Merge branch 'dev' into add-llm-manager-ui

This commit is contained in:
Bentlybro
2026-01-06 10:28:43 +00:00
575 changed files with 79017 additions and 13258 deletions

View File

@@ -44,6 +44,12 @@ 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

View File

@@ -12,6 +12,10 @@ 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

View File

@@ -12,6 +12,10 @@ 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

View File

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

View File

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

View File

@@ -1,4 +1,4 @@
.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
# Run just Supabase + Redis + RabbitMQ
start-core:
@@ -42,7 +42,10 @@ 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:"
@@ -54,4 +57,5 @@ 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 " test-data - Run the test data creator"
@echo " load-store-agents - Load store agents from agents/ folder into test database"

View File

@@ -57,6 +57,9 @@ 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()

View File

@@ -1,29 +1,25 @@
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:
"""
Set up custom OpenAPI schema generation that adds 401 responses
Patch a FastAPI instance's `openapi()` method to add 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 = get_openapi(
title=app.title,
version=app.version,
description=app.description,
routes=app.routes,
)
openapi_schema = wrapped_openapi()
# Add 401 response to all endpoints that have security requirements
for path, methods in openapi_schema["paths"].items():

View File

@@ -108,7 +108,7 @@ import fastapi.testclient
import pytest
from pytest_snapshot.plugin import Snapshot
from backend.server.v2.myroute import router
from backend.api.features.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.server.v2.myroute import router
from backend.api.features.myroute import router
app = fastapi.FastAPI()
app.include_router(router)

View File

@@ -0,0 +1,242 @@
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 youre 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 arent 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 3035 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 Marketplaces 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 YOULL 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,Autocraft LinkedIn gold,"Create researchdriven, highimpact 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 topranked videos so you dont have to
• AIcurated synthesis combines multiple transcripts into one authoritative narrative
• Full creative control adjust style, tone, objective, opinion, clarity, target word count and number of videos
• LinkedInoptimised output hook, 23 key points, CTA, strategic line breaks, 35 hashtags, no markdown
• Oneclick publish returns a readytopost 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
• Thoughtleadership 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 YOULL LOVE IT
Save hours of manual research, avoid surfacelevel hottakes, 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
- Youll 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 35 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
81bcc372-0922-4a36-bc35-f7b1e51d6939,e437cc95-e671-489d-b915-76561fba8c7f,ai-youtube-to-blog-converter,YouTube Video to SEO Blog Writer,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png""]",false,One link. One click. One powerful blog post.,"Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts.
Your videos deserve a second life—in writing.
Make your content work twice as hard by repurposing it into engaging, searchable articles.
Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest.
FEATURES
• 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.
• USER-FRIENDLY
Repurpose your videos into written content to expand your reach and improve accessibility.
Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless.
","[""writing""]",true,true
5c3510d2-fc8b-4053-8e19-67f53c86eb1a,f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9,ai-webpage-copy-improver,AI Webpage Copy Improver,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg""]",false,Boost Your Website's Search Engine Performance,"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.","[""marketing""]",true,true
94d03bd3-7d44-4d47-b60c-edb2f89508d6,b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0,domain-name-finder,Domain Name Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg""]",false,Instantly generate brand-ready domain names that are actually available,"Overview:
Finding a domain name that fits your brand shouldnt take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable.
How It Works
1. Input your product pitch, company name, or core keywords.
2. The agent analyzes brand tone, audience, and industry context.
3. It generates a list of unique, memorable domains that match your criteria.
4. All names are pre-filtered for real-time availability, so you can register immediately.
Business Value
Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains.
Key Use Cases
• Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands.
• Marketers: Test name options across campaigns with instant availability data.
• Entrepreneurs: Validate ideas faster with instant domain options.","[""business""]",false,true
7a831906-daab-426f-9d66-bcf98d869426,516d813b-d1bc-470f-add7-c63a4b2c2bad,ai-function,AI Function,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png""]",false,Never Code Again,"AI FUNCTION MAGIC
Your AIpowered assistant for turning plainEnglish descriptions into working Python functions.
HOW IT WORKS
1. Describe what the function should do.
2. Specify the inputs it needs.
3. Receive the generated Python code.
FEATURES
- Effortless Function Generation: convert naturallanguage 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.
EXAMPLE
Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.”
Input parameter: number_of_people (default 20)
Result: a list of dictionaries such as
[
{ ""name"": ""Emma Martinez"", ""date_of_birth"": ""19921103"", ""job_title"": ""Data Analyst"", ""age"": 32 },
{ ""name"": ""Liam OConnor"", ""date_of_birth"": ""19850719"", ""job_title"": ""Marketing Manager"", ""age"": 39 },
…18 more entries…
]","[""development""]",false,true
1 listing_id storeListingVersionId slug agent_name agent_video agent_image featured sub_heading description categories useForOnboarding is_available
2 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
3 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
4 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
5 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
6 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
7 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
8 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
9 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
10 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
11 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
12 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
13 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
14 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
15 81bcc372-0922-4a36-bc35-f7b1e51d6939 e437cc95-e671-489d-b915-76561fba8c7f ai-youtube-to-blog-converter YouTube Video to SEO Blog Writer ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png"] false One link. One click. One powerful blog post. Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts. Your videos deserve a second life—in writing. Make your content work twice as hard by repurposing it into engaging, searchable articles. Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest. FEATURES • 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. • USER-FRIENDLY Repurpose your videos into written content to expand your reach and improve accessibility. Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless. ["writing"] true true
16 5c3510d2-fc8b-4053-8e19-67f53c86eb1a f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9 ai-webpage-copy-improver AI Webpage Copy Improver ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg"] false Boost Your Website's Search Engine Performance 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. ["marketing"] true true
17 94d03bd3-7d44-4d47-b60c-edb2f89508d6 b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0 domain-name-finder Domain Name Finder ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg"] false Instantly generate brand-ready domain names that are actually available Overview: Finding a domain name that fits your brand shouldn’t take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable. How It Works 1. Input your product pitch, company name, or core keywords. 2. The agent analyzes brand tone, audience, and industry context. 3. It generates a list of unique, memorable domains that match your criteria. 4. All names are pre-filtered for real-time availability, so you can register immediately. Business Value Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains. Key Use Cases • Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands. • Marketers: Test name options across campaigns with instant availability data. • Entrepreneurs: Validate ideas faster with instant domain options. ["business"] false true
18 7a831906-daab-426f-9d66-bcf98d869426 516d813b-d1bc-470f-add7-c63a4b2c2bad ai-function AI Function ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png"] false Never Code Again AI FUNCTION MAGIC Your AI‑powered assistant for turning plain‑English descriptions into working Python functions. HOW IT WORKS 1. Describe what the function should do. 2. Specify the inputs it needs. 3. Receive the generated Python code. FEATURES - 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. EXAMPLE Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.” Input parameter: number_of_people (default 20) Result: a list of dictionaries such as [ { "name": "Emma Martinez", "date_of_birth": "1992‑11‑03", "job_title": "Data Analyst", "age": 32 }, { "name": "Liam O’Connor", "date_of_birth": "1985‑07‑19", "job_title": "Marketing Manager", "age": 39 }, …18 more entries… ] ["development"] false true

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,590 @@
{
"id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"version": 29,
"is_active": false,
"name": "Unspirational Poster Maker",
"description": "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 clich\u00e9s 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.",
"instructions": null,
"recommended_schedule_cron": null,
"nodes": [
{
"id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Generated Image",
"description": "The resulting generated image ready for you to review and post."
},
"metadata": {
"position": {
"x": 2329.937006807125,
"y": 80.49068076698347
}
},
"input_links": [
{
"id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
"source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "20845dda-91de-4508-8077-0504b1a5ae03",
"source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "6524c611-774b-45e9-899d-9a6aa80c549c",
"source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "714a0821-e5ba-4af7-9432-50491adda7b1",
"source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
"block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
"input_default": {
"name": "Theme",
"value": "Cooking"
},
"metadata": {
"position": {
"x": -1219.5966324967521,
"y": 80.50339731789956
}
},
"input_links": [],
"output_links": [
{
"id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
"source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
"sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"source_name": "result",
"sink_name": "prompt_values_#_THEME",
"is_static": true
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
"input_default": {
"upscale": "No Upscale"
},
"metadata": {
"position": {
"x": 1132.373897280427,
"y": 88.44610377514573
}
},
"input_links": [
{
"id": "54588c74-e090-4e49-89e4-844b9952a585",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"output_links": [
{
"id": "20845dda-91de-4508-8077-0504b1a5ae03",
"source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
"input_default": {
"upscale": "No Upscale"
},
"metadata": {
"position": {
"x": 590.7543882245375,
"y": 85.69546832466654
}
},
"input_links": [
{
"id": "66646786-3006-4417-a6b7-0158f2603d1d",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"output_links": [
{
"id": "6524c611-774b-45e9-899d-9a6aa80c549c",
"source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
"input_default": {
"upscale": "No Upscale"
},
"metadata": {
"position": {
"x": 60.48904654237981,
"y": 86.06183359510214
}
},
"input_links": [
{
"id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"output_links": [
{
"id": "714a0821-e5ba-4af7-9432-50491adda7b1",
"source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
"input_default": {
"prompt": "A cat sprawled dramatically across an important-looking document during a work-from-home meeting, making direct eye contact with the camera while knocking over a coffee mug in slow motion. Text Overlay: \"Chaos is a career path. Be the obstacle everyone has to work around.\"",
"upscale": "No Upscale"
},
"metadata": {
"position": {
"x": 1668.3572666956795,
"y": 89.69665262457966
}
},
"input_links": [
{
"id": "509b7587-1940-4a06-808d-edde9a74f400",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"output_links": [
{
"id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
"source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "gpt-4o",
"prompt": "<example_output>\nA photo of a sloth lounging on a desk, with its head resting on a keyboard. The keyboard is on top of a laptop with a blank spreadsheet open. A to-do list is placed beside the laptop, with the top item written as \"Do literally anything\". There is a text overlay that says \"If you can't outwork them, outnap them.\".\n</example_output>\n\nCreate a relatable satirical, snarky, user-deprecating motivational style image based on the theme: \"{{THEME}}\".\n\nOutput only the image description and caption, without any additional commentary or formatting.",
"prompt_values": {}
},
"metadata": {
"position": {
"x": -561.1139207164056,
"y": 78.60434452403524
}
},
"input_links": [
{
"id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
"source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
"sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"source_name": "result",
"sink_name": "prompt_values_#_THEME",
"is_static": true
}
],
"output_links": [
{
"id": "54588c74-e090-4e49-89e4-844b9952a585",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "509b7587-1940-4a06-808d-edde9a74f400",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "66646786-3006-4417-a6b7-0158f2603d1d",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
"graph_version": 29,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"id": "66646786-3006-4417-a6b7-0158f2603d1d",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
"source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "6524c611-774b-45e9-899d-9a6aa80c549c",
"source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "20845dda-91de-4508-8077-0504b1a5ae03",
"source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
"source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
"sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"source_name": "result",
"sink_name": "prompt_values_#_THEME",
"is_static": true
},
{
"id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "714a0821-e5ba-4af7-9432-50491adda7b1",
"source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
"sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
"source_name": "result",
"sink_name": "value",
"is_static": false
},
{
"id": "54588c74-e090-4e49-89e4-844b9952a585",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
},
{
"id": "509b7587-1940-4a06-808d-edde9a74f400",
"source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
"sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
"source_name": "response",
"sink_name": "prompt",
"is_static": false
}
],
"forked_from_id": null,
"forked_from_version": null,
"sub_graphs": [],
"user_id": "",
"created_at": "2024-12-20T19:58:34.390Z",
"input_schema": {
"type": "object",
"properties": {
"Theme": {
"advanced": false,
"secret": false,
"title": "Theme",
"default": "Cooking"
}
},
"required": []
},
"output_schema": {
"type": "object",
"properties": {
"Generated Image": {
"advanced": false,
"secret": false,
"title": "Generated Image",
"description": "The resulting generated image ready for you to review and post."
}
},
"required": [
"Generated Image"
]
},
"has_external_trigger": false,
"has_human_in_the_loop": false,
"trigger_setup_info": null,
"credentials_input_schema": {
"properties": {
"ideogram_api_key_credentials": {
"credentials_provider": [
"ideogram"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "ideogram",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.IDEOGRAM: 'ideogram'>], 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"
]
}
},
"required": [
"ideogram_api_key_credentials",
"openai_api_key_credentials"
],
"title": "UnspirationalPosterMakerCredentialsInputSchema",
"type": "object"
}
}

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,447 @@
{
"id": "622849a7-5848-4838-894d-01f8f07e3fad",
"version": 18,
"is_active": true,
"name": "AI Function",
"description": "## AI-Powered Function Magic: Never code again!\nProvide a description of a python function and your inputs and AI will provide the results.",
"instructions": null,
"recommended_schedule_cron": null,
"nodes": [
{
"id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "return",
"title": null,
"value": null,
"format": "",
"secret": false,
"advanced": false,
"description": "The value returned by the function"
},
"metadata": {
"position": {
"x": 1598.8622921127233,
"y": 291.59140862204725
}
},
"input_links": [
{
"id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
"source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
"source_name": "response",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
"graph_version": 18,
"webhook_id": null,
"webhook": null
},
{
"id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "o3-mini",
"retry": 3,
"prompt": "{{ARGS}}",
"sys_prompt": "You are now the following python function:\n\n```\n# {{DESCRIPTION}}\n{{FUNCTION}}\n```\n\nThe user will provide your input arguments.\nOnly respond with your `return` value.\nDo not include any commentary or additional text in your response. \nDo not include ``` backticks or any other decorators.",
"ollama_host": "localhost:11434",
"prompt_values": {}
},
"metadata": {
"position": {
"x": 995,
"y": 290.50000000000006
}
},
"input_links": [
{
"id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
"source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_FUNCTION",
"is_static": true
},
{
"id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
"source_id": "844530de-2354-46d8-b748-67306b7bbca1",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_ARGS",
"is_static": true
},
{
"id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
"source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_DESCRIPTION",
"is_static": true
}
],
"output_links": [
{
"id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
"source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
"source_name": "response",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
"graph_version": 18,
"webhook_id": null,
"webhook": null
},
{
"id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
"block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
"input_default": {
"name": "Function Definition",
"title": null,
"value": "def fake_people(n: int) -> list[dict]:",
"secret": false,
"advanced": false,
"description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
"placeholder_values": []
},
"metadata": {
"position": {
"x": -672.6908629664215,
"y": 302.42044359789116
}
},
"input_links": [],
"output_links": [
{
"id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
"source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_FUNCTION",
"is_static": true
}
],
"graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
"graph_version": 18,
"webhook_id": null,
"webhook": null
},
{
"id": "844530de-2354-46d8-b748-67306b7bbca1",
"block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
"input_default": {
"name": "Arguments",
"title": null,
"value": "20",
"secret": false,
"advanced": false,
"description": "The function's inputs\n\ne.g \"20\"",
"placeholder_values": []
},
"metadata": {
"position": {
"x": -158.1623599617334,
"y": 295.410856928333
}
},
"input_links": [],
"output_links": [
{
"id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
"source_id": "844530de-2354-46d8-b748-67306b7bbca1",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_ARGS",
"is_static": true
}
],
"graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
"graph_version": 18,
"webhook_id": null,
"webhook": null
},
{
"id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
"block_id": "90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
"input_default": {
"name": "Description",
"title": null,
"value": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.",
"secret": false,
"advanced": false,
"description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
"placeholder_values": []
},
"metadata": {
"position": {
"x": 374.4548658057796,
"y": 290.3779121974126
}
},
"input_links": [],
"output_links": [
{
"id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
"source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_DESCRIPTION",
"is_static": true
}
],
"graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
"graph_version": 18,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
"source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
"source_name": "response",
"sink_name": "value",
"is_static": false
},
{
"id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
"source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_DESCRIPTION",
"is_static": true
},
{
"id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
"source_id": "844530de-2354-46d8-b748-67306b7bbca1",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_ARGS",
"is_static": true
},
{
"id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
"source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
"sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
"source_name": "result",
"sink_name": "prompt_values_#_FUNCTION",
"is_static": true
}
],
"forked_from_id": null,
"forked_from_version": null,
"sub_graphs": [],
"user_id": "",
"created_at": "2025-04-19T17:10:48.857Z",
"input_schema": {
"type": "object",
"properties": {
"Function Definition": {
"advanced": false,
"anyOf": [
{
"format": "short-text",
"type": "string"
},
{
"type": "null"
}
],
"secret": false,
"title": "Function Definition",
"description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
"default": "def fake_people(n: int) -> list[dict]:"
},
"Arguments": {
"advanced": false,
"anyOf": [
{
"format": "short-text",
"type": "string"
},
{
"type": "null"
}
],
"secret": false,
"title": "Arguments",
"description": "The function's inputs\n\ne.g \"20\"",
"default": "20"
},
"Description": {
"advanced": false,
"anyOf": [
{
"format": "long-text",
"type": "string"
},
{
"type": "null"
}
],
"secret": false,
"title": "Description",
"description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
"default": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age."
}
},
"required": []
},
"output_schema": {
"type": "object",
"properties": {
"return": {
"advanced": false,
"secret": false,
"title": "return",
"description": "The value returned by the function"
}
},
"required": [
"return"
]
},
"has_external_trigger": false,
"has_human_in_the_loop": false,
"trigger_setup_info": null,
"credentials_input_schema": {
"properties": {
"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": [
"o3-mini"
]
}
},
"required": [
"openai_api_key_credentials"
],
"title": "AIFunctionCredentialsInputSchema",
"type": "object"
}
}

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

File diff suppressed because one or more lines are too long

File diff suppressed because it is too large Load Diff

File diff suppressed because one or more lines are too long

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,403 @@
{
"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"
},
"metadata": {
"position": {
"x": 872.8268131538296,
"y": 614.9436919065381
}
},
"input_links": [],
"output_links": [],
"graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Generated Image"
},
"metadata": {
"position": {
"x": 1453.6844137728922,
"y": 963.2466395125115
}
},
"input_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
}
],
"output_links": [],
"graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
"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": {
"position": {
"x": -314.43009631839783,
"y": 962.935949165938
}
},
"input_links": [],
"output_links": [
{
"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
}
],
"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,
"y": 966.1604399052493
}
},
"input_links": [
{
"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
}
],
"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
},
{
"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": {
"position": {
"x": 277.3057034159709,
"y": 962.8382498113764
}
},
"input_links": [
{
"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
}
],
"output_links": [
{
"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
}
],
"graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
"graph_version": 12,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"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"
}
}

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

View File

@@ -0,0 +1,505 @@
{
"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.",
"instructions": null,
"recommended_schedule_cron": null,
"nodes": [
{
"id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Improved Webpage Copy"
},
"metadata": {
"position": {
"x": 1039.5884372540172,
"y": -0.8359099621230968
}
},
"input_links": [
{
"id": "d4334477-3616-454f-a430-614ca27f5b36",
"source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
"source_name": "response",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Original Page Analysis",
"description": "Analysis of the webpage as it currently stands."
},
"metadata": {
"position": {
"x": 1037.7724103954706,
"y": -606.5934325506903
}
},
"input_links": [
{
"id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
"source_name": "response",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
"block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
"input_default": {
"name": "Homepage URL",
"value": "https://agpt.co",
"description": "Enter the URL of the homepage you want to improve"
},
"metadata": {
"position": {
"x": -1195.1455674454749,
"y": 0
}
},
"input_links": [],
"output_links": [
{
"id": "cbb12335-fefd-4560-9fff-98675130fbad",
"source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
"sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"source_name": "result",
"sink_name": "url",
"is_static": true
}
],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"block_id": "436c3984-57fd-4b85-8e9a-459b356883bd",
"input_default": {
"raw_content": false
},
"metadata": {
"position": {
"x": -631.7330786555249,
"y": 1.9638396496230826
}
},
"input_links": [
{
"id": "cbb12335-fefd-4560-9fff-98675130fbad",
"source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
"sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"source_name": "result",
"sink_name": "url",
"is_static": true
}
],
"output_links": [
{
"id": "adfa6113-77b3-4e32-b136-3e694b87553e",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
},
{
"id": "5d5656fd-4208-4296-bc70-e39cc31caada",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
}
],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "gpt-4o",
"prompt": "Current Webpage Content:\n```\n{{CONTENT}}\n```\n\nBased on the following analysis of the webpage content:\n\n```\n{{ANALYSIS}}\n```\n\nRewrite and improve the content to address the identified issues. Focus on:\n1. Enhancing clarity and readability\n2. Optimizing for SEO (suggest and incorporate relevant keywords)\n3. Improving calls-to-action for better conversion rates\n4. Refining the structure and organization\n5. Maintaining brand consistency while improving the overall tone\n\nProvide the improved content in HTML format inside a code-block with \"```\" backticks, preserving the original structure where appropriate. Also, include a brief summary of the changes made and their potential impact.",
"prompt_values": {}
},
"metadata": {
"position": {
"x": 488.37278423303917,
"y": 0
}
},
"input_links": [
{
"id": "adfa6113-77b3-4e32-b136-3e694b87553e",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
},
{
"id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "response",
"sink_name": "prompt_values_#_ANALYSIS",
"is_static": false
}
],
"output_links": [
{
"id": "d4334477-3616-454f-a430-614ca27f5b36",
"source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
"source_name": "response",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
},
{
"id": "08612ce2-625b-4c17-accd-3acace7b6477",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "gpt-4o",
"prompt": "Analyze the following webpage content and provide a detailed report on its current state, including strengths and weaknesses in terms of clarity, SEO optimization, and potential for conversion:\n\n{{CONTENT}}\n\nInclude observations on:\n1. Overall readability and clarity\n2. Use of keywords and SEO-friendly language\n3. Effectiveness of calls-to-action\n4. Structure and organization of content\n5. Tone and brand consistency",
"prompt_values": {}
},
"metadata": {
"position": {
"x": -72.66206703605442,
"y": -0.58403945075381
}
},
"input_links": [
{
"id": "5d5656fd-4208-4296-bc70-e39cc31caada",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
}
],
"output_links": [
{
"id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
"source_name": "response",
"sink_name": "value",
"is_static": false
},
{
"id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "response",
"sink_name": "prompt_values_#_ANALYSIS",
"is_static": false
}
],
"graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
"graph_version": 12,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"id": "adfa6113-77b3-4e32-b136-3e694b87553e",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
},
{
"id": "d4334477-3616-454f-a430-614ca27f5b36",
"source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
"source_name": "response",
"sink_name": "value",
"is_static": false
},
{
"id": "5d5656fd-4208-4296-bc70-e39cc31caada",
"source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"source_name": "content",
"sink_name": "prompt_values_#_CONTENT",
"is_static": false
},
{
"id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
"source_name": "response",
"sink_name": "value",
"is_static": false
},
{
"id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
"source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
"sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
"source_name": "response",
"sink_name": "prompt_values_#_ANALYSIS",
"is_static": false
},
{
"id": "cbb12335-fefd-4560-9fff-98675130fbad",
"source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
"sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
"source_name": "result",
"sink_name": "url",
"is_static": true
}
],
"forked_from_id": null,
"forked_from_version": null,
"sub_graphs": [],
"user_id": "",
"created_at": "2024-12-20T19:47:22.036Z",
"input_schema": {
"type": "object",
"properties": {
"Homepage URL": {
"advanced": false,
"secret": false,
"title": "Homepage URL",
"description": "Enter the URL of the homepage you want to improve",
"default": "https://agpt.co"
}
},
"required": []
},
"output_schema": {
"type": "object",
"properties": {
"Improved Webpage Copy": {
"advanced": false,
"secret": false,
"title": "Improved Webpage Copy"
},
"Original Page Analysis": {
"advanced": false,
"secret": false,
"title": "Original Page Analysis",
"description": "Analysis of the webpage as it currently stands."
}
},
"required": [
"Improved Webpage Copy",
"Original Page Analysis"
]
},
"has_external_trigger": false,
"has_human_in_the_loop": false,
"trigger_setup_info": null,
"credentials_input_schema": {
"properties": {
"jina_api_key_credentials": {
"credentials_provider": [
"jina"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "jina",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], 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"
]
}
},
"required": [
"jina_api_key_credentials",
"openai_api_key_credentials"
],
"title": "AIWebpageCopyImproverCredentialsInputSchema",
"type": "object"
}
}

View File

@@ -0,0 +1,615 @@
{
"id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"version": 29,
"is_active": true,
"name": "Email Address Finder",
"description": "Input information of a business and find their email address",
"instructions": null,
"recommended_schedule_cron": null,
"nodes": [
{
"id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
"input_default": {
"name": "Address",
"value": "USA"
},
"metadata": {
"position": {
"x": 1047.9357219838776,
"y": 1067.9123910370954
}
},
"input_links": [],
"output_links": [
{
"id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
"source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_ADDRESS",
"is_static": true
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
"input_default": {
"group": 1,
"pattern": "<email>(.*?)<\\/email>"
},
"metadata": {
"position": {
"x": 3381.2821481740634,
"y": 246.091098184158
}
},
"input_links": [
{
"id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
"source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
}
],
"output_links": [
{
"id": "b15b5143-27b7-486e-a166-4095e72e5235",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
},
{
"id": "23591872-3c6b-4562-87d3-5b6ade698e48",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Email"
},
"metadata": {
"position": {
"x": 4525.4246310882,
"y": 246.36913665010354
}
},
"input_links": [
{
"id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
"source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
},
{
"id": "23591872-3c6b-4562-87d3-5b6ade698e48",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
"input_default": {},
"metadata": {
"position": {
"x": 2182.7499999999995,
"y": 242.00001144409185
}
},
"input_links": [
{
"id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
}
],
"output_links": [
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
"input_default": {
"name": "Business Name",
"value": "Tim Cook"
},
"metadata": {
"position": {
"x": 1049.9704155272595,
"y": 244.49931152418344
}
},
"input_links": [],
"output_links": [
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
"input_default": {
"format": "Email Address of {{NAME}}, {{ADDRESS}}",
"values": {}
},
"metadata": {
"position": {
"x": 1625.25,
"y": 243.25001144409185
}
},
"input_links": [
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
"source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_ADDRESS",
"is_static": true
}
],
"output_links": [
{
"id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
"input_default": {
"format": "Failed to find email. \nResult:\n{{RESULT}}",
"values": {}
},
"metadata": {
"position": {
"x": 3949.7493830805934,
"y": 705.209819698647
}
},
"input_links": [
{
"id": "b15b5143-27b7-486e-a166-4095e72e5235",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
}
],
"output_links": [
{
"id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
"source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "claude-sonnet-4-5-20250929",
"prompt": "<business_website>\n{{WEBSITE_CONTENT}}\n</business_website>\n\nExtract the Contact Email of {{BUSINESS_NAME}}.\n\nIf no email that can be used to contact {{BUSINESS_NAME}} is present, output `N/A`.\nDo not share any emails other than the email for this specific entity.\n\nIf multiple present pick the likely best one.\n\nRespond with the email (or N/A) inside <email></email> tags.\n\nExample Response:\n\n<thoughts_or_comments>\nThere were many emails present, but luckily one was for {{BUSINESS_NAME}} which I have included below.\n</thoughts_or_comments>\n<email>\nexample@email.com\n</email>",
"prompt_values": {}
},
"metadata": {
"position": {
"x": 2774.879259081777,
"y": 243.3102035752969
}
},
"input_links": [
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
},
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"output_links": [
{
"id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
"source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
"source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
},
{
"id": "b15b5143-27b7-486e-a166-4095e72e5235",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
},
{
"id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
"source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
},
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "23591872-3c6b-4562-87d3-5b6ade698e48",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
},
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
},
{
"id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
},
{
"id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
"source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_ADDRESS",
"is_static": true
},
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"forked_from_id": null,
"forked_from_version": null,
"sub_graphs": [],
"user_id": "",
"created_at": "2025-01-03T00:46:30.244Z",
"input_schema": {
"type": "object",
"properties": {
"Address": {
"advanced": false,
"secret": false,
"title": "Address",
"default": "USA"
},
"Business Name": {
"advanced": false,
"secret": false,
"title": "Business Name",
"default": "Tim Cook"
}
},
"required": []
},
"output_schema": {
"type": "object",
"properties": {
"Email": {
"advanced": false,
"secret": false,
"title": "Email"
}
},
"required": [
"Email"
]
},
"has_external_trigger": false,
"has_human_in_the_loop": false,
"trigger_setup_info": null,
"credentials_input_schema": {
"properties": {
"jina_api_key_credentials": {
"credentials_provider": [
"jina"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "jina",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], Literal['api_key']]",
"type": "object",
"discriminator_values": []
},
"anthropic_api_key_credentials": {
"credentials_provider": [
"anthropic"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "anthropic",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], 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": [
"claude-sonnet-4-5-20250929"
]
}
},
"required": [
"jina_api_key_credentials",
"anthropic_api_key_credentials"
],
"title": "EmailAddressFinderCredentialsInputSchema",
"type": "object"
}
}

View File

@@ -3,12 +3,12 @@ from typing import Dict, Set
from fastapi import WebSocket
from backend.api.model import NotificationPayload, WSMessage, WSMethod
from backend.data.execution import (
ExecutionEventType,
GraphExecutionEvent,
NodeExecutionEvent,
)
from backend.server.model import NotificationPayload, WSMessage, WSMethod
_EVENT_TYPE_TO_METHOD_MAP: dict[ExecutionEventType, WSMethod] = {
ExecutionEventType.GRAPH_EXEC_UPDATE: WSMethod.GRAPH_EXECUTION_EVENT,

View File

@@ -4,13 +4,13 @@ from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.api.conn_manager import ConnectionManager
from backend.api.model import NotificationPayload, WSMessage, WSMethod
from backend.data.execution import (
ExecutionStatus,
GraphExecutionEvent,
NodeExecutionEvent,
)
from backend.server.conn_manager import ConnectionManager
from backend.server.model import NotificationPayload, WSMessage, WSMethod
@pytest.fixture

View File

@@ -1,25 +1,23 @@
from fastapi import FastAPI
from backend.api.middleware.security import SecurityHeadersMiddleware
from backend.monitoring.instrumentation import instrument_fastapi
from backend.server.middleware.security import SecurityHeadersMiddleware
from .routes.tools import tools_router
from .routes.v1 import v1_router
from .v1.routes import v1_router
external_app = FastAPI(
external_api = FastAPI(
title="AutoGPT External API",
description="External API for AutoGPT integrations",
docs_url="/docs",
version="1.0",
)
external_app.add_middleware(SecurityHeadersMiddleware)
external_app.include_router(v1_router, prefix="/v1")
external_app.include_router(tools_router, prefix="/v1")
external_api.add_middleware(SecurityHeadersMiddleware)
external_api.include_router(v1_router, prefix="/v1")
# Add Prometheus instrumentation
instrument_fastapi(
external_app,
external_api,
service_name="external-api",
expose_endpoint=True,
endpoint="/metrics",

View File

@@ -0,0 +1,107 @@
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

View File

@@ -0,0 +1,655 @@
"""
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)

View File

@@ -5,46 +5,60 @@ from typing import Annotated, Any, Literal, Optional, Sequence
from fastapi import APIRouter, Body, HTTPException, Security
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
import backend.api.features.store.cache as store_cache
import backend.api.features.store.model as store_model
import backend.data.block
import backend.server.v2.store.cache as store_cache
import backend.server.v2.store.model as store_model
from backend.api.external.middleware import require_permission
from backend.data import execution as execution_db
from backend.data import graph as graph_db
from backend.data.api_key import APIKeyInfo
from backend.data import user as user_db
from backend.data.auth.base import APIAuthorizationInfo
from backend.data.block import BlockInput, CompletedBlockOutput
from backend.executor.utils import add_graph_execution
from backend.server.external.middleware import require_permission
from backend.util.settings import Settings
from .integrations import integrations_router
from .tools import tools_router
settings = Settings()
logger = logging.getLogger(__name__)
v1_router = APIRouter()
class NodeOutput(TypedDict):
key: str
value: Any
v1_router.include_router(integrations_router)
v1_router.include_router(tools_router)
class ExecutionNode(TypedDict):
node_id: str
input: Any
output: dict[str, Any]
class UserInfoResponse(BaseModel):
id: str
name: Optional[str]
email: str
timezone: str = Field(
description="The user's last known timezone (e.g. 'Europe/Amsterdam'), "
"or 'not-set' if not set"
)
class ExecutionNodeOutput(TypedDict):
node_id: str
outputs: list[NodeOutput]
@v1_router.get(
path="/me",
tags=["user", "meta"],
)
async def get_user_info(
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.IDENTITY)
),
) -> UserInfoResponse:
user = await user_db.get_user_by_id(auth.user_id)
class GraphExecutionResult(TypedDict):
execution_id: str
status: str
nodes: list[ExecutionNode]
output: Optional[list[dict[str, str]]]
return UserInfoResponse(
id=user.id,
name=user.name,
email=user.email,
timezone=user.timezone,
)
@v1_router.get(
@@ -65,7 +79,9 @@ async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
async def execute_graph_block(
block_id: str,
data: BlockInput,
api_key: APIKeyInfo = Security(require_permission(APIKeyPermission.EXECUTE_BLOCK)),
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.EXECUTE_BLOCK)
),
) -> CompletedBlockOutput:
obj = backend.data.block.get_block(block_id)
if not obj:
@@ -85,12 +101,14 @@ async def execute_graph(
graph_id: str,
graph_version: int,
node_input: Annotated[dict[str, Any], Body(..., embed=True, default_factory=dict)],
api_key: APIKeyInfo = Security(require_permission(APIKeyPermission.EXECUTE_GRAPH)),
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.EXECUTE_GRAPH)
),
) -> dict[str, Any]:
try:
graph_exec = await add_graph_execution(
graph_id=graph_id,
user_id=api_key.user_id,
user_id=auth.user_id,
inputs=node_input,
graph_version=graph_version,
)
@@ -100,6 +118,19 @@ async def execute_graph(
raise HTTPException(status_code=400, detail=msg)
class ExecutionNode(TypedDict):
node_id: str
input: Any
output: dict[str, Any]
class GraphExecutionResult(TypedDict):
execution_id: str
status: str
nodes: list[ExecutionNode]
output: Optional[list[dict[str, str]]]
@v1_router.get(
path="/graphs/{graph_id}/executions/{graph_exec_id}/results",
tags=["graphs"],
@@ -107,10 +138,12 @@ async def execute_graph(
async def get_graph_execution_results(
graph_id: str,
graph_exec_id: str,
api_key: APIKeyInfo = Security(require_permission(APIKeyPermission.READ_GRAPH)),
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.READ_GRAPH)
),
) -> GraphExecutionResult:
graph_exec = await execution_db.get_graph_execution(
user_id=api_key.user_id,
user_id=auth.user_id,
execution_id=graph_exec_id,
include_node_executions=True,
)
@@ -122,7 +155,7 @@ async def get_graph_execution_results(
if not await graph_db.get_graph(
graph_id=graph_exec.graph_id,
version=graph_exec.graph_version,
user_id=api_key.user_id,
user_id=auth.user_id,
):
raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")

View File

@@ -0,0 +1,152 @@
"""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()

View File

@@ -6,9 +6,10 @@ from fastapi import APIRouter, Body, Security
from prisma.enums import CreditTransactionType
from backend.data.credit import admin_get_user_history, get_user_credit_model
from backend.server.v2.admin.model import AddUserCreditsResponse, UserHistoryResponse
from backend.util.json import SafeJson
from .model import AddUserCreditsResponse, UserHistoryResponse
logger = logging.getLogger(__name__)

View File

@@ -9,14 +9,15 @@ import pytest_mock
from autogpt_libs.auth.jwt_utils import get_jwt_payload
from pytest_snapshot.plugin import Snapshot
import backend.server.v2.admin.credit_admin_routes as credit_admin_routes
import backend.server.v2.admin.model as admin_model
from backend.data.model import UserTransaction
from backend.util.json import SafeJson
from backend.util.models import Pagination
from .credit_admin_routes import router as credit_admin_router
from .model import UserHistoryResponse
app = fastapi.FastAPI()
app.include_router(credit_admin_routes.router)
app.include_router(credit_admin_router)
client = fastapi.testclient.TestClient(app)
@@ -30,7 +31,7 @@ def setup_app_admin_auth(mock_jwt_admin):
def test_add_user_credits_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
configured_snapshot: Snapshot,
admin_user_id: str,
target_user_id: str,
@@ -42,7 +43,7 @@ def test_add_user_credits_success(
return_value=(1500, "transaction-123-uuid")
)
mocker.patch(
"backend.server.v2.admin.credit_admin_routes.get_user_credit_model",
"backend.api.features.admin.credit_admin_routes.get_user_credit_model",
return_value=mock_credit_model,
)
@@ -84,7 +85,7 @@ def test_add_user_credits_success(
def test_add_user_credits_negative_amount(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test credit deduction by admin (negative amount)"""
@@ -94,7 +95,7 @@ def test_add_user_credits_negative_amount(
return_value=(200, "transaction-456-uuid")
)
mocker.patch(
"backend.server.v2.admin.credit_admin_routes.get_user_credit_model",
"backend.api.features.admin.credit_admin_routes.get_user_credit_model",
return_value=mock_credit_model,
)
@@ -119,12 +120,12 @@ def test_add_user_credits_negative_amount(
def test_get_user_history_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test successful retrieval of user credit history"""
# Mock the admin_get_user_history function
mock_history_response = admin_model.UserHistoryResponse(
mock_history_response = UserHistoryResponse(
history=[
UserTransaction(
user_id="user-1",
@@ -150,7 +151,7 @@ def test_get_user_history_success(
)
mocker.patch(
"backend.server.v2.admin.credit_admin_routes.admin_get_user_history",
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)
@@ -170,12 +171,12 @@ def test_get_user_history_success(
def test_get_user_history_with_filters(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test user credit history with search and filter parameters"""
# Mock the admin_get_user_history function
mock_history_response = admin_model.UserHistoryResponse(
mock_history_response = UserHistoryResponse(
history=[
UserTransaction(
user_id="user-3",
@@ -194,7 +195,7 @@ def test_get_user_history_with_filters(
)
mock_get_history = mocker.patch(
"backend.server.v2.admin.credit_admin_routes.admin_get_user_history",
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)
@@ -230,12 +231,12 @@ def test_get_user_history_with_filters(
def test_get_user_history_empty_results(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test user credit history with no results"""
# Mock empty history response
mock_history_response = admin_model.UserHistoryResponse(
mock_history_response = UserHistoryResponse(
history=[],
pagination=Pagination(
total_items=0,
@@ -246,7 +247,7 @@ def test_get_user_history_empty_results(
)
mocker.patch(
"backend.server.v2.admin.credit_admin_routes.admin_get_user_history",
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)

View File

@@ -8,6 +8,10 @@ from fastapi import APIRouter, HTTPException, Security
from pydantic import BaseModel, Field
from backend.blocks.llm import LlmModel
from backend.data.analytics import (
AccuracyTrendsResponse,
get_accuracy_trends_and_alerts,
)
from backend.data.execution import (
ExecutionStatus,
GraphExecutionMeta,
@@ -83,6 +87,18 @@ class ExecutionAnalyticsConfig(BaseModel):
recommended_model: str
class AccuracyTrendsRequest(BaseModel):
graph_id: str = Field(..., description="Graph ID to analyze", min_length=1)
user_id: Optional[str] = Field(None, description="Optional user ID filter")
days_back: int = Field(30, description="Number of days to look back", ge=7, le=90)
drop_threshold: float = Field(
10.0, description="Alert threshold percentage", ge=1.0, le=50.0
)
include_historical: bool = Field(
False, description="Include historical data for charts"
)
router = APIRouter(
prefix="/admin",
tags=["admin", "execution_analytics"],
@@ -426,3 +442,40 @@ async def _process_batch(
return await asyncio.gather(
*[process_single_execution(execution) for execution in executions]
)
@router.get(
"/execution_accuracy_trends",
response_model=AccuracyTrendsResponse,
summary="Get Execution Accuracy Trends and Alerts",
)
async def get_execution_accuracy_trends(
graph_id: str,
user_id: Optional[str] = None,
days_back: int = 30,
drop_threshold: float = 10.0,
include_historical: bool = False,
admin_user_id: str = Security(get_user_id),
) -> AccuracyTrendsResponse:
"""
Get execution accuracy trends with moving averages and alert detection.
Simple single-query approach.
"""
logger.info(
f"Admin user {admin_user_id} requesting accuracy trends for graph {graph_id}"
)
try:
result = await get_accuracy_trends_and_alerts(
graph_id=graph_id,
days_back=days_back,
user_id=user_id,
drop_threshold=drop_threshold,
include_historical=include_historical,
)
return result
except Exception as e:
logger.exception(f"Error getting accuracy trends for graph {graph_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))

View File

@@ -33,7 +33,7 @@ async def _refresh_runtime_state() -> None:
# Clear the /blocks endpoint cache so frontend gets updated schemas
try:
from backend.server.routers.v1 import _get_cached_blocks
from backend.api.features.v1 import _get_cached_blocks
_get_cached_blocks.cache_clear()
logger.info("Cleared /blocks endpoint cache")
@@ -42,7 +42,7 @@ async def _refresh_runtime_state() -> None:
# Clear the v2 builder providers cache (if it exists)
try:
from backend.server.v2.builder import db as builder_db
from backend.api.features.builder import db as builder_db
if hasattr(builder_db, "_get_all_providers"):
builder_db._get_all_providers.cache_clear()

View File

@@ -7,9 +7,9 @@ import fastapi
import fastapi.responses
import prisma.enums
import backend.server.v2.store.cache as store_cache
import backend.server.v2.store.db
import backend.server.v2.store.model
import backend.api.features.store.cache as store_cache
import backend.api.features.store.db as store_db
import backend.api.features.store.model as store_model
import backend.util.json
logger = logging.getLogger(__name__)
@@ -24,7 +24,7 @@ router = fastapi.APIRouter(
@router.get(
"/listings",
summary="Get Admin Listings History",
response_model=backend.server.v2.store.model.StoreListingsWithVersionsResponse,
response_model=store_model.StoreListingsWithVersionsResponse,
)
async def get_admin_listings_with_versions(
status: typing.Optional[prisma.enums.SubmissionStatus] = None,
@@ -48,7 +48,7 @@ async def get_admin_listings_with_versions(
StoreListingsWithVersionsResponse with listings and their versions
"""
try:
listings = await backend.server.v2.store.db.get_admin_listings_with_versions(
listings = await store_db.get_admin_listings_with_versions(
status=status,
search_query=search,
page=page,
@@ -68,11 +68,11 @@ async def get_admin_listings_with_versions(
@router.post(
"/submissions/{store_listing_version_id}/review",
summary="Review Store Submission",
response_model=backend.server.v2.store.model.StoreSubmission,
response_model=store_model.StoreSubmission,
)
async def review_submission(
store_listing_version_id: str,
request: backend.server.v2.store.model.ReviewSubmissionRequest,
request: store_model.ReviewSubmissionRequest,
user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
):
"""
@@ -87,12 +87,10 @@ async def review_submission(
StoreSubmission with updated review information
"""
try:
already_approved = (
await backend.server.v2.store.db.check_submission_already_approved(
store_listing_version_id=store_listing_version_id,
)
already_approved = await store_db.check_submission_already_approved(
store_listing_version_id=store_listing_version_id,
)
submission = await backend.server.v2.store.db.review_store_submission(
submission = await store_db.review_store_submission(
store_listing_version_id=store_listing_version_id,
is_approved=request.is_approved,
external_comments=request.comments,
@@ -136,7 +134,7 @@ async def admin_download_agent_file(
Raises:
HTTPException: If the agent is not found or an unexpected error occurs.
"""
graph_data = await backend.server.v2.store.db.get_agent_as_admin(
graph_data = await store_db.get_agent_as_admin(
user_id=user_id,
store_listing_version_id=store_listing_version_id,
)

View File

@@ -6,10 +6,11 @@ from typing import Annotated
import fastapi
import pydantic
from autogpt_libs.auth import get_user_id
from autogpt_libs.auth.dependencies import requires_user
import backend.data.analytics
router = fastapi.APIRouter()
router = fastapi.APIRouter(dependencies=[fastapi.Security(requires_user)])
logger = logging.getLogger(__name__)

View File

@@ -0,0 +1,340 @@
"""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

View File

@@ -0,0 +1,689 @@
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]

View File

@@ -2,8 +2,8 @@ from typing import Literal
from pydantic import BaseModel
import backend.server.v2.library.model as library_model
import backend.server.v2.store.model as store_model
import backend.api.features.library.model as library_model
import backend.api.features.store.model as store_model
from backend.data.block import BlockInfo
from backend.integrations.providers import ProviderName
from backend.util.models import Pagination
@@ -18,10 +18,17 @@ FilterType = Literal[
BlockType = Literal["all", "input", "action", "output"]
class SearchEntry(BaseModel):
search_query: str | None = None
filter: list[FilterType] | None = None
by_creator: list[str] | None = None
search_id: str | None = None
# Suggestions
class SuggestionsResponse(BaseModel):
otto_suggestions: list[str]
recent_searches: list[str]
recent_searches: list[SearchEntry]
providers: list[ProviderName]
top_blocks: list[BlockInfo]
@@ -32,7 +39,7 @@ class BlockCategoryResponse(BaseModel):
total_blocks: int
blocks: list[BlockInfo]
model_config = {"use_enum_values": False} # <== use enum names like "AI"
model_config = {"use_enum_values": False} # Use enum names like "AI"
# Input/Action/Output and see all for block categories
@@ -53,17 +60,11 @@ class ProviderResponse(BaseModel):
pagination: Pagination
class SearchBlocksResponse(BaseModel):
blocks: BlockResponse
total_block_count: int
total_integration_count: int
class SearchResponse(BaseModel):
items: list[BlockInfo | library_model.LibraryAgent | store_model.StoreAgent]
search_id: str
total_items: dict[FilterType, int]
page: int
more_pages: bool
pagination: Pagination
class CountResponse(BaseModel):

View File

@@ -4,15 +4,12 @@ from typing import Annotated, Sequence
import fastapi
from autogpt_libs.auth.dependencies import get_user_id, requires_user
import backend.server.v2.builder.db as builder_db
import backend.server.v2.builder.model as builder_model
import backend.server.v2.library.db as library_db
import backend.server.v2.library.model as library_model
import backend.server.v2.store.db as store_db
import backend.server.v2.store.model as store_model
from backend.integrations.providers import ProviderName
from backend.util.models import Pagination
from . import db as builder_db
from . import model as builder_model
logger = logging.getLogger(__name__)
router = fastapi.APIRouter(
@@ -45,7 +42,9 @@ def sanitize_query(query: str | None) -> str | None:
summary="Get Builder suggestions",
response_model=builder_model.SuggestionsResponse,
)
async def get_suggestions() -> builder_model.SuggestionsResponse:
async def get_suggestions(
user_id: Annotated[str, fastapi.Security(get_user_id)],
) -> builder_model.SuggestionsResponse:
"""
Get all suggestions for the Blocks Menu.
"""
@@ -55,11 +54,7 @@ async def get_suggestions() -> builder_model.SuggestionsResponse:
"Help me create a list",
"Help me feed my data to Google Maps",
],
recent_searches=[
"image generation",
"deepfake",
"competitor analysis",
],
recent_searches=await builder_db.get_recent_searches(user_id),
providers=[
ProviderName.TWITTER,
ProviderName.GITHUB,
@@ -147,7 +142,6 @@ async def get_providers(
)
# Not using post method because on frontend, orval doesn't support Infinite Query with POST method.
@router.get(
"/search",
summary="Builder search",
@@ -157,7 +151,7 @@ async def get_providers(
async def search(
user_id: Annotated[str, fastapi.Security(get_user_id)],
search_query: Annotated[str | None, fastapi.Query()] = None,
filter: Annotated[list[str] | None, fastapi.Query()] = None,
filter: Annotated[list[builder_model.FilterType] | None, fastapi.Query()] = None,
search_id: Annotated[str | None, fastapi.Query()] = None,
by_creator: Annotated[list[str] | None, fastapi.Query()] = None,
page: Annotated[int, fastapi.Query()] = 1,
@@ -176,69 +170,43 @@ async def search(
]
search_query = sanitize_query(search_query)
# Blocks&Integrations
blocks = builder_model.SearchBlocksResponse(
blocks=builder_model.BlockResponse(
blocks=[],
pagination=Pagination.empty(),
),
total_block_count=0,
total_integration_count=0,
# Get all possible results
cached_results = await builder_db.get_sorted_search_results(
user_id=user_id,
search_query=search_query,
filters=filter,
by_creator=by_creator,
)
if "blocks" in filter or "integrations" in filter:
blocks = builder_db.search_blocks(
include_blocks="blocks" in filter,
include_integrations="integrations" in filter,
query=search_query or "",
page=page,
page_size=page_size,
)
# Library Agents
my_agents = library_model.LibraryAgentResponse(
agents=[],
pagination=Pagination.empty(),
# Paginate results
total_combined_items = len(cached_results.items)
pagination = Pagination(
total_items=total_combined_items,
total_pages=(total_combined_items + page_size - 1) // page_size,
current_page=page,
page_size=page_size,
)
if "my_agents" in filter:
my_agents = await library_db.list_library_agents(
user_id=user_id,
search_term=search_query,
page=page,
page_size=page_size,
)
# Marketplace Agents
marketplace_agents = store_model.StoreAgentsResponse(
agents=[],
pagination=Pagination.empty(),
)
if "marketplace_agents" in filter:
marketplace_agents = await store_db.get_store_agents(
creators=by_creator,
start_idx = (page - 1) * page_size
end_idx = start_idx + page_size
paginated_items = cached_results.items[start_idx:end_idx]
# Update the search entry by id
search_id = await builder_db.update_search(
user_id,
builder_model.SearchEntry(
search_query=search_query,
page=page,
page_size=page_size,
)
more_pages = False
if (
blocks.blocks.pagination.current_page < blocks.blocks.pagination.total_pages
or my_agents.pagination.current_page < my_agents.pagination.total_pages
or marketplace_agents.pagination.current_page
< marketplace_agents.pagination.total_pages
):
more_pages = True
filter=filter,
by_creator=by_creator,
search_id=search_id,
),
)
return builder_model.SearchResponse(
items=blocks.blocks.blocks + my_agents.agents + marketplace_agents.agents,
total_items={
"blocks": blocks.total_block_count,
"integrations": blocks.total_integration_count,
"marketplace_agents": marketplace_agents.pagination.total_items,
"my_agents": my_agents.pagination.total_items,
},
page=page,
more_pages=more_pages,
items=paginated_items,
search_id=search_id,
total_items=cached_results.total_items,
pagination=pagination,
)

View File

@@ -19,9 +19,10 @@ from openai.types.chat.chat_completion_message_tool_call_param import (
from pydantic import BaseModel
from backend.data.redis_client import get_redis_async
from backend.server.v2.chat.config import ChatConfig
from backend.util.exceptions import RedisError
from .config import ChatConfig
logger = logging.getLogger(__name__)
config = ChatConfig()

View File

@@ -1,6 +1,6 @@
import pytest
from backend.server.v2.chat.model import (
from .model import (
ChatMessage,
ChatSession,
Usage,

View File

@@ -0,0 +1,104 @@
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

View File

@@ -9,10 +9,11 @@ from fastapi import APIRouter, Depends, Query, Security
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
import backend.server.v2.chat.service as chat_service
from backend.server.v2.chat.config import ChatConfig
from backend.util.exceptions import NotFoundError
from . import service as chat_service
from .config import ChatConfig
config = ChatConfig()

View File

@@ -7,15 +7,17 @@ import orjson
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
import backend.server.v2.chat.config
from backend.server.v2.chat.model import (
from backend.util.exceptions import NotFoundError
from .config import ChatConfig
from .model import (
ChatMessage,
ChatSession,
Usage,
get_chat_session,
upsert_chat_session,
)
from backend.server.v2.chat.response_model import (
from .response_model import (
StreamBaseResponse,
StreamEnd,
StreamError,
@@ -26,12 +28,11 @@ from backend.server.v2.chat.response_model import (
StreamToolExecutionResult,
StreamUsage,
)
from backend.server.v2.chat.tools import execute_tool, tools
from backend.util.exceptions import NotFoundError
from .tools import execute_tool, tools
logger = logging.getLogger(__name__)
config = backend.server.v2.chat.config.ChatConfig()
config = ChatConfig()
client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)

View File

@@ -3,8 +3,8 @@ from os import getenv
import pytest
import backend.server.v2.chat.service as chat_service
from backend.server.v2.chat.response_model import (
from . import service as chat_service
from .response_model import (
StreamEnd,
StreamError,
StreamTextChunk,

View File

@@ -2,31 +2,22 @@ from typing import TYPE_CHECKING, Any
from openai.types.chat import ChatCompletionToolParam
from backend.server.v2.chat.model import ChatSession
from backend.api.features.chat.model import ChatSession
from .base import BaseTool
from .find_agent import FindAgentTool
from .get_agent_details import GetAgentDetailsTool
from .get_required_setup_info import GetRequiredSetupInfoTool
from .run_agent import RunAgentTool
from .setup_agent import SetupAgentTool
if TYPE_CHECKING:
from backend.server.v2.chat.response_model import StreamToolExecutionResult
from backend.api.features.chat.response_model import StreamToolExecutionResult
# Initialize tool instances
find_agent_tool = FindAgentTool()
get_agent_details_tool = GetAgentDetailsTool()
get_required_setup_info_tool = GetRequiredSetupInfoTool()
setup_agent_tool = SetupAgentTool()
run_agent_tool = RunAgentTool()
# Export tools as OpenAI format
tools: list[ChatCompletionToolParam] = [
find_agent_tool.as_openai_tool(),
get_agent_details_tool.as_openai_tool(),
get_required_setup_info_tool.as_openai_tool(),
setup_agent_tool.as_openai_tool(),
run_agent_tool.as_openai_tool(),
]
@@ -41,9 +32,6 @@ async def execute_tool(
tool_map: dict[str, BaseTool] = {
"find_agent": find_agent_tool,
"get_agent_details": get_agent_details_tool,
"get_required_setup_info": get_required_setup_info_tool,
"schedule_agent": setup_agent_tool,
"run_agent": run_agent_tool,
}
if tool_name not in tool_map:

View File

@@ -5,6 +5,8 @@ from os import getenv
import pytest
from pydantic import SecretStr
from backend.api.features.chat.model import ChatSession
from backend.api.features.store import db as store_db
from backend.blocks.firecrawl.scrape import FirecrawlScrapeBlock
from backend.blocks.io import AgentInputBlock, AgentOutputBlock
from backend.blocks.llm import AITextGeneratorBlock
@@ -13,8 +15,6 @@ from backend.data.graph import Graph, Link, Node, create_graph
from backend.data.model import APIKeyCredentials
from backend.data.user import get_or_create_user
from backend.integrations.credentials_store import IntegrationCredentialsStore
from backend.server.v2.chat.model import ChatSession
from backend.server.v2.store import db as store_db
def make_session(user_id: str | None = None):

View File

@@ -5,8 +5,8 @@ from typing import Any
from openai.types.chat import ChatCompletionToolParam
from backend.server.v2.chat.model import ChatSession
from backend.server.v2.chat.response_model import StreamToolExecutionResult
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.response_model import StreamToolExecutionResult
from .models import ErrorResponse, NeedLoginResponse, ToolResponseBase

View File

@@ -3,17 +3,18 @@
import logging
from typing import Any
from backend.server.v2.chat.model import ChatSession
from backend.server.v2.chat.tools.base import BaseTool
from backend.server.v2.chat.tools.models import (
from backend.api.features.chat.model import ChatSession
from backend.api.features.store import db as store_db
from backend.util.exceptions import DatabaseError, NotFoundError
from .base import BaseTool
from .models import (
AgentCarouselResponse,
AgentInfo,
ErrorResponse,
NoResultsResponse,
ToolResponseBase,
)
from backend.server.v2.store import db as store_db
from backend.util.exceptions import DatabaseError, NotFoundError
logger = logging.getLogger(__name__)

View File

@@ -13,17 +13,9 @@ class ResponseType(str, Enum):
AGENT_CAROUSEL = "agent_carousel"
AGENT_DETAILS = "agent_details"
AGENT_DETAILS_NEED_LOGIN = "agent_details_need_login"
AGENT_DETAILS_NEED_CREDENTIALS = "agent_details_need_credentials"
SETUP_REQUIREMENTS = "setup_requirements"
SCHEDULE_CREATED = "schedule_created"
WEBHOOK_CREATED = "webhook_created"
PRESET_CREATED = "preset_created"
EXECUTION_STARTED = "execution_started"
NEED_LOGIN = "need_login"
NEED_CREDENTIALS = "need_credentials"
INSUFFICIENT_CREDITS = "insufficient_credits"
VALIDATION_ERROR = "validation_error"
ERROR = "error"
NO_RESULTS = "no_results"
SUCCESS = "success"
@@ -112,7 +104,7 @@ class AgentDetails(BaseModel):
class AgentDetailsResponse(ToolResponseBase):
"""Response for get_agent_details tool."""
"""Response for get_details action."""
type: ResponseType = ResponseType.AGENT_DETAILS
agent: AgentDetails
@@ -121,51 +113,7 @@ class AgentDetailsResponse(ToolResponseBase):
graph_version: int | None = None
class AgentDetailsNeedLoginResponse(ToolResponseBase):
"""Response when agent details need login."""
type: ResponseType = ResponseType.AGENT_DETAILS_NEED_LOGIN
agent: AgentDetails
agent_info: dict[str, Any] | None = None
graph_id: str | None = None
graph_version: int | None = None
class AgentDetailsNeedCredentialsResponse(ToolResponseBase):
"""Response when agent needs credentials to be configured."""
type: ResponseType = ResponseType.NEED_CREDENTIALS
agent: AgentDetails
credentials_schema: dict[str, Any]
agent_info: dict[str, Any] | None = None
graph_id: str | None = None
graph_version: int | None = None
# Setup info models
class SetupRequirementInfo(BaseModel):
"""Setup requirement information."""
key: str
provider: str
required: bool = True
user_has: bool = False
credential_id: str | None = None
type: str | None = None
scopes: list[str] | None = None
description: str | None = None
class ExecutionModeInfo(BaseModel):
"""Execution mode information."""
type: str # manual, scheduled, webhook
description: str
supported: bool
config_required: dict[str, str] | None = None
trigger_info: dict[str, Any] | None = None
class UserReadiness(BaseModel):
"""User readiness status."""
@@ -187,11 +135,10 @@ class SetupInfo(BaseModel):
},
)
user_readiness: UserReadiness = Field(default_factory=UserReadiness)
setup_instructions: list[str] = []
class SetupRequirementsResponse(ToolResponseBase):
"""Response for get_required_setup_info tool."""
"""Response for validate action."""
type: ResponseType = ResponseType.SETUP_REQUIREMENTS
setup_info: SetupInfo
@@ -199,70 +146,17 @@ class SetupRequirementsResponse(ToolResponseBase):
graph_version: int | None = None
# Setup agent models
class ScheduleCreatedResponse(ToolResponseBase):
"""Response for scheduled agent setup."""
type: ResponseType = ResponseType.SCHEDULE_CREATED
schedule_id: str
name: str
cron: str
timezone: str = "UTC"
next_run: str | None = None
graph_id: str
graph_name: str
class WebhookCreatedResponse(ToolResponseBase):
"""Response for webhook agent setup."""
type: ResponseType = ResponseType.WEBHOOK_CREATED
webhook_id: str
webhook_url: str
preset_id: str | None = None
name: str
graph_id: str
graph_name: str
class PresetCreatedResponse(ToolResponseBase):
"""Response for preset agent setup."""
type: ResponseType = ResponseType.PRESET_CREATED
preset_id: str
name: str
graph_id: str
graph_name: str
# Run agent models
# Execution models
class ExecutionStartedResponse(ToolResponseBase):
"""Response for agent execution started."""
"""Response for run/schedule actions."""
type: ResponseType = ResponseType.EXECUTION_STARTED
execution_id: str
graph_id: str
graph_name: str
library_agent_id: str | None = None
library_agent_link: str | None = None
status: str = "QUEUED"
ended_at: str | None = None
outputs: dict[str, Any] | None = None
error: str | None = None
timeout_reached: bool | None = None
class InsufficientCreditsResponse(ToolResponseBase):
"""Response for insufficient credits."""
type: ResponseType = ResponseType.INSUFFICIENT_CREDITS
balance: float
class ValidationErrorResponse(ToolResponseBase):
"""Response for validation errors."""
type: ResponseType = ResponseType.VALIDATION_ERROR
error: str
details: dict[str, Any] | None = None
# Auth/error models

View File

@@ -0,0 +1,501 @@
"""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,
)

View File

@@ -0,0 +1,391 @@
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()

View File

@@ -0,0 +1,288 @@
"""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

View File

@@ -7,21 +7,20 @@ import pytest_mock
from prisma.enums import ReviewStatus
from pytest_snapshot.plugin import Snapshot
from backend.server.v2.executions.review.model import PendingHumanReviewModel
from backend.server.v2.executions.review.routes import router
from backend.api.rest_api import handle_internal_http_error
from .model import PendingHumanReviewModel
from .routes import router
# Using a fixed timestamp for reproducible tests
FIXED_NOW = datetime.datetime(2023, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)
app = fastapi.FastAPI()
app.include_router(router, prefix="/api/review")
app.add_exception_handler(ValueError, handle_internal_http_error(400))
client = fastapi.testclient.TestClient(app)
pytestmark = pytest.mark.skip(
reason="Tests failing in CI due to mocking issues - skipping until refactored"
)
@pytest.fixture(autouse=True)
def setup_app_auth(mock_jwt_user):
@@ -34,11 +33,11 @@ def setup_app_auth(mock_jwt_user):
@pytest.fixture
def sample_pending_review() -> PendingHumanReviewModel:
def sample_pending_review(test_user_id: str) -> PendingHumanReviewModel:
"""Create a sample pending review for testing"""
return PendingHumanReviewModel(
node_exec_id="test_node_123",
user_id="test_user",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
@@ -56,12 +55,13 @@ def sample_pending_review() -> PendingHumanReviewModel:
def test_get_pending_reviews_empty(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
test_user_id: str,
) -> None:
"""Test getting pending reviews when none exist"""
mock_get_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_user"
"backend.api.features.executions.review.routes.get_pending_reviews_for_user"
)
mock_get_reviews.return_value = []
@@ -69,17 +69,18 @@ def test_get_pending_reviews_empty(
assert response.status_code == 200
assert response.json() == []
mock_get_reviews.assert_called_once_with("test_user", 1, 25)
mock_get_reviews.assert_called_once_with(test_user_id, 1, 25)
def test_get_pending_reviews_with_data(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
snapshot: Snapshot,
test_user_id: str,
) -> None:
"""Test getting pending reviews with data"""
mock_get_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_user"
"backend.api.features.executions.review.routes.get_pending_reviews_for_user"
)
mock_get_reviews.return_value = [sample_pending_review]
@@ -90,25 +91,26 @@ def test_get_pending_reviews_with_data(
assert len(data) == 1
assert data[0]["node_exec_id"] == "test_node_123"
assert data[0]["status"] == "WAITING"
mock_get_reviews.assert_called_once_with("test_user", 2, 10)
mock_get_reviews.assert_called_once_with(test_user_id, 2, 10)
def test_get_pending_reviews_for_execution_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
snapshot: Snapshot,
test_user_id: str,
) -> None:
"""Test getting pending reviews for specific execution"""
mock_get_graph_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_graph_execution_meta"
"backend.api.features.executions.review.routes.get_graph_execution_meta"
)
mock_get_graph_execution.return_value = {
"id": "test_graph_exec_456",
"user_id": "test_user",
"user_id": test_user_id,
}
mock_get_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews.return_value = [sample_pending_review]
@@ -120,58 +122,73 @@ def test_get_pending_reviews_for_execution_success(
assert data[0]["graph_exec_id"] == "test_graph_exec_456"
def test_get_pending_reviews_for_execution_access_denied(
mocker: pytest_mock.MockFixture,
def test_get_pending_reviews_for_execution_not_available(
mocker: pytest_mock.MockerFixture,
) -> None:
"""Test access denied when user doesn't own the execution"""
mock_get_graph_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_graph_execution_meta"
"backend.api.features.executions.review.routes.get_graph_execution_meta"
)
mock_get_graph_execution.return_value = None
response = client.get("/api/review/execution/test_graph_exec_456")
assert response.status_code == 403
assert "Access denied" in response.json()["detail"]
assert response.status_code == 404
assert "not found" in response.json()["detail"]
def test_process_review_action_approve_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
test_user_id: str,
) -> None:
"""Test successful review approval"""
# Mock the validation functions
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
)
mock_get_pending_review.return_value = sample_pending_review
# Mock the route functions
mock_get_reviews_for_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [sample_pending_review]
mock_process_all_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.process_all_reviews_for_execution"
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
mock_process_all_reviews.return_value = {"test_node_123": sample_pending_review}
# Create approved review for return
approved_review = PendingHumanReviewModel(
node_exec_id="test_node_123",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
payload={"data": "modified payload", "value": 50},
instructions="Please review this data",
editable=True,
status=ReviewStatus.APPROVED,
review_message="Looks good",
was_edited=True,
processed=False,
created_at=FIXED_NOW,
updated_at=FIXED_NOW,
reviewed_at=FIXED_NOW,
)
mock_process_all_reviews.return_value = {"test_node_123": approved_review}
mock_has_pending = mocker.patch(
"backend.data.human_review.has_pending_reviews_for_graph_exec"
"backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
)
mock_has_pending.return_value = False
mocker.patch("backend.executor.utils.add_graph_execution")
mocker.patch("backend.api.features.executions.review.routes.add_graph_execution")
request_data = {
"approved_reviews": [
"reviews": [
{
"node_exec_id": "test_node_123",
"approved": True,
"message": "Looks good",
"reviewed_data": {"data": "modified payload", "value": 50},
}
],
"rejected_review_ids": [],
]
}
response = client.post("/api/review/action", json=request_data)
@@ -185,27 +202,24 @@ def test_process_review_action_approve_success(
def test_process_review_action_reject_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
test_user_id: str,
) -> None:
"""Test successful review rejection"""
# Mock the validation functions
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
)
mock_get_pending_review.return_value = sample_pending_review
# Mock the route functions
mock_get_reviews_for_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [sample_pending_review]
mock_process_all_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.process_all_reviews_for_execution"
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
rejected_review = PendingHumanReviewModel(
node_exec_id="test_node_123",
user_id="test_user",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
@@ -223,11 +237,19 @@ def test_process_review_action_reject_success(
mock_process_all_reviews.return_value = {"test_node_123": rejected_review}
mock_has_pending = mocker.patch(
"backend.data.human_review.has_pending_reviews_for_graph_exec"
"backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
)
mock_has_pending.return_value = False
request_data = {"approved_reviews": [], "rejected_review_ids": ["test_node_123"]}
request_data = {
"reviews": [
{
"node_exec_id": "test_node_123",
"approved": False,
"message": None,
}
]
}
response = client.post("/api/review/action", json=request_data)
@@ -240,14 +262,15 @@ def test_process_review_action_reject_success(
def test_process_review_action_mixed_success(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
test_user_id: str,
) -> None:
"""Test mixed approve/reject operations"""
# Create a second review
second_review = PendingHumanReviewModel(
node_exec_id="test_node_456",
user_id="test_user",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
@@ -263,26 +286,20 @@ def test_process_review_action_mixed_success(
reviewed_at=None,
)
# Mock the validation functions
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
)
mock_get_pending_review.side_effect = lambda node_id, user_id: (
sample_pending_review if node_id == "test_node_123" else second_review
)
# Mock the route functions
mock_get_reviews_for_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [sample_pending_review, second_review]
mock_process_all_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.process_all_reviews_for_execution"
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
# Create approved version of first review
approved_review = PendingHumanReviewModel(
node_exec_id="test_node_123",
user_id="test_user",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
@@ -300,7 +317,7 @@ def test_process_review_action_mixed_success(
# Create rejected version of second review
rejected_review = PendingHumanReviewModel(
node_exec_id="test_node_456",
user_id="test_user",
user_id=test_user_id,
graph_exec_id="test_graph_exec_456",
graph_id="test_graph_789",
graph_version=1,
@@ -321,19 +338,24 @@ def test_process_review_action_mixed_success(
}
mock_has_pending = mocker.patch(
"backend.data.human_review.has_pending_reviews_for_graph_exec"
"backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
)
mock_has_pending.return_value = False
request_data = {
"approved_reviews": [
"reviews": [
{
"node_exec_id": "test_node_123",
"approved": True,
"message": "Approved",
"reviewed_data": {"data": "modified"},
}
],
"rejected_review_ids": ["test_node_456"],
},
{
"node_exec_id": "test_node_456",
"approved": False,
"message": None,
},
]
}
response = client.post("/api/review/action", json=request_data)
@@ -347,117 +369,124 @@ def test_process_review_action_mixed_success(
def test_process_review_action_empty_request(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
test_user_id: str,
) -> None:
"""Test error when no reviews provided"""
request_data = {"approved_reviews": [], "rejected_review_ids": []}
request_data = {"reviews": []}
response = client.post("/api/review/action", json=request_data)
assert response.status_code == 400
assert "At least one review must be provided" in response.json()["detail"]
assert response.status_code == 422
response_data = response.json()
# Pydantic validation error format
assert isinstance(response_data["detail"], list)
assert len(response_data["detail"]) > 0
assert "At least one review must be provided" in response_data["detail"][0]["msg"]
def test_process_review_action_review_not_found(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
test_user_id: str,
) -> None:
"""Test error when review is not found"""
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
# Mock the functions that extract graph execution ID from the request
mock_get_reviews_for_execution = mocker.patch(
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [] # No reviews found
# Mock process_all_reviews to simulate not finding reviews
mock_process_all_reviews = mocker.patch(
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
# This should raise a ValueError with "Reviews not found" message based on the data/human_review.py logic
mock_process_all_reviews.side_effect = ValueError(
"Reviews not found or access denied for IDs: nonexistent_node"
)
mock_get_pending_review.return_value = None
request_data = {
"approved_reviews": [
"reviews": [
{
"node_exec_id": "nonexistent_node",
"approved": True,
"message": "Test",
}
],
"rejected_review_ids": [],
]
}
response = client.post("/api/review/action", json=request_data)
assert response.status_code == 403
assert "not found or access denied" in response.json()["detail"]
assert response.status_code == 400
assert "Reviews not found" in response.json()["detail"]
def test_process_review_action_partial_failure(
mocker: pytest_mock.MockFixture,
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
test_user_id: str,
) -> None:
"""Test handling of partial failures in review processing"""
# Mock successful validation
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
)
mock_get_pending_review.return_value = sample_pending_review
# Mock the route functions
mock_get_reviews_for_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [sample_pending_review]
# Mock partial failure in processing
mock_process_all_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.process_all_reviews_for_execution"
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
mock_process_all_reviews.side_effect = ValueError("Some reviews failed validation")
request_data = {
"approved_reviews": [
"reviews": [
{
"node_exec_id": "test_node_123",
"approved": True,
"message": "Test",
}
],
"rejected_review_ids": [],
]
}
response = client.post("/api/review/action", json=request_data)
assert response.status_code == 200
data = response.json()
assert data["approved_count"] == 0
assert data["rejected_count"] == 0
assert data["failed_count"] == 1
assert "Failed to process reviews" in data["error"]
assert response.status_code == 400
assert "Some reviews failed validation" in response.json()["detail"]
def test_process_review_action_complete_failure(
mocker: pytest_mock.MockFixture,
def test_process_review_action_invalid_node_exec_id(
mocker: pytest_mock.MockerFixture,
sample_pending_review: PendingHumanReviewModel,
test_user_id: str,
) -> None:
"""Test complete failure scenario"""
# Mock successful validation
mock_get_pending_review = mocker.patch(
"backend.data.human_review.get_pending_review_by_node_exec_id"
)
mock_get_pending_review.return_value = sample_pending_review
"""Test failure when trying to process review with invalid node execution ID"""
# Mock the route functions
mock_get_reviews_for_execution = mocker.patch(
"backend.server.v2.executions.review.routes.get_pending_reviews_for_execution"
"backend.api.features.executions.review.routes.get_pending_reviews_for_execution"
)
mock_get_reviews_for_execution.return_value = [sample_pending_review]
# Mock complete failure in processing
# Mock validation failure - this should return 400, not 500
mock_process_all_reviews = mocker.patch(
"backend.server.v2.executions.review.routes.process_all_reviews_for_execution"
"backend.api.features.executions.review.routes.process_all_reviews_for_execution"
)
mock_process_all_reviews.side_effect = ValueError(
"Invalid node execution ID format"
)
mock_process_all_reviews.side_effect = Exception("Database error")
request_data = {
"approved_reviews": [
"reviews": [
{
"node_exec_id": "test_node_123",
"node_exec_id": "invalid-node-format",
"approved": True,
"message": "Test",
}
],
"rejected_review_ids": [],
]
}
response = client.post("/api/review/action", json=request_data)
assert response.status_code == 500
assert "error" in response.json()["detail"].lower()
# Should be a 400 Bad Request, not 500 Internal Server Error
assert response.status_code == 400
assert "Invalid node execution ID format" in response.json()["detail"]

View File

@@ -13,17 +13,14 @@ from backend.data.human_review import (
process_all_reviews_for_execution,
)
from backend.executor.utils import add_graph_execution
from backend.server.v2.executions.review.model import (
PendingHumanReviewModel,
ReviewRequest,
ReviewResponse,
)
from .model import PendingHumanReviewModel, ReviewRequest, ReviewResponse
logger = logging.getLogger(__name__)
router = APIRouter(
tags=["executions", "review", "private"],
tags=["v2", "executions", "review"],
dependencies=[Security(autogpt_auth_lib.requires_user)],
)
@@ -70,8 +67,7 @@ async def list_pending_reviews(
response_model=List[PendingHumanReviewModel],
responses={
200: {"description": "List of pending reviews for the execution"},
400: {"description": "Invalid graph execution ID"},
403: {"description": "Access denied to graph execution"},
404: {"description": "Graph execution not found"},
500: {"description": "Server error", "content": {"application/json": {}}},
},
)
@@ -94,7 +90,7 @@ async def list_pending_reviews_for_execution(
Raises:
HTTPException:
- 403: If user doesn't own the graph execution
- 404: If the graph execution doesn't exist or isn't owned by this user
- 500: If authentication fails or database error occurs
Note:
@@ -108,8 +104,8 @@ async def list_pending_reviews_for_execution(
)
if not graph_exec:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Access denied to graph execution",
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Graph execution #{graph_exec_id} not found",
)
return await get_pending_reviews_for_execution(graph_exec_id, user_id)
@@ -134,18 +130,14 @@ async def process_review_action(
# Build review decisions map
review_decisions = {}
for review in request.reviews:
if review.approved:
review_decisions[review.node_exec_id] = (
ReviewStatus.APPROVED,
review.reviewed_data,
review.message,
)
else:
review_decisions[review.node_exec_id] = (
ReviewStatus.REJECTED,
None,
review.message,
)
review_status = (
ReviewStatus.APPROVED if review.approved else ReviewStatus.REJECTED
)
review_decisions[review.node_exec_id] = (
review_status,
review.reviewed_data,
review.message,
)
# Process all reviews
updated_reviews = await process_all_reviews_for_execution(

View File

@@ -17,6 +17,8 @@ from fastapi import (
from pydantic import BaseModel, Field, SecretStr
from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR, HTTP_502_BAD_GATEWAY
from backend.api.features.library.db import set_preset_webhook, update_preset
from backend.api.features.library.model import LibraryAgentPreset
from backend.data.graph import NodeModel, get_graph, set_node_webhook
from backend.data.integrations import (
WebhookEvent,
@@ -33,7 +35,11 @@ from backend.data.model import (
OAuth2Credentials,
UserIntegrations,
)
from backend.data.onboarding import OnboardingStep, complete_onboarding_step
from backend.data.onboarding import (
OnboardingStep,
complete_onboarding_step,
increment_runs,
)
from backend.data.user import get_user_integrations
from backend.executor.utils import add_graph_execution
from backend.integrations.ayrshare import AyrshareClient, SocialPlatform
@@ -41,13 +47,6 @@ 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.integrations.webhooks import get_webhook_manager
from backend.server.integrations.models import (
ProviderConstants,
ProviderNamesResponse,
get_all_provider_names,
)
from backend.server.v2.library.db import set_preset_webhook, update_preset
from backend.server.v2.library.model import LibraryAgentPreset
from backend.util.exceptions import (
GraphNotInLibraryError,
MissingConfigError,
@@ -56,6 +55,8 @@ from backend.util.exceptions import (
)
from backend.util.settings import Settings
from .models import ProviderConstants, ProviderNamesResponse, get_all_provider_names
if TYPE_CHECKING:
from backend.integrations.oauth import BaseOAuthHandler
@@ -377,6 +378,7 @@ async def webhook_ingress_generic(
return
await complete_onboarding_step(user_id, OnboardingStep.TRIGGER_WEBHOOK)
await increment_runs(user_id)
# Execute all triggers concurrently for better performance
tasks = []

View File

@@ -4,19 +4,18 @@ from typing import Literal, Optional
import fastapi
import prisma.errors
import prisma.fields
import prisma.models
import prisma.types
import backend.api.features.store.exceptions as store_exceptions
import backend.api.features.store.image_gen as store_image_gen
import backend.api.features.store.media as store_media
import backend.data.graph as graph_db
import backend.data.integrations as integrations_db
import backend.server.v2.library.model as library_model
import backend.server.v2.store.exceptions as store_exceptions
import backend.server.v2.store.image_gen as store_image_gen
import backend.server.v2.store.media as store_media
from backend.data.block import BlockInput
from backend.data.db import transaction
from backend.data.execution import get_graph_execution
from backend.data.graph import GraphSettings
from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
from backend.data.model import CredentialsMetaInput
from backend.integrations.creds_manager import IntegrationCredentialsManager
@@ -27,6 +26,8 @@ from backend.util.json import SafeJson
from backend.util.models import Pagination
from backend.util.settings import Config
from . import model as library_model
logger = logging.getLogger(__name__)
config = Config()
integration_creds_manager = IntegrationCredentialsManager()
@@ -400,6 +401,24 @@ async def add_generated_agent_image(
)
def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
"""
Initialize GraphSettings based on graph content.
Args:
graph: The graph to analyze
Returns:
GraphSettings with appropriate human_in_the_loop_safe_mode value
"""
if graph.has_human_in_the_loop:
# Graph has HITL blocks - set safe mode to True by default
return GraphSettings(human_in_the_loop_safe_mode=True)
else:
# Graph has no HITL blocks - keep None
return GraphSettings(human_in_the_loop_safe_mode=None)
async def create_library_agent(
graph: graph_db.GraphModel,
user_id: str,
@@ -445,6 +464,9 @@ async def create_library_agent(
}
}
},
settings=SafeJson(
_initialize_graph_settings(graph_entry).model_dump()
),
),
include=library_agent_include(
user_id, include_nodes=False, include_executions=False
@@ -465,7 +487,7 @@ async def update_agent_version_in_library(
user_id: str,
agent_graph_id: str,
agent_graph_version: int,
) -> None:
) -> library_model.LibraryAgent:
"""
Updates the agent version in the library if useGraphIsActiveVersion is True.
@@ -489,7 +511,7 @@ async def update_agent_version_in_library(
"useGraphIsActiveVersion": True,
},
)
await prisma.models.LibraryAgent.prisma().update(
lib = await prisma.models.LibraryAgent.prisma().update(
where={"id": library_agent.id},
data={
"AgentGraph": {
@@ -501,7 +523,12 @@ async def update_agent_version_in_library(
},
},
},
include={"AgentGraph": True},
)
if lib is None:
raise NotFoundError(f"Library agent {library_agent.id} not found")
return library_model.LibraryAgent.from_db(lib)
except prisma.errors.PrismaError as e:
logger.error(f"Database error updating agent version in library: {e}")
raise DatabaseError("Failed to update agent version in library") from e
@@ -511,9 +538,11 @@ async def update_library_agent(
library_agent_id: str,
user_id: str,
auto_update_version: Optional[bool] = None,
graph_version: Optional[int] = None,
is_favorite: Optional[bool] = None,
is_archived: Optional[bool] = None,
is_deleted: Optional[Literal[False]] = None,
settings: Optional[GraphSettings] = None,
) -> library_model.LibraryAgent:
"""
Updates the specified LibraryAgent record.
@@ -522,8 +551,10 @@ async def update_library_agent(
library_agent_id: The ID of the LibraryAgent to update.
user_id: The owner of this LibraryAgent.
auto_update_version: Whether the agent should auto-update to active version.
graph_version: Specific graph version to update to.
is_favorite: Whether this agent is marked as a favorite.
is_archived: Whether this agent is archived.
settings: User-specific settings for this library agent.
Returns:
The updated LibraryAgent.
@@ -534,8 +565,8 @@ async def update_library_agent(
"""
logger.debug(
f"Updating library agent {library_agent_id} for user {user_id} with "
f"auto_update_version={auto_update_version}, is_favorite={is_favorite}, "
f"is_archived={is_archived}"
f"auto_update_version={auto_update_version}, graph_version={graph_version}, "
f"is_favorite={is_favorite}, is_archived={is_archived}, settings={settings}"
)
update_fields: prisma.types.LibraryAgentUpdateManyMutationInput = {}
if auto_update_version is not None:
@@ -550,10 +581,25 @@ async def update_library_agent(
"Use delete_library_agent() to (soft-)delete library agents"
)
update_fields["isDeleted"] = is_deleted
if not update_fields:
raise ValueError("No values were passed to update")
if settings is not None:
update_fields["settings"] = SafeJson(settings.model_dump())
try:
# If graph_version is provided, update to that specific version
if graph_version is not None:
# Get the current agent to find its graph_id
agent = await get_library_agent(id=library_agent_id, user_id=user_id)
# Update to the specified version using existing function
return await update_agent_version_in_library(
user_id=user_id,
agent_graph_id=agent.graph_id,
agent_graph_version=graph_version,
)
# Otherwise, just update the simple fields
if not update_fields:
raise ValueError("No values were passed to update")
n_updated = await prisma.models.LibraryAgent.prisma().update_many(
where={"id": library_agent_id, "userId": user_id},
data=update_fields,
@@ -570,6 +616,33 @@ async def update_library_agent(
raise DatabaseError("Failed to update library agent") from e
async def update_library_agent_settings(
user_id: str,
agent_id: str,
settings: GraphSettings,
) -> library_model.LibraryAgent:
"""
Updates the settings for a specific LibraryAgent.
Args:
user_id: The owner of the LibraryAgent.
agent_id: The ID of the LibraryAgent to update.
settings: New GraphSettings to apply.
Returns:
The updated LibraryAgent.
Raises:
NotFoundError: If the specified LibraryAgent does not exist.
DatabaseError: If there's an error in the update operation.
"""
return await update_library_agent(
library_agent_id=agent_id,
user_id=user_id,
settings=settings,
)
async def delete_library_agent(
library_agent_id: str, user_id: str, soft_delete: bool = True
) -> None:
@@ -706,6 +779,18 @@ async def add_store_agent_to_library(
graph = store_listing_version.AgentGraph
# Convert to GraphModel to check for HITL blocks
graph_model = await graph_db.get_graph(
graph_id=graph.id,
version=graph.version,
user_id=user_id,
include_subgraphs=False,
)
if not graph_model:
raise store_exceptions.AgentNotFoundError(
f"Graph #{graph.id} v{graph.version} not found or accessible"
)
# Check if user already has this agent
existing_library_agent = await prisma.models.LibraryAgent.prisma().find_unique(
where={
@@ -740,6 +825,9 @@ async def add_store_agent_to_library(
}
},
"isCreatedByUser": False,
"settings": SafeJson(
_initialize_graph_settings(graph_model).model_dump()
),
},
include=library_agent_include(
user_id, include_nodes=False, include_executions=False

View File

@@ -1,16 +1,15 @@
from datetime import datetime
import prisma.enums
import prisma.errors
import prisma.models
import prisma.types
import pytest
import backend.server.v2.library.db as db
import backend.server.v2.store.exceptions
import backend.api.features.store.exceptions
from backend.data.db import connect
from backend.data.includes import library_agent_include
from . import db
@pytest.mark.asyncio
async def test_get_library_agents(mocker):
@@ -32,6 +31,7 @@ async def test_get_library_agents(mocker):
id="ua1",
userId="test-user",
agentGraphId="agent2",
settings="{}", # type: ignore
agentGraphVersion=1,
isCreatedByUser=False,
isDeleted=False,
@@ -87,7 +87,7 @@ async def test_add_agent_to_library(mocker):
await connect()
# Mock the transaction context
mock_transaction = mocker.patch("backend.server.v2.library.db.transaction")
mock_transaction = mocker.patch("backend.api.features.library.db.transaction")
mock_transaction.return_value.__aenter__ = mocker.AsyncMock(return_value=None)
mock_transaction.return_value.__aexit__ = mocker.AsyncMock(return_value=None)
# Mock data
@@ -123,6 +123,7 @@ async def test_add_agent_to_library(mocker):
id="ua1",
userId="test-user",
agentGraphId=mock_store_listing_data.agentGraphId,
settings="{}", # type: ignore
agentGraphVersion=1,
isCreatedByUser=False,
isDeleted=False,
@@ -148,8 +149,18 @@ async def test_add_agent_to_library(mocker):
return_value=mock_library_agent_data
)
# Mock graph_db.get_graph function that's called to check for HITL blocks
mock_graph_db = mocker.patch("backend.api.features.library.db.graph_db")
mock_graph_model = mocker.Mock()
mock_graph_model.nodes = (
[]
) # Empty list so _has_human_in_the_loop_blocks returns False
mock_graph_db.get_graph = mocker.AsyncMock(return_value=mock_graph_model)
# Mock the model conversion
mock_from_db = mocker.patch("backend.server.v2.library.model.LibraryAgent.from_db")
mock_from_db = mocker.patch(
"backend.api.features.library.model.LibraryAgent.from_db"
)
mock_from_db.return_value = mocker.Mock()
# Call function
@@ -169,17 +180,29 @@ async def test_add_agent_to_library(mocker):
},
include={"AgentGraph": True},
)
mock_library_agent.return_value.create.assert_called_once_with(
data={
"User": {"connect": {"id": "test-user"}},
"AgentGraph": {
"connect": {"graphVersionId": {"id": "agent1", "version": 1}}
},
"isCreatedByUser": False,
},
include=library_agent_include(
"test-user", include_nodes=False, include_executions=False
),
# Check that create was called with the expected data including settings
create_call_args = mock_library_agent.return_value.create.call_args
assert create_call_args is not None
# Verify the main structure
expected_data = {
"User": {"connect": {"id": "test-user"}},
"AgentGraph": {"connect": {"graphVersionId": {"id": "agent1", "version": 1}}},
"isCreatedByUser": False,
}
actual_data = create_call_args[1]["data"]
# Check that all expected fields are present
for key, value in expected_data.items():
assert actual_data[key] == value
# Check that settings field is present and is a SafeJson object
assert "settings" in actual_data
assert hasattr(actual_data["settings"], "__class__") # Should be a SafeJson object
# Check include parameter
assert create_call_args[1]["include"] == library_agent_include(
"test-user", include_nodes=False, include_executions=False
)
@@ -195,7 +218,7 @@ async def test_add_agent_to_library_not_found(mocker):
)
# Call function and verify exception
with pytest.raises(backend.server.v2.store.exceptions.AgentNotFoundError):
with pytest.raises(backend.api.features.store.exceptions.AgentNotFoundError):
await db.add_store_agent_to_library("version123", "test-user")
# Verify mock called correctly

View File

@@ -6,8 +6,8 @@ import prisma.enums
import prisma.models
import pydantic
import backend.data.block as block_model
import backend.data.graph as graph_model
from backend.data.block import BlockInput
from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
from backend.data.model import CredentialsMetaInput, is_credentials_field_name
from backend.util.models import Pagination
@@ -72,7 +72,7 @@ class LibraryAgent(pydantic.BaseModel):
has_external_trigger: bool = pydantic.Field(
description="Whether the agent has an external trigger (e.g. webhook) node"
)
trigger_setup_info: Optional[graph_model.GraphTriggerInfo] = None
trigger_setup_info: Optional[GraphTriggerInfo] = None
# Indicates whether there's a new output (based on recent runs)
new_output: bool
@@ -89,6 +89,9 @@ class LibraryAgent(pydantic.BaseModel):
# Recommended schedule cron (from marketplace agents)
recommended_schedule_cron: str | None = None
# User-specific settings for this library agent
settings: GraphSettings = pydantic.Field(default_factory=GraphSettings)
# Marketplace listing information if the agent has been published
marketplace_listing: Optional["MarketplaceListing"] = None
@@ -106,7 +109,7 @@ class LibraryAgent(pydantic.BaseModel):
if not agent.AgentGraph:
raise ValueError("Associated Agent record is required.")
graph = graph_model.GraphModel.from_db(agent.AgentGraph, sub_graphs=sub_graphs)
graph = GraphModel.from_db(agent.AgentGraph, sub_graphs=sub_graphs)
created_at = agent.createdAt
@@ -181,6 +184,7 @@ class LibraryAgent(pydantic.BaseModel):
is_latest_version=is_latest_version,
is_favorite=agent.isFavorite,
recommended_schedule_cron=agent.AgentGraph.recommendedScheduleCron,
settings=GraphSettings.model_validate(agent.settings),
marketplace_listing=marketplace_listing_data,
)
@@ -249,7 +253,7 @@ class LibraryAgentPresetCreatable(pydantic.BaseModel):
graph_id: str
graph_version: int
inputs: block_model.BlockInput
inputs: BlockInput
credentials: dict[str, CredentialsMetaInput]
name: str
@@ -278,7 +282,7 @@ class LibraryAgentPresetUpdatable(pydantic.BaseModel):
Request model used when updating a preset for a library agent.
"""
inputs: Optional[block_model.BlockInput] = None
inputs: Optional[BlockInput] = None
credentials: Optional[dict[str, CredentialsMetaInput]] = None
name: Optional[str] = None
@@ -321,7 +325,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
"Webhook must be included in AgentPreset query when webhookId is set"
)
input_data: block_model.BlockInput = {}
input_data: BlockInput = {}
input_credentials: dict[str, CredentialsMetaInput] = {}
for preset_input in preset.InputPresets:
@@ -381,9 +385,15 @@ class LibraryAgentUpdateRequest(pydantic.BaseModel):
auto_update_version: Optional[bool] = pydantic.Field(
default=None, description="Auto-update the agent version"
)
graph_version: Optional[int] = pydantic.Field(
default=None, description="Specific graph version to update to"
)
is_favorite: Optional[bool] = pydantic.Field(
default=None, description="Mark the agent as a favorite"
)
is_archived: Optional[bool] = pydantic.Field(
default=None, description="Archive the agent"
)
settings: Optional[GraphSettings] = pydantic.Field(
default=None, description="User-specific settings for this library agent"
)

View File

@@ -3,7 +3,7 @@ import datetime
import prisma.models
import pytest
import backend.server.v2.library.model as library_model
from . import model as library_model
@pytest.mark.asyncio

View File

@@ -1,15 +1,18 @@
import logging
from typing import Optional
from typing import Literal, Optional
import autogpt_libs.auth as autogpt_auth_lib
from fastapi import APIRouter, Body, HTTPException, Query, Security, status
from fastapi.responses import Response
from prisma.enums import OnboardingStep
import backend.server.v2.library.db as library_db
import backend.server.v2.library.model as library_model
import backend.server.v2.store.exceptions as store_exceptions
import backend.api.features.store.exceptions as store_exceptions
from backend.data.onboarding import complete_onboarding_step
from backend.util.exceptions import DatabaseError, NotFoundError
from .. import db as library_db
from .. import model as library_model
logger = logging.getLogger(__name__)
router = APIRouter(
@@ -200,6 +203,9 @@ async def get_library_agent_by_store_listing_version_id(
)
async def add_marketplace_agent_to_library(
store_listing_version_id: str = Body(embed=True),
source: Literal["onboarding", "marketplace"] = Body(
default="marketplace", embed=True
),
user_id: str = Security(autogpt_auth_lib.get_user_id),
) -> library_model.LibraryAgent:
"""
@@ -217,10 +223,15 @@ async def add_marketplace_agent_to_library(
HTTPException(500): If a server/database error occurs.
"""
try:
return await library_db.add_store_agent_to_library(
agent = await library_db.add_store_agent_to_library(
store_listing_version_id=store_listing_version_id,
user_id=user_id,
)
if source != "onboarding":
await complete_onboarding_step(
user_id, OnboardingStep.MARKETPLACE_ADD_AGENT
)
return agent
except store_exceptions.AgentNotFoundError as e:
logger.warning(
@@ -274,8 +285,10 @@ async def update_library_agent(
library_agent_id=library_agent_id,
user_id=user_id,
auto_update_version=payload.auto_update_version,
graph_version=payload.graph_version,
is_favorite=payload.is_favorite,
is_archived=payload.is_archived,
settings=payload.settings,
)
except NotFoundError as e:
raise HTTPException(

View File

@@ -4,18 +4,20 @@ from typing import Any, Optional
import autogpt_libs.auth as autogpt_auth_lib
from fastapi import APIRouter, Body, HTTPException, Query, Security, status
import backend.server.v2.library.db as db
import backend.server.v2.library.model as models
from backend.data.execution import GraphExecutionMeta
from backend.data.graph import get_graph
from backend.data.integrations import get_webhook
from backend.data.model import CredentialsMetaInput
from backend.data.onboarding import increment_runs
from backend.executor.utils import add_graph_execution, make_node_credentials_input_map
from backend.integrations.creds_manager import IntegrationCredentialsManager
from backend.integrations.webhooks import get_webhook_manager
from backend.integrations.webhooks.utils import setup_webhook_for_block
from backend.util.exceptions import NotFoundError
from .. import db
from .. import model as models
logger = logging.getLogger(__name__)
credentials_manager = IntegrationCredentialsManager()
@@ -401,6 +403,8 @@ async def execute_preset(
merged_node_input = preset.inputs | inputs
merged_credential_inputs = preset.credentials | credential_inputs
await increment_runs(user_id)
return await add_graph_execution(
user_id=user_id,
graph_id=preset.graph_id,

View File

@@ -1,15 +1,17 @@
import datetime
import json
from unittest.mock import AsyncMock
import fastapi.testclient
import pytest
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
from backend.util.models import Pagination
from . import model as library_model
from .routes import router as library_router
app = fastapi.FastAPI()
app.include_router(library_router)
@@ -85,7 +87,7 @@ async def test_get_library_agents_success(
total_items=2, total_pages=1, current_page=1, page_size=50
),
)
mock_db_call = mocker.patch("backend.server.v2.library.db.list_library_agents")
mock_db_call = mocker.patch("backend.api.features.library.db.list_library_agents")
mock_db_call.return_value = mocked_value
response = client.get("/agents?search_term=test")
@@ -111,7 +113,7 @@ async def test_get_library_agents_success(
def test_get_library_agents_error(mocker: pytest_mock.MockFixture, test_user_id: str):
mock_db_call = mocker.patch("backend.server.v2.library.db.list_library_agents")
mock_db_call = mocker.patch("backend.api.features.library.db.list_library_agents")
mock_db_call.side_effect = Exception("Test error")
response = client.get("/agents?search_term=test")
@@ -160,7 +162,7 @@ async def test_get_favorite_library_agents_success(
),
)
mock_db_call = mocker.patch(
"backend.server.v2.library.db.list_favorite_library_agents"
"backend.api.features.library.db.list_favorite_library_agents"
)
mock_db_call.return_value = mocked_value
@@ -183,7 +185,7 @@ def test_get_favorite_library_agents_error(
mocker: pytest_mock.MockFixture, test_user_id: str
):
mock_db_call = mocker.patch(
"backend.server.v2.library.db.list_favorite_library_agents"
"backend.api.features.library.db.list_favorite_library_agents"
)
mock_db_call.side_effect = Exception("Test error")
@@ -222,9 +224,13 @@ def test_add_agent_to_library_success(
)
mock_db_call = mocker.patch(
"backend.server.v2.library.db.add_store_agent_to_library"
"backend.api.features.library.db.add_store_agent_to_library"
)
mock_db_call.return_value = mock_library_agent
mock_complete_onboarding = mocker.patch(
"backend.api.features.library.routes.agents.complete_onboarding_step",
new_callable=AsyncMock,
)
response = client.post(
"/agents", json={"store_listing_version_id": "test-version-id"}
@@ -239,11 +245,12 @@ def test_add_agent_to_library_success(
mock_db_call.assert_called_once_with(
store_listing_version_id="test-version-id", user_id=test_user_id
)
mock_complete_onboarding.assert_awaited_once()
def test_add_agent_to_library_error(mocker: pytest_mock.MockFixture, test_user_id: str):
mock_db_call = mocker.patch(
"backend.server.v2.library.db.add_store_agent_to_library"
"backend.api.features.library.db.add_store_agent_to_library"
)
mock_db_call.side_effect = Exception("Test error")

View File

@@ -0,0 +1,833 @@
"""
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
)

File diff suppressed because it is too large Load Diff

View File

@@ -6,9 +6,9 @@ import pytest
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.v2.otto.models as otto_models
import backend.server.v2.otto.routes as otto_routes
from backend.server.v2.otto.service import OttoService
from . import models as otto_models
from . import routes as otto_routes
from .service import OttoService
app = fastapi.FastAPI()
app.include_router(otto_routes.router)

View File

@@ -4,12 +4,15 @@ from typing import Annotated
from fastapi import APIRouter, Body, HTTPException, Query, Security
from fastapi.responses import JSONResponse
from backend.api.utils.api_key_auth import APIKeyAuthenticator
from backend.data.user import (
get_user_by_email,
set_user_email_verification,
unsubscribe_user_by_token,
)
from backend.server.routers.postmark.models import (
from backend.util.settings import Settings
from .models import (
PostmarkBounceEnum,
PostmarkBounceWebhook,
PostmarkClickWebhook,
@@ -19,8 +22,6 @@ from backend.server.routers.postmark.models import (
PostmarkSubscriptionChangeWebhook,
PostmarkWebhook,
)
from backend.server.utils.api_key_auth import APIKeyAuthenticator
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
settings = Settings()

View File

@@ -1,8 +1,9 @@
from typing import Literal
import backend.server.v2.store.db
from backend.util.cache import cached
from . import db as store_db
##############################################
############### Caches #######################
##############################################
@@ -29,7 +30,7 @@ async def _get_cached_store_agents(
page_size: int,
):
"""Cached helper to get store agents."""
return await backend.server.v2.store.db.get_store_agents(
return await store_db.get_store_agents(
featured=featured,
creators=[creator] if creator else None,
sorted_by=sorted_by,
@@ -42,10 +43,12 @@ async def _get_cached_store_agents(
# Cache individual agent details for 15 minutes
@cached(maxsize=200, ttl_seconds=300, shared_cache=True)
async def _get_cached_agent_details(username: str, agent_name: str):
async def _get_cached_agent_details(
username: str, agent_name: str, include_changelog: bool = False
):
"""Cached helper to get agent details."""
return await backend.server.v2.store.db.get_store_agent_details(
username=username, agent_name=agent_name
return await store_db.get_store_agent_details(
username=username, agent_name=agent_name, include_changelog=include_changelog
)
@@ -59,7 +62,7 @@ async def _get_cached_store_creators(
page_size: int,
):
"""Cached helper to get store creators."""
return await backend.server.v2.store.db.get_store_creators(
return await store_db.get_store_creators(
featured=featured,
search_query=search_query,
sorted_by=sorted_by,
@@ -72,6 +75,4 @@ async def _get_cached_store_creators(
@cached(maxsize=100, ttl_seconds=300, shared_cache=True)
async def _get_cached_creator_details(username: str):
"""Cached helper to get creator details."""
return await backend.server.v2.store.db.get_store_creator_details(
username=username.lower()
)
return await store_db.get_store_creator_details(username=username.lower())

View File

@@ -10,8 +10,6 @@ import prisma.errors
import prisma.models
import prisma.types
import backend.server.v2.store.exceptions
import backend.server.v2.store.model
from backend.data.db import query_raw_with_schema, transaction
from backend.data.graph import (
GraphMeta,
@@ -30,6 +28,9 @@ from backend.notifications.notifications import queue_notification_async
from backend.util.exceptions import DatabaseError
from backend.util.settings import Settings
from . import exceptions as store_exceptions
from . import model as store_model
logger = logging.getLogger(__name__)
settings = Settings()
@@ -47,7 +48,7 @@ async def get_store_agents(
category: str | None = None,
page: int = 1,
page_size: int = 20,
) -> backend.server.v2.store.model.StoreAgentsResponse:
) -> store_model.StoreAgentsResponse:
"""
Get PUBLIC store agents from the StoreAgent view
"""
@@ -148,10 +149,10 @@ async def get_store_agents(
total_pages = (total + page_size - 1) // page_size
# Convert raw results to StoreAgent models
store_agents: list[backend.server.v2.store.model.StoreAgent] = []
store_agents: list[store_model.StoreAgent] = []
for agent in agents:
try:
store_agent = backend.server.v2.store.model.StoreAgent(
store_agent = store_model.StoreAgent(
slug=agent["slug"],
agent_name=agent["agent_name"],
agent_image=(
@@ -197,11 +198,11 @@ async def get_store_agents(
total = await prisma.models.StoreAgent.prisma().count(where=where_clause)
total_pages = (total + page_size - 1) // page_size
store_agents: list[backend.server.v2.store.model.StoreAgent] = []
store_agents: list[store_model.StoreAgent] = []
for agent in agents:
try:
# Create the StoreAgent object safely
store_agent = backend.server.v2.store.model.StoreAgent(
store_agent = store_model.StoreAgent(
slug=agent.slug,
agent_name=agent.agent_name,
agent_image=agent.agent_image[0] if agent.agent_image else "",
@@ -223,9 +224,9 @@ async def get_store_agents(
continue
logger.debug(f"Found {len(store_agents)} agents")
return backend.server.v2.store.model.StoreAgentsResponse(
return store_model.StoreAgentsResponse(
agents=store_agents,
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=total,
total_pages=total_pages,
@@ -256,8 +257,8 @@ async def log_search_term(search_query: str):
async def get_store_agent_details(
username: str, agent_name: str
) -> backend.server.v2.store.model.StoreAgentDetails:
username: str, agent_name: str, include_changelog: bool = False
) -> store_model.StoreAgentDetails:
"""Get PUBLIC store agent details from the StoreAgent view"""
logger.debug(f"Getting store agent details for {username}/{agent_name}")
@@ -268,7 +269,7 @@ async def get_store_agent_details(
if not agent:
logger.warning(f"Agent not found: {username}/{agent_name}")
raise backend.server.v2.store.exceptions.AgentNotFoundError(
raise store_exceptions.AgentNotFoundError(
f"Agent {username}/{agent_name} not found"
)
@@ -321,12 +322,34 @@ async def get_store_agent_details(
else:
recommended_schedule_cron = None
# Fetch changelog data if requested
changelog_data = None
if include_changelog and store_listing:
changelog_versions = (
await prisma.models.StoreListingVersion.prisma().find_many(
where={
"storeListingId": store_listing.id,
"submissionStatus": prisma.enums.SubmissionStatus.APPROVED,
},
order=[{"version": "desc"}],
)
)
changelog_data = [
store_model.ChangelogEntry(
version=str(version.version),
changes_summary=version.changesSummary or "No changes recorded",
date=version.createdAt,
)
for version in changelog_versions
]
logger.debug(f"Found agent details for {username}/{agent_name}")
return backend.server.v2.store.model.StoreAgentDetails(
return store_model.StoreAgentDetails(
store_listing_version_id=agent.storeListingVersionId,
slug=agent.slug,
agent_name=agent.agent_name,
agent_video=agent.agent_video or "",
agent_output_demo=agent.agent_output_demo or "",
agent_image=agent.agent_image,
creator=agent.creator_username or "",
creator_avatar=agent.creator_avatar or "",
@@ -336,12 +359,15 @@ async def get_store_agent_details(
runs=agent.runs,
rating=agent.rating,
versions=agent.versions,
agentGraphVersions=agent.agentGraphVersions,
agentGraphId=agent.agentGraphId,
last_updated=agent.updated_at,
active_version_id=active_version_id,
has_approved_version=has_approved_version,
recommended_schedule_cron=recommended_schedule_cron,
changelog=changelog_data,
)
except backend.server.v2.store.exceptions.AgentNotFoundError:
except store_exceptions.AgentNotFoundError:
raise
except Exception as e:
logger.error(f"Error getting store agent details: {e}")
@@ -377,7 +403,7 @@ async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
async def get_store_agent_by_version_id(
store_listing_version_id: str,
) -> backend.server.v2.store.model.StoreAgentDetails:
) -> store_model.StoreAgentDetails:
logger.debug(f"Getting store agent details for {store_listing_version_id}")
try:
@@ -387,16 +413,17 @@ async def get_store_agent_by_version_id(
if not agent:
logger.warning(f"Agent not found: {store_listing_version_id}")
raise backend.server.v2.store.exceptions.AgentNotFoundError(
raise store_exceptions.AgentNotFoundError(
f"Agent {store_listing_version_id} not found"
)
logger.debug(f"Found agent details for {store_listing_version_id}")
return backend.server.v2.store.model.StoreAgentDetails(
return store_model.StoreAgentDetails(
store_listing_version_id=agent.storeListingVersionId,
slug=agent.slug,
agent_name=agent.agent_name,
agent_video=agent.agent_video or "",
agent_output_demo=agent.agent_output_demo or "",
agent_image=agent.agent_image,
creator=agent.creator_username or "",
creator_avatar=agent.creator_avatar or "",
@@ -406,9 +433,11 @@ async def get_store_agent_by_version_id(
runs=agent.runs,
rating=agent.rating,
versions=agent.versions,
agentGraphVersions=agent.agentGraphVersions,
agentGraphId=agent.agentGraphId,
last_updated=agent.updated_at,
)
except backend.server.v2.store.exceptions.AgentNotFoundError:
except store_exceptions.AgentNotFoundError:
raise
except Exception as e:
logger.error(f"Error getting store agent details: {e}")
@@ -421,7 +450,7 @@ async def get_store_creators(
sorted_by: Literal["agent_rating", "agent_runs", "num_agents"] | None = None,
page: int = 1,
page_size: int = 20,
) -> backend.server.v2.store.model.CreatorsResponse:
) -> store_model.CreatorsResponse:
"""Get PUBLIC store creators from the Creator view"""
logger.debug(
f"Getting store creators. featured={featured}, search={search_query}, sorted_by={sorted_by}, page={page}"
@@ -496,7 +525,7 @@ async def get_store_creators(
# Convert to response model
creator_models = [
backend.server.v2.store.model.Creator(
store_model.Creator(
username=creator.username,
name=creator.name,
description=creator.description,
@@ -510,9 +539,9 @@ async def get_store_creators(
]
logger.debug(f"Found {len(creator_models)} creators")
return backend.server.v2.store.model.CreatorsResponse(
return store_model.CreatorsResponse(
creators=creator_models,
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=total,
total_pages=total_pages,
@@ -526,7 +555,7 @@ async def get_store_creators(
async def get_store_creator_details(
username: str,
) -> backend.server.v2.store.model.CreatorDetails:
) -> store_model.CreatorDetails:
logger.debug(f"Getting store creator details for {username}")
try:
@@ -537,12 +566,10 @@ async def get_store_creator_details(
if not creator:
logger.warning(f"Creator not found: {username}")
raise backend.server.v2.store.exceptions.CreatorNotFoundError(
f"Creator {username} not found"
)
raise store_exceptions.CreatorNotFoundError(f"Creator {username} not found")
logger.debug(f"Found creator details for {username}")
return backend.server.v2.store.model.CreatorDetails(
return store_model.CreatorDetails(
name=creator.name,
username=creator.username,
description=creator.description,
@@ -552,7 +579,7 @@ async def get_store_creator_details(
agent_runs=creator.agent_runs,
top_categories=creator.top_categories,
)
except backend.server.v2.store.exceptions.CreatorNotFoundError:
except store_exceptions.CreatorNotFoundError:
raise
except Exception as e:
logger.error(f"Error getting store creator details: {e}")
@@ -561,7 +588,7 @@ async def get_store_creator_details(
async def get_store_submissions(
user_id: str, page: int = 1, page_size: int = 20
) -> backend.server.v2.store.model.StoreSubmissionsResponse:
) -> store_model.StoreSubmissionsResponse:
"""Get store submissions for the authenticated user -- not an admin"""
logger.debug(f"Getting store submissions for user {user_id}, page={page}")
@@ -586,7 +613,7 @@ async def get_store_submissions(
# Convert to response models
submission_models = []
for sub in submissions:
submission_model = backend.server.v2.store.model.StoreSubmission(
submission_model = store_model.StoreSubmission(
agent_id=sub.agent_id,
agent_version=sub.agent_version,
name=sub.name,
@@ -611,9 +638,9 @@ async def get_store_submissions(
submission_models.append(submission_model)
logger.debug(f"Found {len(submission_models)} submissions")
return backend.server.v2.store.model.StoreSubmissionsResponse(
return store_model.StoreSubmissionsResponse(
submissions=submission_models,
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=total,
total_pages=total_pages,
@@ -624,9 +651,9 @@ async def get_store_submissions(
except Exception as e:
logger.error(f"Error fetching store submissions: {e}")
# Return empty response rather than exposing internal errors
return backend.server.v2.store.model.StoreSubmissionsResponse(
return store_model.StoreSubmissionsResponse(
submissions=[],
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=0,
total_pages=0,
@@ -659,7 +686,7 @@ async def delete_store_submission(
if not submission:
logger.warning(f"Submission not found for user {user_id}: {submission_id}")
raise backend.server.v2.store.exceptions.SubmissionNotFoundError(
raise store_exceptions.SubmissionNotFoundError(
f"Submission not found for this user. User ID: {user_id}, Submission ID: {submission_id}"
)
@@ -683,6 +710,7 @@ async def create_store_submission(
slug: str,
name: str,
video_url: str | None = None,
agent_output_demo_url: str | None = None,
image_urls: list[str] = [],
description: str = "",
instructions: str | None = None,
@@ -690,7 +718,7 @@ async def create_store_submission(
categories: list[str] = [],
changes_summary: str | None = "Initial Submission",
recommended_schedule_cron: str | None = None,
) -> backend.server.v2.store.model.StoreSubmission:
) -> store_model.StoreSubmission:
"""
Create the first (and only) store listing and thus submission as a normal user
@@ -731,7 +759,7 @@ async def create_store_submission(
logger.warning(
f"Agent not found for user {user_id}: {agent_id} v{agent_version}"
)
raise backend.server.v2.store.exceptions.AgentNotFoundError(
raise store_exceptions.AgentNotFoundError(
f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
)
@@ -777,6 +805,7 @@ async def create_store_submission(
agentGraphVersion=agent_version,
name=name,
videoUrl=video_url,
agentOutputDemoUrl=agent_output_demo_url,
imageUrls=image_urls,
description=description,
instructions=instructions,
@@ -803,7 +832,7 @@ async def create_store_submission(
logger.debug(f"Created store listing for agent {agent_id}")
# Return submission details
return backend.server.v2.store.model.StoreSubmission(
return store_model.StoreSubmission(
agent_id=agent_id,
agent_version=agent_version,
name=name,
@@ -826,7 +855,7 @@ async def create_store_submission(
logger.debug(
f"Slug '{slug}' is already in use by another agent (agent_id: {agent_id}) for user {user_id}"
)
raise backend.server.v2.store.exceptions.SlugAlreadyInUseError(
raise store_exceptions.SlugAlreadyInUseError(
f"The URL slug '{slug}' is already in use by another one of your agents. Please choose a different slug."
) from exc
else:
@@ -835,8 +864,8 @@ async def create_store_submission(
f"Unique constraint violated (not slug): {error_str}"
) from exc
except (
backend.server.v2.store.exceptions.AgentNotFoundError,
backend.server.v2.store.exceptions.ListingExistsError,
store_exceptions.AgentNotFoundError,
store_exceptions.ListingExistsError,
):
raise
except prisma.errors.PrismaError as e:
@@ -849,6 +878,7 @@ async def edit_store_submission(
store_listing_version_id: str,
name: str,
video_url: str | None = None,
agent_output_demo_url: str | None = None,
image_urls: list[str] = [],
description: str = "",
sub_heading: str = "",
@@ -856,7 +886,7 @@ async def edit_store_submission(
changes_summary: str | None = "Update submission",
recommended_schedule_cron: str | None = None,
instructions: str | None = None,
) -> backend.server.v2.store.model.StoreSubmission:
) -> store_model.StoreSubmission:
"""
Edit an existing store listing submission.
@@ -898,7 +928,7 @@ async def edit_store_submission(
)
if not current_version:
raise backend.server.v2.store.exceptions.SubmissionNotFoundError(
raise store_exceptions.SubmissionNotFoundError(
f"Store listing version not found: {store_listing_version_id}"
)
@@ -907,7 +937,7 @@ async def edit_store_submission(
not current_version.StoreListing
or current_version.StoreListing.owningUserId != user_id
):
raise backend.server.v2.store.exceptions.UnauthorizedError(
raise store_exceptions.UnauthorizedError(
f"User {user_id} does not own submission {store_listing_version_id}"
)
@@ -916,7 +946,7 @@ async def edit_store_submission(
# Check if we can edit this submission
if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
raise backend.server.v2.store.exceptions.InvalidOperationError(
raise store_exceptions.InvalidOperationError(
"Cannot edit a rejected submission"
)
@@ -930,6 +960,7 @@ async def edit_store_submission(
store_listing_id=current_version.storeListingId,
name=name,
video_url=video_url,
agent_output_demo_url=agent_output_demo_url,
image_urls=image_urls,
description=description,
sub_heading=sub_heading,
@@ -947,6 +978,7 @@ async def edit_store_submission(
data=prisma.types.StoreListingVersionUpdateInput(
name=name,
videoUrl=video_url,
agentOutputDemoUrl=agent_output_demo_url,
imageUrls=image_urls,
description=description,
categories=categories,
@@ -963,7 +995,7 @@ async def edit_store_submission(
if not updated_version:
raise DatabaseError("Failed to update store listing version")
return backend.server.v2.store.model.StoreSubmission(
return store_model.StoreSubmission(
agent_id=current_version.agentGraphId,
agent_version=current_version.agentGraphVersion,
name=name,
@@ -984,16 +1016,16 @@ async def edit_store_submission(
)
else:
raise backend.server.v2.store.exceptions.InvalidOperationError(
raise store_exceptions.InvalidOperationError(
f"Cannot edit submission with status: {current_version.submissionStatus}"
)
except (
backend.server.v2.store.exceptions.SubmissionNotFoundError,
backend.server.v2.store.exceptions.UnauthorizedError,
backend.server.v2.store.exceptions.AgentNotFoundError,
backend.server.v2.store.exceptions.ListingExistsError,
backend.server.v2.store.exceptions.InvalidOperationError,
store_exceptions.SubmissionNotFoundError,
store_exceptions.UnauthorizedError,
store_exceptions.AgentNotFoundError,
store_exceptions.ListingExistsError,
store_exceptions.InvalidOperationError,
):
raise
except prisma.errors.PrismaError as e:
@@ -1008,6 +1040,7 @@ async def create_store_version(
store_listing_id: str,
name: str,
video_url: str | None = None,
agent_output_demo_url: str | None = None,
image_urls: list[str] = [],
description: str = "",
instructions: str | None = None,
@@ -1015,7 +1048,7 @@ async def create_store_version(
categories: list[str] = [],
changes_summary: str | None = "Initial submission",
recommended_schedule_cron: str | None = None,
) -> backend.server.v2.store.model.StoreSubmission:
) -> store_model.StoreSubmission:
"""
Create a new version for an existing store listing
@@ -1048,7 +1081,7 @@ async def create_store_version(
)
if not listing:
raise backend.server.v2.store.exceptions.ListingNotFoundError(
raise store_exceptions.ListingNotFoundError(
f"Store listing not found. User ID: {user_id}, Listing ID: {store_listing_id}"
)
@@ -1060,7 +1093,7 @@ async def create_store_version(
)
if not agent:
raise backend.server.v2.store.exceptions.AgentNotFoundError(
raise store_exceptions.AgentNotFoundError(
f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
)
@@ -1077,6 +1110,7 @@ async def create_store_version(
agentGraphVersion=agent_version,
name=name,
videoUrl=video_url,
agentOutputDemoUrl=agent_output_demo_url,
imageUrls=image_urls,
description=description,
instructions=instructions,
@@ -1094,7 +1128,7 @@ async def create_store_version(
f"Created new version for listing {store_listing_id} of agent {agent_id}"
)
# Return submission details
return backend.server.v2.store.model.StoreSubmission(
return store_model.StoreSubmission(
agent_id=agent_id,
agent_version=agent_version,
name=name,
@@ -1121,7 +1155,7 @@ async def create_store_review(
store_listing_version_id: str,
score: int,
comments: str | None = None,
) -> backend.server.v2.store.model.StoreReview:
) -> store_model.StoreReview:
"""Create a review for a store listing as a user to detail their experience"""
try:
data = prisma.types.StoreListingReviewUpsertInput(
@@ -1146,7 +1180,7 @@ async def create_store_review(
data=data,
)
return backend.server.v2.store.model.StoreReview(
return store_model.StoreReview(
score=review.score,
comments=review.comments,
)
@@ -1158,7 +1192,7 @@ async def create_store_review(
async def get_user_profile(
user_id: str,
) -> backend.server.v2.store.model.ProfileDetails | None:
) -> store_model.ProfileDetails | None:
logger.debug(f"Getting user profile for {user_id}")
try:
@@ -1168,7 +1202,7 @@ async def get_user_profile(
if not profile:
return None
return backend.server.v2.store.model.ProfileDetails(
return store_model.ProfileDetails(
name=profile.name,
username=profile.username,
description=profile.description,
@@ -1181,8 +1215,8 @@ async def get_user_profile(
async def update_profile(
user_id: str, profile: backend.server.v2.store.model.Profile
) -> backend.server.v2.store.model.CreatorDetails:
user_id: str, profile: store_model.Profile
) -> store_model.CreatorDetails:
"""
Update the store profile for a user or create a new one if it doesn't exist.
Args:
@@ -1205,7 +1239,7 @@ async def update_profile(
where={"userId": user_id}
)
if not existing_profile:
raise backend.server.v2.store.exceptions.ProfileNotFoundError(
raise store_exceptions.ProfileNotFoundError(
f"Profile not found for user {user_id}. This should not be possible."
)
@@ -1241,7 +1275,7 @@ async def update_profile(
logger.error(f"Failed to update profile for user {user_id}")
raise DatabaseError("Failed to update profile")
return backend.server.v2.store.model.CreatorDetails(
return store_model.CreatorDetails(
name=updated_profile.name,
username=updated_profile.username,
description=updated_profile.description,
@@ -1261,7 +1295,7 @@ async def get_my_agents(
user_id: str,
page: int = 1,
page_size: int = 20,
) -> backend.server.v2.store.model.MyAgentsResponse:
) -> store_model.MyAgentsResponse:
"""Get the agents for the authenticated user"""
logger.debug(f"Getting my agents for user {user_id}, page={page}")
@@ -1298,7 +1332,7 @@ async def get_my_agents(
total_pages = (total + page_size - 1) // page_size
my_agents = [
backend.server.v2.store.model.MyAgent(
store_model.MyAgent(
agent_id=graph.id,
agent_version=graph.version,
agent_name=graph.name or "",
@@ -1311,9 +1345,9 @@ async def get_my_agents(
if (graph := library_agent.AgentGraph)
]
return backend.server.v2.store.model.MyAgentsResponse(
return store_model.MyAgentsResponse(
agents=my_agents,
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=total,
total_pages=total_pages,
@@ -1460,7 +1494,7 @@ async def review_store_submission(
external_comments: str,
internal_comments: str,
reviewer_id: str,
) -> backend.server.v2.store.model.StoreSubmission:
) -> store_model.StoreSubmission:
"""Review a store listing submission as an admin."""
try:
store_listing_version = (
@@ -1673,7 +1707,7 @@ async def review_store_submission(
pass
# Convert to Pydantic model for consistency
return backend.server.v2.store.model.StoreSubmission(
return store_model.StoreSubmission(
agent_id=submission.agentGraphId,
agent_version=submission.agentGraphVersion,
name=submission.name,
@@ -1708,7 +1742,7 @@ async def get_admin_listings_with_versions(
search_query: str | None = None,
page: int = 1,
page_size: int = 20,
) -> backend.server.v2.store.model.StoreListingsWithVersionsResponse:
) -> store_model.StoreListingsWithVersionsResponse:
"""
Get store listings for admins with all their versions.
@@ -1807,10 +1841,10 @@ async def get_admin_listings_with_versions(
# Convert to response models
listings_with_versions = []
for listing in listings:
versions: list[backend.server.v2.store.model.StoreSubmission] = []
versions: list[store_model.StoreSubmission] = []
# If we have versions, turn them into StoreSubmission models
for version in listing.Versions or []:
version_model = backend.server.v2.store.model.StoreSubmission(
version_model = store_model.StoreSubmission(
agent_id=version.agentGraphId,
agent_version=version.agentGraphVersion,
name=version.name,
@@ -1838,26 +1872,24 @@ async def get_admin_listings_with_versions(
creator_email = listing.OwningUser.email if listing.OwningUser else None
listing_with_versions = (
backend.server.v2.store.model.StoreListingWithVersions(
listing_id=listing.id,
slug=listing.slug,
agent_id=listing.agentGraphId,
agent_version=listing.agentGraphVersion,
active_version_id=listing.activeVersionId,
has_approved_version=listing.hasApprovedVersion,
creator_email=creator_email,
latest_version=latest_version,
versions=versions,
)
listing_with_versions = store_model.StoreListingWithVersions(
listing_id=listing.id,
slug=listing.slug,
agent_id=listing.agentGraphId,
agent_version=listing.agentGraphVersion,
active_version_id=listing.activeVersionId,
has_approved_version=listing.hasApprovedVersion,
creator_email=creator_email,
latest_version=latest_version,
versions=versions,
)
listings_with_versions.append(listing_with_versions)
logger.debug(f"Found {len(listings_with_versions)} listings for admin")
return backend.server.v2.store.model.StoreListingsWithVersionsResponse(
return store_model.StoreListingsWithVersionsResponse(
listings=listings_with_versions,
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=total,
total_pages=total_pages,
@@ -1867,9 +1899,9 @@ async def get_admin_listings_with_versions(
except Exception as e:
logger.error(f"Error fetching admin store listings: {e}")
# Return empty response rather than exposing internal errors
return backend.server.v2.store.model.StoreListingsWithVersionsResponse(
return store_model.StoreListingsWithVersionsResponse(
listings=[],
pagination=backend.server.v2.store.model.Pagination(
pagination=store_model.Pagination(
current_page=page,
total_items=0,
total_pages=0,

Some files were not shown because too many files have changed in this diff Show More