The issue number")
-        issue: dict = SchemaField(description="The full issue object")
-        issue_url: str = SchemaField(description="URL to the issue")
-        issue_title: str = SchemaField(description="The issue title")
-        issue_body: str = SchemaField(description="The issue body/description")
-        labels: list = SchemaField(description="List of labels on the issue")
-        assignees: list = SchemaField(description="List of assignees")
-        state: str = SchemaField(description="Issue state ('open' or 'closed')")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="b2605464-e486-4bf4-aad3-d8a213c8a48a",
-            description="This block triggers on GitHub issues events. "
-            "Useful for automated triage, notifications, and welcoming first-time contributors.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubIssuesTriggerBlock.Input,
-            output_schema=GithubIssuesTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="issues.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"opened": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["issue"]["number"]),
-                ("issue", example_payload["issue"]),
-                ("issue_url", example_payload["issue"]["html_url"]),
-                ("issue_title", example_payload["issue"]["title"]),
-                ("issue_body", example_payload["issue"]["body"]),
-                ("labels", example_payload["issue"]["labels"]),
-                ("assignees", example_payload["issue"]["assignees"]),
-                ("state", example_payload["issue"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        issue = input_data.payload["issue"]
-        yield "event", input_data.payload["action"]
-        yield "number", issue["number"]
-        yield "issue", issue
-        yield "issue_url", issue["html_url"]
-        yield "issue_title", issue["title"]
-        yield "issue_body", issue.get("body") or ""
-        yield "labels", issue["labels"]
-        yield "assignees", issue["assignees"]
-        yield "state", issue["state"]
-
-
-class GithubDiscussionTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub discussion events - perfect for community Q&A sync."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "discussion.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#discussion
-            """
-
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            answered: bool = False
-            unanswered: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            category_changed: bool = False
-            transferred: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The discussion events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The discussion event that triggered the webhook"
-        )
-        number: int = SchemaField(description="The discussion number")
-        discussion: dict = SchemaField(description="The full discussion object")
-        discussion_url: str = SchemaField(description="URL to the discussion")
-        title: str = SchemaField(description="The discussion title")
-        body: str = SchemaField(description="The discussion body")
-        category: dict = SchemaField(description="The discussion category object")
-        category_name: str = SchemaField(description="Name of the category")
-        state: str = SchemaField(description="Discussion state")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="87f847b3-d81a-424e-8e89-acadb5c9d52b",
-            description="This block triggers on GitHub Discussions events. "
-            "Great for syncing Q&A to Discord or auto-responding to common questions. "
-            "Note: Discussions must be enabled on the repository.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubDiscussionTriggerBlock.Input,
-            output_schema=GithubDiscussionTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="discussion.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["discussion"]["number"]),
-                ("discussion", example_payload["discussion"]),
-                ("discussion_url", example_payload["discussion"]["html_url"]),
-                ("title", example_payload["discussion"]["title"]),
-                ("body", example_payload["discussion"]["body"]),
-                ("category", example_payload["discussion"]["category"]),
-                ("category_name", example_payload["discussion"]["category"]["name"]),
-                ("state", example_payload["discussion"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        discussion = input_data.payload["discussion"]
-        yield "event", input_data.payload["action"]
-        yield "number", discussion["number"]
-        yield "discussion", discussion
-        yield "discussion_url", discussion["html_url"]
-        yield "title", discussion["title"]
-        yield "body", discussion.get("body") or ""
-        yield "category", discussion["category"]
-        yield "category_name", discussion["category"]["name"]
-        yield "state", discussion["state"]

autogpt_platform/backend/backend/blocks/google/_drive.py

@@ -1,8 +1,16 @@
+import asyncio
+import mimetypes
+import uuid
+from pathlib import Path
 from typing import Any, Literal, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from backend.data.model import SchemaField
+from backend.util.file import get_exec_file_path
+from backend.util.request import Requests
+from backend.util.type import MediaFileType
+from backend.util.virus_scanner import scan_content_safe
 
 AttachmentView = Literal[
     "DOCS",
@@ -22,8 +30,8 @@ ATTACHMENT_VIEWS: tuple[AttachmentView, ...] = (
 )
 
 
-class _GoogleDriveFileBase(BaseModel):
-    """Internal base class for Google Drive file representation."""
+class GoogleDriveFile(BaseModel):
+    """Represents a single file/folder picked from Google Drive"""
 
     model_config = ConfigDict(populate_by_name=True)
 
@@ -41,115 +49,146 @@ class _GoogleDriveFileBase(BaseModel):
     )
 
 
-class GoogleDriveFile(_GoogleDriveFileBase):
-    """
-    Represents a Google Drive file/folder with optional credentials for chaining.
-
-    Used for both inputs and outputs in Google Drive blocks. The `_credentials_id`
-    field enables chaining between blocks - when one block outputs a file, the
-    next block can use the same credentials to access it.
-
-    When used with GoogleDriveFileField(), the frontend renders a combined
-    auth + file picker UI that automatically populates `_credentials_id`.
-    """
-
-    # Hidden field for credential ID - populated by frontend, preserved in outputs
-    credentials_id: Optional[str] = Field(
-        None,
-        alias="_credentials_id",
-        description="Internal: credential ID for authentication",
-    )
-
-
-def GoogleDriveFileField(
-    *,
-    title: str,
-    description: str | None = None,
-    credentials_kwarg: str = "credentials",
-    credentials_scopes: list[str] | None = None,
-    allowed_views: list[AttachmentView] | None = None,
-    allowed_mime_types: list[str] | None = None,
-    placeholder: str | None = None,
-    **kwargs: Any,
+def GoogleDrivePickerField(
+    multiselect: bool = False,
+    allow_folder_selection: bool = False,
+    allowed_views: Optional[list[AttachmentView]] = None,
+    allowed_mime_types: Optional[list[str]] = None,
+    scopes: Optional[list[str]] = None,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    placeholder: Optional[str] = None,
+    **kwargs,
 ) -> Any:
     """
-    Creates a Google Drive file input field with auto-generated credentials.
-
-    This field type produces a single UI element that handles both:
-    1. Google OAuth authentication
-    2. File selection via Google Drive Picker
-
-    The system automatically generates a credentials field, and the credentials
-    are passed to the run() method using the specified kwarg name.
+    Creates a Google Drive Picker input field.
 
     Args:
+        multiselect: Allow selecting multiple files/folders (default: False)
+        allow_folder_selection: Allow selecting folders (default: False)
+        allowed_views: List of view types to show in picker (default: ["DOCS"])
+        allowed_mime_types: Filter by MIME types (e.g., ["application/pdf"])
         title: Field title shown in UI
         description: Field description/help text
-        credentials_kwarg: Name of the kwarg that will receive GoogleCredentials
-                          in the run() method (default: "credentials")
-        credentials_scopes: OAuth scopes required (default: drive.file)
-        allowed_views: List of view types to show in picker (default: ["DOCS"])
-        allowed_mime_types: Filter by MIME types
         placeholder: Placeholder text for the button
-        **kwargs: Additional SchemaField arguments
+        **kwargs: Additional SchemaField arguments (advanced, hidden, etc.)
 
     Returns:
-        Field definition that produces GoogleDriveFile
+        Field definition that produces:
+        - Single GoogleDriveFile when multiselect=False
+        - list[GoogleDriveFile] when multiselect=True
 
     Example:
         >>> class MyBlock(Block):
-        ...     class Input(BlockSchemaInput):
-        ...         spreadsheet: GoogleDriveFile = GoogleDriveFileField(
-        ...             title="Select Spreadsheet",
-        ...             credentials_kwarg="creds",
-        ...             allowed_views=["SPREADSHEETS"],
+        ...     class Input(BlockSchema):
+        ...         document: GoogleDriveFile = GoogleDrivePickerField(
+        ...             title="Select Document",
+        ...             allowed_views=["DOCUMENTS"],
         ...         )
         ...
-        ...     async def run(
-        ...         self, input_data: Input, *, creds: GoogleCredentials, **kwargs
-        ...     ):
-        ...         # creds is automatically populated
-        ...         file = input_data.spreadsheet
+        ...         files: list[GoogleDriveFile] = GoogleDrivePickerField(
+        ...             title="Select Multiple Files",
+        ...             multiselect=True,
+        ...             allow_folder_selection=True,
+        ...         )
     """
-
-    # Determine scopes - drive.file is sufficient for picker-selected files
-    scopes = credentials_scopes or ["https://www.googleapis.com/auth/drive.file"]
-
-    # Build picker configuration with auto_credentials embedded
+    # Build configuration that will be sent to frontend
     picker_config = {
-        "multiselect": False,
-        "allow_folder_selection": False,
+        "multiselect": multiselect,
+        "allow_folder_selection": allow_folder_selection,
         "allowed_views": list(allowed_views) if allowed_views else ["DOCS"],
-        "scopes": scopes,
-        # Auto-credentials config tells frontend to include _credentials_id in output
-        "auto_credentials": {
-            "provider": "google",
-            "type": "oauth2",
-            "scopes": scopes,
-            "kwarg_name": credentials_kwarg,
-        },
     }
 
+    # Add optional configurations
     if allowed_mime_types:
         picker_config["allowed_mime_types"] = list(allowed_mime_types)
 
+    # Determine required scopes based on config
+    base_scopes = scopes if scopes is not None else []
+    picker_scopes: set[str] = set(base_scopes)
+    if allow_folder_selection:
+        picker_scopes.add("https://www.googleapis.com/auth/drive")
+    else:
+        # Use drive.file for minimal scope - only access files selected by user in picker
+        # This scope is sufficient for the picker to work. The actual API operations
+        # (read/write) use the block's credentials field which has the appropriate scopes.
+        picker_scopes.add("https://www.googleapis.com/auth/drive.file")
+
+    picker_config["scopes"] = sorted(picker_scopes)
+
+    # Set appropriate default value
+    default_value = [] if multiselect else None
+
+    # Use SchemaField to handle format properly
     return SchemaField(
-        default=None,
+        default=default_value,
         title=title,
         description=description,
-        placeholder=placeholder or "Select from Google Drive",
-        # Use google-drive-picker format so frontend renders existing component
+        placeholder=placeholder or "Choose from Google Drive",
         format="google-drive-picker",
         advanced=False,
         json_schema_extra={
             "google_drive_picker_config": picker_config,
-            # Also keep auto_credentials at top level for backend detection
-            "auto_credentials": {
-                "provider": "google",
-                "type": "oauth2",
-                "scopes": scopes,
-                "kwarg_name": credentials_kwarg,
-            },
             **kwargs,
         },
     )
+
+
+DRIVE_API_URL = "https://www.googleapis.com/drive/v3/files"
+_requests = Requests(trusted_origins=["https://www.googleapis.com"])
+
+
+def GoogleDriveAttachmentField(
+    *,
+    title: str,
+    description: str | None = None,
+    placeholder: str | None = None,
+    multiselect: bool = True,
+    allowed_mime_types: list[str] | None = None,
+    **extra: Any,
+) -> Any:
+    return GoogleDrivePickerField(
+        multiselect=multiselect,
+        allowed_views=list(ATTACHMENT_VIEWS),
+        allowed_mime_types=allowed_mime_types,
+        title=title,
+        description=description,
+        placeholder=placeholder or "Choose files from Google Drive",
+        **extra,
+    )
+
+
+async def drive_file_to_media_file(
+    drive_file: GoogleDriveFile, *, graph_exec_id: str, access_token: str
+) -> MediaFileType:
+    if drive_file.is_folder:
+        raise ValueError("Google Drive selection must be a file.")
+    if not access_token:
+        raise ValueError("Google Drive access token is required for file download.")
+
+    url = f"{DRIVE_API_URL}/{drive_file.id}?alt=media"
+    response = await _requests.get(
+        url, headers={"Authorization": f"Bearer {access_token}"}
+    )
+
+    mime_type = drive_file.mime_type or response.headers.get(
+        "content-type", "application/octet-stream"
+    )
+
+    MAX_FILE_SIZE = 100 * 1024 * 1024
+    if len(response.content) > MAX_FILE_SIZE:
+        raise ValueError(
+            f"File too large: {len(response.content)} bytes > {MAX_FILE_SIZE} bytes"
+        )
+
+    base_path = Path(get_exec_file_path(graph_exec_id, ""))
+    base_path.mkdir(parents=True, exist_ok=True)
+
+    extension = mimetypes.guess_extension(mime_type, strict=False) or ".bin"
+    filename = f"{uuid.uuid4()}{extension}"
+    target_path = base_path / filename
+
+    await scan_content_safe(response.content, filename=filename)
+    await asyncio.to_thread(target_path.write_bytes, response.content)
+
+    return MediaFileType(str(target_path.relative_to(base_path)))

autogpt_platform/backend/backend/blocks/http.py

@@ -184,13 +184,7 @@ class SendWebRequestBlock(Block):
             )
 
         # ─── Execute request ─────────────────────────────────────────
-        # Use raise_for_status=False so HTTP errors (4xx, 5xx) are returned
-        # as response objects instead of raising exceptions, allowing proper
-        # handling via client_error and server_error outputs
-        response = await Requests(
-            raise_for_status=False,
-            retry_max_attempts=1,  # allow callers to handle HTTP errors immediately
-        ).request(
+        response = await Requests().request(
             input_data.method.value,
             input_data.url,
             headers=input_data.headers,

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any
+from typing import Any, Literal
 
 from prisma.enums import ReviewStatus
 
@@ -9,9 +9,8 @@ from backend.data.block import (
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
-    BlockType,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus
+from backend.data.execution import ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField
 from backend.executor.manager import async_update_node_execution_status
@@ -45,11 +44,11 @@ class HumanInTheLoopBlock(Block):
         )
 
     class Output(BlockSchemaOutput):
-        approved_data: Any = SchemaField(
-            description="The data when approved (may be modified by reviewer)"
+        reviewed_data: Any = SchemaField(
+            description="The data after human review (may be modified)"
         )
-        rejected_data: Any = SchemaField(
-            description="The data when rejected (may be modified by reviewer)"
+        status: Literal["approved", "rejected"] = SchemaField(
+            description="Status of the review: 'approved' or 'rejected'"
         )
         review_message: str = SchemaField(
             description="Any message provided by the reviewer", default=""
@@ -62,14 +61,15 @@ class HumanInTheLoopBlock(Block):
             categories={BlockCategory.BASIC},
             input_schema=HumanInTheLoopBlock.Input,
             output_schema=HumanInTheLoopBlock.Output,
-            block_type=BlockType.HUMAN_IN_THE_LOOP,
             test_input={
                 "data": {"name": "John Doe", "age": 30},
                 "name": "User profile data",
                 "editable": True,
             },
             test_output=[
-                ("approved_data", {"name": "John Doe", "age": 30}),
+                ("reviewed_data", {"name": "John Doe", "age": 30}),
+                ("status", "approved"),
+                ("review_message", ""),
             ],
             test_mock={
                 "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
@@ -80,25 +80,9 @@ class HumanInTheLoopBlock(Block):
                     node_exec_id="test-node-exec-id",
                 ),
                 "update_node_execution_status": lambda *_args, **_kwargs: None,
-                "update_review_processed_status": lambda *_args, **_kwargs: None,
             },
         )
 
-    async def get_or_create_human_review(self, **kwargs):
-        return await get_database_manager_async_client().get_or_create_human_review(
-            **kwargs
-        )
-
-    async def update_node_execution_status(self, **kwargs):
-        return await async_update_node_execution_status(
-            db_client=get_database_manager_async_client(), **kwargs
-        )
-
-    async def update_review_processed_status(self, node_exec_id: str, processed: bool):
-        return await get_database_manager_async_client().update_review_processed_status(
-            node_exec_id, processed
-        )
-
     async def run(
         self,
         input_data: Input,
@@ -108,19 +92,20 @@ class HumanInTheLoopBlock(Block):
         graph_exec_id: str,
         graph_id: str,
         graph_version: int,
-        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
-        if not execution_context.safe_mode:
-            logger.info(
-                f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
-            )
-            yield "approved_data", input_data.data
-            yield "review_message", "Auto-approved (safe mode disabled)"
-            return
+        """
+        Execute the Human In The Loop block.
 
+        This method uses one function to handle the complete workflow - checking existing reviews
+        and creating pending ones as needed.
+        """
         try:
-            result = await self.get_or_create_human_review(
+            logger.debug(f"HITL block executing for node {node_exec_id}")
+
+            # Use the data layer to handle the complete workflow
+            db_client = get_database_manager_async_client()
+            result = await db_client.get_or_create_human_review(
                 user_id=user_id,
                 node_exec_id=node_exec_id,
                 graph_exec_id=graph_exec_id,
@@ -134,15 +119,21 @@ class HumanInTheLoopBlock(Block):
             logger.error(f"Error in HITL block for node {node_exec_id}: {str(e)}")
             raise
 
+        # Check if we're waiting for human input
         if result is None:
             logger.info(
                 f"HITL block pausing execution for node {node_exec_id} - awaiting human review"
             )
             try:
-                await self.update_node_execution_status(
+                # Set node status to REVIEW so execution manager can't mark it as COMPLETED
+                # The VALID_STATUS_TRANSITIONS will then prevent any unwanted status changes
+                # Use the proper wrapper function to ensure websocket events are published
+                await async_update_node_execution_status(
+                    db_client=db_client,
                     exec_id=node_exec_id,
                     status=ExecutionStatus.REVIEW,
                 )
+                # Execution pauses here until API routes process the review
                 return
             except Exception as e:
                 logger.error(
@@ -150,17 +141,20 @@ class HumanInTheLoopBlock(Block):
                 )
                 raise
 
+        # Review is complete (approved or rejected) - check if unprocessed
         if not result.processed:
-            await self.update_review_processed_status(
+            # Mark as processed before yielding
+            await db_client.update_review_processed_status(
                 node_exec_id=node_exec_id, processed=True
             )
 
             if result.status == ReviewStatus.APPROVED:
-                yield "approved_data", result.data
+                yield "status", "approved"
+                yield "reviewed_data", result.data
                 if result.message:
                     yield "review_message", result.message
 
             elif result.status == ReviewStatus.REJECTED:
-                yield "rejected_data", result.data
+                yield "status", "rejected"
                 if result.message:
                     yield "review_message", result.message

autogpt_platform/backend/backend/blocks/ideogram.py

@@ -2,6 +2,7 @@ from enum import Enum
 from typing import Any, Dict, Literal, Optional
 
 from pydantic import SecretStr
+from requests.exceptions import RequestException
 
 from backend.data.block import (
     Block,
@@ -331,8 +332,8 @@ class IdeogramModelBlock(Block):
         try:
             response = await Requests().post(url, headers=headers, json=data)
             return response.json()["data"][0]["url"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch image with V3 endpoint: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to fetch image with V3 endpoint: {str(e)}")
 
     async def _run_model_legacy(
         self,
@@ -384,8 +385,8 @@ class IdeogramModelBlock(Block):
         try:
             response = await Requests().post(url, headers=headers, json=data)
             return response.json()["data"][0]["url"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch image with legacy endpoint: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to fetch image with legacy endpoint: {str(e)}")
 
     async def upscale_image(self, api_key: SecretStr, image_url: str):
         url = "https://api.ideogram.ai/upscale"
@@ -412,5 +413,5 @@ class IdeogramModelBlock(Block):
 
             return (response.json())["data"][0]["url"]
 
-        except Exception as e:
-            raise ValueError(f"Failed to upscale image: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to upscale image: {str(e)}")

autogpt_platform/backend/backend/blocks/io.py

@@ -2,8 +2,6 @@ import copy
 from datetime import date, time
 from typing import Any, Optional
 
-# Import for Google Drive file input block
-from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -648,119 +646,6 @@ class AgentTableInputBlock(AgentInputBlock):
         yield "result", input_data.value if input_data.value is not None else []
 
 
-class AgentGoogleDriveFileInputBlock(AgentInputBlock):
-    """
-    This block allows users to select a file from Google Drive.
-
-    It provides a Google Drive file picker UI that handles both authentication
-    and file selection. The selected file information (ID, name, URL, etc.)
-    is output for use by other blocks like Google Sheets Read.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[GoogleDriveFile] = SchemaField(
-            description="The selected Google Drive file.",
-            default=None,
-            advanced=False,
-            title="Selected File",
-        )
-        allowed_views: list[AttachmentView] = SchemaField(
-            description="Which views to show in the file picker (DOCS, SPREADSHEETS, PRESENTATIONS, etc.).",
-            default_factory=lambda: ["DOCS", "SPREADSHEETS", "PRESENTATIONS"],
-            advanced=False,
-            title="Allowed Views",
-        )
-        allow_folder_selection: bool = SchemaField(
-            description="Whether to allow selecting folders.",
-            default=False,
-            advanced=True,
-            title="Allow Folder Selection",
-        )
-
-        def generate_schema(self):
-            """Generate schema for the value field with Google Drive picker format."""
-            schema = super().generate_schema()
-
-            # Default scopes for drive.file access
-            scopes = ["https://www.googleapis.com/auth/drive.file"]
-
-            # Build picker configuration
-            picker_config = {
-                "multiselect": False,  # Single file selection only for now
-                "allow_folder_selection": self.allow_folder_selection,
-                "allowed_views": (
-                    list(self.allowed_views) if self.allowed_views else ["DOCS"]
-                ),
-                "scopes": scopes,
-                # Auto-credentials config tells frontend to include _credentials_id in output
-                "auto_credentials": {
-                    "provider": "google",
-                    "type": "oauth2",
-                    "scopes": scopes,
-                    "kwarg_name": "credentials",
-                },
-            }
-
-            # Set format and config for frontend to render Google Drive picker
-            schema["format"] = "google-drive-picker"
-            schema["google_drive_picker_config"] = picker_config
-            # Also keep auto_credentials at top level for backend detection
-            schema["auto_credentials"] = {
-                "provider": "google",
-                "type": "oauth2",
-                "scopes": scopes,
-                "kwarg_name": "credentials",
-            }
-
-            if self.value is not None:
-                schema["default"] = self.value.model_dump()
-
-            return schema
-
-    class Output(AgentInputBlock.Output):
-        result: GoogleDriveFile = SchemaField(
-            description="The selected Google Drive file with ID, name, URL, and other metadata."
-        )
-
-    def __init__(self):
-        test_file = GoogleDriveFile.model_validate(
-            {
-                "id": "test-file-id",
-                "name": "Test Spreadsheet",
-                "mimeType": "application/vnd.google-apps.spreadsheet",
-                "url": "https://docs.google.com/spreadsheets/d/test-file-id",
-            }
-        )
-        super().__init__(
-            id="d3b32f15-6fd7-40e3-be52-e083f51b19a2",
-            description="Block for selecting a file from Google Drive.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentGoogleDriveFileInputBlock.Input,
-            output_schema=AgentGoogleDriveFileInputBlock.Output,
-            test_input=[
-                {
-                    "name": "spreadsheet_input",
-                    "description": "Select a spreadsheet from Google Drive",
-                    "allowed_views": ["SPREADSHEETS"],
-                    "value": {
-                        "id": "test-file-id",
-                        "name": "Test Spreadsheet",
-                        "mimeType": "application/vnd.google-apps.spreadsheet",
-                        "url": "https://docs.google.com/spreadsheets/d/test-file-id",
-                    },
-                }
-            ],
-            test_output=[("result", test_file)],
-        )
-
-    async def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        """
-        Yields the selected Google Drive file.
-        """
-        if input_data.value is not None:
-            yield "result", input_data.value
-
-
 IO_BLOCK_IDs = [
     AgentInputBlock().id,
     AgentOutputBlock().id,
@@ -773,5 +658,4 @@ IO_BLOCK_IDs = [
     AgentDropdownInputBlock().id,
     AgentToggleInputBlock().id,
     AgentTableInputBlock().id,
-    AgentGoogleDriveFileInputBlock().id,
 ]

autogpt_platform/backend/backend/blocks/jina/search.py

@@ -16,7 +16,6 @@ from backend.data.block import (
     BlockSchemaOutput,
 )
 from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
 
 
 class SearchTheWebBlock(Block, GetRequest):
@@ -57,17 +56,7 @@ class SearchTheWebBlock(Block, GetRequest):
 
         # Prepend the Jina Search URL to the encoded query
         jina_search_url = f"https://s.jina.ai/{encoded_query}"
-
-        try:
-            results = await self.get_request(
-                jina_search_url, headers=headers, json=False
-            )
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Search failed: {e}",
-                block_name=self.name,
-                block_id=self.id,
-            ) from e
+        results = await self.get_request(jina_search_url, headers=headers, json=False)
 
         # Output the search results
         yield "results", results

autogpt_platform/backend/backend/blocks/llm.py

@@ -92,9 +92,8 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     O1 = "o1"
     O1_MINI = "o1-mini"
     # GPT-5 models
-    GPT5_2 = "gpt-5.2-2025-12-11"
-    GPT5_1 = "gpt-5.1-2025-11-13"
     GPT5 = "gpt-5-2025-08-07"
+    GPT5_1 = "gpt-5.1-2025-11-13"
     GPT5_MINI = "gpt-5-mini-2025-08-07"
     GPT5_NANO = "gpt-5-nano-2025-08-07"
     GPT5_CHAT = "gpt-5-chat-latest"
@@ -195,9 +194,8 @@ MODEL_METADATA = {
     LlmModel.O1: ModelMetadata("openai", 200000, 100000),  # o1-2024-12-17
     LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
     # GPT-5 models
-    LlmModel.GPT5_2: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
+    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
@@ -305,8 +303,6 @@ MODEL_METADATA = {
     LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
 }
 
-DEFAULT_LLM_MODEL = LlmModel.GPT5_2
-
 for model in LlmModel:
     if model not in MODEL_METADATA:
         raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
@@ -794,7 +790,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -859,7 +855,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
             test_input={
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "expected_format": {
                     "key1": "value1",
@@ -1225,7 +1221,7 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -1321,7 +1317,7 @@ class AITextSummarizerBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for summarizing the text.",
         )
         focus: str = SchemaField(
@@ -1538,7 +1534,7 @@ class AIConversationBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for the conversation.",
         )
         credentials: AICredentials = AICredentialsField()
@@ -1576,7 +1572,7 @@ class AIConversationBlock(AIBlockBase):
                     },
                     {"role": "user", "content": "Where was it played?"},
                 ],
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
@@ -1639,7 +1635,7 @@ class AIListGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default=LlmModel.GPT4O,
             description="The language model to use for generating the list.",
             advanced=True,
         )
@@ -1696,7 +1692,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
                     "fictional worlds."
                 ),
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
                 "force_json_output": False,

autogpt_platform/backend/backend/blocks/reddit.py

@@ -1,4 +1,3 @@
-import logging
 from datetime import datetime, timezone
 from typing import Iterator, Literal
 
@@ -65,7 +64,6 @@ class RedditComment(BaseModel):
 
 
 settings = Settings()
-logger = logging.getLogger(__name__)
 
 
 def get_praw(creds: RedditCredentials) -> praw.Reddit:
@@ -79,7 +77,7 @@ def get_praw(creds: RedditCredentials) -> praw.Reddit:
     me = client.user.me()
     if not me:
         raise ValueError("Invalid Reddit credentials.")
-    logger.info(f"Logged in as Reddit user: {me.name}")
+    print(f"Logged in as Reddit user: {me.name}")
     return client
 
 

autogpt_platform/backend/backend/blocks/replicate/replicate_block.py

@@ -18,7 +18,6 @@ from backend.data.block import (
     BlockSchemaOutput,
 )
 from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
-from backend.util.exceptions import BlockExecutionError, BlockInputError
 
 logger = logging.getLogger(__name__)
 
@@ -112,27 +111,9 @@ class ReplicateModelBlock(Block):
             yield "status", "succeeded"
             yield "model_name", input_data.model_name
         except Exception as e:
-            error_msg = str(e)
-            logger.error(f"Error running Replicate model: {error_msg}")
-
-            # Input validation errors (422, 400) → BlockInputError
-            if (
-                "422" in error_msg
-                or "Input validation failed" in error_msg
-                or "400" in error_msg
-            ):
-                raise BlockInputError(
-                    message=f"Invalid model inputs: {error_msg}",
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from e
-            # Everything else → BlockExecutionError
-            else:
-                raise BlockExecutionError(
-                    message=f"Replicate model error: {error_msg}",
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from e
+            error_msg = f"Unexpected error running Replicate model: {str(e)}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
     async def run_model(self, model_ref: str, model_inputs: dict, api_key: SecretStr):
         """

autogpt_platform/backend/backend/blocks/search.py

@@ -45,16 +45,10 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.topic
         url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
-
-        # Note: User-Agent is now automatically set by the request library
-        # to comply with Wikimedia's robot policy (https://w.wiki/4wJS)
-        try:
-            response = await self.get_request(url, json=True)
-            if "extract" not in response:
-                raise ValueError(f"Unable to parse Wikipedia response: {response}")
-            yield "summary", response["extract"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch Wikipedia summary: {e}") from e
+        response = await self.get_request(url, json=True)
+        if "extract" not in response:
+            raise RuntimeError(f"Unable to parse Wikipedia response: {response}")
+        yield "summary", response["extract"]
 
 
 TEST_CREDENTIALS = APIKeyCredentials(

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -1,11 +1,8 @@
 import logging
 import re
 from collections import Counter
-from concurrent.futures import Future
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel
-
 import backend.blocks.llm as llm
 from backend.blocks.agent import AgentExecutorBlock
 from backend.data.block import (
@@ -23,41 +20,16 @@ from backend.data.dynamic_fields import (
     is_dynamic_field,
     is_tool_pin,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
-from backend.util.prompt import MAIN_OBJECTIVE_PREFIX
 
 if TYPE_CHECKING:
     from backend.data.graph import Link, Node
-    from backend.executor.manager import ExecutionProcessor
 
 logger = logging.getLogger(__name__)
 
 
-class ToolInfo(BaseModel):
-    """Processed tool call information."""
-
-    tool_call: Any  # The original tool call object from LLM response
-    tool_name: str  # The function name
-    tool_def: dict[str, Any]  # The tool definition from tool_functions
-    input_data: dict[str, Any]  # Processed input data ready for tool execution
-    field_mapping: dict[str, str]  # Field name mapping for the tool
-
-
-class ExecutionParams(BaseModel):
-    """Tool execution parameters."""
-
-    user_id: str
-    graph_id: str
-    node_id: str
-    graph_version: int
-    graph_exec_id: str
-    node_exec_id: str
-    execution_context: "ExecutionContext"
-
-
 def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
     """
     Return a list of tool_call_ids if the entry is a tool request.
@@ -133,50 +105,6 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
-def _combine_tool_responses(tool_outputs: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """
-    Combine multiple Anthropic tool responses into a single user message.
-    For non-Anthropic formats, returns the original list unchanged.
-    """
-    if len(tool_outputs) <= 1:
-        return tool_outputs
-
-    # Anthropic responses have role="user", type="message", and content is a list with tool_result items
-    anthropic_responses = [
-        output
-        for output in tool_outputs
-        if (
-            output.get("role") == "user"
-            and output.get("type") == "message"
-            and isinstance(output.get("content"), list)
-            and any(
-                item.get("type") == "tool_result"
-                for item in output.get("content", [])
-                if isinstance(item, dict)
-            )
-        )
-    ]
-
-    if len(anthropic_responses) > 1:
-        combined_content = [
-            item for response in anthropic_responses for item in response["content"]
-        ]
-
-        combined_response = {
-            "role": "user",
-            "type": "message",
-            "content": combined_content,
-        }
-
-        non_anthropic_responses = [
-            output for output in tool_outputs if output not in anthropic_responses
-        ]
-
-        return [combined_response] + non_anthropic_responses
-
-    return tool_outputs
-
-
 def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
     """
     Safely convert raw_response to dictionary format for conversation history.
@@ -226,7 +154,7 @@ class SmartDecisionMakerBlock(Block):
         )
         model: llm.LlmModel = SchemaField(
             title="LLM Model",
-            default=llm.DEFAULT_LLM_MODEL,
+            default=llm.LlmModel.GPT4O,
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
@@ -276,17 +204,6 @@ class SmartDecisionMakerBlock(Block):
             default="localhost:11434",
             description="Ollama host for local  models",
         )
-        agent_mode_max_iterations: int = SchemaField(
-            title="Agent Mode Max Iterations",
-            description="Maximum iterations for agent mode. 0 = traditional mode (single LLM call, yield tool calls for external execution), -1 = infinite agent mode (loop until finished), 1+ = agent mode with max iterations limit.",
-            advanced=True,
-            default=0,
-        )
-        conversation_compaction: bool = SchemaField(
-            default=True,
-            title="Context window auto-compaction",
-            description="Automatically compact the context window once it hits the limit",
-        )
 
         @classmethod
         def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
@@ -589,7 +506,6 @@ class SmartDecisionMakerBlock(Block):
         Returns the response if successful, raises ValueError if validation fails.
         """
         resp = await llm.llm_call(
-            compress_prompt_to_fit=input_data.conversation_compaction,
             credentials=credentials,
             llm_model=input_data.model,
             prompt=current_prompt,
@@ -677,291 +593,6 @@ class SmartDecisionMakerBlock(Block):
 
         return resp
 
-    def _process_tool_calls(
-        self, response, tool_functions: list[dict[str, Any]]
-    ) -> list[ToolInfo]:
-        """Process tool calls and extract tool definitions, arguments, and input data.
-
-        Returns a list of tool info dicts with:
-        - tool_call: The original tool call object
-        - tool_name: The function name
-        - tool_def: The tool definition from tool_functions
-        - input_data: Processed input data dict (includes None values)
-        - field_mapping: Field name mapping for the tool
-        """
-        if not response.tool_calls:
-            return []
-
-        processed_tools = []
-        for tool_call in response.tool_calls:
-            tool_name = tool_call.function.name
-            tool_args = json.loads(tool_call.function.arguments)
-
-            tool_def = next(
-                (
-                    tool
-                    for tool in tool_functions
-                    if tool["function"]["name"] == tool_name
-                ),
-                None,
-            )
-            if not tool_def:
-                if len(tool_functions) == 1:
-                    tool_def = tool_functions[0]
-                else:
-                    continue
-
-            # Build input data for the tool
-            input_data = {}
-            field_mapping = tool_def["function"].get("_field_mapping", {})
-            if "function" in tool_def and "parameters" in tool_def["function"]:
-                expected_args = tool_def["function"]["parameters"].get("properties", {})
-                for clean_arg_name in expected_args:
-                    original_field_name = field_mapping.get(
-                        clean_arg_name, clean_arg_name
-                    )
-                    arg_value = tool_args.get(clean_arg_name)
-                    # Include all expected parameters, even if None (for backward compatibility with tests)
-                    input_data[original_field_name] = arg_value
-
-            processed_tools.append(
-                ToolInfo(
-                    tool_call=tool_call,
-                    tool_name=tool_name,
-                    tool_def=tool_def,
-                    input_data=input_data,
-                    field_mapping=field_mapping,
-                )
-            )
-
-        return processed_tools
-
-    def _update_conversation(
-        self, prompt: list[dict], response, tool_outputs: list | None = None
-    ):
-        """Update conversation history with response and tool outputs."""
-        # Don't add separate reasoning message with tool calls (breaks Anthropic's tool_use->tool_result pairing)
-        assistant_message = _convert_raw_response_to_dict(response.raw_response)
-        has_tool_calls = isinstance(assistant_message.get("content"), list) and any(
-            item.get("type") == "tool_use"
-            for item in assistant_message.get("content", [])
-        )
-
-        if response.reasoning and not has_tool_calls:
-            prompt.append(
-                {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
-            )
-
-        prompt.append(assistant_message)
-
-        if tool_outputs:
-            prompt.extend(tool_outputs)
-
-    async def _execute_single_tool_with_manager(
-        self,
-        tool_info: ToolInfo,
-        execution_params: ExecutionParams,
-        execution_processor: "ExecutionProcessor",
-    ) -> dict:
-        """Execute a single tool using the execution manager for proper integration."""
-        # Lazy imports to avoid circular dependencies
-        from backend.data.execution import NodeExecutionEntry
-
-        tool_call = tool_info.tool_call
-        tool_def = tool_info.tool_def
-        raw_input_data = tool_info.input_data
-
-        # Get sink node and field mapping
-        sink_node_id = tool_def["function"]["_sink_node_id"]
-
-        # Use proper database operations for tool execution
-        db_client = get_database_manager_async_client()
-
-        # Get target node
-        target_node = await db_client.get_node(sink_node_id)
-        if not target_node:
-            raise ValueError(f"Target node {sink_node_id} not found")
-
-        # Create proper node execution using upsert_execution_input
-        node_exec_result = None
-        final_input_data = None
-
-        # Add all inputs to the execution
-        if not raw_input_data:
-            raise ValueError(f"Tool call has no input data: {tool_call}")
-
-        for input_name, input_value in raw_input_data.items():
-            node_exec_result, final_input_data = await db_client.upsert_execution_input(
-                node_id=sink_node_id,
-                graph_exec_id=execution_params.graph_exec_id,
-                input_name=input_name,
-                input_data=input_value,
-            )
-
-        assert node_exec_result is not None, "node_exec_result should not be None"
-
-        # Create NodeExecutionEntry for execution manager
-        node_exec_entry = NodeExecutionEntry(
-            user_id=execution_params.user_id,
-            graph_exec_id=execution_params.graph_exec_id,
-            graph_id=execution_params.graph_id,
-            graph_version=execution_params.graph_version,
-            node_exec_id=node_exec_result.node_exec_id,
-            node_id=sink_node_id,
-            block_id=target_node.block_id,
-            inputs=final_input_data or {},
-            execution_context=execution_params.execution_context,
-        )
-
-        # Use the execution manager to execute the tool node
-        try:
-            # Get NodeExecutionProgress from the execution manager's running nodes
-            node_exec_progress = execution_processor.running_node_execution[
-                sink_node_id
-            ]
-
-            # Use the execution manager's own graph stats
-            graph_stats_pair = (
-                execution_processor.execution_stats,
-                execution_processor.execution_stats_lock,
-            )
-
-            # Create a completed future for the task tracking system
-            node_exec_future = Future()
-            node_exec_progress.add_task(
-                node_exec_id=node_exec_result.node_exec_id,
-                task=node_exec_future,
-            )
-
-            # Execute the node directly since we're in the SmartDecisionMaker context
-            node_exec_future.set_result(
-                await execution_processor.on_node_execution(
-                    node_exec=node_exec_entry,
-                    node_exec_progress=node_exec_progress,
-                    nodes_input_masks=None,
-                    graph_stats_pair=graph_stats_pair,
-                )
-            )
-
-            # Get outputs from database after execution completes using database manager client
-            node_outputs = await db_client.get_execution_outputs_by_node_exec_id(
-                node_exec_result.node_exec_id
-            )
-
-            # Create tool response
-            tool_response_content = (
-                json.dumps(node_outputs)
-                if node_outputs
-                else "Tool executed successfully"
-            )
-            return _create_tool_response(tool_call.id, tool_response_content)
-
-        except Exception as e:
-            logger.error(f"Tool execution with manager failed: {e}")
-            # Return error response
-            return _create_tool_response(
-                tool_call.id, f"Tool execution failed: {str(e)}"
-            )
-
-    async def _execute_tools_agent_mode(
-        self,
-        input_data,
-        credentials,
-        tool_functions: list[dict[str, Any]],
-        prompt: list[dict],
-        graph_exec_id: str,
-        node_id: str,
-        node_exec_id: str,
-        user_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
-    ):
-        """Execute tools in agent mode with a loop until finished."""
-        max_iterations = input_data.agent_mode_max_iterations
-        iteration = 0
-
-        # Execution parameters for tool execution
-        execution_params = ExecutionParams(
-            user_id=user_id,
-            graph_id=graph_id,
-            node_id=node_id,
-            graph_version=graph_version,
-            graph_exec_id=graph_exec_id,
-            node_exec_id=node_exec_id,
-            execution_context=execution_context,
-        )
-
-        current_prompt = list(prompt)
-
-        while max_iterations < 0 or iteration < max_iterations:
-            iteration += 1
-            logger.debug(f"Agent mode iteration {iteration}")
-
-            # Prepare prompt for this iteration
-            iteration_prompt = list(current_prompt)
-
-            # On the last iteration, add a special system message to encourage completion
-            if max_iterations > 0 and iteration == max_iterations:
-                last_iteration_message = {
-                    "role": "system",
-                    "content": f"{MAIN_OBJECTIVE_PREFIX}This is your last iteration ({iteration}/{max_iterations}). "
-                    "Try to complete the task with the information you have. If you cannot fully complete it, "
-                    "provide a summary of what you've accomplished and what remains to be done. "
-                    "Prefer finishing with a clear response rather than making additional tool calls.",
-                }
-                iteration_prompt.append(last_iteration_message)
-
-            # Get LLM response
-            try:
-                response = await self._attempt_llm_call_with_validation(
-                    credentials, input_data, iteration_prompt, tool_functions
-                )
-            except Exception as e:
-                yield "error", f"LLM call failed in agent mode iteration {iteration}: {str(e)}"
-                return
-
-            # Process tool calls
-            processed_tools = self._process_tool_calls(response, tool_functions)
-
-            # If no tool calls, we're done
-            if not processed_tools:
-                yield "finished", response.response
-                self._update_conversation(current_prompt, response)
-                yield "conversations", current_prompt
-                return
-
-            # Execute tools and collect responses
-            tool_outputs = []
-            for tool_info in processed_tools:
-                try:
-                    tool_response = await self._execute_single_tool_with_manager(
-                        tool_info, execution_params, execution_processor
-                    )
-                    tool_outputs.append(tool_response)
-                except Exception as e:
-                    logger.error(f"Tool execution failed: {e}")
-                    # Create error response for the tool
-                    error_response = _create_tool_response(
-                        tool_info.tool_call.id, f"Error: {str(e)}"
-                    )
-                    tool_outputs.append(error_response)
-
-            tool_outputs = _combine_tool_responses(tool_outputs)
-
-            self._update_conversation(current_prompt, response, tool_outputs)
-
-            # Yield intermediate conversation state
-            yield "conversations", current_prompt
-
-        # If we reach max iterations, yield the current state
-        if max_iterations < 0:
-            yield "finished", f"Agent mode completed after {iteration} iterations"
-        else:
-            yield "finished", f"Agent mode completed after {max_iterations} iterations (limit reached)"
-        yield "conversations", current_prompt
-
     async def run(
         self,
         input_data: Input,
@@ -972,12 +603,8 @@ class SmartDecisionMakerBlock(Block):
         graph_exec_id: str,
         node_exec_id: str,
         user_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
         **kwargs,
     ) -> BlockOutput:
-
         tool_functions = await self._create_tool_node_signatures(node_id)
         yield "tool_functions", json.dumps(tool_functions)
 
@@ -1021,52 +648,24 @@ class SmartDecisionMakerBlock(Block):
             input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
             input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
 
+        prefix = "[Main Objective Prompt]: "
+
         if input_data.sys_prompt and not any(
-            p["role"] == "system" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {
-                    "role": "system",
-                    "content": MAIN_OBJECTIVE_PREFIX + input_data.sys_prompt,
-                }
-            )
+            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
 
         if input_data.prompt and not any(
-            p["role"] == "user" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {"role": "user", "content": MAIN_OBJECTIVE_PREFIX + input_data.prompt}
-            )
+            prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        # Execute tools based on the selected mode
-        if input_data.agent_mode_max_iterations != 0:
-            # In agent mode, execute tools directly in a loop until finished
-            async for result in self._execute_tools_agent_mode(
-                input_data=input_data,
-                credentials=credentials,
-                tool_functions=tool_functions,
-                prompt=prompt,
-                graph_exec_id=graph_exec_id,
-                node_id=node_id,
-                node_exec_id=node_exec_id,
-                user_id=user_id,
-                graph_id=graph_id,
-                graph_version=graph_version,
-                execution_context=execution_context,
-                execution_processor=execution_processor,
-            ):
-                yield result
-            return
-
-        # One-off mode: single LLM call and yield tool calls for external execution
         current_prompt = list(prompt)
         max_attempts = max(1, int(input_data.retry))
         response = None
 
         last_error = None
-        for _ in range(max_attempts):
+        for attempt in range(max_attempts):
             try:
                 response = await self._attempt_llm_call_with_validation(
                     credentials, input_data, current_prompt, tool_functions

autogpt_platform/backend/backend/blocks/test/test_block.py

@@ -1,9 +1,8 @@
-from typing import Any, Type
+from typing import Type
 
 import pytest
 
-from backend.data.block import Block, BlockSchemaInput, get_blocks
-from backend.data.model import SchemaField
+from backend.data.block import Block, get_blocks
 from backend.util.test import execute_block_test
 
 SKIP_BLOCK_TESTS = {
@@ -133,148 +132,3 @@ async def test_block_ids_valid(block: Type[Block]):
         ), f"Block {block.name} ID is UUID version {parsed_uuid.version}, expected version 4"
     except ValueError:
         pytest.fail(f"Block {block.name} has invalid UUID format: {block_instance.id}")
-
-
-class TestAutoCredentialsFieldsValidation:
-    """Tests for auto_credentials field validation in BlockSchema."""
-
-    def test_duplicate_auto_credentials_kwarg_name_raises_error(self):
-        """Test that duplicate kwarg_name in auto_credentials raises ValueError."""
-
-        class DuplicateKwargSchema(BlockSchemaInput):
-            """Schema with duplicate auto_credentials kwarg_name."""
-
-            # Both fields explicitly use the same kwarg_name "credentials"
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "credentials",
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "credentials",  # Duplicate kwarg_name!
-                    }
-                },
-            )
-
-        with pytest.raises(ValueError) as exc_info:
-            DuplicateKwargSchema.get_auto_credentials_fields()
-
-        error_message = str(exc_info.value)
-        assert "Duplicate auto_credentials kwarg_name 'credentials'" in error_message
-        assert "file1" in error_message
-        assert "file2" in error_message
-
-    def test_unique_auto_credentials_kwarg_names_succeed(self):
-        """Test that unique kwarg_name values work correctly."""
-
-        class UniqueKwargSchema(BlockSchemaInput):
-            """Schema with unique auto_credentials kwarg_name values."""
-
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "file1_credentials",
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "file2_credentials",  # Different kwarg_name
-                    }
-                },
-            )
-
-        # Should not raise
-        result = UniqueKwargSchema.get_auto_credentials_fields()
-
-        assert "file1_credentials" in result
-        assert "file2_credentials" in result
-        assert result["file1_credentials"]["field_name"] == "file1"
-        assert result["file2_credentials"]["field_name"] == "file2"
-
-    def test_default_kwarg_name_is_credentials(self):
-        """Test that missing kwarg_name defaults to 'credentials'."""
-
-        class DefaultKwargSchema(BlockSchemaInput):
-            """Schema with auto_credentials missing kwarg_name."""
-
-            file: dict[str, Any] | None = SchemaField(
-                description="File input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name specified - should default to "credentials"
-                    }
-                },
-            )
-
-        result = DefaultKwargSchema.get_auto_credentials_fields()
-
-        assert "credentials" in result
-        assert result["credentials"]["field_name"] == "file"
-
-    def test_duplicate_default_kwarg_name_raises_error(self):
-        """Test that two fields with default kwarg_name raises ValueError."""
-
-        class DefaultDuplicateSchema(BlockSchemaInput):
-            """Schema where both fields omit kwarg_name, defaulting to 'credentials'."""
-
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name - defaults to "credentials"
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name - also defaults to "credentials"
-                    }
-                },
-            )
-
-        with pytest.raises(ValueError) as exc_info:
-            DefaultDuplicateSchema.get_auto_credentials_fields()
-
-        assert "Duplicate auto_credentials kwarg_name 'credentials'" in str(
-            exc_info.value
-        )

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -196,15 +196,6 @@ class TestXMLParserBlockSecurity:
             async for _ in block.run(XMLParserBlock.Input(input_xml=large_xml)):
                 pass
 
-    async def test_rejects_text_outside_root(self):
-        """Ensure parser surfaces readable errors for invalid root text."""
-        block = XMLParserBlock()
-        invalid_xml = "<root><child>value</child></root> trailing"
-
-        with pytest.raises(ValueError, match="text outside the root element"):
-            async for _ in block.run(XMLParserBlock.Input(input_xml=invalid_xml)):
-                pass
-
 
 class TestStoreMediaFileSecurity:
     """Test file storage security limits."""

autogpt_platform/backend/backend/blocks/test/test_llm.py

@@ -28,7 +28,7 @@ class TestLLMStatsTracking:
 
             response = await llm.llm_call(
                 credentials=llm.TEST_CREDENTIALS,
-                llm_model=llm.DEFAULT_LLM_MODEL,
+                llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
                 max_tokens=100,
             )
@@ -65,7 +65,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test prompt",
             expected_format={"key1": "desc1", "key2": "desc2"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore  # type: ignore
         )
 
@@ -109,7 +109,7 @@ class TestLLMStatsTracking:
         # Run the block
         input_data = llm.AITextGeneratorBlock.Input(
             prompt="Generate text",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -170,7 +170,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test prompt",
             expected_format={"key1": "desc1", "key2": "desc2"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
         )
@@ -228,7 +228,7 @@ class TestLLMStatsTracking:
 
         input_data = llm.AITextSummarizerBlock.Input(
             text=long_text,
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_tokens=100,  # Small chunks
             chunk_overlap=10,
@@ -299,7 +299,7 @@ class TestLLMStatsTracking:
             # Test with very short text (should only need 1 chunk + 1 final summary)
             input_data = llm.AITextSummarizerBlock.Input(
                 text="This is a short text.",
-                model=llm.DEFAULT_LLM_MODEL,
+                model=llm.LlmModel.GPT4O,
                 credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
                 max_tokens=1000,  # Large enough to avoid chunking
             )
@@ -346,7 +346,7 @@ class TestLLMStatsTracking:
                 {"role": "assistant", "content": "Hi there!"},
                 {"role": "user", "content": "How are you?"},
             ],
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -387,7 +387,7 @@ class TestLLMStatsTracking:
         # Run the block
         input_data = llm.AIListGeneratorBlock.Input(
             focus="test items",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_retries=3,
         )
@@ -469,7 +469,7 @@ class TestLLMStatsTracking:
         input_data = llm.AIStructuredResponseGeneratorBlock.Input(
             prompt="Test",
             expected_format={"result": "desc"},
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -513,7 +513,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             style=llm.SummaryStyle.BULLET_POINTS,
         )
@@ -558,7 +558,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             style=llm.SummaryStyle.BULLET_POINTS,
             max_tokens=1000,
@@ -593,7 +593,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 
@@ -623,7 +623,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
             max_tokens=1000,
         )
@@ -654,7 +654,7 @@ class TestAITextSummarizerValidation:
         # Create input data
         input_data = llm.AITextSummarizerBlock.Input(
             text="Some text to summarize",
-            model=llm.DEFAULT_LLM_MODEL,
+            model=llm.LlmModel.GPT4O,
             credentials=llm.TEST_CREDENTIALS_INPUT,  # type: ignore
         )
 

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1,14 +1,10 @@
 import logging
-import threading
-from collections import defaultdict
-from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.api.model import CreateGraph
-from backend.api.rest_api import AgentServer
-from backend.data.execution import ExecutionContext
 from backend.data.model import ProviderName, User
+from backend.server.model import CreateGraph
+from backend.server.rest_api import AgentServer
 from backend.usecases.sample import create_test_graph, create_test_user
 from backend.util.test import SpinTestServer, wait_execution
 
@@ -21,10 +17,10 @@ async def create_graph(s: SpinTestServer, g, u: User):
 
 
 async def create_credentials(s: SpinTestServer, u: User):
-    import backend.blocks.llm as llm_module
+    import backend.blocks.llm as llm
 
     provider = ProviderName.OPENAI
-    credentials = llm_module.TEST_CREDENTIALS
+    credentials = llm.TEST_CREDENTIALS
     return await s.agent_server.test_create_credentials(u.id, provider, credentials)
 
 
@@ -200,6 +196,8 @@ async def test_smart_decision_maker_function_signature(server: SpinTestServer):
 @pytest.mark.asyncio
 async def test_smart_decision_maker_tracks_llm_stats():
     """Test that SmartDecisionMakerBlock correctly tracks LLM usage stats."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -218,6 +216,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_tool_node_signatures method to avoid database calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call",
@@ -233,21 +232,12 @@ async def test_smart_decision_maker_tracks_llm_stats():
         # Create test input
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Should I continue with this task?",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Execute the block
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -256,9 +246,6 @@ async def test_smart_decision_maker_tracks_llm_stats():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -276,6 +263,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_parameter_validation():
     """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -322,6 +311,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_with_typo.reasoning = None
     mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -335,20 +326,11 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,  # Set retry to 2 for testing
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError after retries due to typo'd parameter name
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -360,9 +342,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -389,6 +368,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_missing_required.reasoning = None
     mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -402,19 +383,10 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError due to missing required parameter
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -426,9 +398,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -449,6 +418,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_valid.reasoning = None
     mock_response_valid.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -462,21 +433,12 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed - optional parameter missing is OK
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -485,9 +447,6 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -513,6 +472,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_all_params.reasoning = None
     mock_response_all_params.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -526,21 +487,12 @@ async def test_smart_decision_maker_parameter_validation():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Search for keywords",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed with all parameters
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -549,9 +501,6 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -564,6 +513,8 @@ async def test_smart_decision_maker_parameter_validation():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_raw_response_conversion():
     """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -633,6 +584,7 @@ async def test_smart_decision_maker_raw_response_conversion():
     )
 
     # Mock llm_call to return different responses on different calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call", new_callable=AsyncMock
@@ -648,22 +600,13 @@ async def test_smart_decision_maker_raw_response_conversion():
 
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Test prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed after retry, demonstrating our helper function works
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -672,9 +615,6 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -710,6 +650,8 @@ async def test_smart_decision_maker_raw_response_conversion():
         "I'll help you with that."  # Ollama returns string
     )
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -722,20 +664,11 @@ async def test_smart_decision_maker_raw_response_conversion():
     ):
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Simple prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -744,9 +677,6 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -766,6 +696,8 @@ async def test_smart_decision_maker_raw_response_conversion():
         "content": "Test response",
     }  # Dict format
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -778,20 +710,11 @@ async def test_smart_decision_maker_raw_response_conversion():
     ):
         input_data = SmartDecisionMakerBlock.Input(
             prompt="Another test",
-            model=llm_module.DEFAULT_LLM_MODEL,
+            model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -800,260 +723,8 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         assert "finished" in outputs
         assert outputs["finished"] == "Test response"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_agent_mode():
-    """Test that agent mode executes tools directly and loops until finished."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call that requires multiple iterations
-    mock_tool_call_1 = MagicMock()
-    mock_tool_call_1.id = "call_1"
-    mock_tool_call_1.function.name = "search_keywords"
-    mock_tool_call_1.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response_1 = MagicMock()
-    mock_response_1.response = None
-    mock_response_1.tool_calls = [mock_tool_call_1]
-    mock_response_1.prompt_tokens = 50
-    mock_response_1.completion_tokens = 25
-    mock_response_1.reasoning = "Using search tool"
-    mock_response_1.raw_response = {
-        "role": "assistant",
-        "content": None,
-        "tool_calls": [{"id": "call_1", "type": "function"}],
-    }
-
-    # Final response with no tool calls (finished)
-    mock_response_2 = MagicMock()
-    mock_response_2.response = "Task completed successfully"
-    mock_response_2.tool_calls = []
-    mock_response_2.prompt_tokens = 30
-    mock_response_2.completion_tokens = 15
-    mock_response_2.reasoning = None
-    mock_response_2.raw_response = {
-        "role": "assistant",
-        "content": "Task completed successfully",
-    }
-
-    # Mock the LLM call to return different responses on each iteration
-    llm_call_mock = AsyncMock()
-    llm_call_mock.side_effect = [mock_response_1, mock_response_2]
-
-    # Mock tool node signatures
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    # Mock database and execution components
-    mock_db_client = AsyncMock()
-    mock_node = MagicMock()
-    mock_node.block_id = "test-block-id"
-    mock_db_client.get_node.return_value = mock_node
-
-    # Mock upsert_execution_input to return proper NodeExecutionResult and input data
-    mock_node_exec_result = MagicMock()
-    mock_node_exec_result.node_exec_id = "test-tool-exec-id"
-    mock_input_data = {"query": "test", "max_keyword_difficulty": 50}
-    mock_db_client.upsert_execution_input.return_value = (
-        mock_node_exec_result,
-        mock_input_data,
-    )
-
-    # No longer need mock_execute_node since we use execution_processor.on_node_execution
-
-    with patch("backend.blocks.llm.llm_call", llm_call_mock), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ), patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ), patch(
-        "backend.executor.manager.async_update_node_execution_status",
-        new_callable=AsyncMock,
-    ), patch(
-        "backend.integrations.creds_manager.IntegrationCredentialsManager"
-    ):
-
-        # Create a mock execution context
-
-        mock_execution_context = ExecutionContext(
-            safe_mode=False,
-        )
-
-        # Create a mock execution processor for agent mode tests
-
-        mock_execution_processor = AsyncMock()
-        # Configure the execution processor mock with required attributes
-        mock_execution_processor.running_node_execution = defaultdict(MagicMock)
-        mock_execution_processor.execution_stats = MagicMock()
-        mock_execution_processor.execution_stats_lock = threading.Lock()
-
-        # Mock the on_node_execution method to return successful stats
-        mock_node_stats = MagicMock()
-        mock_node_stats.error = None  # No error
-        mock_execution_processor.on_node_execution = AsyncMock(
-            return_value=mock_node_stats
-        )
-
-        # Mock the get_execution_outputs_by_node_exec_id method
-        mock_db_client.get_execution_outputs_by_node_exec_id.return_value = {
-            "result": {"status": "success", "data": "search completed"}
-        }
-
-        # Test agent mode with max_iterations = 3
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Complete this task using tools",
-            model=llm_module.DEFAULT_LLM_MODEL,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=3,  # Enable agent mode with 3 max iterations
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify agent mode behavior
-        assert "tool_functions" in outputs  # tool_functions is yielded in both modes
-        assert "finished" in outputs
-        assert outputs["finished"] == "Task completed successfully"
-        assert "conversations" in outputs
-
-        # Verify the conversation includes tool responses
-        conversations = outputs["conversations"]
-        assert len(conversations) > 2  # Should have multiple conversation entries
-
-        # Verify LLM was called twice (once for tool call, once for finish)
-        assert llm_call_mock.call_count == 2
-
-        # Verify tool was executed via execution processor
-        assert mock_execution_processor.on_node_execution.call_count == 1
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_traditional_mode_default():
-    """Test that default behavior (agent_mode_max_iterations=0) works as traditional mode."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call
-    mock_tool_call = MagicMock()
-    mock_tool_call.function.name = "search_keywords"
-    mock_tool_call.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response = MagicMock()
-    mock_response.response = None
-    mock_response.tool_calls = [mock_tool_call]
-    mock_response.prompt_tokens = 50
-    mock_response.completion_tokens = 25
-    mock_response.reasoning = None
-    mock_response.raw_response = {"role": "assistant", "content": None}
-
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response,
-    ), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ):
-
-        # Test default behavior (traditional mode)
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Test prompt",
-            model=llm_module.DEFAULT_LLM_MODEL,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,  # Traditional mode
-        )
-
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify traditional mode behavior
-        assert (
-            "tool_functions" in outputs
-        )  # Should yield tool_functions in traditional mode
-        assert (
-            "tools_^_test-sink-node-id_~_query" in outputs
-        )  # Should yield individual tool parameters
-        assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
-        assert "conversations" in outputs

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -1,7 +1,7 @@
 """Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
 
 import json
-from unittest.mock import AsyncMock, MagicMock, Mock, patch
+from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
@@ -308,47 +308,10 @@ async def test_output_yielding_with_dynamic_fields():
     ) as mock_llm:
         mock_llm.return_value = mock_response
 
-        # Mock the database manager to avoid HTTP calls during tool execution
-        with patch(
-            "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-        ) as mock_db_manager, patch.object(
+        # Mock the function signature creation
+        with patch.object(
             block, "_create_tool_node_signatures", new_callable=AsyncMock
         ) as mock_sig:
-            # Set up the mock database manager
-            mock_db_client = AsyncMock()
-            mock_db_manager.return_value = mock_db_client
-
-            # Mock the node retrieval
-            mock_target_node = Mock()
-            mock_target_node.id = "test-sink-node-id"
-            mock_target_node.block_id = "CreateDictionaryBlock"
-            mock_target_node.block = Mock()
-            mock_target_node.block.name = "Create Dictionary"
-            mock_db_client.get_node.return_value = mock_target_node
-
-            # Mock the execution result creation
-            mock_node_exec_result = Mock()
-            mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-            mock_final_input_data = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.upsert_execution_input.return_value = (
-                mock_node_exec_result,
-                mock_final_input_data,
-            )
-
-            # Mock the output retrieval
-            mock_outputs = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                mock_outputs
-            )
-
             mock_sig.return_value = [
                 {
                     "type": "function",
@@ -373,17 +336,11 @@ async def test_output_yielding_with_dynamic_fields():
             input_data = block.input_schema(
                 prompt="Create a user dictionary",
                 credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.DEFAULT_LLM_MODEL,
-                agent_mode_max_iterations=0,  # Use traditional mode to test output yielding
+                model=llm.LlmModel.GPT4O,
             )
 
             # Run the block
             outputs = {}
-            from backend.data.execution import ExecutionContext
-
-            mock_execution_context = ExecutionContext(safe_mode=False)
-            mock_execution_processor = MagicMock()
-
             async for output_name, output_value in block.run(
                 input_data,
                 credentials=llm.TEST_CREDENTIALS,
@@ -392,9 +349,6 @@ async def test_output_yielding_with_dynamic_fields():
                 graph_exec_id="test_exec",
                 node_exec_id="test_node_exec",
                 user_id="test_user",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_value
 
@@ -557,108 +511,45 @@ async def test_validation_errors_dont_pollute_conversation():
                 }
             ]
 
-            # Mock the database manager to avoid HTTP calls during tool execution
-            with patch(
-                "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-            ) as mock_db_manager:
-                # Set up the mock database manager for agent mode
-                mock_db_client = AsyncMock()
-                mock_db_manager.return_value = mock_db_client
+            # Create input data
+            from backend.blocks import llm
 
-                # Mock the node retrieval
-                mock_target_node = Mock()
-                mock_target_node.id = "test-sink-node-id"
-                mock_target_node.block_id = "TestBlock"
-                mock_target_node.block = Mock()
-                mock_target_node.block.name = "Test Block"
-                mock_db_client.get_node.return_value = mock_target_node
+            input_data = block.input_schema(
+                prompt="Test prompt",
+                credentials=llm.TEST_CREDENTIALS_INPUT,
+                model=llm.LlmModel.GPT4O,
+                retry=3,  # Allow retries
+            )
 
-                # Mock the execution result creation
-                mock_node_exec_result = Mock()
-                mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-                mock_final_input_data = {"correct_param": "value"}
-                mock_db_client.upsert_execution_input.return_value = (
-                    mock_node_exec_result,
-                    mock_final_input_data,
-                )
+            # Run the block
+            outputs = {}
+            async for output_name, output_value in block.run(
+                input_data,
+                credentials=llm.TEST_CREDENTIALS,
+                graph_id="test_graph",
+                node_id="test_node",
+                graph_exec_id="test_exec",
+                node_exec_id="test_node_exec",
+                user_id="test_user",
+            ):
+                outputs[output_name] = output_value
 
-                # Mock the output retrieval
-                mock_outputs = {"correct_param": "value"}
-                mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                    mock_outputs
-                )
+            # Verify we had 2 LLM calls (initial + retry)
+            assert call_count == 2
 
-                # Create input data
-                from backend.blocks import llm
+            # Check the final conversation output
+            final_conversation = outputs.get("conversations", [])
 
-                input_data = block.input_schema(
-                    prompt="Test prompt",
-                    credentials=llm.TEST_CREDENTIALS_INPUT,
-                    model=llm.DEFAULT_LLM_MODEL,
-                    retry=3,  # Allow retries
-                    agent_mode_max_iterations=1,
-                )
+            # The final conversation should NOT contain the validation error message
+            error_messages = [
+                msg
+                for msg in final_conversation
+                if msg.get("role") == "user"
+                and "parameter errors" in msg.get("content", "")
+            ]
+            assert (
+                len(error_messages) == 0
+            ), "Validation error leaked into final conversation"
 
-                # Run the block
-                outputs = {}
-                from backend.data.execution import ExecutionContext
-
-                mock_execution_context = ExecutionContext(safe_mode=False)
-
-                # Create a proper mock execution processor for agent mode
-                from collections import defaultdict
-
-                mock_execution_processor = AsyncMock()
-                mock_execution_processor.execution_stats = MagicMock()
-                mock_execution_processor.execution_stats_lock = MagicMock()
-
-                # Create a mock NodeExecutionProgress for the sink node
-                mock_node_exec_progress = MagicMock()
-                mock_node_exec_progress.add_task = MagicMock()
-                mock_node_exec_progress.pop_output = MagicMock(
-                    return_value=None
-                )  # No outputs to process
-
-                # Set up running_node_execution as a defaultdict that returns our mock for any key
-                mock_execution_processor.running_node_execution = defaultdict(
-                    lambda: mock_node_exec_progress
-                )
-
-                # Mock the on_node_execution method that gets called during tool execution
-                mock_node_stats = MagicMock()
-                mock_node_stats.error = None
-                mock_execution_processor.on_node_execution.return_value = (
-                    mock_node_stats
-                )
-
-                async for output_name, output_value in block.run(
-                    input_data,
-                    credentials=llm.TEST_CREDENTIALS,
-                    graph_id="test_graph",
-                    node_id="test_node",
-                    graph_exec_id="test_exec",
-                    node_exec_id="test_node_exec",
-                    user_id="test_user",
-                    graph_version=1,
-                    execution_context=mock_execution_context,
-                    execution_processor=mock_execution_processor,
-                ):
-                    outputs[output_name] = output_value
-
-                # Verify we had at least 1 LLM call
-                assert call_count >= 1
-
-                # Check the final conversation output
-                final_conversation = outputs.get("conversations", [])
-
-                # The final conversation should NOT contain validation error messages
-                # Even if retries don't happen in agent mode, we should not leak errors
-                error_messages = [
-                    msg
-                    for msg in final_conversation
-                    if msg.get("role") == "user"
-                    and "parameter errors" in msg.get("content", "")
-                ]
-                assert (
-                    len(error_messages) == 0
-                ), "Validation error leaked into final conversation"
+            # The final conversation should only have the successful response
+            assert final_conversation[-1]["content"] == "valid"

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -14,7 +14,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
+from backend.data.execution import UserContext
 from backend.data.model import SchemaField
 
 # Shared timezone literal type for all time/date blocks
@@ -188,9 +188,10 @@ class GetCurrentTimeBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
+        self, input_data: Input, *, user_context: UserContext, **kwargs
     ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+        # Extract timezone from user_context (always present)
+        effective_timezone = user_context.timezone
 
         # Get the appropriate timezone
         tz = _get_timezone(input_data.format_type, effective_timezone)
@@ -297,10 +298,10 @@ class GetCurrentDateBlock(Block):
             ],
         )
 
-    async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
-    ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # Extract timezone from user_context (required keyword argument)
+        user_context: UserContext = kwargs["user_context"]
+        effective_timezone = user_context.timezone
 
         try:
             offset = int(input_data.offset)
@@ -403,10 +404,10 @@ class GetCurrentDateAndTimeBlock(Block):
             ],
         )
 
-    async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
-    ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # Extract timezone from user_context (required keyword argument)
+        user_context: UserContext = kwargs["user_context"]
+        effective_timezone = user_context.timezone
 
         # Get the appropriate timezone
         tz = _get_timezone(input_data.format_type, effective_timezone)

autogpt_platform/backend/backend/blocks/twitter/_builders.py

@@ -1,4 +1,4 @@
-from datetime import datetime, timedelta, timezone
+from datetime import datetime
 from typing import Any, Dict
 
 from backend.blocks.twitter._mappers import (
@@ -237,12 +237,6 @@ class TweetDurationBuilder:
 
     def add_start_time(self, start_time: datetime | None):
         if start_time:
-            # Twitter API requires start_time to be at least 10 seconds before now
-            max_start_time = datetime.now(timezone.utc) - timedelta(seconds=10)
-            if start_time.tzinfo is None:
-                start_time = start_time.replace(tzinfo=timezone.utc)
-            if start_time > max_start_time:
-                start_time = max_start_time
             self.params["start_time"] = start_time
         return self
 

autogpt_platform/backend/backend/blocks/twitter/_serializer.py

@@ -51,10 +51,8 @@ class ResponseDataSerializer(BaseSerializer):
         return serialized_item
 
     @classmethod
-    def serialize_list(cls, data: List[Dict[str, Any]] | None) -> List[Dict[str, Any]]:
+    def serialize_list(cls, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         """Serializes a list of dictionary items"""
-        if not data:
-            return []
         return [cls.serialize_dict(item) for item in data]
 
 

autogpt_platform/backend/backend/blocks/twitter/_types.py

@@ -408,7 +408,7 @@ class ListExpansionInputs(BlockSchemaInput):
 
 class TweetTimeWindowInputs(BlockSchemaInput):
     start_time: datetime | None = SchemaField(
-        description="Start time in YYYY-MM-DDTHH:mm:ssZ format. If set to a time less than 10 seconds ago, it will be automatically adjusted to 10 seconds ago (Twitter API requirement).",
+        description="Start time in YYYY-MM-DDTHH:mm:ssZ format",
         placeholder="Enter start time",
         default=None,
         advanced=False,

autogpt_platform/backend/backend/blocks/xml_parser.py

@@ -1,5 +1,5 @@
 from gravitasml.parser import Parser
-from gravitasml.token import Token, tokenize
+from gravitasml.token import tokenize
 
 from backend.data.block import Block, BlockOutput, BlockSchemaInput, BlockSchemaOutput
 from backend.data.model import SchemaField
@@ -25,38 +25,6 @@ class XMLParserBlock(Block):
             ],
         )
 
-    @staticmethod
-    def _validate_tokens(tokens: list[Token]) -> None:
-        """Ensure the XML has a single root element and no stray text."""
-        if not tokens:
-            raise ValueError("XML input is empty.")
-
-        depth = 0
-        root_seen = False
-
-        for token in tokens:
-            if token.type == "TAG_OPEN":
-                if depth == 0 and root_seen:
-                    raise ValueError("XML must have a single root element.")
-                depth += 1
-                if depth == 1:
-                    root_seen = True
-            elif token.type == "TAG_CLOSE":
-                depth -= 1
-                if depth < 0:
-                    raise SyntaxError("Unexpected closing tag in XML input.")
-            elif token.type in {"TEXT", "ESCAPE"}:
-                if depth == 0 and token.value:
-                    raise ValueError(
-                        "XML contains text outside the root element; "
-                        "wrap content in a single root tag."
-                    )
-
-        if depth != 0:
-            raise SyntaxError("Unclosed tag detected in XML input.")
-        if not root_seen:
-            raise ValueError("XML must include a root element.")
-
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         # Security fix: Add size limits to prevent XML bomb attacks
         MAX_XML_SIZE = 10 * 1024 * 1024  # 10MB limit for XML input
@@ -67,9 +35,7 @@ class XMLParserBlock(Block):
             )
 
         try:
-            tokens = list(tokenize(input_data.input_xml))
-            self._validate_tokens(tokens)
-
+            tokens = tokenize(input_data.input_xml)
             parser = Parser(tokens)
             parsed_result = parser.parse()
             yield "parsed_xml", parsed_result

autogpt_platform/backend/backend/blocks/youtube.py

@@ -1,13 +1,9 @@
-import logging
-from typing import Literal
 from urllib.parse import parse_qs, urlparse
 
-from pydantic import SecretStr
 from youtube_transcript_api._api import YouTubeTranscriptApi
 from youtube_transcript_api._errors import NoTranscriptFound
 from youtube_transcript_api._transcripts import FetchedTranscript
 from youtube_transcript_api.formatters import TextFormatter
-from youtube_transcript_api.proxies import WebshareProxyConfig
 
 from backend.data.block import (
     Block,
@@ -16,42 +12,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.model import (
-    CredentialsField,
-    CredentialsMetaInput,
-    SchemaField,
-    UserPasswordCredentials,
-)
-from backend.integrations.providers import ProviderName
-
-logger = logging.getLogger(__name__)
-
-TEST_CREDENTIALS = UserPasswordCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="webshare_proxy",
-    username=SecretStr("mock-webshare-username"),
-    password=SecretStr("mock-webshare-password"),
-    title="Mock Webshare Proxy credentials",
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-WebshareProxyCredentials = UserPasswordCredentials
-WebshareProxyCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.WEBSHARE_PROXY],
-    Literal["user_password"],
-]
-
-
-def WebshareProxyCredentialsField() -> WebshareProxyCredentialsInput:
-    return CredentialsField(
-        description="Webshare proxy credentials for fetching YouTube transcripts",
-    )
+from backend.data.model import SchemaField
 
 
 class TranscribeYoutubeVideoBlock(Block):
@@ -61,7 +22,6 @@ class TranscribeYoutubeVideoBlock(Block):
             description="The URL of the YouTube video to transcribe",
             placeholder="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
         )
-        credentials: WebshareProxyCredentialsInput = WebshareProxyCredentialsField()
 
     class Output(BlockSchemaOutput):
         video_id: str = SchemaField(description="The extracted YouTube video ID")
@@ -75,12 +35,9 @@ class TranscribeYoutubeVideoBlock(Block):
             id="f3a8f7e1-4b1d-4e5f-9f2a-7c3d5a2e6b4c",
             input_schema=TranscribeYoutubeVideoBlock.Input,
             output_schema=TranscribeYoutubeVideoBlock.Output,
-            description="Transcribes a YouTube video using a proxy.",
+            description="Transcribes a YouTube video.",
             categories={BlockCategory.SOCIAL},
-            test_input={
-                "youtube_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
+            test_input={"youtube_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"},
             test_output=[
                 ("video_id", "dQw4w9WgXcQ"),
                 (
@@ -88,9 +45,8 @@ class TranscribeYoutubeVideoBlock(Block):
                     "Never gonna give you up\nNever gonna let you down",
                 ),
             ],
-            test_credentials=TEST_CREDENTIALS,
             test_mock={
-                "get_transcript": lambda video_id, credentials: [
+                "get_transcript": lambda video_id: [
                     {"text": "Never gonna give you up"},
                     {"text": "Never gonna let you down"},
                 ],
@@ -111,31 +67,18 @@ class TranscribeYoutubeVideoBlock(Block):
                 return parsed_url.path.split("/")[2]
             if parsed_url.path[:3] == "/v/":
                 return parsed_url.path.split("/")[2]
-            if parsed_url.path.startswith("/shorts/"):
-                return parsed_url.path.split("/")[2]
         raise ValueError(f"Invalid YouTube URL: {url}")
 
-    def get_transcript(
-        self, video_id: str, credentials: WebshareProxyCredentials
-    ) -> FetchedTranscript:
+    @staticmethod
+    def get_transcript(video_id: str) -> FetchedTranscript:
         """
         Get transcript for a video, preferring English but falling back to any available language.
 
         :param video_id: The YouTube video ID
-        :param credentials: The Webshare proxy credentials
         :return: The fetched transcript
         :raises: Any exception except NoTranscriptFound for requested languages
         """
-        logger.warning(
-            "Using Webshare proxy for YouTube transcript fetch (video_id=%s)",
-            video_id,
-        )
-        proxy_config = WebshareProxyConfig(
-            proxy_username=credentials.username.get_secret_value(),
-            proxy_password=credentials.password.get_secret_value(),
-        )
-
-        api = YouTubeTranscriptApi(proxy_config=proxy_config)
+        api = YouTubeTranscriptApi()
         try:
             # Try to get English transcript first (default behavior)
             return api.fetch(video_id=video_id)
@@ -158,17 +101,11 @@ class TranscribeYoutubeVideoBlock(Block):
         transcript_text = formatter.format_transcript(transcript)
         return transcript_text
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: WebshareProxyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         video_id = self.extract_video_id(input_data.youtube_url)
         yield "video_id", video_id
 
-        transcript = self.get_transcript(video_id, credentials)
+        transcript = self.get_transcript(video_id)
         transcript_text = self.format_transcript(transcript=transcript)
 
         yield "transcript", transcript_text

autogpt_platform/backend/backend/cli.py

@@ -244,7 +244,11 @@ def websocket(server_address: str, graph_exec_id: str):
 
     import websockets.asyncio.client
 
-    from backend.api.ws_api import WSMessage, WSMethod, WSSubscribeGraphExecutionRequest
+    from backend.server.ws_api import (
+        WSMessage,
+        WSMethod,
+        WSSubscribeGraphExecutionRequest,
+    )
 
     async def send_message(server_address: str):
         uri = f"ws://{server_address}"

autogpt_platform/backend/backend/cli/__init__.py

@@ -1 +0,0 @@
-"""CLI utilities for backend development & administration"""

autogpt_platform/backend/backend/cli/generate_openapi_json.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-"""
-Script to generate OpenAPI JSON specification for the FastAPI app.
-
-This script imports the FastAPI app from backend.api.rest_api and outputs
-the OpenAPI specification as JSON to stdout or a specified file.
-
-Usage:
-  `poetry run python generate_openapi_json.py`
-  `poetry run python generate_openapi_json.py --output openapi.json`
-  `poetry run python generate_openapi_json.py --indent 4 --output openapi.json`
-"""
-
-import json
-import os
-from pathlib import Path
-
-import click
-
-
-@click.command()
-@click.option(
-    "--output",
-    type=click.Path(dir_okay=False, path_type=Path),
-    help="Output file path (default: stdout)",
-)
-@click.option(
-    "--pretty",
-    type=click.BOOL,
-    default=False,
-    help="Pretty-print JSON output (indented 2 spaces)",
-)
-def main(output: Path, pretty: bool):
-    """Generate and output the OpenAPI JSON specification."""
-    openapi_schema = get_openapi_schema()
-
-    json_output = json.dumps(openapi_schema, indent=2 if pretty else None)
-
-    if output:
-        output.write_text(json_output)
-        click.echo(f"✅ OpenAPI specification written to {output}\n\nPreview:")
-        click.echo(f"\n{json_output[:500]} ...")
-    else:
-        print(json_output)
-
-
-def get_openapi_schema():
-    """Get the OpenAPI schema from the FastAPI app"""
-    from backend.api.rest_api import app
-
-    return app.openapi()
-
-
-if __name__ == "__main__":
-    os.environ["LOG_LEVEL"] = "ERROR"  # disable stdout log output
-
-    main()

autogpt_platform/backend/backend/data/__init__.py

@@ -1,4 +1,4 @@
-from backend.api.features.library.model import LibraryAgentPreset
+from backend.server.v2.library.model import LibraryAgentPreset
 
 from .graph import NodeModel
 from .integrations import Webhook  # noqa: F401

autogpt_platform/backend/backend/data/analytics.py

@@ -1,45 +1,12 @@
 import logging
-from datetime import datetime, timedelta, timezone
-from typing import Optional
 
 import prisma.types
-from pydantic import BaseModel
 
-from backend.data.db import query_raw_with_schema
 from backend.util.json import SafeJson
 
 logger = logging.getLogger(__name__)
 
 
-class AccuracyAlertData(BaseModel):
-    """Alert data when accuracy drops significantly."""
-
-    graph_id: str
-    user_id: Optional[str]
-    drop_percent: float
-    three_day_avg: float
-    seven_day_avg: float
-    detected_at: datetime
-
-
-class AccuracyLatestData(BaseModel):
-    """Latest execution accuracy data point."""
-
-    date: datetime
-    daily_score: Optional[float]
-    three_day_avg: Optional[float]
-    seven_day_avg: Optional[float]
-    fourteen_day_avg: Optional[float]
-
-
-class AccuracyTrendsResponse(BaseModel):
-    """Response model for accuracy trends and alerts."""
-
-    latest_data: AccuracyLatestData
-    alert: Optional[AccuracyAlertData]
-    historical_data: Optional[list[AccuracyLatestData]] = None
-
-
 async def log_raw_analytics(
     user_id: str,
     type: str,
@@ -76,217 +43,3 @@ async def log_raw_metric(
     )
 
     return result
-
-
-async def get_accuracy_trends_and_alerts(
-    graph_id: str,
-    days_back: int = 30,
-    user_id: Optional[str] = None,
-    drop_threshold: float = 10.0,
-    include_historical: bool = False,
-) -> AccuracyTrendsResponse:
-    """Get accuracy trends and detect alerts for a specific graph."""
-    query_template = """
-    WITH daily_scores AS (
-        SELECT 
-            DATE(e."createdAt") as execution_date,
-            AVG(CASE 
-                WHEN e.stats IS NOT NULL 
-                AND e.stats::json->>'correctness_score' IS NOT NULL
-                AND e.stats::json->>'correctness_score' != 'null'
-                THEN (e.stats::json->>'correctness_score')::float * 100
-                ELSE NULL 
-            END) as daily_score
-        FROM {schema_prefix}"AgentGraphExecution" e
-        WHERE e."agentGraphId" = $1::text
-            AND e."isDeleted" = false
-            AND e."createdAt" >= $2::timestamp
-            AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-            {user_filter}
-        GROUP BY DATE(e."createdAt")
-        HAVING COUNT(*) >= 3  -- Need at least 3 executions per day
-    ),
-    trends AS (
-        SELECT 
-            execution_date,
-            daily_score,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
-            ) as three_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
-            ) as seven_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 13 PRECEDING AND CURRENT ROW
-            ) as fourteen_day_avg
-        FROM daily_scores
-    )
-    SELECT *,
-        CASE 
-            WHEN three_day_avg IS NOT NULL AND seven_day_avg IS NOT NULL AND seven_day_avg > 0
-            THEN ((seven_day_avg - three_day_avg) / seven_day_avg * 100)
-            ELSE NULL
-        END as drop_percent
-    FROM trends
-    ORDER BY execution_date DESC
-    {limit_clause}
-    """
-
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    params = [graph_id, start_date]
-    user_filter = ""
-    if user_id:
-        user_filter = 'AND e."userId" = $3::text'
-        params.append(user_id)
-
-    # Determine limit clause
-    limit_clause = "" if include_historical else "LIMIT 1"
-
-    final_query = query_template.format(
-        schema_prefix="{schema_prefix}",
-        user_filter=user_filter,
-        limit_clause=limit_clause,
-    )
-
-    result = await query_raw_with_schema(final_query, *params)
-
-    if not result:
-        return AccuracyTrendsResponse(
-            latest_data=AccuracyLatestData(
-                date=datetime.now(timezone.utc),
-                daily_score=None,
-                three_day_avg=None,
-                seven_day_avg=None,
-                fourteen_day_avg=None,
-            ),
-            alert=None,
-        )
-
-    latest = result[0]
-
-    alert = None
-    if (
-        latest["drop_percent"] is not None
-        and latest["drop_percent"] >= drop_threshold
-        and latest["three_day_avg"] is not None
-        and latest["seven_day_avg"] is not None
-    ):
-        alert = AccuracyAlertData(
-            graph_id=graph_id,
-            user_id=user_id,
-            drop_percent=float(latest["drop_percent"]),
-            three_day_avg=float(latest["three_day_avg"]),
-            seven_day_avg=float(latest["seven_day_avg"]),
-            detected_at=datetime.now(timezone.utc),
-        )
-
-    # Prepare historical data if requested
-    historical_data = None
-    if include_historical:
-        historical_data = []
-        for row in result:
-            historical_data.append(
-                AccuracyLatestData(
-                    date=row["execution_date"],
-                    daily_score=(
-                        float(row["daily_score"])
-                        if row["daily_score"] is not None
-                        else None
-                    ),
-                    three_day_avg=(
-                        float(row["three_day_avg"])
-                        if row["three_day_avg"] is not None
-                        else None
-                    ),
-                    seven_day_avg=(
-                        float(row["seven_day_avg"])
-                        if row["seven_day_avg"] is not None
-                        else None
-                    ),
-                    fourteen_day_avg=(
-                        float(row["fourteen_day_avg"])
-                        if row["fourteen_day_avg"] is not None
-                        else None
-                    ),
-                )
-            )
-
-    return AccuracyTrendsResponse(
-        latest_data=AccuracyLatestData(
-            date=latest["execution_date"],
-            daily_score=(
-                float(latest["daily_score"])
-                if latest["daily_score"] is not None
-                else None
-            ),
-            three_day_avg=(
-                float(latest["three_day_avg"])
-                if latest["three_day_avg"] is not None
-                else None
-            ),
-            seven_day_avg=(
-                float(latest["seven_day_avg"])
-                if latest["seven_day_avg"] is not None
-                else None
-            ),
-            fourteen_day_avg=(
-                float(latest["fourteen_day_avg"])
-                if latest["fourteen_day_avg"] is not None
-                else None
-            ),
-        ),
-        alert=alert,
-        historical_data=historical_data,
-    )
-
-
-class MarketplaceGraphData(BaseModel):
-    """Data structure for marketplace graph monitoring."""
-
-    graph_id: str
-    user_id: Optional[str]
-    execution_count: int
-
-
-async def get_marketplace_graphs_for_monitoring(
-    days_back: int = 30,
-    min_executions: int = 10,
-) -> list[MarketplaceGraphData]:
-    """Get published marketplace graphs with recent executions for monitoring."""
-    query_template = """
-    WITH marketplace_graphs AS (
-        SELECT DISTINCT 
-            slv."agentGraphId" as graph_id,
-            slv."agentGraphVersion" as graph_version
-        FROM {schema_prefix}"StoreListing" sl
-        JOIN {schema_prefix}"StoreListingVersion" slv ON sl."activeVersionId" = slv."id"
-        WHERE sl."hasApprovedVersion" = true
-            AND sl."isDeleted" = false
-    )
-    SELECT DISTINCT 
-        mg.graph_id,
-        NULL as user_id,  -- Marketplace graphs don't have a specific user_id for monitoring
-        COUNT(*) as execution_count
-    FROM marketplace_graphs mg
-    JOIN {schema_prefix}"AgentGraphExecution" e ON e."agentGraphId" = mg.graph_id
-    WHERE e."createdAt" >= $1::timestamp
-        AND e."isDeleted" = false
-        AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-    GROUP BY mg.graph_id
-    HAVING COUNT(*) >= $2
-    ORDER BY execution_count DESC
-    """
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    result = await query_raw_with_schema(query_template, start_date, min_executions)
-
-    return [
-        MarketplaceGraphData(
-            graph_id=row["graph_id"],
-            user_id=row["user_id"],
-            execution_count=int(row["execution_count"]),
-        )
-        for row in result
-    ]

autogpt_platform/backend/backend/data/api_key.py

@@ -1,24 +1,22 @@
 import logging
 import uuid
 from datetime import datetime, timezone
-from typing import Literal, Optional
+from typing import Optional
 
 from autogpt_libs.api_key.keysmith import APIKeySmith
 from prisma.enums import APIKeyPermission, APIKeyStatus
 from prisma.models import APIKey as PrismaAPIKey
 from prisma.types import APIKeyWhereUniqueInput
-from pydantic import Field
+from pydantic import BaseModel, Field
 
 from backend.data.includes import MAX_USER_API_KEYS_FETCH
 from backend.util.exceptions import NotAuthorizedError, NotFoundError
 
-from .base import APIAuthorizationInfo
-
 logger = logging.getLogger(__name__)
 keysmith = APIKeySmith()
 
 
-class APIKeyInfo(APIAuthorizationInfo):
+class APIKeyInfo(BaseModel):
     id: str
     name: str
     head: str = Field(
@@ -28,9 +26,12 @@ class APIKeyInfo(APIAuthorizationInfo):
         description=f"The last {APIKeySmith.TAIL_LENGTH} characters of the key"
     )
     status: APIKeyStatus
+    permissions: list[APIKeyPermission]
+    created_at: datetime
+    last_used_at: Optional[datetime] = None
+    revoked_at: Optional[datetime] = None
     description: Optional[str] = None
-
-    type: Literal["api_key"] = "api_key"  # type: ignore
+    user_id: str
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
@@ -40,7 +41,7 @@ class APIKeyInfo(APIAuthorizationInfo):
             head=api_key.head,
             tail=api_key.tail,
             status=APIKeyStatus(api_key.status),
-            scopes=[APIKeyPermission(p) for p in api_key.permissions],
+            permissions=[APIKeyPermission(p) for p in api_key.permissions],
             created_at=api_key.createdAt,
             last_used_at=api_key.lastUsedAt,
             revoked_at=api_key.revokedAt,
@@ -210,7 +211,7 @@ async def suspend_api_key(key_id: str, user_id: str) -> APIKeyInfo:
 
 
 def has_permission(api_key: APIKeyInfo, required_permission: APIKeyPermission) -> bool:
-    return required_permission in api_key.scopes
+    return required_permission in api_key.permissions
 
 
 async def get_api_key_by_id(key_id: str, user_id: str) -> Optional[APIKeyInfo]:

autogpt_platform/backend/backend/data/auth/base.py

@@ -1,15 +0,0 @@
-from datetime import datetime
-from typing import Literal, Optional
-
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel
-
-
-class APIAuthorizationInfo(BaseModel):
-    user_id: str
-    scopes: list[APIKeyPermission]
-    type: Literal["oauth", "api_key"]
-    created_at: datetime
-    expires_at: Optional[datetime] = None
-    last_used_at: Optional[datetime] = None
-    revoked_at: Optional[datetime] = None

autogpt_platform/backend/backend/data/auth/oauth.py

@@ -1,872 +0,0 @@
-"""
-OAuth 2.0 Provider Data Layer
-
-Handles management of OAuth applications, authorization codes,
-access tokens, and refresh tokens.
-
-Hashing strategy:
-- Access tokens & Refresh tokens: SHA256 (deterministic, allows direct lookup by hash)
-- Client secrets: Scrypt with salt (lookup by client_id, then verify with salt)
-"""
-
-import hashlib
-import logging
-import secrets
-import uuid
-from datetime import datetime, timedelta, timezone
-from typing import Literal, Optional
-
-from autogpt_libs.api_key.keysmith import APIKeySmith
-from prisma.enums import APIKeyPermission as APIPermission
-from prisma.models import OAuthAccessToken as PrismaOAuthAccessToken
-from prisma.models import OAuthApplication as PrismaOAuthApplication
-from prisma.models import OAuthAuthorizationCode as PrismaOAuthAuthorizationCode
-from prisma.models import OAuthRefreshToken as PrismaOAuthRefreshToken
-from prisma.types import OAuthApplicationUpdateInput
-from pydantic import BaseModel, Field, SecretStr
-
-from .base import APIAuthorizationInfo
-
-logger = logging.getLogger(__name__)
-keysmith = APIKeySmith()  # Only used for client secret hashing (Scrypt)
-
-
-def _generate_token() -> str:
-    """Generate a cryptographically secure random token."""
-    return secrets.token_urlsafe(32)
-
-
-def _hash_token(token: str) -> str:
-    """Hash a token using SHA256 (deterministic, for direct lookup)."""
-    return hashlib.sha256(token.encode()).hexdigest()
-
-
-# Token TTLs
-AUTHORIZATION_CODE_TTL = timedelta(minutes=10)
-ACCESS_TOKEN_TTL = timedelta(hours=1)
-REFRESH_TOKEN_TTL = timedelta(days=30)
-
-ACCESS_TOKEN_PREFIX = "agpt_xt_"
-REFRESH_TOKEN_PREFIX = "agpt_rt_"
-
-
-# ============================================================================
-# Exception Classes
-# ============================================================================
-
-
-class OAuthError(Exception):
-    """Base OAuth error"""
-
-    pass
-
-
-class InvalidClientError(OAuthError):
-    """Invalid client_id or client_secret"""
-
-    pass
-
-
-class InvalidGrantError(OAuthError):
-    """Invalid or expired authorization code/refresh token"""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Invalid grant: {reason}")
-
-
-class InvalidTokenError(OAuthError):
-    """Invalid, expired, or revoked token"""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Invalid token: {reason}")
-
-
-# ============================================================================
-# Data Models
-# ============================================================================
-
-
-class OAuthApplicationInfo(BaseModel):
-    """OAuth application information (without client secret hash)"""
-
-    id: str
-    name: str
-    description: Optional[str] = None
-    logo_url: Optional[str] = None
-    client_id: str
-    redirect_uris: list[str]
-    grant_types: list[str]
-    scopes: list[APIPermission]
-    owner_id: str
-    is_active: bool
-    created_at: datetime
-    updated_at: datetime
-
-    @staticmethod
-    def from_db(app: PrismaOAuthApplication):
-        return OAuthApplicationInfo(
-            id=app.id,
-            name=app.name,
-            description=app.description,
-            logo_url=app.logoUrl,
-            client_id=app.clientId,
-            redirect_uris=app.redirectUris,
-            grant_types=app.grantTypes,
-            scopes=[APIPermission(s) for s in app.scopes],
-            owner_id=app.ownerId,
-            is_active=app.isActive,
-            created_at=app.createdAt,
-            updated_at=app.updatedAt,
-        )
-
-
-class OAuthApplicationInfoWithSecret(OAuthApplicationInfo):
-    """OAuth application with client secret hash (for validation)"""
-
-    client_secret_hash: str
-    client_secret_salt: str
-
-    @staticmethod
-    def from_db(app: PrismaOAuthApplication):
-        return OAuthApplicationInfoWithSecret(
-            **OAuthApplicationInfo.from_db(app).model_dump(),
-            client_secret_hash=app.clientSecret,
-            client_secret_salt=app.clientSecretSalt,
-        )
-
-    def verify_secret(self, plaintext_secret: str) -> bool:
-        """Verify a plaintext client secret against the stored hash"""
-        # Use keysmith.verify_key() with stored salt
-        return keysmith.verify_key(
-            plaintext_secret, self.client_secret_hash, self.client_secret_salt
-        )
-
-
-class OAuthAuthorizationCodeInfo(BaseModel):
-    """Authorization code information"""
-
-    id: str
-    code: str
-    created_at: datetime
-    expires_at: datetime
-    application_id: str
-    user_id: str
-    scopes: list[APIPermission]
-    redirect_uri: str
-    code_challenge: Optional[str] = None
-    code_challenge_method: Optional[str] = None
-    used_at: Optional[datetime] = None
-
-    @property
-    def is_used(self) -> bool:
-        return self.used_at is not None
-
-    @staticmethod
-    def from_db(code: PrismaOAuthAuthorizationCode):
-        return OAuthAuthorizationCodeInfo(
-            id=code.id,
-            code=code.code,
-            created_at=code.createdAt,
-            expires_at=code.expiresAt,
-            application_id=code.applicationId,
-            user_id=code.userId,
-            scopes=[APIPermission(s) for s in code.scopes],
-            redirect_uri=code.redirectUri,
-            code_challenge=code.codeChallenge,
-            code_challenge_method=code.codeChallengeMethod,
-            used_at=code.usedAt,
-        )
-
-
-class OAuthAccessTokenInfo(APIAuthorizationInfo):
-    """Access token information"""
-
-    id: str
-    expires_at: datetime  # type: ignore
-    application_id: str
-
-    type: Literal["oauth"] = "oauth"  # type: ignore
-
-    @staticmethod
-    def from_db(token: PrismaOAuthAccessToken):
-        return OAuthAccessTokenInfo(
-            id=token.id,
-            user_id=token.userId,
-            scopes=[APIPermission(s) for s in token.scopes],
-            created_at=token.createdAt,
-            expires_at=token.expiresAt,
-            last_used_at=None,
-            revoked_at=token.revokedAt,
-            application_id=token.applicationId,
-        )
-
-
-class OAuthAccessToken(OAuthAccessTokenInfo):
-    """Access token with plaintext token included (sensitive)"""
-
-    token: SecretStr = Field(description="Plaintext token (sensitive)")
-
-    @staticmethod
-    def from_db(token: PrismaOAuthAccessToken, plaintext_token: str):  # type: ignore
-        return OAuthAccessToken(
-            **OAuthAccessTokenInfo.from_db(token).model_dump(),
-            token=SecretStr(plaintext_token),
-        )
-
-
-class OAuthRefreshTokenInfo(BaseModel):
-    """Refresh token information"""
-
-    id: str
-    user_id: str
-    scopes: list[APIPermission]
-    created_at: datetime
-    expires_at: datetime
-    application_id: str
-    revoked_at: Optional[datetime] = None
-
-    @property
-    def is_revoked(self) -> bool:
-        return self.revoked_at is not None
-
-    @staticmethod
-    def from_db(token: PrismaOAuthRefreshToken):
-        return OAuthRefreshTokenInfo(
-            id=token.id,
-            user_id=token.userId,
-            scopes=[APIPermission(s) for s in token.scopes],
-            created_at=token.createdAt,
-            expires_at=token.expiresAt,
-            application_id=token.applicationId,
-            revoked_at=token.revokedAt,
-        )
-
-
-class OAuthRefreshToken(OAuthRefreshTokenInfo):
-    """Refresh token with plaintext token included (sensitive)"""
-
-    token: SecretStr = Field(description="Plaintext token (sensitive)")
-
-    @staticmethod
-    def from_db(token: PrismaOAuthRefreshToken, plaintext_token: str):  # type: ignore
-        return OAuthRefreshToken(
-            **OAuthRefreshTokenInfo.from_db(token).model_dump(),
-            token=SecretStr(plaintext_token),
-        )
-
-
-class TokenIntrospectionResult(BaseModel):
-    """Result of token introspection (RFC 7662)"""
-
-    active: bool
-    scopes: Optional[list[str]] = None
-    client_id: Optional[str] = None
-    user_id: Optional[str] = None
-    exp: Optional[int] = None  # Unix timestamp
-    token_type: Optional[Literal["access_token", "refresh_token"]] = None
-
-
-# ============================================================================
-# OAuth Application Management
-# ============================================================================
-
-
-async def get_oauth_application(client_id: str) -> Optional[OAuthApplicationInfo]:
-    """Get OAuth application by client ID (without secret)"""
-    app = await PrismaOAuthApplication.prisma().find_unique(
-        where={"clientId": client_id}
-    )
-    if not app:
-        return None
-    return OAuthApplicationInfo.from_db(app)
-
-
-async def get_oauth_application_with_secret(
-    client_id: str,
-) -> Optional[OAuthApplicationInfoWithSecret]:
-    """Get OAuth application by client ID (with secret hash for validation)"""
-    app = await PrismaOAuthApplication.prisma().find_unique(
-        where={"clientId": client_id}
-    )
-    if not app:
-        return None
-    return OAuthApplicationInfoWithSecret.from_db(app)
-
-
-async def validate_client_credentials(
-    client_id: str, client_secret: str
-) -> OAuthApplicationInfo:
-    """
-    Validate client credentials and return application info.
-
-    Raises:
-        InvalidClientError: If client_id or client_secret is invalid, or app is inactive
-    """
-    app = await get_oauth_application_with_secret(client_id)
-    if not app:
-        raise InvalidClientError("Invalid client_id")
-
-    if not app.is_active:
-        raise InvalidClientError("Application is not active")
-
-    # Verify client secret
-    if not app.verify_secret(client_secret):
-        raise InvalidClientError("Invalid client_secret")
-
-    # Return without secret hash
-    return OAuthApplicationInfo(**app.model_dump(exclude={"client_secret_hash"}))
-
-
-def validate_redirect_uri(app: OAuthApplicationInfo, redirect_uri: str) -> bool:
-    """Validate that redirect URI is registered for the application"""
-    return redirect_uri in app.redirect_uris
-
-
-def validate_scopes(
-    app: OAuthApplicationInfo, requested_scopes: list[APIPermission]
-) -> bool:
-    """Validate that all requested scopes are allowed for the application"""
-    return all(scope in app.scopes for scope in requested_scopes)
-
-
-# ============================================================================
-# Authorization Code Flow
-# ============================================================================
-
-
-def _generate_authorization_code() -> str:
-    """Generate a cryptographically secure authorization code"""
-    # 32 bytes = 256 bits of entropy
-    return secrets.token_urlsafe(32)
-
-
-async def create_authorization_code(
-    application_id: str,
-    user_id: str,
-    scopes: list[APIPermission],
-    redirect_uri: str,
-    code_challenge: Optional[str] = None,
-    code_challenge_method: Optional[Literal["S256", "plain"]] = None,
-) -> OAuthAuthorizationCodeInfo:
-    """
-    Create a new authorization code.
-    Expires in 10 minutes and can only be used once.
-    """
-    code = _generate_authorization_code()
-    now = datetime.now(timezone.utc)
-    expires_at = now + AUTHORIZATION_CODE_TTL
-
-    saved_code = await PrismaOAuthAuthorizationCode.prisma().create(
-        data={
-            "id": str(uuid.uuid4()),
-            "code": code,
-            "expiresAt": expires_at,
-            "applicationId": application_id,
-            "userId": user_id,
-            "scopes": [s for s in scopes],
-            "redirectUri": redirect_uri,
-            "codeChallenge": code_challenge,
-            "codeChallengeMethod": code_challenge_method,
-        }
-    )
-
-    return OAuthAuthorizationCodeInfo.from_db(saved_code)
-
-
-async def consume_authorization_code(
-    code: str,
-    application_id: str,
-    redirect_uri: str,
-    code_verifier: Optional[str] = None,
-) -> tuple[str, list[APIPermission]]:
-    """
-    Consume an authorization code and return (user_id, scopes).
-
-    This marks the code as used and validates:
-    - Code exists and matches application
-    - Code is not expired
-    - Code has not been used
-    - Redirect URI matches
-    - PKCE code verifier matches (if code challenge was provided)
-
-    Raises:
-        InvalidGrantError: If code is invalid, expired, used, or PKCE fails
-    """
-    auth_code = await PrismaOAuthAuthorizationCode.prisma().find_unique(
-        where={"code": code}
-    )
-
-    if not auth_code:
-        raise InvalidGrantError("authorization code not found")
-
-    # Validate application
-    if auth_code.applicationId != application_id:
-        raise InvalidGrantError(
-            "authorization code does not belong to this application"
-        )
-
-    # Check if already used
-    if auth_code.usedAt is not None:
-        raise InvalidGrantError(
-            f"authorization code already used at {auth_code.usedAt}"
-        )
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if auth_code.expiresAt < now:
-        raise InvalidGrantError("authorization code expired")
-
-    # Validate redirect URI
-    if auth_code.redirectUri != redirect_uri:
-        raise InvalidGrantError("redirect_uri mismatch")
-
-    # Validate PKCE if code challenge was provided
-    if auth_code.codeChallenge:
-        if not code_verifier:
-            raise InvalidGrantError("code_verifier required but not provided")
-
-        if not _verify_pkce(
-            code_verifier, auth_code.codeChallenge, auth_code.codeChallengeMethod
-        ):
-            raise InvalidGrantError("PKCE verification failed")
-
-    # Mark code as used
-    await PrismaOAuthAuthorizationCode.prisma().update(
-        where={"code": code},
-        data={"usedAt": now},
-    )
-
-    return auth_code.userId, [APIPermission(s) for s in auth_code.scopes]
-
-
-def _verify_pkce(
-    code_verifier: str, code_challenge: str, code_challenge_method: Optional[str]
-) -> bool:
-    """
-    Verify PKCE code verifier against code challenge.
-
-    Supports:
-    - S256: SHA256(code_verifier) == code_challenge
-    - plain: code_verifier == code_challenge
-    """
-    if code_challenge_method == "S256":
-        # Hash the verifier with SHA256 and base64url encode
-        hashed = hashlib.sha256(code_verifier.encode("ascii")).digest()
-        computed_challenge = (
-            secrets.token_urlsafe(len(hashed)).encode("ascii").decode("ascii")
-        )
-        # For proper base64url encoding
-        import base64
-
-        computed_challenge = (
-            base64.urlsafe_b64encode(hashed).decode("ascii").rstrip("=")
-        )
-        return secrets.compare_digest(computed_challenge, code_challenge)
-    elif code_challenge_method == "plain" or code_challenge_method is None:
-        # Plain comparison
-        return secrets.compare_digest(code_verifier, code_challenge)
-    else:
-        logger.warning(f"Unsupported code challenge method: {code_challenge_method}")
-        return False
-
-
-# ============================================================================
-# Access Token Management
-# ============================================================================
-
-
-async def create_access_token(
-    application_id: str, user_id: str, scopes: list[APIPermission]
-) -> OAuthAccessToken:
-    """
-    Create a new access token.
-    Returns OAuthAccessToken (with plaintext token).
-    """
-    plaintext_token = ACCESS_TOKEN_PREFIX + _generate_token()
-    token_hash = _hash_token(plaintext_token)
-    now = datetime.now(timezone.utc)
-    expires_at = now + ACCESS_TOKEN_TTL
-
-    saved_token = await PrismaOAuthAccessToken.prisma().create(
-        data={
-            "id": str(uuid.uuid4()),
-            "token": token_hash,  # SHA256 hash for direct lookup
-            "expiresAt": expires_at,
-            "applicationId": application_id,
-            "userId": user_id,
-            "scopes": [s for s in scopes],
-        }
-    )
-
-    return OAuthAccessToken.from_db(saved_token, plaintext_token=plaintext_token)
-
-
-async def validate_access_token(
-    token: str,
-) -> tuple[OAuthAccessTokenInfo, OAuthApplicationInfo]:
-    """
-    Validate an access token and return token info.
-
-    Raises:
-        InvalidTokenError: If token is invalid, expired, or revoked
-        InvalidClientError: If the client application is not marked as active
-    """
-    token_hash = _hash_token(token)
-
-    # Direct lookup by hash
-    access_token = await PrismaOAuthAccessToken.prisma().find_unique(
-        where={"token": token_hash}, include={"Application": True}
-    )
-
-    if not access_token:
-        raise InvalidTokenError("access token not found")
-
-    if not access_token.Application:  # should be impossible
-        raise InvalidClientError("Client application not found")
-
-    if not access_token.Application.isActive:
-        raise InvalidClientError("Client application is disabled")
-
-    if access_token.revokedAt is not None:
-        raise InvalidTokenError("access token has been revoked")
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if access_token.expiresAt < now:
-        raise InvalidTokenError("access token expired")
-
-    return (
-        OAuthAccessTokenInfo.from_db(access_token),
-        OAuthApplicationInfo.from_db(access_token.Application),
-    )
-
-
-async def revoke_access_token(
-    token: str, application_id: str
-) -> OAuthAccessTokenInfo | None:
-    """
-    Revoke an access token.
-
-    Args:
-        token: The plaintext access token to revoke
-        application_id: The application ID making the revocation request.
-            Only tokens belonging to this application will be revoked.
-
-    Returns:
-        OAuthAccessTokenInfo if token was found and revoked, None otherwise.
-
-    Note:
-        Always performs exactly 2 DB queries regardless of outcome to prevent
-        timing side-channel attacks that could reveal token existence.
-    """
-    try:
-        token_hash = _hash_token(token)
-
-        # Use update_many to filter by both token and applicationId
-        updated_count = await PrismaOAuthAccessToken.prisma().update_many(
-            where={
-                "token": token_hash,
-                "applicationId": application_id,
-                "revokedAt": None,
-            },
-            data={"revokedAt": datetime.now(timezone.utc)},
-        )
-
-        # Always perform second query to ensure constant time
-        result = await PrismaOAuthAccessToken.prisma().find_unique(
-            where={"token": token_hash}
-        )
-
-        # Only return result if we actually revoked something
-        if updated_count == 0:
-            return None
-
-        return OAuthAccessTokenInfo.from_db(result) if result else None
-    except Exception as e:
-        logger.exception(f"Error revoking access token: {e}")
-        return None
-
-
-# ============================================================================
-# Refresh Token Management
-# ============================================================================
-
-
-async def create_refresh_token(
-    application_id: str, user_id: str, scopes: list[APIPermission]
-) -> OAuthRefreshToken:
-    """
-    Create a new refresh token.
-    Returns OAuthRefreshToken (with plaintext token).
-    """
-    plaintext_token = REFRESH_TOKEN_PREFIX + _generate_token()
-    token_hash = _hash_token(plaintext_token)
-    now = datetime.now(timezone.utc)
-    expires_at = now + REFRESH_TOKEN_TTL
-
-    saved_token = await PrismaOAuthRefreshToken.prisma().create(
-        data={
-            "id": str(uuid.uuid4()),
-            "token": token_hash,  # SHA256 hash for direct lookup
-            "expiresAt": expires_at,
-            "applicationId": application_id,
-            "userId": user_id,
-            "scopes": [s for s in scopes],
-        }
-    )
-
-    return OAuthRefreshToken.from_db(saved_token, plaintext_token=plaintext_token)
-
-
-async def refresh_tokens(
-    refresh_token: str, application_id: str
-) -> tuple[OAuthAccessToken, OAuthRefreshToken]:
-    """
-    Use a refresh token to create new access and refresh tokens.
-    Returns (new_access_token, new_refresh_token) both with plaintext tokens included.
-
-    Raises:
-        InvalidGrantError: If refresh token is invalid, expired, or revoked
-    """
-    token_hash = _hash_token(refresh_token)
-
-    # Direct lookup by hash
-    rt = await PrismaOAuthRefreshToken.prisma().find_unique(where={"token": token_hash})
-
-    if not rt:
-        raise InvalidGrantError("refresh token not found")
-
-    # NOTE: no need to check Application.isActive, this is checked by the token endpoint
-
-    if rt.revokedAt is not None:
-        raise InvalidGrantError("refresh token has been revoked")
-
-    # Validate application
-    if rt.applicationId != application_id:
-        raise InvalidGrantError("refresh token does not belong to this application")
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if rt.expiresAt < now:
-        raise InvalidGrantError("refresh token expired")
-
-    # Revoke old refresh token
-    await PrismaOAuthRefreshToken.prisma().update(
-        where={"token": token_hash},
-        data={"revokedAt": now},
-    )
-
-    # Create new access and refresh tokens with same scopes
-    scopes = [APIPermission(s) for s in rt.scopes]
-    new_access_token = await create_access_token(
-        rt.applicationId,
-        rt.userId,
-        scopes,
-    )
-    new_refresh_token = await create_refresh_token(
-        rt.applicationId,
-        rt.userId,
-        scopes,
-    )
-
-    return new_access_token, new_refresh_token
-
-
-async def revoke_refresh_token(
-    token: str, application_id: str
-) -> OAuthRefreshTokenInfo | None:
-    """
-    Revoke a refresh token.
-
-    Args:
-        token: The plaintext refresh token to revoke
-        application_id: The application ID making the revocation request.
-            Only tokens belonging to this application will be revoked.
-
-    Returns:
-        OAuthRefreshTokenInfo if token was found and revoked, None otherwise.
-
-    Note:
-        Always performs exactly 2 DB queries regardless of outcome to prevent
-        timing side-channel attacks that could reveal token existence.
-    """
-    try:
-        token_hash = _hash_token(token)
-
-        # Use update_many to filter by both token and applicationId
-        updated_count = await PrismaOAuthRefreshToken.prisma().update_many(
-            where={
-                "token": token_hash,
-                "applicationId": application_id,
-                "revokedAt": None,
-            },
-            data={"revokedAt": datetime.now(timezone.utc)},
-        )
-
-        # Always perform second query to ensure constant time
-        result = await PrismaOAuthRefreshToken.prisma().find_unique(
-            where={"token": token_hash}
-        )
-
-        # Only return result if we actually revoked something
-        if updated_count == 0:
-            return None
-
-        return OAuthRefreshTokenInfo.from_db(result) if result else None
-    except Exception as e:
-        logger.exception(f"Error revoking refresh token: {e}")
-        return None
-
-
-# ============================================================================
-# Token Introspection
-# ============================================================================
-
-
-async def introspect_token(
-    token: str,
-    token_type_hint: Optional[Literal["access_token", "refresh_token"]] = None,
-) -> TokenIntrospectionResult:
-    """
-    Introspect a token and return its metadata (RFC 7662).
-
-    Returns TokenIntrospectionResult with active=True and metadata if valid,
-    or active=False if the token is invalid/expired/revoked.
-    """
-    # Try as access token first (or if hint says "access_token")
-    if token_type_hint != "refresh_token":
-        try:
-            token_info, app = await validate_access_token(token)
-            return TokenIntrospectionResult(
-                active=True,
-                scopes=list(s.value for s in token_info.scopes),
-                client_id=app.client_id if app else None,
-                user_id=token_info.user_id,
-                exp=int(token_info.expires_at.timestamp()),
-                token_type="access_token",
-            )
-        except InvalidTokenError:
-            pass  # Try as refresh token
-
-    # Try as refresh token
-    token_hash = _hash_token(token)
-    refresh_token = await PrismaOAuthRefreshToken.prisma().find_unique(
-        where={"token": token_hash}
-    )
-
-    if refresh_token and refresh_token.revokedAt is None:
-        # Check if valid (not expired)
-        now = datetime.now(timezone.utc)
-        if refresh_token.expiresAt > now:
-            app = await get_oauth_application_by_id(refresh_token.applicationId)
-            return TokenIntrospectionResult(
-                active=True,
-                scopes=list(s for s in refresh_token.scopes),
-                client_id=app.client_id if app else None,
-                user_id=refresh_token.userId,
-                exp=int(refresh_token.expiresAt.timestamp()),
-                token_type="refresh_token",
-            )
-
-    # Token not found or inactive
-    return TokenIntrospectionResult(active=False)
-
-
-async def get_oauth_application_by_id(app_id: str) -> Optional[OAuthApplicationInfo]:
-    """Get OAuth application by ID"""
-    app = await PrismaOAuthApplication.prisma().find_unique(where={"id": app_id})
-    if not app:
-        return None
-    return OAuthApplicationInfo.from_db(app)
-
-
-async def list_user_oauth_applications(user_id: str) -> list[OAuthApplicationInfo]:
-    """Get all OAuth applications owned by a user"""
-    apps = await PrismaOAuthApplication.prisma().find_many(
-        where={"ownerId": user_id},
-        order={"createdAt": "desc"},
-    )
-    return [OAuthApplicationInfo.from_db(app) for app in apps]
-
-
-async def update_oauth_application(
-    app_id: str,
-    *,
-    owner_id: str,
-    is_active: Optional[bool] = None,
-    logo_url: Optional[str] = None,
-) -> Optional[OAuthApplicationInfo]:
-    """
-    Update OAuth application active status.
-    Only the owner can update their app's status.
-
-    Returns the updated app info, or None if app not found or not owned by user.
-    """
-    # First verify ownership
-    app = await PrismaOAuthApplication.prisma().find_first(
-        where={"id": app_id, "ownerId": owner_id}
-    )
-    if not app:
-        return None
-
-    patch: OAuthApplicationUpdateInput = {}
-    if is_active is not None:
-        patch["isActive"] = is_active
-    if logo_url:
-        patch["logoUrl"] = logo_url
-    if not patch:
-        return OAuthApplicationInfo.from_db(app)  # return unchanged
-
-    updated_app = await PrismaOAuthApplication.prisma().update(
-        where={"id": app_id},
-        data=patch,
-    )
-    return OAuthApplicationInfo.from_db(updated_app) if updated_app else None
-
-
-# ============================================================================
-# Token Cleanup
-# ============================================================================
-
-
-async def cleanup_expired_oauth_tokens() -> dict[str, int]:
-    """
-    Delete expired OAuth tokens from the database.
-
-    This removes:
-    - Expired authorization codes (10 min TTL)
-    - Expired access tokens (1 hour TTL)
-    - Expired refresh tokens (30 day TTL)
-
-    Returns a dict with counts of deleted tokens by type.
-    """
-    now = datetime.now(timezone.utc)
-
-    # Delete expired authorization codes
-    codes_result = await PrismaOAuthAuthorizationCode.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    # Delete expired access tokens
-    access_result = await PrismaOAuthAccessToken.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    # Delete expired refresh tokens
-    refresh_result = await PrismaOAuthRefreshToken.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    deleted = {
-        "authorization_codes": codes_result,
-        "access_tokens": access_result,
-        "refresh_tokens": refresh_result,
-    }
-
-    total = sum(deleted.values())
-    if total > 0:
-        logger.info(f"Cleaned up {total} expired OAuth tokens: {deleted}")
-
-    return deleted

autogpt_platform/backend/backend/data/block.py

@@ -71,7 +71,6 @@ class BlockType(Enum):
     AGENT = "Agent"
     AI = "AI"
     AYRSHARE = "Ayrshare"
-    HUMAN_IN_THE_LOOP = "Human In The Loop"
 
 
 class BlockCategory(Enum):
@@ -266,61 +265,14 @@ class BlockSchema(BaseModel):
             )
         }
 
-    @classmethod
-    def get_auto_credentials_fields(cls) -> dict[str, dict[str, Any]]:
-        """
-        Get fields that have auto_credentials metadata (e.g., GoogleDriveFileInput).
-
-        Returns a dict mapping kwarg_name -> {field_name, auto_credentials_config}
-
-        Raises:
-            ValueError: If multiple fields have the same kwarg_name, as this would
-                cause silent overwriting and only the last field would be processed.
-        """
-        result: dict[str, dict[str, Any]] = {}
-        schema = cls.jsonschema()
-        properties = schema.get("properties", {})
-
-        for field_name, field_schema in properties.items():
-            auto_creds = field_schema.get("auto_credentials")
-            if auto_creds:
-                kwarg_name = auto_creds.get("kwarg_name", "credentials")
-                if kwarg_name in result:
-                    raise ValueError(
-                        f"Duplicate auto_credentials kwarg_name '{kwarg_name}' "
-                        f"in fields '{result[kwarg_name]['field_name']}' and "
-                        f"'{field_name}' on {cls.__qualname__}"
-                    )
-                result[kwarg_name] = {
-                    "field_name": field_name,
-                    "config": auto_creds,
-                }
-        return result
-
     @classmethod
     def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
-        result = {}
-
-        # Regular credentials fields
-        for field_name in cls.get_credentials_fields().keys():
-            result[field_name] = CredentialsFieldInfo.model_validate(
+        return {
+            field_name: CredentialsFieldInfo.model_validate(
                 cls.get_field_schema(field_name), by_alias=True
             )
-
-        # Auto-generated credentials fields (from GoogleDriveFileInput etc.)
-        for kwarg_name, info in cls.get_auto_credentials_fields().items():
-            config = info["config"]
-            # Build a schema-like dict that CredentialsFieldInfo can parse
-            auto_schema = {
-                "credentials_provider": [config.get("provider", "google")],
-                "credentials_types": [config.get("type", "oauth2")],
-                "credentials_scopes": config.get("scopes"),
-            }
-            result[kwarg_name] = CredentialsFieldInfo.model_validate(
-                auto_schema, by_alias=True
-            )
-
-        return result
+            for field_name in cls.get_credentials_fields().keys()
+        }
 
     @classmethod
     def get_input_defaults(cls, data: BlockInput) -> BlockInput:
@@ -601,18 +553,14 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             async for output_name, output_data in self._execute(input_data, **kwargs):
                 yield output_name, output_data
         except Exception as ex:
-            if isinstance(ex, BlockError):
-                raise ex
-            else:
-                raise (
-                    BlockExecutionError
-                    if isinstance(ex, ValueError)
-                    else BlockUnknownError
-                )(
+            if not isinstance(ex, BlockError):
+                raise BlockUnknownError(
                     message=str(ex),
                     block_name=self.name,
                     block_id=self.id,
                 ) from ex
+            else:
+                raise ex
 
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
         if error := self.input_schema.validate_data(input_data):
@@ -848,12 +796,3 @@ def get_io_block_ids() -> Sequence[str]:
         for id, B in get_blocks().items()
         if B().block_type in (BlockType.INPUT, BlockType.OUTPUT)
     ]
-
-
-@cached(ttl_seconds=3600)
-def get_human_in_the_loop_block_ids() -> Sequence[str]:
-    return [
-        id
-        for id, B in get_blocks().items()
-        if B().block_type == BlockType.HUMAN_IN_THE_LOOP
-    ]

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -59,13 +59,12 @@ from backend.integrations.credentials_store import (
 
 MODEL_COST: dict[LlmModel, int] = {
     LlmModel.O3: 4,
-    LlmModel.O3_MINI: 2,
-    LlmModel.O1: 16,
+    LlmModel.O3_MINI: 2,  # $1.10 / $4.40
+    LlmModel.O1: 16,  # $15 / $60
     LlmModel.O1_MINI: 4,
     # GPT-5 models
-    LlmModel.GPT5_2: 6,
-    LlmModel.GPT5_1: 5,
     LlmModel.GPT5: 2,
+    LlmModel.GPT5_1: 5,
     LlmModel.GPT5_MINI: 1,
     LlmModel.GPT5_NANO: 1,
     LlmModel.GPT5_CHAT: 5,
@@ -88,7 +87,7 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.AIML_API_LLAMA3_3_70B: 1,
     LlmModel.AIML_API_META_LLAMA_3_1_70B: 1,
     LlmModel.AIML_API_LLAMA_3_2_3B: 1,
-    LlmModel.LLAMA3_3_70B: 1,
+    LlmModel.LLAMA3_3_70B: 1,  # $0.59 / $0.79
     LlmModel.LLAMA3_1_8B: 1,
     LlmModel.OLLAMA_LLAMA3_3: 1,
     LlmModel.OLLAMA_LLAMA3_2: 1,

autogpt_platform/backend/backend/data/credit.py

@@ -16,7 +16,6 @@ from prisma.models import CreditRefundRequest, CreditTransaction, User, UserBala
 from prisma.types import CreditRefundRequestCreateInput, CreditTransactionWhereInput
 from pydantic import BaseModel
 
-from backend.api.features.admin.model import UserHistoryResponse
 from backend.data.block_cost_config import BLOCK_COSTS
 from backend.data.db import query_raw_with_schema
 from backend.data.includes import MAX_CREDIT_REFUND_REQUESTS_FETCH
@@ -30,6 +29,7 @@ from backend.data.model import (
 from backend.data.notifications import NotificationEventModel, RefundRequestData
 from backend.data.user import get_user_by_id, get_user_email_by_id
 from backend.notifications.notifications import queue_notification_async
+from backend.server.v2.admin.model import UserHistoryResponse
 from backend.util.exceptions import InsufficientBalanceError
 from backend.util.feature_flag import Flag, is_feature_enabled
 from backend.util.json import SafeJson, dumps
@@ -341,19 +341,6 @@ class UserCreditBase(ABC):
 
         if result:
             # UserBalance is already updated by the CTE
-
-            # Clear insufficient funds notification flags when credits are added
-            # so user can receive alerts again if they run out in the future.
-            if transaction.amount > 0 and transaction.type in [
-                CreditTransactionType.GRANT,
-                CreditTransactionType.TOP_UP,
-            ]:
-                from backend.executor.manager import (
-                    clear_insufficient_funds_notifications,
-                )
-
-                await clear_insufficient_funds_notifications(user_id)
-
             return result[0]["balance"]
 
     async def _add_transaction(
@@ -543,22 +530,6 @@ class UserCreditBase(ABC):
         if result:
             new_balance, tx_key = result[0]["balance"], result[0]["transactionKey"]
             # UserBalance is already updated by the CTE
-
-            # Clear insufficient funds notification flags when credits are added
-            # so user can receive alerts again if they run out in the future.
-            if (
-                amount > 0
-                and is_active
-                and transaction_type
-                in [CreditTransactionType.GRANT, CreditTransactionType.TOP_UP]
-            ):
-                # Lazy import to avoid circular dependency with executor.manager
-                from backend.executor.manager import (
-                    clear_insufficient_funds_notifications,
-                )
-
-                await clear_insufficient_funds_notifications(user_id)
-
             return new_balance, tx_key
 
         # If no result, either user doesn't exist or insufficient balance

autogpt_platform/backend/backend/data/credit_test.py

@@ -7,7 +7,7 @@ from prisma.models import CreditTransaction, UserBalance
 from backend.blocks.llm import AITextGeneratorBlock
 from backend.data.block import get_block
 from backend.data.credit import BetaUserCredit, UsageTransactionMetadata
-from backend.data.execution import ExecutionContext, NodeExecutionEntry
+from backend.data.execution import NodeExecutionEntry, UserContext
 from backend.data.user import DEFAULT_USER_ID
 from backend.executor.utils import block_usage_cost
 from backend.integrations.credentials_store import openai_credentials
@@ -86,7 +86,7 @@ async def test_block_credit_usage(server: SpinTestServer):
                     "type": openai_credentials.type,
                 },
             },
-            execution_context=ExecutionContext(user_timezone="UTC"),
+            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_1 > 0
@@ -101,7 +101,7 @@ async def test_block_credit_usage(server: SpinTestServer):
             node_exec_id="test_node_exec",
             block_id=AITextGeneratorBlock().id,
             inputs={"model": "gpt-4-turbo", "api_key": "owned_api_key"},
-            execution_context=ExecutionContext(user_timezone="UTC"),
+            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_2 == 0

autogpt_platform/backend/backend/data/db.py

@@ -111,7 +111,7 @@ def get_database_schema() -> str:
 async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
     """Execute raw SQL query with proper schema handling."""
     schema = get_database_schema()
-    schema_prefix = f'"{schema}".' if schema != "public" else ""
+    schema_prefix = f"{schema}." if schema != "public" else ""
     formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module