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Swifty
2025-12-05 16:09:11 +01:00
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10
autogpt_platform/Makefile
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@@ -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"

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

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{
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"version": 29,
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"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,
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},
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{
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}
}

615
autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json Normal file
View File

@@ -0,0 +1,615 @@
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},
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},
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{
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},
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],
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},
"anthropic_api_key_credentials": {
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],
"credentials_types": [
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],
"properties": {
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"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"
}
}

2
autogpt_platform/backend/backend/blocks/airtable/_api.py
View File

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

4
autogpt_platform/backend/backend/blocks/reddit.py
View File

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

12
autogpt_platform/backend/backend/data/block.py
View File

@@ -601,14 +601,18 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
async for output_name, output_data in self._execute(input_data, **kwargs):
yield output_name, output_data
except Exception as ex:
if not isinstance(ex, BlockError):
raise BlockUnknownError(
if isinstance(ex, BlockError):
raise ex
else:
raise (
BlockExecutionError
if isinstance(ex, ValueError)
else BlockUnknownError
)(
message=str(ex),
block_name=self.name,
block_id=self.id,
) from ex
else:
raise ex
async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
if error := self.input_schema.validate_data(input_data):

19
autogpt_platform/backend/backend/data/model.py
View File

@@ -22,7 +22,7 @@ from typing import (
from urllib.parse import urlparse
from uuid import uuid4
from prisma.enums import CreditTransactionType
from prisma.enums import CreditTransactionType, OnboardingStep
from pydantic import (
BaseModel,
ConfigDict,
@@ -868,3 +868,20 @@ class UserExecutionSummaryStats(BaseModel):
total_execution_time: float = Field(default=0)
average_execution_time: float = Field(default=0)
cost_breakdown: dict[str, float] = Field(default_factory=dict)
class UserOnboarding(BaseModel):
userId: str
completedSteps: list[OnboardingStep]
walletShown: bool
notified: list[OnboardingStep]
rewardedFor: list[OnboardingStep]
usageReason: Optional[str]
integrations: list[str]
otherIntegrations: Optional[str]
selectedStoreListingVersionId: Optional[str]
agentInput: Optional[dict[str, Any]]
onboardingAgentExecutionId: Optional[str]
agentRuns: int
lastRunAt: Optional[datetime]
consecutiveRunDays: int

7
autogpt_platform/backend/backend/data/notification_bus.py
View File

@@ -2,7 +2,7 @@ from __future__ import annotations
from typing import AsyncGenerator
from pydantic import BaseModel
from pydantic import BaseModel, field_serializer
from backend.data.event_bus import AsyncRedisEventBus
from backend.server.model import NotificationPayload
@@ -15,6 +15,11 @@ class NotificationEvent(BaseModel):
user_id: str
payload: NotificationPayload
@field_serializer("payload")
def serialize_payload(self, payload: NotificationPayload):
"""Ensure extra fields survive Redis serialization."""
return payload.model_dump()
class AsyncRedisNotificationEventBus(AsyncRedisEventBus[NotificationEvent]):
Model = NotificationEvent # type: ignore

177
autogpt_platform/backend/backend/data/onboarding.py
View File

@@ -1,6 +1,7 @@
import re
from datetime import datetime
from typing import Any, Optional
from datetime import datetime, timedelta, timezone
from typing import Any, Literal, Optional
from zoneinfo import ZoneInfo
import prisma
import pydantic
@@ -8,17 +9,18 @@ from prisma.enums import OnboardingStep
from prisma.models import UserOnboarding
from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
from backend.data.block import get_blocks
from backend.data import execution as execution_db
from backend.data.credit import get_user_credit_model
from backend.data.model import CredentialsMetaInput
from backend.data.notification_bus import (
AsyncRedisNotificationEventBus,
NotificationEvent,
)
from backend.data.user import get_user_by_id
from backend.server.model import OnboardingNotificationPayload
from backend.server.v2.store.model import StoreAgentDetails
from backend.util.cache import cached
from backend.util.json import SafeJson
from backend.util.timezone_utils import get_user_timezone_or_utc
# Mapping from user reason id to categories to search for when choosing agent to show
REASON_MAPPING: dict[str, list[str]] = {
@@ -31,9 +33,20 @@ REASON_MAPPING: dict[str, list[str]] = {
POINTS_AGENT_COUNT = 50 # Number of agents to calculate points for
MIN_AGENT_COUNT = 2 # Minimum number of marketplace agents to enable onboarding
FrontendOnboardingStep = Literal[
OnboardingStep.WELCOME,
OnboardingStep.USAGE_REASON,
OnboardingStep.INTEGRATIONS,
OnboardingStep.AGENT_CHOICE,
OnboardingStep.AGENT_NEW_RUN,
OnboardingStep.AGENT_INPUT,
OnboardingStep.CONGRATS,
OnboardingStep.MARKETPLACE_VISIT,
OnboardingStep.BUILDER_OPEN,
]
class UserOnboardingUpdate(pydantic.BaseModel):
completedSteps: Optional[list[OnboardingStep]] = None
walletShown: Optional[bool] = None
notified: Optional[list[OnboardingStep]] = None
usageReason: Optional[str] = None
@@ -42,9 +55,6 @@ class UserOnboardingUpdate(pydantic.BaseModel):
selectedStoreListingVersionId: Optional[str] = None
agentInput: Optional[dict[str, Any]] = None
onboardingAgentExecutionId: Optional[str] = None
agentRuns: Optional[int] = None
lastRunAt: Optional[datetime] = None
consecutiveRunDays: Optional[int] = None
async def get_user_onboarding(user_id: str):
@@ -83,14 +93,6 @@ async def reset_user_onboarding(user_id: str):
async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
update: UserOnboardingUpdateInput = {}
onboarding = await get_user_onboarding(user_id)
if data.completedSteps is not None:
update["completedSteps"] = list(
set(data.completedSteps + onboarding.completedSteps)
)
for step in data.completedSteps:
if step not in onboarding.completedSteps:
await _reward_user(user_id, onboarding, step)
await _send_onboarding_notification(user_id, step)
if data.walletShown:
update["walletShown"] = data.walletShown
if data.notified is not None:
@@ -107,12 +109,6 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
update["agentInput"] = SafeJson(data.agentInput)
if data.onboardingAgentExecutionId is not None:
update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
if data.agentRuns is not None and data.agentRuns > onboarding.agentRuns:
update["agentRuns"] = data.agentRuns
if data.lastRunAt is not None:
update["lastRunAt"] = data.lastRunAt
if data.consecutiveRunDays is not None:
update["consecutiveRunDays"] = data.consecutiveRunDays
return await UserOnboarding.prisma().upsert(
where={"userId": user_id},
@@ -161,14 +157,12 @@ async def _reward_user(user_id: str, onboarding: UserOnboarding, step: Onboardin
if step in onboarding.rewardedFor:
return
onboarding.rewardedFor.append(step)
user_credit_model = await get_user_credit_model(user_id)
await user_credit_model.onboarding_reward(user_id, reward, step)
await UserOnboarding.prisma().update(
where={"userId": user_id},
data={
"completedSteps": list(set(onboarding.completedSteps + [step])),
"rewardedFor": onboarding.rewardedFor,
"rewardedFor": list(set(onboarding.rewardedFor + [step])),
},
)
@@ -177,31 +171,52 @@ async def complete_onboarding_step(user_id: str, step: OnboardingStep):
"""
Completes the specified onboarding step for the user if not already completed.
"""
onboarding = await get_user_onboarding(user_id)
if step not in onboarding.completedSteps:
await update_user_onboarding(
user_id,
UserOnboardingUpdate(completedSteps=onboarding.completedSteps + [step]),
await UserOnboarding.prisma().update(
where={"userId": user_id},
data={
"completedSteps": list(set(onboarding.completedSteps + [step])),
},
)
await _reward_user(user_id, onboarding, step)
await _send_onboarding_notification(user_id, step)
async def _send_onboarding_notification(user_id: str, step: OnboardingStep):
async def _send_onboarding_notification(
user_id: str, step: OnboardingStep | None, event: str = "step_completed"
):
"""
Sends an onboarding notification to the user for the specified step.
Sends an onboarding notification to the user.
"""
payload = OnboardingNotificationPayload(
type="onboarding",
event="step_completed",
step=step.value,
event=event,
step=step,
)
await AsyncRedisNotificationEventBus().publish(
NotificationEvent(user_id=user_id, payload=payload)
)
def clean_and_split(text: str) -> list[str]:
async def complete_re_run_agent(user_id: str, graph_id: str) -> None:
"""
Complete RE_RUN_AGENT step when a user runs a graph they've run before.
Keeps overhead low by only counting executions if the step is still pending.
"""
onboarding = await get_user_onboarding(user_id)
if OnboardingStep.RE_RUN_AGENT in onboarding.completedSteps:
return
# Includes current execution, so count > 1 means there was at least one prior run.
previous_exec_count = await execution_db.get_graph_executions_count(
user_id=user_id, graph_id=graph_id
)
if previous_exec_count > 1:
await complete_onboarding_step(user_id, OnboardingStep.RE_RUN_AGENT)
def _clean_and_split(text: str) -> list[str]:
"""
Removes all special characters from a string, truncates it to 100 characters,
and splits it by whitespace and commas.
@@ -224,7 +239,7 @@ def clean_and_split(text: str) -> list[str]:
return words
def calculate_points(
def _calculate_points(
agent, categories: list[str], custom: list[str], integrations: list[str]
) -> int:
"""
@@ -268,18 +283,85 @@ def calculate_points(
return int(points)
def get_credentials_blocks() -> dict[str, str]:
# Returns a dictionary of block id to credentials field name
creds: dict[str, str] = {}
blocks = get_blocks()
for id, block in blocks.items():
for field_name, field_info in block().input_schema.model_fields.items():
if field_info.annotation == CredentialsMetaInput:
creds[id] = field_name
return creds
def _normalize_datetime(value: datetime | None) -> datetime | None:
if value is None:
return None
if value.tzinfo is None:
return value.replace(tzinfo=timezone.utc)
return value.astimezone(timezone.utc)
CREDENTIALS_FIELDS: dict[str, str] = get_credentials_blocks()
def _calculate_consecutive_run_days(
last_run_at: datetime | None, current_consecutive_days: int, user_timezone: str
) -> tuple[datetime, int]:
tz = ZoneInfo(user_timezone)
local_now = datetime.now(tz)
normalized_last_run = _normalize_datetime(last_run_at)
if normalized_last_run is None:
return local_now.astimezone(timezone.utc), 1
last_run_local = normalized_last_run.astimezone(tz)
last_run_date = last_run_local.date()
today = local_now.date()
if last_run_date == today:
return local_now.astimezone(timezone.utc), current_consecutive_days
if last_run_date == today - timedelta(days=1):
return local_now.astimezone(timezone.utc), current_consecutive_days + 1
return local_now.astimezone(timezone.utc), 1
def _get_run_milestone_steps(
new_run_count: int, consecutive_days: int
) -> list[OnboardingStep]:
milestones: list[OnboardingStep] = []
if new_run_count >= 10:
milestones.append(OnboardingStep.RUN_AGENTS)
if new_run_count >= 100:
milestones.append(OnboardingStep.RUN_AGENTS_100)
if consecutive_days >= 3:
milestones.append(OnboardingStep.RUN_3_DAYS)
if consecutive_days >= 14:
milestones.append(OnboardingStep.RUN_14_DAYS)
return milestones
async def _get_user_timezone(user_id: str) -> str:
user = await get_user_by_id(user_id)
return get_user_timezone_or_utc(user.timezone if user else None)
async def increment_runs(user_id: str):
"""
Increment a user's run counters and trigger any onboarding milestones.
"""
user_timezone = await _get_user_timezone(user_id)
onboarding = await get_user_onboarding(user_id)
new_run_count = onboarding.agentRuns + 1
last_run_at, consecutive_run_days = _calculate_consecutive_run_days(
onboarding.lastRunAt, onboarding.consecutiveRunDays, user_timezone
)
await UserOnboarding.prisma().update(
where={"userId": user_id},
data={
"agentRuns": {"increment": 1},
"lastRunAt": last_run_at,
"consecutiveRunDays": consecutive_run_days,
},
)
milestones = _get_run_milestone_steps(new_run_count, consecutive_run_days)
new_steps = [step for step in milestones if step not in onboarding.completedSteps]
for step in new_steps:
await complete_onboarding_step(user_id, step)
# Send progress notification if no steps were completed, so client refetches onboarding state
if not new_steps:
await _send_onboarding_notification(user_id, None, event="increment_runs")
async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
@@ -288,7 +370,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
where_clause: dict[str, Any] = {}
custom = clean_and_split((user_onboarding.usageReason or "").lower())
custom = _clean_and_split((user_onboarding.usageReason or "").lower())
if categories:
where_clause["OR"] = [
@@ -336,7 +418,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
# Calculate points for the first X agents and choose the top 2
agent_points = []
for agent in storeAgents[:POINTS_AGENT_COUNT]:
points = calculate_points(
points = _calculate_points(
agent, categories, custom, user_onboarding.integrations
)
agent_points.append((agent, points))
@@ -350,6 +432,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
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,
creator_avatar=agent.creator_avatar,

2
autogpt_platform/backend/backend/executor/scheduler.py
View File

@@ -26,6 +26,7 @@ from sqlalchemy import MetaData, create_engine
from backend.data.block import BlockInput
from backend.data.execution import GraphExecutionWithNodes
from backend.data.model import CredentialsMetaInput
from backend.data.onboarding import increment_runs
from backend.executor import utils as execution_utils
from backend.monitoring import (
NotificationJobArgs,
@@ -153,6 +154,7 @@ async def _execute_graph(**kwargs):
inputs=args.input_data,
graph_credentials_inputs=args.input_credentials,
)
await increment_runs(args.user_id)
elapsed = asyncio.get_event_loop().time() - start_time
logger.info(
f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "

4
autogpt_platform/backend/backend/integrations/webhooks/_manual_base.py
View File

@@ -18,7 +18,9 @@ class ManualWebhookManagerBase(BaseWebhooksManager[WT]):
ingress_url: str,
secret: str,
) -> tuple[str, dict]:
print(ingress_url) # FIXME: pass URL to user in front end
# TODO: pass ingress_url to user in frontend
# See: https://github.com/Significant-Gravitas/AutoGPT/issues/8537
logger.debug(f"Manual webhook registered with ingress URL: {ingress_url}")
return "", {}

22
autogpt_platform/backend/backend/server/external/routes/integrations.py vendored
View File

@@ -273,6 +273,8 @@ async def list_providers(
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
@@ -281,13 +283,27 @@ async def list_providers(
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=True, # All providers can accept API keys
supports_user_password=name in ("smtp",), # SMTP uses user/password
supports_host_scoped=name == "http", # HTTP block uses host-scoped
supports_api_key=supports_api_key,
supports_user_password=supports_user_password,
supports_host_scoped=supports_host_scoped,
default_scopes=default_scopes,
)
)

7
autogpt_platform/backend/backend/server/integrations/router.py
View File

@@ -33,7 +33,11 @@ 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
@@ -377,6 +381,7 @@ 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 = []

12
autogpt_platform/backend/backend/server/model.py
View File

@@ -1,7 +1,8 @@
import enum
from typing import Any, Optional
from typing import Any, Literal, Optional
import pydantic
from prisma.enums import OnboardingStep
from backend.data.api_key import APIKeyInfo, APIKeyPermission
from backend.data.graph import Graph
@@ -35,8 +36,13 @@ class WSSubscribeGraphExecutionsRequest(pydantic.BaseModel):
graph_id: str
GraphCreationSource = Literal["builder", "upload"]
GraphExecutionSource = Literal["builder", "library", "onboarding"]
class CreateGraph(pydantic.BaseModel):
graph: Graph
source: GraphCreationSource | None = None
class CreateAPIKeyRequest(pydantic.BaseModel):
@@ -83,6 +89,8 @@ class NotificationPayload(pydantic.BaseModel):
type: str
event: str
model_config = pydantic.ConfigDict(extra="allow")
class OnboardingNotificationPayload(NotificationPayload):
step: str
step: OnboardingStep | None

73
autogpt_platform/backend/backend/server/routers/v1.py
View File

@@ -5,7 +5,7 @@ import time
import uuid
from collections import defaultdict
from datetime import datetime, timezone
from typing import Annotated, Any, Sequence
from typing import Annotated, Any, Sequence, get_args
import pydantic
import stripe
@@ -45,12 +45,17 @@ from backend.data.credit import (
set_auto_top_up,
)
from backend.data.graph import GraphSettings
from backend.data.model import CredentialsMetaInput
from backend.data.model import CredentialsMetaInput, UserOnboarding
from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
from backend.data.onboarding import (
FrontendOnboardingStep,
OnboardingStep,
UserOnboardingUpdate,
complete_onboarding_step,
complete_re_run_agent,
get_recommended_agents,
get_user_onboarding,
increment_runs,
onboarding_enabled,
reset_user_onboarding,
update_user_onboarding,
@@ -78,6 +83,7 @@ from backend.server.model import (
CreateAPIKeyRequest,
CreateAPIKeyResponse,
CreateGraph,
GraphExecutionSource,
RequestTopUp,
SetGraphActiveVersion,
TimezoneResponse,
@@ -85,6 +91,7 @@ from backend.server.model import (
UpdateTimezoneRequest,
UploadFileResponse,
)
from backend.server.v2.store.model import StoreAgentDetails
from backend.util.cache import cached
from backend.util.clients import get_scheduler_client
from backend.util.cloud_storage import get_cloud_storage_handler
@@ -274,9 +281,10 @@ async def update_preferences(
@v1_router.get(
"/onboarding",
summary="Get onboarding status",
summary="Onboarding state",
tags=["onboarding"],
dependencies=[Security(requires_user)],
response_model=UserOnboarding,
)
async def get_onboarding(user_id: Annotated[str, Security(get_user_id)]):
return await get_user_onboarding(user_id)
@@ -284,9 +292,10 @@ async def get_onboarding(user_id: Annotated[str, Security(get_user_id)]):
@v1_router.patch(
"/onboarding",
summary="Update onboarding progress",
summary="Update onboarding state",
tags=["onboarding"],
dependencies=[Security(requires_user)],
response_model=UserOnboarding,
)
async def update_onboarding(
user_id: Annotated[str, Security(get_user_id)], data: UserOnboardingUpdate
@@ -294,25 +303,39 @@ async def update_onboarding(
return await update_user_onboarding(user_id, data)
@v1_router.post(
"/onboarding/step",
summary="Complete onboarding step",
tags=["onboarding"],
dependencies=[Security(requires_user)],
)
async def onboarding_complete_step(
user_id: Annotated[str, Security(get_user_id)], step: FrontendOnboardingStep
):
if step not in get_args(FrontendOnboardingStep):
raise HTTPException(status_code=400, detail="Invalid onboarding step")
return await complete_onboarding_step(user_id, step)
@v1_router.get(
"/onboarding/agents",
summary="Get recommended agents",
summary="Recommended onboarding agents",
tags=["onboarding"],
dependencies=[Security(requires_user)],
)
async def get_onboarding_agents(
user_id: Annotated[str, Security(get_user_id)],
):
) -> list[StoreAgentDetails]:
return await get_recommended_agents(user_id)
@v1_router.get(
"/onboarding/enabled",
summary="Check onboarding enabled",
summary="Is onboarding enabled",
tags=["onboarding", "public"],
dependencies=[Security(requires_user)],
)
async def is_onboarding_enabled():
async def is_onboarding_enabled() -> bool:
return await onboarding_enabled()
@@ -321,6 +344,7 @@ async def is_onboarding_enabled():
summary="Reset onboarding progress",
tags=["onboarding"],
dependencies=[Security(requires_user)],
response_model=UserOnboarding,
)
async def reset_onboarding(user_id: Annotated[str, Security(get_user_id)]):
return await reset_user_onboarding(user_id)
@@ -809,7 +833,12 @@ async def create_new_graph(
# as the graph already valid and no sub-graphs are returned back.
await graph_db.create_graph(graph, user_id=user_id)
await library_db.create_library_agent(graph, user_id=user_id)
return await on_graph_activate(graph, user_id=user_id)
activated_graph = await on_graph_activate(graph, user_id=user_id)
if create_graph.source == "builder":
await complete_onboarding_step(user_id, OnboardingStep.BUILDER_SAVE_AGENT)
return activated_graph
@v1_router.delete(
@@ -967,6 +996,7 @@ async def execute_graph(
credentials_inputs: Annotated[
dict[str, CredentialsMetaInput], Body(..., embed=True, default_factory=dict)
],
source: Annotated[GraphExecutionSource | None, Body(embed=True)] = None,
graph_version: Optional[int] = None,
preset_id: Optional[str] = None,
) -> execution_db.GraphExecutionMeta:
@@ -990,6 +1020,14 @@ async def execute_graph(
# Record successful graph execution
record_graph_execution(graph_id=graph_id, status="success", user_id=user_id)
record_graph_operation(operation="execute", status="success")
await increment_runs(user_id)
await complete_re_run_agent(user_id, graph_id)
if source == "library":
await complete_onboarding_step(
user_id, OnboardingStep.MARKETPLACE_RUN_AGENT
)
elif source == "builder":
await complete_onboarding_step(user_id, OnboardingStep.BUILDER_RUN_AGENT)
return result
except GraphValidationError as e:
# Record failed graph execution
@@ -1103,6 +1141,15 @@ async def list_graph_executions(
filtered_executions = await hide_activity_summaries_if_disabled(
paginated_result.executions, user_id
)
onboarding = await get_user_onboarding(user_id)
if (
onboarding.onboardingAgentExecutionId
and onboarding.onboardingAgentExecutionId
in [exec.id for exec in filtered_executions]
and OnboardingStep.GET_RESULTS not in onboarding.completedSteps
):
await complete_onboarding_step(user_id, OnboardingStep.GET_RESULTS)
return execution_db.GraphExecutionsPaginated(
executions=filtered_executions, pagination=paginated_result.pagination
)
@@ -1140,6 +1187,12 @@ async def get_graph_execution(
# Apply feature flags to filter out disabled features
result = await hide_activity_summary_if_disabled(result, user_id)
onboarding = await get_user_onboarding(user_id)
if (
onboarding.onboardingAgentExecutionId == graph_exec_id
and OnboardingStep.GET_RESULTS not in onboarding.completedSteps
):
await complete_onboarding_step(user_id, OnboardingStep.GET_RESULTS)
return result
@@ -1316,6 +1369,8 @@ async def create_graph_execution_schedule(
result.next_run_time, user_timezone
)
await complete_onboarding_step(user_id, OnboardingStep.SCHEDULE_AGENT)
return result

14
autogpt_platform/backend/backend/server/v2/library/routes/agents.py
View File

@@ -1,13 +1,15 @@
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
from backend.data.onboarding import complete_onboarding_step
from backend.util.exceptions import DatabaseError, NotFoundError
logger = logging.getLogger(__name__)
@@ -200,6 +202,9 @@ 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 +222,15 @@ 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(

3
autogpt_platform/backend/backend/server/v2/library/routes/presets.py
View File

@@ -10,6 +10,7 @@ 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
@@ -401,6 +402,8 @@ 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,

6
autogpt_platform/backend/backend/server/v2/library/routes_test.py
View File

@@ -1,5 +1,6 @@
import datetime
import json
from unittest.mock import AsyncMock
import fastapi.testclient
import pytest
@@ -225,6 +226,10 @@ def test_add_agent_to_library_success(
"backend.server.v2.library.db.add_store_agent_to_library"
)
mock_db_call.return_value = mock_library_agent
mock_complete_onboarding = mocker.patch(
"backend.server.v2.library.routes.agents.complete_onboarding_step",
new_callable=AsyncMock,
)
response = client.post(
"/agents", json={"store_listing_version_id": "test-version-id"}
@@ -239,6 +244,7 @@ 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):

9
autogpt_platform/backend/backend/server/v2/store/db.py
View File

@@ -394,6 +394,7 @@ async def get_store_agent_details(
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 "",
@@ -464,6 +465,7 @@ async def get_store_agent_by_version_id(
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 "",
@@ -750,6 +752,7 @@ 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,
@@ -844,6 +847,7 @@ 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,
@@ -922,6 +926,7 @@ 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 = "",
@@ -1003,6 +1008,7 @@ 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,
@@ -1020,6 +1026,7 @@ 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,
@@ -1087,6 +1094,7 @@ 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,
@@ -1156,6 +1164,7 @@ 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,

4
autogpt_platform/backend/backend/server/v2/store/model.py
View File

@@ -44,6 +44,7 @@ class StoreAgentDetails(pydantic.BaseModel):
slug: str
agent_name: str
agent_video: str
agent_output_demo: str
agent_image: list[str]
creator: str
creator_avatar: str
@@ -121,6 +122,7 @@ class StoreSubmission(pydantic.BaseModel):
# Additional fields for editing
video_url: str | None = None
agent_output_demo_url: str | None = None
categories: list[str] = []
@@ -157,6 +159,7 @@ class StoreSubmissionRequest(pydantic.BaseModel):
name: str
sub_heading: str
video_url: str | None = None
agent_output_demo_url: str | None = None
image_urls: list[str] = []
description: str = ""
instructions: str | None = None
@@ -169,6 +172,7 @@ class StoreSubmissionEditRequest(pydantic.BaseModel):
name: str
sub_heading: str
video_url: str | None = None
agent_output_demo_url: str | None = None
image_urls: list[str] = []
description: str = ""
instructions: str | None = None

1
autogpt_platform/backend/backend/server/v2/store/model_test.py
View File

@@ -62,6 +62,7 @@ def test_store_agent_details():
slug="test-agent",
agent_name="Test Agent",
agent_video="video.mp4",
agent_output_demo="demo.mp4",
agent_image=["image1.jpg", "image2.jpg"],
creator="creator1",
creator_avatar="avatar.jpg",

2
autogpt_platform/backend/backend/server/v2/store/routes.py
View File

@@ -438,6 +438,7 @@ async def create_submission(
slug=submission_request.slug,
name=submission_request.name,
video_url=submission_request.video_url,
agent_output_demo_url=submission_request.agent_output_demo_url,
image_urls=submission_request.image_urls,
description=submission_request.description,
instructions=submission_request.instructions,
@@ -481,6 +482,7 @@ async def edit_submission(
store_listing_version_id=store_listing_version_id,
name=submission_request.name,
video_url=submission_request.video_url,
agent_output_demo_url=submission_request.agent_output_demo_url,
image_urls=submission_request.image_urls,
description=submission_request.description,
instructions=submission_request.instructions,

1
autogpt_platform/backend/backend/server/v2/store/routes_test.py
View File

@@ -378,6 +378,7 @@ def test_get_agent_details(
slug="test-agent",
agent_name="Test Agent",
agent_video="video.mp4",
agent_output_demo="demo.mp4",
agent_image=["image1.jpg", "image2.jpg"],
creator="creator1",
creator_avatar="avatar1.jpg",

64
autogpt_platform/backend/migrations/20251204012214_add_marketplace_agent_output_video_column/migration.sql Normal file
View File

@@ -0,0 +1,64 @@
-- AlterTable
ALTER TABLE "StoreListingVersion" ADD COLUMN "agentOutputDemoUrl" TEXT;
-- Drop and recreate the StoreAgent view with agentOutputDemoUrl field
DROP VIEW IF EXISTS "StoreAgent";
CREATE OR REPLACE VIEW "StoreAgent" AS
WITH latest_versions AS (
SELECT
"storeListingId",
MAX(version) AS max_version
FROM "StoreListingVersion"
WHERE "submissionStatus" = 'APPROVED'
GROUP BY "storeListingId"
),
agent_versions AS (
SELECT
"storeListingId",
array_agg(DISTINCT version::text ORDER BY version::text) AS versions
FROM "StoreListingVersion"
WHERE "submissionStatus" = 'APPROVED'
GROUP BY "storeListingId"
)
SELECT
sl.id AS listing_id,
slv.id AS "storeListingVersionId",
slv."createdAt" AS updated_at,
sl.slug,
COALESCE(slv.name, '') AS agent_name,
slv."videoUrl" AS agent_video,
slv."agentOutputDemoUrl" AS agent_output_demo,
COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
slv."isFeatured" AS featured,
p.username AS creator_username, -- Allow NULL for malformed sub-agents
p."avatarUrl" AS creator_avatar, -- Allow NULL for malformed sub-agents
slv."subHeading" AS sub_heading,
slv.description,
slv.categories,
slv.search,
COALESCE(ar.run_count, 0::bigint) AS runs,
COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
slv."isAvailable" AS is_available,
COALESCE(sl."useForOnboarding", false) AS "useForOnboarding"
FROM "StoreListing" sl
JOIN latest_versions lv
ON sl.id = lv."storeListingId"
JOIN "StoreListingVersion" slv
ON slv."storeListingId" = lv."storeListingId"
AND slv.version = lv.max_version
AND slv."submissionStatus" = 'APPROVED'
JOIN "AgentGraph" a
ON slv."agentGraphId" = a.id
AND slv."agentGraphVersion" = a.version
LEFT JOIN "Profile" p
ON sl."owningUserId" = p."userId"
LEFT JOIN "mv_review_stats" rs
ON sl.id = rs."storeListingId"
LEFT JOIN "mv_agent_run_counts" ar
ON a.id = ar."agentGraphId"
LEFT JOIN agent_versions av
ON sl.id = av."storeListingId"
WHERE sl."isDeleted" = false
AND sl."hasApprovedVersion" = true;

1
autogpt_platform/backend/pyproject.toml
View File

@@ -114,6 +114,7 @@ cli = "backend.cli:main"
format = "linter:format"
lint = "linter:lint"
test = "run_tests:test"
load-store-agents = "test.load_store_agents:run"
[tool.isort]
profile = "black"

24
autogpt_platform/backend/schema.prisma
View File

@@ -701,10 +701,11 @@ view StoreAgent {
storeListingVersionId String
updated_at DateTime
slug String
agent_name String
agent_video String?
agent_image String[]
slug String
agent_name String
agent_video String?
agent_output_demo String?
agent_image String[]
featured Boolean @default(false)
creator_username String?
@@ -833,13 +834,14 @@ model StoreListingVersion {
AgentGraph AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
// Content fields
name String
subHeading String
videoUrl String?
imageUrls String[]
description String
instructions String?
categories String[]
name String
subHeading String
videoUrl String?
agentOutputDemoUrl String?
imageUrls String[]
description String
instructions String?
categories String[]
isFeatured Boolean @default(false)

1
autogpt_platform/backend/snapshots/agt_details
View File

@@ -3,6 +3,7 @@
"slug": "test-agent",
"agent_name": "Test Agent",
"agent_video": "video.mp4",
"agent_output_demo": "demo.mp4",
"agent_image": [
"image1.jpg",
"image2.jpg"

1
autogpt_platform/backend/snapshots/sub_success
View File

@@ -23,6 +23,7 @@
"reviewed_at": null,
"changes_summary": null,
"video_url": "test.mp4",
"agent_output_demo_url": null,
"categories": [
"test-category"
]

455
autogpt_platform/backend/test/load_store_agents.py Normal file
View File

@@ -0,0 +1,455 @@
"""
Load Store Agents Script
This script loads the exported store agents from the agents/ folder into the test database.
It creates:
- A user and profile for the 'autogpt' creator
- AgentGraph records from JSON files
- StoreListing and StoreListingVersion records from CSV metadata
- Approves agents that have is_available=true in the CSV
Usage:
cd backend
poetry run load-store-agents
"""
import asyncio
import csv
import json
import re
from datetime import datetime
from pathlib import Path
import prisma.enums
from prisma import Json, Prisma
from prisma.types import (
AgentBlockCreateInput,
AgentGraphCreateInput,
AgentNodeCreateInput,
AgentNodeLinkCreateInput,
ProfileCreateInput,
StoreListingCreateInput,
StoreListingVersionCreateInput,
UserCreateInput,
)
# Path to agents folder (relative to backend directory)
AGENTS_DIR = Path(__file__).parent.parent / "agents"
CSV_FILE = AGENTS_DIR / "StoreAgent_rows.csv"
# User constants for the autogpt creator (test data, not production)
# Fixed uuid4 for idempotency - same user is reused across script runs
AUTOGPT_USER_ID = "79d96c73-e6f5-4656-a83a-185b41ee0d06"
AUTOGPT_EMAIL = "autogpt-test@agpt.co"
AUTOGPT_USERNAME = "autogpt"
async def initialize_blocks(db: Prisma) -> set[str]:
"""Initialize agent blocks in the database from the registered blocks.
Returns a set of block IDs that exist in the database.
"""
from backend.data.block import get_blocks
print(" Initializing agent blocks...")
blocks = get_blocks()
created_count = 0
block_ids = set()
for block_cls in blocks.values():
block = block_cls()
block_ids.add(block.id)
existing_block = await db.agentblock.find_first(
where={"OR": [{"id": block.id}, {"name": block.name}]}
)
if not existing_block:
await db.agentblock.create(
data=AgentBlockCreateInput(
id=block.id,
name=block.name,
inputSchema=json.dumps(block.input_schema.jsonschema()),
outputSchema=json.dumps(block.output_schema.jsonschema()),
)
)
created_count += 1
elif block.id != existing_block.id or block.name != existing_block.name:
await db.agentblock.update(
where={"id": existing_block.id},
data={
"id": block.id,
"name": block.name,
"inputSchema": json.dumps(block.input_schema.jsonschema()),
"outputSchema": json.dumps(block.output_schema.jsonschema()),
},
)
print(f" Initialized {len(blocks)} blocks ({created_count} new)")
return block_ids
async def ensure_block_exists(
db: Prisma, block_id: str, known_blocks: set[str]
) -> bool:
"""Ensure a block exists in the database, create a placeholder if needed.
Returns True if the block exists (or was created), False otherwise.
"""
if block_id in known_blocks:
return True
# Check if it already exists in the database
existing = await db.agentblock.find_unique(where={"id": block_id})
if existing:
known_blocks.add(block_id)
return True
# Create a placeholder block
print(f" Creating placeholder block: {block_id}")
try:
await db.agentblock.create(
data=AgentBlockCreateInput(
id=block_id,
name=f"Placeholder_{block_id[:8]}",
inputSchema="{}",
outputSchema="{}",
)
)
known_blocks.add(block_id)
return True
except Exception as e:
print(f" Warning: Could not create placeholder block {block_id}: {e}")
return False
def parse_image_urls(image_str: str) -> list[str]:
"""Parse the image URLs from CSV format like ["url1","url2"]."""
if not image_str or image_str == "[]":
return []
try:
return json.loads(image_str)
except json.JSONDecodeError:
return []
def parse_categories(categories_str: str) -> list[str]:
"""Parse categories from CSV format like ["cat1","cat2"]."""
if not categories_str or categories_str == "[]":
return []
try:
return json.loads(categories_str)
except json.JSONDecodeError:
return []
def sanitize_slug(slug: str) -> str:
"""Ensure slug only contains valid characters."""
return re.sub(r"[^a-z0-9-]", "", slug.lower())
async def create_user_and_profile(db: Prisma) -> None:
"""Create the autogpt user and profile if they don't exist."""
# Check if user exists
existing_user = await db.user.find_unique(where={"id": AUTOGPT_USER_ID})
if existing_user:
print(f"User {AUTOGPT_USER_ID} already exists, skipping user creation")
else:
print(f"Creating user {AUTOGPT_USER_ID}")
await db.user.create(
data=UserCreateInput(
id=AUTOGPT_USER_ID,
email=AUTOGPT_EMAIL,
name="AutoGPT",
metadata=Json({}),
integrations="",
)
)
# Check if profile exists
existing_profile = await db.profile.find_first(where={"userId": AUTOGPT_USER_ID})
if existing_profile:
print(
f"Profile for user {AUTOGPT_USER_ID} already exists, skipping profile creation"
)
else:
print(f"Creating profile for user {AUTOGPT_USER_ID}")
await db.profile.create(
data=ProfileCreateInput(
userId=AUTOGPT_USER_ID,
name="AutoGPT",
username=AUTOGPT_USERNAME,
description="Official AutoGPT agents and templates",
links=["https://agpt.co"],
avatarUrl="https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/4b5781a6-49e1-433c-9a75-65af1be5c02d.png",
)
)
async def load_csv_metadata() -> dict[str, dict]:
"""Load CSV metadata and return a dict keyed by storeListingVersionId."""
metadata = {}
with open(CSV_FILE, "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
version_id = row["storeListingVersionId"]
metadata[version_id] = {
"listing_id": row["listing_id"],
"store_listing_version_id": version_id,
"slug": sanitize_slug(row["slug"]),
"agent_name": row["agent_name"],
"agent_video": row["agent_video"] if row["agent_video"] else None,
"agent_image": parse_image_urls(row["agent_image"]),
"featured": row["featured"].lower() == "true",
"sub_heading": row["sub_heading"],
"description": row["description"],
"categories": parse_categories(row["categories"]),
"use_for_onboarding": row["useForOnboarding"].lower() == "true",
"is_available": row["is_available"].lower() == "true",
}
return metadata
async def load_agent_json(json_path: Path) -> dict:
"""Load and parse an agent JSON file."""
with open(json_path, "r", encoding="utf-8") as f:
return json.load(f)
async def create_agent_graph(
db: Prisma, agent_data: dict, known_blocks: set[str]
) -> tuple[str, int]:
"""Create an AgentGraph and its nodes/links from JSON data."""
graph_id = agent_data["id"]
version = agent_data.get("version", 1)
# Check if graph already exists
existing_graph = await db.agentgraph.find_unique(
where={"graphVersionId": {"id": graph_id, "version": version}}
)
if existing_graph:
print(f" Graph {graph_id} v{version} already exists, skipping")
return graph_id, version
print(
f" Creating graph {graph_id} v{version}: {agent_data.get('name', 'Unnamed')}"
)
# Create the main graph
await db.agentgraph.create(
data=AgentGraphCreateInput(
id=graph_id,
version=version,
name=agent_data.get("name"),
description=agent_data.get("description"),
instructions=agent_data.get("instructions"),
recommendedScheduleCron=agent_data.get("recommended_schedule_cron"),
isActive=agent_data.get("is_active", True),
userId=AUTOGPT_USER_ID,
forkedFromId=agent_data.get("forked_from_id"),
forkedFromVersion=agent_data.get("forked_from_version"),
)
)
# Create nodes
nodes = agent_data.get("nodes", [])
for node in nodes:
block_id = node["block_id"]
# Ensure the block exists (create placeholder if needed)
block_exists = await ensure_block_exists(db, block_id, known_blocks)
if not block_exists:
print(
f" Skipping node {node['id']} - block {block_id} could not be created"
)
continue
await db.agentnode.create(
data=AgentNodeCreateInput(
id=node["id"],
agentBlockId=block_id,
agentGraphId=graph_id,
agentGraphVersion=version,
constantInput=Json(node.get("input_default", {})),
metadata=Json(node.get("metadata", {})),
)
)
# Create links
links = agent_data.get("links", [])
for link in links:
await db.agentnodelink.create(
data=AgentNodeLinkCreateInput(
id=link["id"],
agentNodeSourceId=link["source_id"],
agentNodeSinkId=link["sink_id"],
sourceName=link["source_name"],
sinkName=link["sink_name"],
isStatic=link.get("is_static", False),
)
)
# Handle sub_graphs recursively
sub_graphs = agent_data.get("sub_graphs", [])
for sub_graph in sub_graphs:
await create_agent_graph(db, sub_graph, known_blocks)
return graph_id, version
async def create_store_listing(
db: Prisma,
graph_id: str,
graph_version: int,
metadata: dict,
) -> None:
"""Create StoreListing and StoreListingVersion for an agent."""
listing_id = metadata["listing_id"]
version_id = metadata["store_listing_version_id"]
# Check if listing already exists
existing_listing = await db.storelisting.find_unique(where={"id": listing_id})
if existing_listing:
print(f" Store listing {listing_id} already exists, skipping")
return
print(f" Creating store listing: {metadata['agent_name']}")
# Determine if this should be approved
is_approved = metadata["is_available"]
submission_status = (
prisma.enums.SubmissionStatus.APPROVED
if is_approved
else prisma.enums.SubmissionStatus.PENDING
)
# Create the store listing first (without activeVersionId - will update after)
await db.storelisting.create(
data=StoreListingCreateInput(
id=listing_id,
slug=metadata["slug"],
agentGraphId=graph_id,
agentGraphVersion=graph_version,
owningUserId=AUTOGPT_USER_ID,
hasApprovedVersion=is_approved,
useForOnboarding=metadata["use_for_onboarding"],
)
)
# Create the store listing version
await db.storelistingversion.create(
data=StoreListingVersionCreateInput(
id=version_id,
version=1,
agentGraphId=graph_id,
agentGraphVersion=graph_version,
name=metadata["agent_name"],
subHeading=metadata["sub_heading"],
videoUrl=metadata["agent_video"],
imageUrls=metadata["agent_image"],
description=metadata["description"],
categories=metadata["categories"],
isFeatured=metadata["featured"],
isAvailable=metadata["is_available"],
submissionStatus=submission_status,
submittedAt=datetime.now() if is_approved else None,
reviewedAt=datetime.now() if is_approved else None,
storeListingId=listing_id,
)
)
# Update the store listing with the active version if approved
if is_approved:
await db.storelisting.update(
where={"id": listing_id},
data={"ActiveVersion": {"connect": {"id": version_id}}},
)
async def main():
"""Main function to load all store agents."""
print("=" * 60)
print("Loading Store Agents into Test Database")
print("=" * 60)
db = Prisma()
await db.connect()
try:
# Step 0: Initialize agent blocks
print("\n[Step 0] Initializing agent blocks...")
known_blocks = await initialize_blocks(db)
# Step 1: Create user and profile
print("\n[Step 1] Creating user and profile...")
await create_user_and_profile(db)
# Step 2: Load CSV metadata
print("\n[Step 2] Loading CSV metadata...")
csv_metadata = await load_csv_metadata()
print(f" Found {len(csv_metadata)} store listing entries in CSV")
# Step 3: Find all JSON files and match with CSV
print("\n[Step 3] Processing agent JSON files...")
json_files = list(AGENTS_DIR.glob("agent_*.json"))
print(f" Found {len(json_files)} agent JSON files")
# Build mapping from version_id to json file
loaded_graphs = {} # graph_id -> (graph_id, version)
failed_agents = []
for json_file in json_files:
# Extract the version ID from filename (agent_<version_id>.json)
version_id = json_file.stem.replace("agent_", "")
if version_id not in csv_metadata:
print(
f" Warning: {json_file.name} not found in CSV metadata, skipping"
)
continue
metadata = csv_metadata[version_id]
agent_name = metadata["agent_name"]
print(f"\nProcessing: {agent_name}")
# Use a transaction per agent to prevent dangling resources
try:
async with db.tx() as tx:
# Load and create the agent graph
agent_data = await load_agent_json(json_file)
graph_id, graph_version = await create_agent_graph(
tx, agent_data, known_blocks
)
loaded_graphs[graph_id] = (graph_id, graph_version)
# Create store listing
await create_store_listing(tx, graph_id, graph_version, metadata)
except Exception as e:
print(f" Error loading agent '{agent_name}': {e}")
failed_agents.append(agent_name)
continue
# Step 4: Refresh materialized views
print("\n[Step 4] Refreshing materialized views...")
try:
await db.execute_raw("SELECT refresh_store_materialized_views();")
print(" Materialized views refreshed successfully")
except Exception as e:
print(f" Warning: Could not refresh materialized views: {e}")
print("\n" + "=" * 60)
print(f"Successfully loaded {len(loaded_graphs)} agents")
if failed_agents:
print(
f"Failed to load {len(failed_agents)} agents: {', '.join(failed_agents)}"
)
print("=" * 60)
finally:
await db.disconnect()
def run():
"""Entry point for poetry script."""
asyncio.run(main())
if __name__ == "__main__":
run()

2
autogpt_platform/frontend/package.json
View File

@@ -82,7 +82,7 @@
"lodash": "4.17.21",
"lucide-react": "0.552.0",
"moment": "2.30.1",
"next": "15.4.7",
"next": "15.4.8",
"next-themes": "0.4.6",
"nuqs": "2.7.2",
"party-js": "2.2.0",

126
autogpt_platform/frontend/pnpm-lock.yaml generated
View File

@@ -16,7 +16,7 @@ importers:
version: 5.2.2(react-hook-form@7.66.0(react@18.3.1))
'@next/third-parties':
specifier: 15.4.6
version: 15.4.6(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
version: 15.4.6(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
'@phosphor-icons/react':
specifier: 2.1.10
version: 2.1.10(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -88,7 +88,7 @@ importers:
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version: 10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))
version: 10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))
'@supabase/ssr':
specifier: 0.7.0
version: 0.7.0(@supabase/supabase-js@2.78.0)
@@ -106,10 +106,10 @@ importers:
version: 0.2.4
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specifier: 1.5.0
version: 1.5.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
version: 1.5.0(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
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specifier: 1.2.0
version: 1.2.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
version: 1.2.0(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
'@xyflow/react':
specifier: 12.9.2
version: 12.9.2(@types/react@18.3.17)(immer@10.1.3)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -148,7 +148,7 @@ importers:
version: 12.23.24(@emotion/is-prop-valid@1.2.2)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
geist:
specifier: 1.5.1
version: 1.5.1(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))
version: 1.5.1(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))
highlight.js:
specifier: 11.11.1
version: 11.11.1
@@ -171,14 +171,14 @@ importers:
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version: 2.30.1
next:
specifier: 15.4.7
version: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
specifier: 15.4.8
version: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next-themes:
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version: 0.4.6(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
nuqs:
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version: 2.7.2(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
version: 2.7.2(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
party-js:
specifier: 2.2.0
version: 2.2.0
@@ -284,7 +284,7 @@ importers:
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specifier: 5.91.2
version: 5.91.2(eslint@8.57.1)(typescript@5.9.3)
@@ -1602,56 +1602,56 @@ packages:
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engines: {node: '>= 10'}
cpu: [x64]
os: [win32]
@@ -5920,8 +5920,8 @@ packages:
react: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
react-dom: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
next@15.4.7:
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engines: {node: ^18.18.0 || ^19.8.0 || >= 20.0.0}
hasBin: true
peerDependencies:
@@ -9003,39 +9003,39 @@ snapshots:
'@neoconfetti/react@1.0.0': {}
'@next/env@15.4.7': {}
'@next/env@15.4.8': {}
'@next/eslint-plugin-next@15.5.2':
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'@next/swc-darwin-arm64@15.4.8':
optional: true
'@next/swc-darwin-x64@15.4.7':
'@next/swc-darwin-x64@15.4.8':
optional: true
'@next/swc-linux-arm64-gnu@15.4.7':
'@next/swc-linux-arm64-gnu@15.4.8':
optional: true
'@next/swc-linux-arm64-musl@15.4.7':
'@next/swc-linux-arm64-musl@15.4.8':
optional: true
'@next/swc-linux-x64-gnu@15.4.7':
'@next/swc-linux-x64-gnu@15.4.8':
optional: true
'@next/swc-linux-x64-musl@15.4.7':
'@next/swc-linux-x64-musl@15.4.8':
optional: true
'@next/swc-win32-arm64-msvc@15.4.7':
'@next/swc-win32-arm64-msvc@15.4.8':
optional: true
'@next/swc-win32-x64-msvc@15.4.7':
'@next/swc-win32-x64-msvc@15.4.8':
optional: true
'@next/third-parties@15.4.6(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
'@next/third-parties@15.4.6(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
dependencies:
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
react: 18.3.1
third-party-capital: 1.0.20
@@ -10267,7 +10267,7 @@ snapshots:
'@sentry/core@10.27.0': {}
'@sentry/nextjs@10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))':
'@sentry/nextjs@10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))':
dependencies:
'@opentelemetry/api': 1.9.0
'@opentelemetry/semantic-conventions': 1.37.0
@@ -10280,7 +10280,7 @@ snapshots:
'@sentry/react': 10.27.0(react@18.3.1)
'@sentry/vercel-edge': 10.27.0
'@sentry/webpack-plugin': 4.3.0(webpack@5.101.3(esbuild@0.25.9))
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
resolve: 1.22.8
rollup: 4.52.2
stacktrace-parser: 0.1.11
@@ -10642,7 +10642,7 @@ snapshots:
react: 18.3.1
react-dom: 18.3.1(react@18.3.1)
'@storybook/nextjs@9.1.5(esbuild@0.25.9)(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))':
'@storybook/nextjs@9.1.5(esbuild@0.25.9)(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))':
dependencies:
'@babel/core': 7.28.4
'@babel/plugin-syntax-bigint': 7.8.3(@babel/core@7.28.4)
@@ -10666,7 +10666,7 @@ snapshots:
css-loader: 6.11.0(webpack@5.101.3(esbuild@0.25.9))
image-size: 2.0.2
loader-utils: 3.3.1
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
node-polyfill-webpack-plugin: 2.0.1(webpack@5.101.3(esbuild@0.25.9))
postcss: 8.5.6
postcss-loader: 8.2.0(postcss@8.5.6)(typescript@5.9.3)(webpack@5.101.3(esbuild@0.25.9))
@@ -11271,14 +11271,14 @@ snapshots:
'@unrs/resolver-binding-win32-x64-msvc@1.11.1':
optional: true
'@vercel/analytics@1.5.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
'@vercel/analytics@1.5.0(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
optionalDependencies:
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
react: 18.3.1
'@vercel/speed-insights@1.2.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
'@vercel/speed-insights@1.2.0(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
optionalDependencies:
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
react: 18.3.1
'@vitest/expect@3.2.4':
@@ -12954,9 +12954,9 @@ snapshots:
functions-have-names@1.2.3: {}
geist@1.5.1(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)):
geist@1.5.1(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)):
dependencies:
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
gensync@1.0.0-beta.2: {}
@@ -14226,9 +14226,9 @@ snapshots:
react: 18.3.1
react-dom: 18.3.1(react@18.3.1)
next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
dependencies:
'@next/env': 15.4.7
'@next/env': 15.4.8
'@swc/helpers': 0.5.15
caniuse-lite: 1.0.30001741
postcss: 8.4.31
@@ -14236,14 +14236,14 @@ snapshots:
react-dom: 18.3.1(react@18.3.1)
styled-jsx: 5.1.6(@babel/core@7.28.4)(react@18.3.1)
optionalDependencies:
'@next/swc-darwin-arm64': 15.4.7
'@next/swc-darwin-x64': 15.4.7
'@next/swc-linux-arm64-gnu': 15.4.7
'@next/swc-linux-arm64-musl': 15.4.7
'@next/swc-linux-x64-gnu': 15.4.7
'@next/swc-linux-x64-musl': 15.4.7
'@next/swc-win32-arm64-msvc': 15.4.7
'@next/swc-win32-x64-msvc': 15.4.7
'@next/swc-darwin-arm64': 15.4.8
'@next/swc-darwin-x64': 15.4.8
'@next/swc-linux-arm64-gnu': 15.4.8
'@next/swc-linux-arm64-musl': 15.4.8
'@next/swc-linux-x64-gnu': 15.4.8
'@next/swc-linux-x64-musl': 15.4.8
'@next/swc-win32-arm64-msvc': 15.4.8
'@next/swc-win32-x64-msvc': 15.4.8
'@opentelemetry/api': 1.9.0
'@playwright/test': 1.56.1
sharp: 0.34.3
@@ -14321,12 +14321,12 @@ snapshots:
dependencies:
boolbase: 1.0.0
nuqs@2.7.2(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1):
nuqs@2.7.2(next@15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1):
dependencies:
'@standard-schema/spec': 1.0.0
react: 18.3.1
optionalDependencies:
next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
next: 15.4.8(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
oas-kit-common@1.0.8:
dependencies:
@@ -15340,7 +15340,7 @@ snapshots:
dependencies:
color: 4.2.3
detect-libc: 2.0.4
semver: 7.7.2
semver: 7.7.3
optionalDependencies:
'@img/sharp-darwin-arm64': 0.34.3
'@img/sharp-darwin-x64': 0.34.3

8
autogpt_platform/frontend/src/app/(no-navbar)/onboarding/4-agent/page.tsx
View File

@@ -1,6 +1,4 @@
"use client";
import { StoreAgentDetails } from "@/lib/autogpt-server-api";
import { useBackendAPI } from "@/lib/autogpt-server-api/context";
import { isEmptyOrWhitespace } from "@/lib/utils";
import { useRouter } from "next/navigation";
import { useEffect, useState } from "react";
@@ -13,15 +11,17 @@ import {
OnboardingStep,
} from "../components/OnboardingStep";
import { OnboardingText } from "../components/OnboardingText";
import { getV1RecommendedOnboardingAgents } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
import { resolveResponse } from "@/app/api/helpers";
import { StoreAgentDetails } from "@/app/api/__generated__/models/storeAgentDetails";
export default function Page() {
const { state, updateState, completeStep } = useOnboarding(4, "INTEGRATIONS");
const [agents, setAgents] = useState<StoreAgentDetails[]>([]);
const api = useBackendAPI();
const router = useRouter();
useEffect(() => {
api.getOnboardingAgents().then((agents) => {
resolveResponse(getV1RecommendedOnboardingAgents()).then((agents) => {
if (agents.length < 2) {
completeStep("CONGRATS");
router.replace("/");

25
autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/useOnboardingRunStep.tsx
View File

@@ -12,6 +12,9 @@ import {
useGetV2GetAgentByVersion,
useGetV2GetAgentGraph,
} from "@/app/api/__generated__/endpoints/store/store";
import { resolveResponse } from "@/app/api/helpers";
import { postV2AddMarketplaceAgent } from "@/app/api/__generated__/endpoints/library/library";
import { GraphID } from "@/lib/autogpt-server-api";
export function useOnboardingRunStep() {
const onboarding = useOnboarding(undefined, "AGENT_CHOICE");
@@ -77,12 +80,7 @@ export function useOnboardingRunStep() {
setShowInput(true);
onboarding.setStep(6);
onboarding.updateState({
completedSteps: [
...(onboarding.state.completedSteps || []),
"AGENT_NEW_RUN",
],
});
onboarding.completeStep("AGENT_NEW_RUN");
}
function handleSetAgentInput(key: string, value: string) {
@@ -111,21 +109,22 @@ export function useOnboardingRunStep() {
setRunningAgent(true);
try {
const libraryAgent = await api.addMarketplaceAgentToLibrary(
storeAgent?.store_listing_version_id || "",
const libraryAgent = await resolveResponse(
postV2AddMarketplaceAgent({
store_listing_version_id: storeAgent?.store_listing_version_id || "",
source: "onboarding",
}),
);
const { id: runID } = await api.executeGraph(
libraryAgent.graph_id,
libraryAgent.graph_id as GraphID,
libraryAgent.graph_version,
onboarding.state.agentInput || {},
inputCredentials,
"onboarding",
);
onboarding.updateState({
onboardingAgentExecutionId: runID,
agentRuns: (onboarding.state.agentRuns || 0) + 1,
});
onboarding.updateState({ onboardingAgentExecutionId: runID });
router.push("/onboarding/6-congrats");
} catch (error) {

13
autogpt_platform/frontend/src/app/(no-navbar)/onboarding/6-congrats/page.tsx
View File

@@ -5,6 +5,9 @@ import { useRouter } from "next/navigation";
import * as party from "party-js";
import { useEffect, useRef, useState } from "react";
import { useOnboarding } from "../../../../providers/onboarding/onboarding-provider";
import { resolveResponse } from "@/app/api/helpers";
import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
import { postV2AddMarketplaceAgent } from "@/app/api/__generated__/endpoints/library/library";
export default function Page() {
const { completeStep } = useOnboarding(7, "AGENT_INPUT");
@@ -37,11 +40,15 @@ export default function Page() {
completeStep("CONGRATS");
try {
const onboarding = await api.getUserOnboarding();
const onboarding = await resolveResponse(getV1OnboardingState());
if (onboarding?.selectedStoreListingVersionId) {
try {
const libraryAgent = await api.addMarketplaceAgentToLibrary(
onboarding.selectedStoreListingVersionId,
const libraryAgent = await resolveResponse(
postV2AddMarketplaceAgent({
store_listing_version_id:
onboarding.selectedStoreListingVersionId,
source: "onboarding",
}),
);
router.replace(`/library/agents/${libraryAgent.id}`);
} catch (error) {

2
autogpt_platform/frontend/src/app/(no-navbar)/onboarding/components/OnboardingAgentCard.tsx
View File

@@ -1,7 +1,7 @@
import { cn } from "@/lib/utils";
import StarRating from "./StarRating";
import { StoreAgentDetails } from "@/lib/autogpt-server-api";
import SmartImage from "@/components/__legacy__/SmartImage";
import { StoreAgentDetails } from "@/app/api/__generated__/models/storeAgentDetails";
type OnboardingAgentCardProps = {
agent?: StoreAgentDetails;

10
autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx
View File

@@ -1,24 +1,24 @@
"use client";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { useBackendAPI } from "@/lib/autogpt-server-api/context";
import { useRouter } from "next/navigation";
import { useEffect } from "react";
import { resolveResponse, shouldShowOnboarding } from "@/app/api/helpers";
import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
export default function OnboardingPage() {
const router = useRouter();
const api = useBackendAPI();
useEffect(() => {
async function redirectToStep() {
try {
// Check if onboarding is enabled
const isEnabled = await api.isOnboardingEnabled();
const isEnabled = await shouldShowOnboarding();
if (!isEnabled) {
router.replace("/");
return;
}
const onboarding = await api.getUserOnboarding();
const onboarding = await resolveResponse(getV1OnboardingState());
// Handle completed onboarding
if (onboarding.completedSteps.includes("GET_RESULTS")) {
@@ -66,7 +66,7 @@ export default function OnboardingPage() {
}
redirectToStep();
}, [api, router]);
}, [router]);
return <LoadingSpinner size="large" cover />;
}

2
autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunGraph/useRunGraph.ts
View File

@@ -116,7 +116,7 @@ export const useRunGraph = () => {
await executeGraph({
graphId: flowID ?? "",
graphVersion: flowVersion || null,
data: { inputs: {}, credentials_inputs: {} },
data: { inputs: {}, credentials_inputs: {}, source: "builder" },
});
}
};

6
autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts
View File

@@ -79,7 +79,11 @@ export const useRunInputDialog = ({
await executeGraph({
graphId: flowID ?? "",
graphVersion: flowVersion || null,
data: { inputs: inputValues, credentials_inputs: credentialValues },
data: {
inputs: inputValues,
credentials_inputs: credentialValues,
source: "builder",
},
});
// Optimistically set running state immediately for responsive UI
setIsGraphRunning(true);

20
autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeInputs.tsx
View File

@@ -981,15 +981,17 @@ const NodeArrayInput: FC<{
);
return (
<div key={entryKey}>
<NodeHandle
title={`#${index + 1}`}
className="text-sm text-gray-500"
keyName={entryKey}
schema={schema.items!}
isConnected={isConnected}
isRequired={false}
side="left"
/>
{schema.items && (
<NodeHandle
title={`#${index + 1}`}
className="text-sm text-gray-500"
keyName={entryKey}
schema={schema.items}
isConnected={isConnected}
isRequired={false}
side="left"
/>
)}
<div className="mb-2 flex space-x-2">
{!isConnected &&
(schema.items ? (

1
autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/RunnerInputUI.tsx
View File

@@ -83,7 +83,6 @@ export function RunnerInputDialog({
onRun={doRun ? undefined : doClose}
doCreateSchedule={doCreateSchedule ? handleSchedule : undefined}
onCreateSchedule={doCreateSchedule ? undefined : doClose}
runCount={0}
/>
</DialogContent>
</Dialog>

4
autogpt_platform/frontend/src/app/(platform)/build/hooks/useSaveGraph.ts
View File

@@ -152,7 +152,9 @@ export const useSaveGraph = ({
links: graphLinks,
};
const response = await createNewGraph({ data: { graph: data } });
const response = await createNewGraph({
data: { graph: data, source: "builder" },
});
const graphData = response.data as GraphModel;
setGraphSchemas(
graphData.input_schema,

5
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/useAgentRunModal.ts
View File

@@ -16,7 +16,6 @@ import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutio
import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
import { useGetV1GetUserTimezone } from "@/app/api/__generated__/endpoints/auth/auth";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { analytics } from "@/services/analytics";
export type RunVariant =
@@ -50,7 +49,6 @@ export function useAgentRunModal(
const [cronExpression, setCronExpression] = useState(
agent.recommended_schedule_cron || "0 9 * * 1",
);
const { completeStep: completeOnboardingStep } = useOnboarding();
// Get user timezone for scheduling
const { data: userTimezone } = useGetV1GetUserTimezone({
@@ -290,6 +288,7 @@ export function useAgentRunModal(
data: {
inputs: inputValues,
credentials_inputs: inputCredentials,
source: "library",
},
});
}
@@ -335,8 +334,6 @@ export function useAgentRunModal(
userTimezone && userTimezone !== "not-set" ? userTimezone : undefined,
},
});
completeOnboardingStep("SCHEDULE_AGENT");
}, [
allRequiredInputsAreSet,
scheduleName,

2
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/RunDetailCard/RunDetailCard.tsx
View File

@@ -4,7 +4,7 @@ import { cn } from "@/lib/utils";
type Props = {
children: React.ReactNode;
className?: string;
title?: string;
title?: React.ReactNode;
};
export function RunDetailCard({ children, className, title }: Props) {

8
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/RunDetailHeader/RunDetailHeader.tsx
View File

@@ -60,14 +60,6 @@ export function RunDetailHeader({ agent, run, scheduleRecurrence }: Props) {
</Text>
</>
)}
{run.stats?.activity_status && (
<>
<span className="mx-1 inline-block text-zinc-200">|</span>
<Text variant="small" className="text-zinc-500">
{String(run.stats.activity_status)}
</Text>
</>
)}
</div>
) : scheduleRecurrence ? (
<Text variant="small" className="mt-1 !text-zinc-600">

31
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/SelectedRunView.tsx
View File

@@ -4,9 +4,16 @@ import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecut
import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { Text } from "@/components/atoms/Text/Text";
import {
Tooltip,
TooltipContent,
TooltipProvider,
TooltipTrigger,
} from "@/components/atoms/Tooltip/BaseTooltip";
import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
import { PendingReviewsList } from "@/components/organisms/PendingReviewsList/PendingReviewsList";
import { usePendingReviewsForExecution } from "@/hooks/usePendingReviews";
import { InfoIcon } from "@phosphor-icons/react";
import { useEffect } from "react";
import { AGENT_LIBRARY_SECTION_PADDING_X } from "../../../helpers";
import { AgentInputsReadOnly } from "../../modals/AgentInputsReadOnly/AgentInputsReadOnly";
@@ -120,7 +127,29 @@ export function SelectedRunView({
{/* Summary Section */}
{withSummary && (
<div id="summary" className="scroll-mt-4">
<RunDetailCard title="Summary">
<RunDetailCard
title={
<div>
<TooltipProvider>
<Tooltip>
<TooltipTrigger asChild>
<InfoIcon
size={8}
className="cursor-help text-neutral-500 hover:text-neutral-700"
/>
</TooltipTrigger>
<TooltipContent>
<p className="max-w-xs">
This AI-generated summary describes how the agent
handled your task. It&apos;s an experimental
feature and may occasionally be inaccurate.
</p>
</TooltipContent>
</Tooltip>
</TooltipProvider>
</div>
}
>
<RunSummary run={run} />
</RunDetailCard>
</div>

111
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/components/RunSummary.tsx
View File

@@ -8,7 +8,6 @@ import {
TooltipProvider,
TooltipTrigger,
} from "@/components/atoms/Tooltip/BaseTooltip";
import { RunDetailCard } from "../../RunDetailCard/RunDetailCard";
interface Props {
run: GetV1GetExecutionDetails200;
@@ -20,81 +19,61 @@ export function RunSummary({ run }: Props) {
const correctnessScore = run.stats.correctness_score;
return (
<RunDetailCard>
<div className="space-y-4">
<div className="flex items-center gap-2">
<h2 className="text-lg font-semibold">Task Summary</h2>
<div className="space-y-4">
<p className="text-sm leading-relaxed text-neutral-700">
{run.stats.activity_status}
</p>
{typeof correctnessScore === "number" && (
<div className="flex items-center gap-3 rounded-lg bg-neutral-50 p-3">
<div className="flex items-center gap-2">
<span className="text-sm font-medium text-neutral-600">
Success Estimate:
</span>
<div className="flex items-center gap-2">
<div className="relative h-2 w-16 overflow-hidden rounded-full bg-neutral-200">
<div
className={`h-full transition-all ${
correctnessScore >= 0.8
? "bg-green-500"
: correctnessScore >= 0.6
? "bg-yellow-500"
: correctnessScore >= 0.4
? "bg-orange-500"
: "bg-red-500"
}`}
style={{
width: `${Math.round(correctnessScore * 100)}%`,
}}
/>
</div>
<span className="text-sm font-medium">
{Math.round(correctnessScore * 100)}%
</span>
</div>
</div>
<TooltipProvider>
<Tooltip>
<TooltipTrigger asChild>
<IconCircleAlert className="size-4 cursor-help text-neutral-500 hover:text-neutral-700" />
<IconCircleAlert className="size-4 cursor-help text-neutral-400 hover:text-neutral-600" />
</TooltipTrigger>
<TooltipContent>
<p className="max-w-xs">
This AI-generated summary describes how the agent handled your
task. It&apos;s an experimental feature and may occasionally
be inaccurate.
AI-generated estimate of how well this execution achieved its
intended purpose. This score indicates
{correctnessScore >= 0.8
? " the agent was highly successful."
: correctnessScore >= 0.6
? " the agent was mostly successful with minor issues."
: correctnessScore >= 0.4
? " the agent was partially successful with some gaps."
: " the agent had limited success with significant issues."}
</p>
</TooltipContent>
</Tooltip>
</TooltipProvider>
</div>
<p className="text-sm leading-relaxed text-neutral-700">
{run.stats.activity_status}
</p>
{typeof correctnessScore === "number" && (
<div className="flex items-center gap-3 rounded-lg bg-neutral-50 p-3">
<div className="flex items-center gap-2">
<span className="text-sm font-medium text-neutral-600">
Success Estimate:
</span>
<div className="flex items-center gap-2">
<div className="relative h-2 w-16 overflow-hidden rounded-full bg-neutral-200">
<div
className={`h-full transition-all ${
correctnessScore >= 0.8
? "bg-green-500"
: correctnessScore >= 0.6
? "bg-yellow-500"
: correctnessScore >= 0.4
? "bg-orange-500"
: "bg-red-500"
}`}
style={{
width: `${Math.round(correctnessScore * 100)}%`,
}}
/>
</div>
<span className="text-sm font-medium">
{Math.round(correctnessScore * 100)}%
</span>
</div>
</div>
<TooltipProvider>
<Tooltip>
<TooltipTrigger asChild>
<IconCircleAlert className="size-4 cursor-help text-neutral-400 hover:text-neutral-600" />
</TooltipTrigger>
<TooltipContent>
<p className="max-w-xs">
AI-generated estimate of how well this execution achieved
its intended purpose. This score indicates
{correctnessScore >= 0.8
? " the agent was highly successful."
: correctnessScore >= 0.6
? " the agent was mostly successful with minor issues."
: correctnessScore >= 0.4
? " the agent was partially successful with some gaps."
: " the agent had limited success with significant issues."}
</p>
</TooltipContent>
</Tooltip>
</TooltipProvider>
</div>
)}
</div>
</RunDetailCard>
)}
</div>
);
}

1
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/components/SelectedRunActions/useSelectedRunActions.ts
View File

@@ -77,6 +77,7 @@ export function useSelectedRunActions(args: Args) {
data: {
inputs: args.run.inputs || {},
credentials_inputs: args.run.credential_inputs || {},
source: "library",
},
});

30
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/OldAgentLibraryView.tsx
View File

@@ -47,7 +47,6 @@ import { CreatePresetDialog } from "./components/create-preset-dialog";
import { useAgentRunsInfinite } from "./use-agent-runs";
import { AgentRunsSelectorList } from "./components/agent-runs-selector-list";
import { AgentScheduleDetailsView } from "./components/agent-schedule-details-view";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
export function OldAgentLibraryView() {
const { id: agentID }: { id: LibraryAgentID } = useParams();
@@ -84,11 +83,6 @@ export function OldAgentLibraryView() {
useState<GraphExecutionMeta | null>(null);
const [confirmingDeleteAgentPreset, setConfirmingDeleteAgentPreset] =
useState<LibraryAgentPresetID | null>(null);
const {
state: onboardingState,
updateState: updateOnboardingState,
incrementRuns,
} = useOnboarding();
const [copyAgentDialogOpen, setCopyAgentDialogOpen] = useState(false);
const [creatingPresetFromExecutionID, setCreatingPresetFromExecutionID] =
useState<GraphExecutionID | null>(null);
@@ -136,22 +130,6 @@ export function OldAgentLibraryView() {
[api, graphVersions, loadingGraphVersions],
);
// Reward user for viewing results of their onboarding agent
useEffect(() => {
if (
!onboardingState ||
!selectedRun ||
onboardingState.completedSteps.includes("GET_RESULTS")
)
return;
if (selectedRun.id === onboardingState.onboardingAgentExecutionId) {
updateOnboardingState({
completedSteps: [...onboardingState.completedSteps, "GET_RESULTS"],
});
}
}, [selectedRun, onboardingState, updateOnboardingState]);
const lastRefresh = useRef<number>(0);
const refreshPageData = useCallback(() => {
if (Date.now() - lastRefresh.current < 2e3) return; // 2 second debounce
@@ -285,10 +263,6 @@ export function OldAgentLibraryView() {
(data) => {
if (data.graph_id != agent?.graph_id) return;
if (data.status == "COMPLETED") {
incrementRuns();
}
agentRunsQuery.upsertAgentRun(data);
if (data.id === selectedView.id) {
// Update currently viewed run
@@ -300,7 +274,7 @@ export function OldAgentLibraryView() {
return () => {
detachExecUpdateHandler();
};
}, [api, agent?.graph_id, selectedView.id, incrementRuns]);
}, [api, agent?.graph_id, selectedView.id]);
// Pre-load selectedRun based on selectedView
useEffect(() => {
@@ -558,7 +532,6 @@ export function OldAgentLibraryView() {
onCreateSchedule={onCreateSchedule}
onCreatePreset={onCreatePreset}
agentActions={agentActions}
runCount={agentRuns.length}
recommendedScheduleCron={agent?.recommended_schedule_cron || null}
/>
) : selectedView.type == "preset" ? (
@@ -574,7 +547,6 @@ export function OldAgentLibraryView() {
onUpdatePreset={onUpdatePreset}
doDeletePreset={setConfirmingDeleteAgentPreset}
agentActions={agentActions}
runCount={agentRuns.length}
/>
) : selectedView.type == "schedule" ? (
selectedSchedule &&

5
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-run-details-view.tsx
View File

@@ -38,7 +38,6 @@ import { AgentRunStatus, agentRunStatusMap } from "./agent-run-status-chip";
import useCredits from "@/hooks/useCredits";
import { AgentRunOutputView } from "./agent-run-output-view";
import { analytics } from "@/services/analytics";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { PendingReviewsList } from "@/components/organisms/PendingReviewsList/PendingReviewsList";
import { usePendingReviewsForExecution } from "@/hooks/usePendingReviews";
@@ -67,8 +66,6 @@ export function AgentRunDetailsView({
[run],
);
const { completeStep } = useOnboarding();
const {
pendingReviews,
isLoading: reviewsLoading,
@@ -166,13 +163,13 @@ export function AgentRunDetailsView({
graph.version,
run.inputs!,
run.credential_inputs!,
"library",
)
.then(({ id }) => {
analytics.sendDatafastEvent("run_agent", {
name: graph.name,
id: graph.id,
});
completeStep("RE_RUN_AGENT");
onRun(id);
})
.catch(toastOnFail("execute agent"));

33
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-run-draft-view.tsx
View File

@@ -40,9 +40,8 @@ import { cn, isEmpty } from "@/lib/utils";
import { ClockIcon, CopyIcon, InfoIcon } from "@phosphor-icons/react";
import { CalendarClockIcon, Trash2Icon } from "lucide-react";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { analytics } from "@/services/analytics";
import { AgentStatus, AgentStatusChip } from "./agent-status-chip";
import { analytics } from "@/services/analytics";
export function AgentRunDraftView({
graph,
@@ -55,7 +54,6 @@ export function AgentRunDraftView({
doCreateSchedule: _doCreateSchedule,
onCreateSchedule,
agentActions,
runCount,
className,
recommendedScheduleCron,
}: {
@@ -74,7 +72,6 @@ export function AgentRunDraftView({
credentialsInputs: Record<string, CredentialsMetaInput>,
) => Promise<void>;
onCreateSchedule?: (schedule: Schedule) => void;
runCount: number;
className?: string;
} & (
| {
@@ -103,7 +100,6 @@ export function AgentRunDraftView({
const [changedPresetAttributes, setChangedPresetAttributes] = useState<
Set<keyof LibraryAgentPresetUpdatable>
>(new Set());
const { completeStep: completeOnboardingStep } = useOnboarding();
const [cronScheduleDialogOpen, setCronScheduleDialogOpen] = useState(false);
// Update values if agentPreset parameter is changed
@@ -193,7 +189,13 @@ export function AgentRunDraftView({
}
// TODO: on executing preset with changes, ask for confirmation and offer save+run
const newRun = await api
.executeGraph(graph.id, graph.version, inputValues, inputCredentials)
.executeGraph(
graph.id,
graph.version,
inputValues,
inputCredentials,
"library",
)
.catch(toastOnFail("execute agent"));
if (newRun && onRun) onRun(newRun.id);
@@ -203,26 +205,12 @@ export function AgentRunDraftView({
.then((newRun) => onRun && onRun(newRun.id))
.catch(toastOnFail("execute agent preset"));
}
// Mark run agent onboarding step as completed
completeOnboardingStep("MARKETPLACE_RUN_AGENT");
analytics.sendDatafastEvent("run_agent", {
name: graph.name,
id: graph.id,
});
if (runCount > 0) {
completeOnboardingStep("RE_RUN_AGENT");
}
}, [
api,
graph,
inputValues,
inputCredentials,
onRun,
toastOnFail,
completeOnboardingStep,
]);
}, [api, graph, inputValues, inputCredentials, onRun, toastOnFail]);
const doCreatePreset = useCallback(async () => {
if (!onCreatePreset) return;
@@ -256,7 +244,6 @@ export function AgentRunDraftView({
onCreatePreset,
toast,
toastOnFail,
completeOnboardingStep,
]);
const doUpdatePreset = useCallback(async () => {
@@ -295,7 +282,6 @@ export function AgentRunDraftView({
onUpdatePreset,
toast,
toastOnFail,
completeOnboardingStep,
]);
const doSetPresetActive = useCallback(
@@ -342,7 +328,6 @@ export function AgentRunDraftView({
onCreatePreset,
toast,
toastOnFail,
completeOnboardingStep,
]);
const openScheduleDialog = useCallback(() => {

1
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-schedule-details-view.tsx
View File

@@ -100,6 +100,7 @@ export function AgentScheduleDetailsView({
graph.version,
schedule.input_data,
schedule.input_credentials,
"library",
)
.then((run) => onForcedRun(run.id))
.catch(toastOnFail("execute agent")),

3
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/cron-scheduler-dialog.tsx
View File

@@ -7,7 +7,6 @@ import { Dialog } from "@/components/molecules/Dialog/Dialog";
import { useGetV1GetUserTimezone } from "@/app/api/__generated__/endpoints/auth/auth";
import { getTimezoneDisplayName } from "@/lib/timezone-utils";
import { InfoIcon } from "lucide-react";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
// Base type for cron expression only
type CronOnlyCallback = (cronExpression: string) => void;
@@ -49,7 +48,6 @@ export function CronSchedulerDialog(props: CronSchedulerDialogProps) {
const [scheduleName, setScheduleName] = useState<string>(
props.mode === "with-name" ? props.defaultScheduleName || "" : "",
);
const { completeStep } = useOnboarding();
// Get user's timezone
const { data: userTimezone } = useGetV1GetUserTimezone({
@@ -94,7 +92,6 @@ export function CronSchedulerDialog(props: CronSchedulerDialogProps) {
props.onSubmit(cronExpression);
}
setOpen(false);
completeStep("SCHEDULE_AGENT");
};
return (

1
autogpt_platform/frontend/src/app/(platform)/library/components/LibraryUploadAgentDialog/useLibraryUploadAgentDialog.ts
View File

@@ -62,6 +62,7 @@ export const useLibraryUploadAgentDialog = () => {
await createGraph({
data: {
graph: payload,
source: "upload",
},
});
};

4
autogpt_platform/frontend/src/app/(platform)/marketplace/components/AgentInfo/useAgentInfo.ts
View File

@@ -6,7 +6,6 @@ import { useToast } from "@/components/molecules/Toast/use-toast";
import { useRouter } from "next/navigation";
import * as Sentry from "@sentry/nextjs";
import { useGetV2DownloadAgentFile } from "@/app/api/__generated__/endpoints/store/store";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { analytics } from "@/services/analytics";
import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { useQueryClient } from "@tanstack/react-query";
@@ -18,7 +17,6 @@ interface UseAgentInfoProps {
export const useAgentInfo = ({ storeListingVersionId }: UseAgentInfoProps) => {
const { toast } = useToast();
const router = useRouter();
const { completeStep } = useOnboarding();
const queryClient = useQueryClient();
const {
@@ -49,8 +47,6 @@ export const useAgentInfo = ({ storeListingVersionId }: UseAgentInfoProps) => {
const data = response as LibraryAgent;
if (isAddingAgentFirstTime) {
completeStep("MARKETPLACE_ADD_AGENT");
await queryClient.invalidateQueries({
queryKey: getGetV2ListLibraryAgentsQueryKey(),
});

30
autogpt_platform/frontend/src/app/(platform)/marketplace/components/MainAgentPage/MainAgentPage.tsx
View File

@@ -97,11 +97,31 @@ export const MainAgentPage = ({ params }: MainAgentPageProps) => {
/>
</div>
<AgentImages
images={
agent.agent_video
? [agent.agent_video, ...agent.agent_image]
: agent.agent_image
}
images={(() => {
const orderedImages: string[] = [];
// 1. YouTube/Overview video (if it exists)
if (agent.agent_video) {
orderedImages.push(agent.agent_video);
}
// 2. First image (hero)
if (agent.agent_image.length > 0) {
orderedImages.push(agent.agent_image[0]);
}
// 3. Agent Output Demo (if it exists)
if ((agent as any).agent_output_demo) {
orderedImages.push((agent as any).agent_output_demo);
}
// 4. Additional images
if (agent.agent_image.length > 1) {
orderedImages.push(...agent.agent_image.slice(1));
}
return orderedImages;
})()}
/>
</div>
<Separator className="mb-[25px] mt-[60px]" />

2
autogpt_platform/frontend/src/app/(platform)/monitoring/components/AgentImportForm.tsx
View File

@@ -62,7 +62,7 @@ export const AgentImportForm: React.FC<
};
api
.createGraph(payload)
.createGraph(payload, "upload")
.then((response) => {
const qID = "flowID";
window.location.href = `/build?${qID}=${response.id}`;

68
autogpt_platform/frontend/src/app/api/helpers.ts
View File

@@ -1,4 +1,7 @@
import BackendAPI from "@/lib/autogpt-server-api";
import {
getV1IsOnboardingEnabled,
getV1OnboardingState,
} from "./__generated__/endpoints/onboarding/onboarding";
/**
* Narrow an orval response to its success payload if and only if it is a `200` status with OK shape.
@@ -26,10 +29,67 @@ export function okData<T>(res: unknown): T | undefined {
return (res as { data: T }).data;
}
type ResponseWithData = { status: number; data: unknown };
type ExtractResponseData<T extends ResponseWithData> = T extends {
data: infer D;
}
? D
: never;
type SuccessfulResponses<T extends ResponseWithData> = T extends {
status: infer S;
}
? S extends number
? `${S}` extends `2${string}`
? T
: never
: never
: never;
/**
* Resolve an Orval response to its payload after asserting the status is either the explicit
* `expected` code or any other 2xx status if `expected` is omitted.
*
* Usage with server actions:
* ```ts
* const onboarding = await expectStatus(getV1OnboardingState());
* const agent = await expectStatus(
* postV2AddMarketplaceAgent({ store_listing_version_id }),
* 201,
* );
* ```
*/
export function resolveResponse<
TSuccess extends ResponseWithData,
TCode extends number,
>(
promise: Promise<TSuccess>,
expected: TCode,
): Promise<ExtractResponseData<Extract<TSuccess, { status: TCode }>>>;
export function resolveResponse<TSuccess extends ResponseWithData>(
promise: Promise<TSuccess>,
): Promise<ExtractResponseData<SuccessfulResponses<TSuccess>>>;
export async function resolveResponse<
TSuccess extends ResponseWithData,
TCode extends number,
>(promise: Promise<TSuccess>, expected?: TCode) {
const res = await promise;
const isSuccessfulStatus =
typeof res.status === "number" && res.status >= 200 && res.status < 300;
if (typeof expected === "number") {
if (res.status !== expected) {
throw new Error(`Unexpected status ${res.status}`);
}
} else if (!isSuccessfulStatus) {
throw new Error(`Unexpected status ${res.status}`);
}
return res.data;
}
export async function shouldShowOnboarding() {
const api = new BackendAPI();
const isEnabled = await api.isOnboardingEnabled();
const onboarding = await api.getUserOnboarding();
const isEnabled = await resolveResponse(getV1IsOnboardingEnabled());
const onboarding = await resolveResponse(getV1OnboardingState());
const isCompleted = onboarding.completedSteps.includes("CONGRATS");
return isEnabled && !isCompleted;
}

246
autogpt_platform/frontend/src/app/api/openapi.json
View File

@@ -827,12 +827,16 @@
"/api/onboarding": {
"get": {
"tags": ["v1", "onboarding"],
"summary": "Get onboarding status",
"operationId": "getV1Get onboarding status",
"summary": "Onboarding state",
"operationId": "getV1Onboarding state",
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/UserOnboarding" }
}
}
},
"401": {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
@@ -842,8 +846,8 @@
},
"patch": {
"tags": ["v1", "onboarding"],
"summary": "Update onboarding progress",
"operationId": "patchV1Update onboarding progress",
"summary": "Update onboarding state",
"operationId": "patchV1Update onboarding state",
"requestBody": {
"content": {
"application/json": {
@@ -855,7 +859,11 @@
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/UserOnboarding" }
}
}
},
"422": {
"description": "Validation Error",
@@ -872,16 +880,71 @@
"security": [{ "HTTPBearerJWT": [] }]
}
},
"/api/onboarding/agents": {
"get": {
"/api/onboarding/step": {
"post": {
"tags": ["v1", "onboarding"],
"summary": "Get recommended agents",
"operationId": "getV1Get recommended agents",
"summary": "Complete onboarding step",
"operationId": "postV1Complete onboarding step",
"security": [{ "HTTPBearerJWT": [] }],
"parameters": [
{
"name": "step",
"in": "query",
"required": true,
"schema": {
"enum": [
"WELCOME",
"USAGE_REASON",
"INTEGRATIONS",
"AGENT_CHOICE",
"AGENT_NEW_RUN",
"AGENT_INPUT",
"CONGRATS",
"MARKETPLACE_VISIT",
"BUILDER_OPEN"
],
"type": "string",
"title": "Step"
}
}
],
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/HTTPValidationError" }
}
}
},
"401": {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
}
}
}
},
"/api/onboarding/agents": {
"get": {
"tags": ["v1", "onboarding"],
"summary": "Recommended onboarding agents",
"operationId": "getV1Recommended onboarding agents",
"responses": {
"200": {
"description": "Successful Response",
"content": {
"application/json": {
"schema": {
"items": { "$ref": "#/components/schemas/StoreAgentDetails" },
"type": "array",
"title": "Response Getv1Recommended Onboarding Agents"
}
}
}
},
"401": {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
}
@@ -892,12 +955,19 @@
"/api/onboarding/enabled": {
"get": {
"tags": ["v1", "onboarding", "public"],
"summary": "Check onboarding enabled",
"operationId": "getV1Check onboarding enabled",
"summary": "Is onboarding enabled",
"operationId": "getV1Is onboarding enabled",
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
"content": {
"application/json": {
"schema": {
"type": "boolean",
"title": "Response Getv1Is Onboarding Enabled"
}
}
}
},
"401": {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
@@ -914,7 +984,11 @@
"responses": {
"200": {
"description": "Successful Response",
"content": { "application/json": { "schema": {} } }
"content": {
"application/json": {
"schema": { "$ref": "#/components/schemas/UserOnboarding" }
}
}
},
"401": {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
@@ -5665,6 +5739,16 @@
},
"type": "object",
"title": "Credentials Inputs"
},
"source": {
"anyOf": [
{
"type": "string",
"enum": ["builder", "library", "onboarding"]
},
{ "type": "null" }
],
"title": "Source"
}
},
"type": "object",
@@ -5712,6 +5796,12 @@
"store_listing_version_id": {
"type": "string",
"title": "Store Listing Version Id"
},
"source": {
"type": "string",
"enum": ["onboarding", "marketplace"],
"title": "Source",
"default": "marketplace"
}
},
"type": "object",
@@ -5819,7 +5909,16 @@
"title": "CreateAPIKeyResponse"
},
"CreateGraph": {
"properties": { "graph": { "$ref": "#/components/schemas/Graph" } },
"properties": {
"graph": { "$ref": "#/components/schemas/Graph" },
"source": {
"anyOf": [
{ "type": "string", "enum": ["builder", "upload"] },
{ "type": "null" }
],
"title": "Source"
}
},
"type": "object",
"required": ["graph"],
"title": "CreateGraph"
@@ -8586,6 +8685,10 @@
"slug": { "type": "string", "title": "Slug" },
"agent_name": { "type": "string", "title": "Agent Name" },
"agent_video": { "type": "string", "title": "Agent Video" },
"agent_output_demo": {
"type": "string",
"title": "Agent Output Demo"
},
"agent_image": {
"items": { "type": "string" },
"type": "array",
@@ -8636,6 +8739,7 @@
"slug",
"agent_name",
"agent_video",
"agent_output_demo",
"agent_image",
"creator",
"creator_avatar",
@@ -8803,6 +8907,10 @@
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Video Url"
},
"agent_output_demo_url": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Agent Output Demo Url"
},
"categories": {
"items": { "type": "string" },
"type": "array",
@@ -8834,6 +8942,10 @@
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Video Url"
},
"agent_output_demo_url": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Agent Output Demo Url"
},
"image_urls": {
"items": { "type": "string" },
"type": "array",
@@ -8879,6 +8991,10 @@
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Video Url"
},
"agent_output_demo_url": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Agent Output Demo Url"
},
"image_urls": {
"items": { "type": "string" },
"type": "array",
@@ -10289,18 +10405,87 @@
"title": "UserHistoryResponse",
"description": "Response model for listings with version history"
},
"UserOnboardingUpdate": {
"UserOnboarding": {
"properties": {
"userId": { "type": "string", "title": "Userid" },
"completedSteps": {
"anyOf": [
{
"items": { "$ref": "#/components/schemas/OnboardingStep" },
"type": "array"
},
{ "type": "null" }
],
"items": { "$ref": "#/components/schemas/OnboardingStep" },
"type": "array",
"title": "Completedsteps"
},
"walletShown": { "type": "boolean", "title": "Walletshown" },
"notified": {
"items": { "$ref": "#/components/schemas/OnboardingStep" },
"type": "array",
"title": "Notified"
},
"rewardedFor": {
"items": { "$ref": "#/components/schemas/OnboardingStep" },
"type": "array",
"title": "Rewardedfor"
},
"usageReason": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Usagereason"
},
"integrations": {
"items": { "type": "string" },
"type": "array",
"title": "Integrations"
},
"otherIntegrations": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Otherintegrations"
},
"selectedStoreListingVersionId": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Selectedstorelistingversionid"
},
"agentInput": {
"anyOf": [
{ "additionalProperties": true, "type": "object" },
{ "type": "null" }
],
"title": "Agentinput"
},
"onboardingAgentExecutionId": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Onboardingagentexecutionid"
},
"agentRuns": { "type": "integer", "title": "Agentruns" },
"lastRunAt": {
"anyOf": [
{ "type": "string", "format": "date-time" },
{ "type": "null" }
],
"title": "Lastrunat"
},
"consecutiveRunDays": {
"type": "integer",
"title": "Consecutiverundays"
}
},
"type": "object",
"required": [
"userId",
"completedSteps",
"walletShown",
"notified",
"rewardedFor",
"usageReason",
"integrations",
"otherIntegrations",
"selectedStoreListingVersionId",
"agentInput",
"onboardingAgentExecutionId",
"agentRuns",
"lastRunAt",
"consecutiveRunDays"
],
"title": "UserOnboarding"
},
"UserOnboardingUpdate": {
"properties": {
"walletShown": {
"anyOf": [{ "type": "boolean" }, { "type": "null" }],
"title": "Walletshown"
@@ -10344,21 +10529,6 @@
"onboardingAgentExecutionId": {
"anyOf": [{ "type": "string" }, { "type": "null" }],
"title": "Onboardingagentexecutionid"
},
"agentRuns": {
"anyOf": [{ "type": "integer" }, { "type": "null" }],
"title": "Agentruns"
},
"lastRunAt": {
"anyOf": [
{ "type": "string", "format": "date-time" },
{ "type": "null" }
],
"title": "Lastrunat"
},
"consecutiveRunDays": {
"anyOf": [{ "type": "integer" }, { "type": "null" }],
"title": "Consecutiverundays"
}
},
"type": "object",

23
autogpt_platform/frontend/src/components/contextual/EditAgentModal/components/useEditAgentForm.ts
View File

@@ -6,6 +6,7 @@ import {
import { StoreSubmission } from "@/app/api/__generated__/models/storeSubmission";
import { StoreSubmissionEditRequest } from "@/app/api/__generated__/models/storeSubmissionEditRequest";
import { useToast } from "@/components/molecules/Toast/use-toast";
import { validateYouTubeUrl } from "@/lib/utils";
import { zodResolver } from "@hookform/resolvers/zod";
import { useQueryClient } from "@tanstack/react-query";
import React from "react";
@@ -35,22 +36,7 @@ export const useEditAgentForm = ({
.max(200, "Subheader must be less than 200 characters"),
youtubeLink: z
.string()
.optional()
.refine((val) => {
if (!val) return true;
try {
const url = new URL(val);
const allowedHosts = [
"youtube.com",
"www.youtube.com",
"youtu.be",
"www.youtu.be",
];
return allowedHosts.includes(url.hostname);
} catch {
return false;
}
}, "Please enter a valid YouTube URL"),
.refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),
category: z.string().min(1, "Category is required"),
description: z
.string()
@@ -60,6 +46,9 @@ export const useEditAgentForm = ({
.string()
.min(1, "Changes summary is required")
.max(200, "Changes summary must be less than 200 characters"),
agentOutputDemo: z
.string()
.refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),
});
type EditAgentFormData = z.infer<typeof editAgentSchema>;
@@ -91,6 +80,7 @@ export const useEditAgentForm = ({
category: submission.categories?.[0] || "",
description: submission.description,
changes_summary: submission.changes_summary || "",
agentOutputDemo: submission.agent_output_demo_url || "",
},
});
@@ -134,6 +124,7 @@ export const useEditAgentForm = ({
description: data.description,
image_urls: images,
video_url: data.youtubeLink || "",
agent_output_demo_url: data.agentOutputDemo || "",
categories: filteredCategories,
changes_summary: data.changes_summary,
},

15
autogpt_platform/frontend/src/components/contextual/PublishAgentModal/components/AgentInfoStep/AgentInfoStep.tsx
View File

@@ -163,6 +163,21 @@ export function AgentInfoStep({
)}
/>
<FormField
control={form.control}
name="agentOutputDemo"
render={({ field }) => (
<Input
id={field.name}
label="Agent Output Demo"
type="url"
placeholder="Add a short video showing the agent's results in action."
error={form.formState.errors.agentOutputDemo?.message}
{...field}
/>
)}
/>
<FormField
control={form.control}
name="instructions"

22
autogpt_platform/frontend/src/components/contextual/PublishAgentModal/components/AgentInfoStep/helpers.ts
View File

@@ -1,4 +1,5 @@
import z from "zod";
import { validateYouTubeUrl } from "@/lib/utils";
export const publishAgentSchema = z.object({
title: z
@@ -19,22 +20,7 @@ export const publishAgentSchema = z.object({
),
youtubeLink: z
.string()
.optional()
.refine((val) => {
if (!val) return true;
try {
const url = new URL(val);
const allowedHosts = [
"youtube.com",
"www.youtube.com",
"youtu.be",
"www.youtu.be",
];
return allowedHosts.includes(url.hostname);
} catch {
return false;
}
}, "Please enter a valid YouTube URL"),
.refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),
category: z.string().min(1, "Category is required"),
description: z
.string()
@@ -48,6 +34,9 @@ export const publishAgentSchema = z.object({
(val) => !val || val.length <= 2000,
"Instructions must be less than 2000 characters",
),
agentOutputDemo: z
.string()
.refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),
});
export type PublishAgentFormData = z.infer<typeof publishAgentSchema>;
@@ -64,4 +53,5 @@ export interface PublishAgentInfoInitialData {
additionalImages?: string[];
recommendedScheduleCron?: string;
instructions?: string;
agentOutputDemo?: string;
}

5
autogpt_platform/frontend/src/components/contextual/PublishAgentModal/components/AgentInfoStep/useAgentInfoStep.ts
View File

@@ -46,6 +46,7 @@ export function useAgentInfoStep({
description: "",
recommendedScheduleCron: "",
instructions: "",
agentOutputDemo: "",
},
});
@@ -68,6 +69,7 @@ export function useAgentInfoStep({
description: initialData.description,
recommendedScheduleCron: initialData.recommendedScheduleCron || "",
instructions: initialData.instructions || "",
agentOutputDemo: initialData.agentOutputDemo || "",
});
}
}, [initialData, form]);
@@ -99,12 +101,13 @@ export function useAgentInfoStep({
instructions: data.instructions || null,
image_urls: images,
video_url: data.youtubeLink || "",
agent_output_demo_url: data.agentOutputDemo || "",
agent_id: selectedAgentId || "",
agent_version: selectedAgentVersion || 0,
slug: data.slug.replace(/\s+/g, "-"),
categories: filteredCategories,
recommended_schedule_cron: data.recommendedScheduleCron || null,
});
} as any);
await queryClient.invalidateQueries({
queryKey: getGetV2ListMySubmissionsQueryKey(),

46
autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx
View File

@@ -250,31 +250,41 @@ export function Wallet() {
[],
);
// Confetti effect on the wallet button
// React to onboarding notifications emitted by the provider
const handleNotification = useCallback(
(notification: WebSocketNotification) => {
if (notification.type !== "onboarding") {
if (
notification.type !== "onboarding" ||
notification.event !== "step_completed" ||
!walletRef.current
) {
return;
}
if (walletRef.current) {
// Fix confetti appearing in the top left corner
const rect = walletRef.current.getBoundingClientRect();
if (rect.width === 0 || rect.height === 0) {
return;
}
fetchCredits();
party.confetti(walletRef.current!, {
count: 30,
spread: 120,
shapes: ["square", "circle"],
size: party.variation.range(1, 2),
speed: party.variation.range(200, 300),
modules: [fadeOut],
});
// Only trigger confetti for tasks that are in groups
const taskIds = groups
.flatMap((group) => group.tasks)
.map((task) => task.id);
if (!taskIds.includes(notification.step as OnboardingStep)) {
return;
}
const rect = walletRef.current.getBoundingClientRect();
if (rect.width === 0 || rect.height === 0) {
return;
}
fetchCredits();
party.confetti(walletRef.current, {
count: 30,
spread: 120,
shapes: ["square", "circle"],
size: party.variation.range(1, 2),
speed: party.variation.range(200, 300),
modules: [fadeOut],
});
},
[],
[fetchCredits, fadeOut],
);
// WebSocket setup for onboarding notifications

14
autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/components/WalletTaskGroups.tsx
View File

@@ -16,7 +16,10 @@ export function TaskGroups({ groups }: Props) {
const [openGroups, setOpenGroups] = useState<Record<string, boolean>>(() => {
const initialState: Record<string, boolean> = {};
groups.forEach((group) => {
initialState[group.name] = true;
const completed = group.tasks.every((task) =>
state?.completedSteps?.includes(task.id),
);
initialState[group.name] = !completed;
});
return initialState;
});
@@ -62,7 +65,7 @@ export function TaskGroups({ groups }: Props) {
{} as Record<string, boolean>,
),
);
}, [state?.completedSteps, isGroupCompleted]);
}, [state?.completedSteps, isGroupCompleted, groups]);
const setRef = (name: string) => (el: HTMLDivElement | null) => {
if (el) {
@@ -101,9 +104,10 @@ export function TaskGroups({ groups }: Props) {
useEffect(() => {
groups.forEach((group) => {
const groupCompleted = isGroupCompleted(group);
// Check if the last task in the group is completed
const alreadyCelebrated = state?.notified.includes(
group.tasks[group.tasks.length - 1].id,
// Check if all tasks in the group were already celebrated
// last task completed triggers group completion
const alreadyCelebrated = group.tasks.every((task) =>
state?.notified.includes(task.id),
);
if (groupCompleted) {

19
autogpt_platform/frontend/src/hooks/useAgentGraph.tsx
View File

@@ -26,7 +26,6 @@ import { default as NextLink } from "next/link";
import { usePathname, useRouter, useSearchParams } from "next/navigation";
import { useCallback, useEffect, useMemo, useRef, useState } from "react";
import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { useQueryClient } from "@tanstack/react-query";
import { getGetV2ListLibraryAgentsQueryKey } from "@/app/api/__generated__/endpoints/library/library";
@@ -67,7 +66,6 @@ export default function useAgentGraph(
>(null);
const [xyNodes, setXYNodes] = useState<CustomNode[]>([]);
const [xyEdges, setXYEdges] = useState<CustomEdge[]>([]);
const { state, completeStep, incrementRuns } = useOnboarding();
const betaBlocks = useGetFlag(Flag.BETA_BLOCKS);
// Filter blocks based on beta flags
@@ -563,14 +561,13 @@ export default function useAgentGraph(
setIsRunning(false);
setIsStopping(false);
setActiveExecutionID(null);
incrementRuns();
}
},
);
};
fetchExecutions();
}, [flowID, flowExecutionID, incrementRuns]);
}, [flowID, flowExecutionID]);
const prepareNodeInputData = useCallback(
(node: CustomNode) => {
@@ -679,7 +676,7 @@ export default function useAgentGraph(
...payload,
id: savedAgent.id,
})
: await api.createGraph(payload);
: await api.createGraph(payload, "builder");
console.debug("Response from the API:", newSavedAgent);
}
@@ -751,8 +748,6 @@ export default function useAgentGraph(
await queryClient.invalidateQueries({
queryKey: getGetV2ListLibraryAgentsQueryKey(),
});
completeStep("BUILDER_SAVE_AGENT");
} catch (error) {
const errorMessage =
error instanceof Error ? error.message : String(error);
@@ -765,7 +760,7 @@ export default function useAgentGraph(
} finally {
setIsSaving(false);
}
}, [_saveAgent, toast, completeStep]);
}, [_saveAgent, toast]);
const saveAndRun = useCallback(
async (
@@ -780,7 +775,6 @@ export default function useAgentGraph(
let savedAgent: Graph;
try {
savedAgent = await _saveAgent();
completeStep("BUILDER_SAVE_AGENT");
} catch (error) {
const errorMessage =
error instanceof Error ? error.message : String(error);
@@ -808,6 +802,7 @@ export default function useAgentGraph(
savedAgent.version,
inputs,
credentialsInputs,
"builder",
);
setActiveExecutionID(graphExecution.id);
@@ -818,10 +813,6 @@ export default function useAgentGraph(
path.set("flowVersion", savedAgent.version.toString());
path.set("flowExecutionID", graphExecution.id);
router.push(`${pathname}?${path.toString()}`);
if (state?.completedSteps.includes("BUILDER_SAVE_AGENT")) {
completeStep("BUILDER_RUN_AGENT");
}
} catch (error) {
// Check if this is a structured validation error from the backend
if (error instanceof ApiError && error.isGraphValidationError()) {
@@ -871,12 +862,10 @@ export default function useAgentGraph(
[
_saveAgent,
toast,
completeStep,
api,
searchParams,
pathname,
router,
state,
isSaving,
isRunning,
processedUpdates,

80
autogpt_platform/frontend/src/lib/autogpt-server-api/client.ts
View File

@@ -55,7 +55,6 @@ import type {
Schedule,
ScheduleCreatable,
ScheduleID,
StoreAgentDetails,
StoreAgentsResponse,
StoreListingsWithVersionsResponse,
StoreReview,
@@ -66,7 +65,6 @@ import type {
SubmissionStatus,
TransactionHistory,
User,
UserOnboarding,
UserPasswordCredentials,
UsersBalanceHistoryResponse,
WebSocketNotification,
@@ -193,29 +191,6 @@ export default class BackendAPI {
return this._request("PATCH", "/credits");
}
////////////////////////////////////////
////////////// ONBOARDING //////////////
////////////////////////////////////////
getUserOnboarding(): Promise<UserOnboarding> {
return this._get("/onboarding");
}
updateUserOnboarding(
onboarding: Omit<Partial<UserOnboarding>, "rewardedFor">,
): Promise<void> {
return this._request("PATCH", "/onboarding", onboarding);
}
getOnboardingAgents(): Promise<StoreAgentDetails[]> {
return this._get("/onboarding/agents");
}
/** Check if onboarding is enabled not if user finished it or not. */
isOnboardingEnabled(): Promise<boolean> {
return this._get("/onboarding/enabled");
}
////////////////////////////////////////
//////////////// GRAPHS ////////////////
////////////////////////////////////////
@@ -249,8 +224,14 @@ export default class BackendAPI {
return this._get(`/graphs/${id}/versions`);
}
createGraph(graph: GraphCreatable): Promise<Graph> {
const requestBody = { graph } as GraphCreateRequestBody;
createGraph(
graph: GraphCreatable,
source?: GraphCreationSource,
): Promise<Graph> {
const requestBody: GraphCreateRequestBody = { graph };
if (source) {
requestBody.source = source;
}
return this._request("POST", "/graphs", requestBody);
}
@@ -274,11 +255,13 @@ export default class BackendAPI {
version: number,
inputs: { [key: string]: any } = {},
credentials_inputs: { [key: string]: CredentialsMetaInput } = {},
source?: GraphExecutionSource,
): Promise<GraphExecutionMeta> {
return this._request("POST", `/graphs/${id}/execute/${version}`, {
inputs,
credentials_inputs,
});
const body: GraphExecuteRequestBody = { inputs, credentials_inputs };
if (source) {
body.source = source;
}
return this._request("POST", `/graphs/${id}/execute/${version}`, body);
}
getExecutions(): Promise<GraphExecutionMeta[]> {
@@ -468,29 +451,12 @@ export default class BackendAPI {
return this._get("/store/agents", params);
}
getStoreAgent(
username: string,
agentName: string,
): Promise<StoreAgentDetails> {
return this._get(
`/store/agents/${encodeURIComponent(username)}/${encodeURIComponent(
agentName,
)}`,
);
}
getGraphMetaByStoreListingVersionID(
storeListingVersionID: string,
): Promise<GraphMeta> {
return this._get(`/store/graph/${storeListingVersionID}`);
}
getStoreAgentByVersionId(
storeListingVersionID: string,
): Promise<StoreAgentDetails> {
return this._get(`/store/agents/${storeListingVersionID}`);
}
getStoreCreators(params?: {
featured?: boolean;
search_query?: string;
@@ -689,14 +655,6 @@ export default class BackendAPI {
});
}
addMarketplaceAgentToLibrary(
storeListingVersionID: string,
): Promise<LibraryAgent> {
return this._request("POST", "/library/agents", {
store_listing_version_id: storeListingVersionID,
});
}
updateLibraryAgent(
libraryAgentId: LibraryAgentID,
params: {
@@ -1356,8 +1314,18 @@ declare global {
/* *** UTILITY TYPES *** */
type GraphCreationSource = "builder" | "upload";
type GraphExecutionSource = "builder" | "library" | "onboarding";
type GraphCreateRequestBody = {
graph: GraphCreatable;
source?: GraphCreationSource;
};
type GraphExecuteRequestBody = {
inputs: { [key: string]: any };
credentials_inputs: { [key: string]: CredentialsMetaInput };
source?: GraphExecutionSource;
};
type WebsocketMessageTypeMap = {

26
autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts
View File

@@ -761,28 +761,6 @@ export type StoreAgentsResponse = {
pagination: Pagination;
};
export type StoreAgentDetails = {
store_listing_version_id: string;
slug: string;
updated_at: string;
agent_name: string;
agent_video: string;
agent_image: string[];
creator: string;
creator_avatar: string;
sub_heading: string;
description: string;
categories: string[];
runs: number;
rating: number;
versions: string[];
// Approval and status fields
active_version_id?: string;
has_approved_version?: boolean;
is_available?: boolean;
};
export type Creator = {
name: string;
username: string;
@@ -1028,8 +1006,8 @@ export interface UserOnboarding {
export interface OnboardingNotificationPayload {
type: "onboarding";
event: string;
step: OnboardingStep;
event: "step_completed" | "increment_runs";
step: OnboardingStep | null;
}
export type WebSocketNotification =

17
autogpt_platform/frontend/src/lib/utils.ts
View File

@@ -428,3 +428,20 @@ export function isEmpty(value: any): boolean {
export function isObject(value: unknown): value is Record<string, unknown> {
return typeof value === "object" && value !== null && !Array.isArray(value);
}
/** Validate YouTube URL */
export function validateYouTubeUrl(val: string): boolean {
if (!val) return true;
try {
const url = new URL(val);
const allowedHosts = [
"youtube.com",
"www.youtube.com",
"youtu.be",
"www.youtu.be",
];
return allowedHosts.includes(url.hostname);
} catch {
return false;
}
}

135
autogpt_platform/frontend/src/providers/onboarding/helpers.ts
View File

@@ -1,78 +1,32 @@
import { OnboardingStep, UserOnboarding } from "@/lib/autogpt-server-api";
import {
GraphExecutionID,
OnboardingStep,
UserOnboarding,
} from "@/lib/autogpt-server-api";
import { UserOnboarding as RawUserOnboarding } from "@/app/api/__generated__/models/userOnboarding";
export function isToday(date: Date): boolean {
const today = new Date();
return (
date.getDate() === today.getDate() &&
date.getMonth() === today.getMonth() &&
date.getFullYear() === today.getFullYear()
);
}
export type LocalOnboardingStateUpdate = Omit<
Partial<UserOnboarding>,
| "completedSteps"
| "rewardedFor"
| "lastRunAt"
| "consecutiveRunDays"
| "agentRuns"
>;
export function isYesterday(date: Date): boolean {
const yesterday = new Date();
yesterday.setDate(yesterday.getDate() - 1);
return (
date.getDate() === yesterday.getDate() &&
date.getMonth() === yesterday.getMonth() &&
date.getFullYear() === yesterday.getFullYear()
);
}
export function calculateConsecutiveDays(
lastRunAt: Date | null,
currentConsecutiveDays: number,
): { lastRunAt: Date; consecutiveRunDays: number } {
const now = new Date();
if (lastRunAt === null || isYesterday(lastRunAt)) {
return {
lastRunAt: now,
consecutiveRunDays: currentConsecutiveDays + 1,
};
}
if (!isToday(lastRunAt)) {
return {
lastRunAt: now,
consecutiveRunDays: 1,
};
}
return {
lastRunAt: now,
consecutiveRunDays: currentConsecutiveDays,
};
}
export function getRunMilestoneSteps(
newRunCount: number,
consecutiveDays: number,
): OnboardingStep[] {
const steps: OnboardingStep[] = [];
if (newRunCount >= 10) steps.push("RUN_AGENTS");
if (newRunCount >= 100) steps.push("RUN_AGENTS_100");
if (consecutiveDays >= 3) steps.push("RUN_3_DAYS");
if (consecutiveDays >= 14) steps.push("RUN_14_DAYS");
return steps;
}
export function processOnboardingData(
onboarding: UserOnboarding,
export function fromBackendUserOnboarding(
onboarding: RawUserOnboarding,
): UserOnboarding {
// Patch for TRIGGER_WEBHOOK - only set on backend then overwritten by frontend
const completeWebhook =
onboarding.rewardedFor.includes("TRIGGER_WEBHOOK") &&
!onboarding.completedSteps.includes("TRIGGER_WEBHOOK")
? (["TRIGGER_WEBHOOK"] as OnboardingStep[])
: [];
return {
...onboarding,
completedSteps: [...completeWebhook, ...onboarding.completedSteps],
usageReason: onboarding.usageReason || null,
otherIntegrations: onboarding.otherIntegrations || null,
selectedStoreListingVersionId:
onboarding.selectedStoreListingVersionId || null,
agentInput:
(onboarding.agentInput as Record<string, string | number>) || null,
onboardingAgentExecutionId:
(onboarding.onboardingAgentExecutionId as GraphExecutionID) || null,
lastRunAt: onboarding.lastRunAt ? new Date(onboarding.lastRunAt) : null,
};
}
@@ -87,23 +41,30 @@ export function shouldRedirectFromOnboarding(
);
}
export function createInitialOnboardingState(
newState: Omit<Partial<UserOnboarding>, "rewardedFor">,
): UserOnboarding {
export function updateOnboardingState(
prevState: UserOnboarding | null,
newState: LocalOnboardingStateUpdate,
): UserOnboarding | null {
return {
completedSteps: [],
walletShown: true,
notified: [],
rewardedFor: [],
usageReason: null,
integrations: [],
otherIntegrations: null,
selectedStoreListingVersionId: null,
agentInput: null,
onboardingAgentExecutionId: null,
agentRuns: 0,
lastRunAt: null,
consecutiveRunDays: 0,
...newState,
completedSteps: prevState?.completedSteps ?? [],
walletShown: newState.walletShown ?? prevState?.walletShown ?? false,
notified: newState.notified ?? prevState?.notified ?? [],
rewardedFor: prevState?.rewardedFor ?? [],
usageReason: newState.usageReason ?? prevState?.usageReason ?? null,
integrations: newState.integrations ?? prevState?.integrations ?? [],
otherIntegrations:
newState.otherIntegrations ?? prevState?.otherIntegrations ?? null,
selectedStoreListingVersionId:
newState.selectedStoreListingVersionId ??
prevState?.selectedStoreListingVersionId ??
null,
agentInput: newState.agentInput ?? prevState?.agentInput ?? null,
onboardingAgentExecutionId:
newState.onboardingAgentExecutionId ??
prevState?.onboardingAgentExecutionId ??
null,
lastRunAt: prevState?.lastRunAt ?? null,
consecutiveRunDays: prevState?.consecutiveRunDays ?? 0,
agentRuns: prevState?.agentRuns ?? 0,
};
}

188
autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx
View File

@@ -10,7 +10,10 @@ import {
} from "@/components/__legacy__/ui/dialog";
import { useToast } from "@/components/molecules/Toast/use-toast";
import { useOnboardingTimezoneDetection } from "@/hooks/useOnboardingTimezoneDetection";
import { OnboardingStep, UserOnboarding } from "@/lib/autogpt-server-api";
import {
UserOnboarding,
WebSocketNotification,
} from "@/lib/autogpt-server-api";
import { useBackendAPI } from "@/lib/autogpt-server-api/context";
import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
import Link from "next/link";
@@ -25,28 +28,37 @@ import {
useState,
} from "react";
import {
calculateConsecutiveDays,
createInitialOnboardingState,
getRunMilestoneSteps,
processOnboardingData,
updateOnboardingState,
fromBackendUserOnboarding,
shouldRedirectFromOnboarding,
LocalOnboardingStateUpdate,
} from "./helpers";
import { resolveResponse } from "@/app/api/helpers";
import {
getV1IsOnboardingEnabled,
getV1OnboardingState,
patchV1UpdateOnboardingState,
postV1CompleteOnboardingStep,
} from "@/app/api/__generated__/endpoints/onboarding/onboarding";
import { PostV1CompleteOnboardingStepStep } from "@/app/api/__generated__/models/postV1CompleteOnboardingStepStep";
type FrontendOnboardingStep = PostV1CompleteOnboardingStepStep;
const OnboardingContext = createContext<
| {
state: UserOnboarding | null;
updateState: (
state: Omit<Partial<UserOnboarding>, "rewardedFor">,
) => void;
updateState: (state: LocalOnboardingStateUpdate) => void;
step: number;
setStep: (step: number) => void;
completeStep: (step: OnboardingStep) => void;
incrementRuns: () => void;
completeStep: (step: FrontendOnboardingStep) => void;
}
| undefined
>(undefined);
export function useOnboarding(step?: number, completeStep?: OnboardingStep) {
export function useOnboarding(
step?: number,
completeStep?: FrontendOnboardingStep,
) {
const context = useContext(OnboardingContext);
if (!context)
@@ -56,15 +68,13 @@ export function useOnboarding(step?: number, completeStep?: OnboardingStep) {
if (
!completeStep ||
!context.state ||
!context.state.completedSteps ||
context.state.completedSteps.includes(completeStep)
)
) {
return;
}
context.updateState({
completedSteps: [...context.state.completedSteps, completeStep],
});
}, [completeStep, context, context.updateState]);
context.completeStep(completeStep);
}, [completeStep, context]);
useEffect(() => {
if (step && context.step !== step) {
@@ -113,6 +123,15 @@ export default function OnboardingProvider({
const isOnOnboardingRoute = pathname.startsWith("/onboarding");
const fetchOnboarding = useCallback(async () => {
const onboarding = await resolveResponse(getV1OnboardingState());
const processedOnboarding = fromBackendUserOnboarding(onboarding);
if (isMounted.current) {
setState(processedOnboarding);
}
return processedOnboarding;
}, []);
useEffect(() => {
// Prevent multiple initializations
if (hasInitialized.current || !isLoggedIn) {
@@ -125,26 +144,19 @@ export default function OnboardingProvider({
try {
// Check onboarding enabled only for onboarding routes
if (isOnOnboardingRoute) {
const enabled = await api.isOnboardingEnabled();
const enabled = await resolveResponse(getV1IsOnboardingEnabled());
if (!enabled) {
router.push("/marketplace");
return;
}
}
const onboarding = await api.getUserOnboarding();
if (!onboarding) return;
const processedOnboarding = processOnboardingData(onboarding);
setState(processedOnboarding);
const onboarding = await fetchOnboarding();
// Handle redirects for completed onboarding
if (
isOnOnboardingRoute &&
shouldRedirectFromOnboarding(
processedOnboarding.completedSteps,
pathname,
)
shouldRedirectFromOnboarding(onboarding.completedSteps, pathname)
) {
router.push("/marketplace");
}
@@ -163,21 +175,53 @@ export default function OnboardingProvider({
initializeOnboarding();
}, [api, isOnOnboardingRoute, router, isLoggedIn, pathname]);
const updateState = useCallback(
(newState: Omit<Partial<UserOnboarding>, "rewardedFor">) => {
if (!isLoggedIn || !isMounted.current) return;
const handleOnboardingNotification = useCallback(
(notification: WebSocketNotification) => {
if (!isLoggedIn || notification.type !== "onboarding") {
return;
}
// Update local state immediately
setState((prev) => {
if (!prev) {
return createInitialOnboardingState(newState);
}
return { ...prev, ...newState };
if (notification.step === "RUN_AGENTS") {
setNpsDialogOpen(true);
}
fetchOnboarding().catch((error) => {
console.error(
"Failed to refresh onboarding after notification:",
error,
);
});
},
[fetchOnboarding, isLoggedIn],
);
useEffect(() => {
const detachMessage = api.onWebSocketMessage(
"notification",
handleOnboardingNotification,
);
if (isLoggedIn) {
api.connectWebSocket();
}
return () => {
detachMessage();
};
}, [api, handleOnboardingNotification, isLoggedIn]);
const updateState = useCallback(
(newState: LocalOnboardingStateUpdate) => {
if (!isLoggedIn) {
return;
}
setState((prev) => updateOnboardingState(prev, newState));
const updatePromise = (async () => {
try {
await api.updateUserOnboarding(newState);
if (!isMounted.current) return;
await patchV1UpdateOnboardingState(newState);
} catch (error) {
console.error("Failed to update user onboarding:", error);
@@ -188,58 +232,54 @@ export default function OnboardingProvider({
}
})();
// Track this pending update
pendingUpdatesRef.current.add(updatePromise);
updatePromise.finally(() => {
pendingUpdatesRef.current.delete(updatePromise);
});
},
[api, isLoggedIn, isMounted],
[toast, isLoggedIn, fetchOnboarding, api, setState],
);
const completeStep = useCallback(
(step: OnboardingStep) => {
if (!state?.completedSteps?.includes(step)) {
updateState({
completedSteps: [...(state?.completedSteps || []), step],
});
(step: FrontendOnboardingStep) => {
if (!isLoggedIn || state?.completedSteps?.includes(step)) {
return;
}
const completionPromise = (async () => {
try {
await postV1CompleteOnboardingStep({ step });
await fetchOnboarding();
} catch (error) {
if (isMounted.current) {
console.error("Failed to complete onboarding step:", error);
}
toast({
title: "Failed to complete onboarding step",
variant: "destructive",
});
}
})();
pendingUpdatesRef.current.add(completionPromise);
completionPromise.finally(() => {
pendingUpdatesRef.current.delete(completionPromise);
});
},
[state?.completedSteps, updateState],
[isLoggedIn, state?.completedSteps, fetchOnboarding, toast],
);
const incrementRuns = useCallback(() => {
if (!state?.completedSteps) return;
const newRunCount = state.agentRuns + 1;
const consecutiveData = calculateConsecutiveDays(
state.lastRunAt,
state.consecutiveRunDays,
);
const milestoneSteps = getRunMilestoneSteps(
newRunCount,
consecutiveData.consecutiveRunDays,
);
// Show NPS dialog at 10 runs
if (newRunCount === 10) {
setNpsDialogOpen(true);
}
updateState({
agentRuns: newRunCount,
completedSteps: Array.from(
new Set([...state.completedSteps, ...milestoneSteps]),
),
...consecutiveData,
});
}, [state, updateState]);
return (
<OnboardingContext.Provider
value={{ state, updateState, step, setStep, completeStep, incrementRuns }}
value={{
state,
updateState,
step,
setStep,
completeStep,
}}
>
<Dialog onOpenChange={setNpsDialogOpen} open={npsDialogOpen}>
<DialogContent>