Compare commits

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17 Commits

Author SHA1 Message Date
SwiftyOS
1b5f2c6f11 elevenlabs integration 2025-03-12 15:36:04 +01:00
SwiftyOS
8aba4a5d48 Dict -> dict 2025-03-12 12:59:16 +01:00
SwiftyOS
6ba9fd9cb4 add preference for pydantic objects over dicts 2025-03-12 12:58:52 +01:00
SwiftyOS
b16c2eed52 update comments 2025-03-12 12:58:26 +01:00
Swifty
5af718c9f5 Update autogpt_platform/backend/backend/integrations/webhooks/example.py
Co-authored-by: Reinier van der Leer <pwuts@agpt.co>
2025-03-12 12:51:29 +01:00
SwiftyOS
a588cf1dc5 best practice improvements 2025-03-12 12:51:29 +01:00
SwiftyOS
98a1adc397 code quality improvements 2025-03-12 12:51:29 +01:00
SwiftyOS
19728ebc05 improved the example 2025-03-12 12:51:29 +01:00
SwiftyOS
3e117aac5d Added example of yielding multiple each item from the list as well as the list itself 2025-03-12 12:51:29 +01:00
SwiftyOS
8dabe6c70d fmt 2025-03-12 12:51:29 +01:00
SwiftyOS
a04919beca change credential handling 2025-03-12 12:51:29 +01:00
SwiftyOS
c6b22842a4 movec api key down the .env file 2025-03-12 12:51:29 +01:00
SwiftyOS
614f751a90 Added example provider to the frontend 2025-03-12 12:51:29 +01:00
SwiftyOS
c458bec9c7 Added example api key to settings 2025-03-12 12:51:29 +01:00
SwiftyOS
040bde3f49 fix import 2025-03-12 12:51:29 +01:00
SwiftyOS
71028d57d7 refactor example block into its own folder 2025-03-12 12:51:29 +01:00
SwiftyOS
dbf014f936 Example block 2025-03-12 12:51:29 +01:00
2093 changed files with 59693 additions and 289980 deletions

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@@ -1,37 +0,0 @@
{
"worktreeCopyPatterns": [
".env*",
".vscode/**",
".auth/**",
".claude/**",
"autogpt_platform/.env*",
"autogpt_platform/backend/.env*",
"autogpt_platform/frontend/.env*",
"autogpt_platform/frontend/.auth/**",
"autogpt_platform/db/docker/.env*"
],
"worktreeCopyIgnores": [
"**/node_modules/**",
"**/dist/**",
"**/.git/**",
"**/Thumbs.db",
"**/.DS_Store",
"**/.next/**",
"**/__pycache__/**",
"**/.ruff_cache/**",
"**/.pytest_cache/**",
"**/*.pyc",
"**/playwright-report/**",
"**/logs/**",
"**/site/**"
],
"worktreePathTemplate": "$BASE_PATH.worktree",
"postCreateCmd": [
"cd autogpt_platform/autogpt_libs && poetry install",
"cd autogpt_platform/backend && poetry install && poetry run prisma generate",
"cd autogpt_platform/frontend && pnpm install",
"cd docs && pip install -r requirements.txt"
],
"terminalCommand": "code .",
"deleteBranchWithWorktree": false
}

View File

@@ -9,14 +9,11 @@
# Platform - Backend
!autogpt_platform/backend/backend/
!autogpt_platform/backend/test/e2e_test_data.py
!autogpt_platform/backend/migrations/
!autogpt_platform/backend/schema.prisma
!autogpt_platform/backend/pyproject.toml
!autogpt_platform/backend/poetry.lock
!autogpt_platform/backend/README.md
!autogpt_platform/backend/.env
!autogpt_platform/backend/gen_prisma_types_stub.py
# Platform - Market
!autogpt_platform/market/market/
@@ -29,15 +26,13 @@
# Platform - Frontend
!autogpt_platform/frontend/src/
!autogpt_platform/frontend/public/
!autogpt_platform/frontend/scripts/
!autogpt_platform/frontend/package.json
!autogpt_platform/frontend/pnpm-lock.yaml
!autogpt_platform/frontend/yarn.lock
!autogpt_platform/frontend/tsconfig.json
!autogpt_platform/frontend/README.md
## config
!autogpt_platform/frontend/*.config.*
!autogpt_platform/frontend/.env.*
!autogpt_platform/frontend/.env
# Classic - AutoGPT
!classic/original_autogpt/autogpt/

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@@ -24,8 +24,7 @@
</details>
#### For configuration changes:
- [ ] `.env.default` is updated or already compatible with my changes
- [ ] `.env.example` is updated or already compatible with my changes
- [ ] `docker-compose.yml` is updated or already compatible with my changes
- [ ] I have included a list of my configuration changes in the PR description (under **Changes**)

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@@ -1,322 +0,0 @@
# GitHub Copilot Instructions for AutoGPT
This file provides comprehensive onboarding information for GitHub Copilot coding agent to work efficiently with the AutoGPT repository.
## Repository Overview
**AutoGPT** is a powerful platform for creating, deploying, and managing continuous AI agents that automate complex workflows. This is a large monorepo (~150MB) containing multiple components:
- **AutoGPT Platform** (`autogpt_platform/`) - Main focus: Modern AI agent platform (Polyform Shield License)
- **Classic AutoGPT** (`classic/`) - Legacy agent system (MIT License)
- **Documentation** (`docs/`) - MkDocs-based documentation site
- **Infrastructure** - Docker configurations, CI/CD, and development tools
**Primary Languages & Frameworks:**
- **Backend**: Python 3.10-3.13, FastAPI, Prisma ORM, PostgreSQL, RabbitMQ
- **Frontend**: TypeScript, Next.js 15, React, Tailwind CSS, Radix UI
- **Development**: Docker, Poetry, pnpm, Playwright, Storybook
## Build and Validation Instructions
### Essential Setup Commands
**Always run these commands in the correct directory and in this order:**
1. **Initial Setup** (required once):
```bash
# Clone and enter repository
git clone <repo> && cd AutoGPT
# Start all services (database, redis, rabbitmq, clamav)
cd autogpt_platform && docker compose --profile local up deps --build --detach
```
2. **Backend Setup** (always run before backend development):
```bash
cd autogpt_platform/backend
poetry install # Install dependencies
poetry run prisma migrate dev # Run database migrations
poetry run prisma generate # Generate Prisma client
```
3. **Frontend Setup** (always run before frontend development):
```bash
cd autogpt_platform/frontend
pnpm install # Install dependencies
```
### Runtime Requirements
**Critical:** Always ensure Docker services are running before starting development:
```bash
cd autogpt_platform && docker compose --profile local up deps --build --detach
```
**Python Version:** Use Python 3.11 (required; managed by Poetry via pyproject.toml)
**Node.js Version:** Use Node.js 21+ with pnpm package manager
### Development Commands
**Backend Development:**
```bash
cd autogpt_platform/backend
poetry run serve # Start development server (port 8000)
poetry run test # Run all tests (requires ~5 minutes)
poetry run pytest path/to/test.py # Run specific test
poetry run format # Format code (Black + isort) - always run first
poetry run lint # Lint code (ruff) - run after format
```
**Frontend Development:**
```bash
cd autogpt_platform/frontend
pnpm dev # Start development server (port 3000) - use for active development
pnpm build # Build for production (only needed for E2E tests or deployment)
pnpm test # Run Playwright E2E tests (requires build first)
pnpm test-ui # Run tests with UI
pnpm format # Format and lint code
pnpm storybook # Start component development server
```
### Testing Strategy
**Backend Tests:**
- **Block Tests**: `poetry run pytest backend/blocks/test/test_block.py -xvs` (validates all blocks)
- **Specific Block**: `poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[BlockName]' -xvs`
- **Snapshot Tests**: Use `--snapshot-update` when output changes, always review with `git diff`
**Frontend Tests:**
- **E2E Tests**: Always run `pnpm dev` before `pnpm test` (Playwright requires running instance)
- **Component Tests**: Use Storybook for isolated component development
### Critical Validation Steps
**Before committing changes:**
1. Run `poetry run format` (backend) and `pnpm format` (frontend)
2. Ensure all tests pass in modified areas
3. Verify Docker services are still running
4. Check that database migrations apply cleanly
**Common Issues & Workarounds:**
- **Prisma issues**: Run `poetry run prisma generate` after schema changes
- **Permission errors**: Ensure Docker has proper permissions
- **Port conflicts**: Check the `docker-compose.yml` file for the current list of exposed ports. You can list all mapped ports with:
- **Test timeouts**: Backend tests can take 5+ minutes, use `-x` flag to stop on first failure
## Project Layout & Architecture
### Core Architecture
**AutoGPT Platform** (`autogpt_platform/`):
- `backend/` - FastAPI server with async support
- `backend/backend/` - Core API logic
- `backend/blocks/` - Agent execution blocks
- `backend/data/` - Database models and schemas
- `schema.prisma` - Database schema definition
- `frontend/` - Next.js application
- `src/app/` - App Router pages and layouts
- `src/components/` - Reusable React components
- `src/lib/` - Utilities and configurations
- `autogpt_libs/` - Shared Python utilities
- `docker-compose.yml` - Development stack orchestration
**Key Configuration Files:**
- `pyproject.toml` - Python dependencies and tooling
- `package.json` - Node.js dependencies and scripts
- `schema.prisma` - Database schema and migrations
- `next.config.mjs` - Next.js configuration
- `tailwind.config.ts` - Styling configuration
### Security & Middleware
**Cache Protection**: Backend includes middleware preventing sensitive data caching in browsers/proxies
**Authentication**: JWT-based with Supabase integration
**User ID Validation**: All data access requires user ID checks - verify this for any `data/*.py` changes
### Development Workflow
**GitHub Actions**: Multiple CI/CD workflows in `.github/workflows/`
- `platform-backend-ci.yml` - Backend testing and validation
- `platform-frontend-ci.yml` - Frontend testing and validation
- `platform-fullstack-ci.yml` - End-to-end integration tests
**Pre-commit Hooks**: Run linting and formatting checks
**Conventional Commits**: Use format `type(scope): description` (e.g., `feat(backend): add API`)
### Key Source Files
**Backend Entry Points:**
- `backend/backend/server/server.py` - FastAPI application setup
- `backend/backend/data/` - Database models and user management
- `backend/blocks/` - Agent execution blocks and logic
**Frontend Entry Points:**
- `frontend/src/app/layout.tsx` - Root application layout
- `frontend/src/app/page.tsx` - Home page
- `frontend/src/lib/supabase/` - Authentication and database client
**Protected Routes**: Update `frontend/lib/supabase/middleware.ts` when adding protected routes
### Agent Block System
Agents are built using a visual block-based system where each block performs a single action. Blocks are defined in `backend/blocks/` and must include:
- Block definition with input/output schemas
- Execution logic with proper error handling
- Tests validating functionality
### Database & ORM
**Prisma ORM** with PostgreSQL backend including pgvector for embeddings:
- Schema in `schema.prisma`
- Migrations in `backend/migrations/`
- Always run `prisma migrate dev` and `prisma generate` after schema changes
## Environment Configuration
### Configuration Files Priority Order
1. **Backend**: `/backend/.env.default` → `/backend/.env` (user overrides)
2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)
3. **Platform**: `/.env.default` (Supabase/shared) → `/.env` (user overrides)
4. Docker Compose `environment:` sections override file-based config
5. Shell environment variables have highest precedence
### Docker Environment Setup
- All services use hardcoded defaults (no `${VARIABLE}` substitutions)
- The `env_file` directive loads variables INTO containers at runtime
- Backend/Frontend services use YAML anchors for consistent configuration
- Copy `.env.default` files to `.env` for local development customization
## Advanced Development Patterns
### Adding New Blocks
1. Create file in `/backend/backend/blocks/`
2. Inherit from `Block` base class with input/output schemas
3. Implement `run` method with proper error handling
4. Generate block UUID using `uuid.uuid4()`
5. Register in block registry
6. Write tests alongside block implementation
7. Consider how inputs/outputs connect with other blocks in graph editor
### API Development
1. Update routes in `/backend/backend/server/routers/`
2. Add/update Pydantic models in same directory
3. Write tests alongside route files
4. For `data/*.py` changes, validate user ID checks
5. Run `poetry run test` to verify changes
### Frontend Development
**📖 Complete Frontend Guide**: See `autogpt_platform/frontend/CONTRIBUTING.md` and `autogpt_platform/frontend/.cursorrules` for comprehensive patterns and conventions.
**Quick Reference:**
**Component Structure:**
- Separate render logic from data/behavior
- Structure: `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
- Exception: Small components (3-4 lines of logic) can be inline
- Render-only components can be direct files without folders
**Data Fetching:**
- Use generated API hooks from `@/app/api/__generated__/endpoints/`
- Generated via Orval from backend OpenAPI spec
- Pattern: `use{Method}{Version}{OperationName}`
- Example: `useGetV2ListLibraryAgents`
- Regenerate with: `pnpm generate:api`
- **Never** use deprecated `BackendAPI` or `src/lib/autogpt-server-api/*`
**Code Conventions:**
- Use function declarations for components and handlers (not arrow functions)
- Only arrow functions for small inline lambdas (map, filter, etc.)
- Components: `PascalCase`, Hooks: `camelCase` with `use` prefix
- No barrel files or `index.ts` re-exports
- Minimal comments (code should be self-documenting)
**Styling:**
- Use Tailwind CSS utilities only
- Use design system components from `src/components/` (atoms, molecules, organisms)
- Never use `src/components/__legacy__/*`
- Only use Phosphor Icons (`@phosphor-icons/react`)
- Prefer design tokens over hardcoded values
**Error Handling:**
- Render errors: Use `<ErrorCard />` component
- Mutation errors: Display with toast notifications
- Manual exceptions: Use `Sentry.captureException()`
- Global error boundaries already configured
**Testing:**
- Add/update Storybook stories for UI components (`pnpm storybook`)
- Run Playwright E2E tests with `pnpm test`
- Verify in Chromatic after PR
**Architecture:**
- Default to client components ("use client")
- Server components only for SEO or extreme TTFB needs
- Use React Query for server state (via generated hooks)
- Co-locate UI state in components/hooks
### Security Guidelines
**Cache Protection Middleware** (`/backend/backend/server/middleware/security.py`):
- Default: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
- Uses allow list approach for cacheable paths (static assets, health checks, public pages)
- Prevents sensitive data caching in browsers/proxies
- Add new cacheable endpoints to `CACHEABLE_PATHS`
### CI/CD Alignment
The repository has comprehensive CI workflows that test:
- **Backend**: Python 3.11-3.13, services (Redis/RabbitMQ/ClamAV), Prisma migrations, Poetry lock validation
- **Frontend**: Node.js 21, pnpm, Playwright with Docker Compose stack, API schema validation
- **Integration**: Full-stack type checking and E2E testing
Match these patterns when developing locally - the copilot setup environment mirrors these CI configurations.
## Collaboration with Other AI Assistants
This repository is actively developed with assistance from Claude (via CLAUDE.md files). When working on this codebase:
- Check for existing CLAUDE.md files that provide additional context
- Follow established patterns and conventions already in the codebase
- Maintain consistency with existing code style and architecture
- Consider that changes may be reviewed and extended by both human developers and AI assistants
## Trust These Instructions
These instructions are comprehensive and tested. Only perform additional searches if:
1. Information here is incomplete for your specific task
2. You encounter errors not covered by the workarounds
3. You need to understand implementation details not covered above
For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.

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@@ -10,19 +10,17 @@ updates:
commit-message:
prefix: "chore(libs/deps)"
prefix-development: "chore(libs/deps-dev)"
ignore:
- dependency-name: "poetry"
groups:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# backend (Poetry project)
- package-ecosystem: "pip"
@@ -34,19 +32,17 @@ updates:
commit-message:
prefix: "chore(backend/deps)"
prefix-development: "chore(backend/deps-dev)"
ignore:
- dependency-name: "poetry"
groups:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# frontend (Next.js project)
- package-ecosystem: "npm"
@@ -62,13 +58,13 @@ updates:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# infra (Terraform)
- package-ecosystem: "terraform"
@@ -85,13 +81,14 @@ updates:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# GitHub Actions
- package-ecosystem: "github-actions"
@@ -104,13 +101,14 @@ updates:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# Docker
- package-ecosystem: "docker"
@@ -123,16 +121,40 @@ updates:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
# Submodules
- package-ecosystem: "gitsubmodule"
directory: "autogpt_platform/supabase"
schedule:
interval: "weekly"
open-pull-requests-limit: 1
target-branch: "dev"
commit-message:
prefix: "chore(platform/deps)"
prefix-development: "chore(platform/deps-dev)"
groups:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
# Docs
- package-ecosystem: "pip"
- package-ecosystem: 'pip'
directory: "docs/"
schedule:
interval: "weekly"
@@ -144,10 +166,10 @@ updates:
production-dependencies:
dependency-type: "production"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"
development-dependencies:
dependency-type: "development"
update-types:
- "minor"
- "patch"
- "minor"
- "patch"

5
.github/labeler.yml vendored
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@@ -24,9 +24,8 @@ platform/frontend:
platform/backend:
- changed-files:
- all-globs-to-any-file:
- autogpt_platform/backend/**
- '!autogpt_platform/backend/backend/blocks/**'
- any-glob-to-any-file: autogpt_platform/backend/**
- all-globs-to-all-files: '!autogpt_platform/backend/backend/blocks/**'
platform/blocks:
- changed-files:

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@@ -1,97 +0,0 @@
name: Auto Fix CI Failures
on:
workflow_run:
workflows: ["CI"]
types:
- completed
permissions:
contents: write
pull-requests: write
actions: read
issues: write
id-token: write # Required for OIDC token exchange
jobs:
auto-fix:
if: |
github.event.workflow_run.conclusion == 'failure' &&
github.event.workflow_run.pull_requests[0] &&
!startsWith(github.event.workflow_run.head_branch, 'claude-auto-fix-ci-')
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ github.event.workflow_run.head_branch }}
fetch-depth: 0
token: ${{ secrets.GITHUB_TOKEN }}
- name: Setup git identity
run: |
git config --global user.email "claude[bot]@users.noreply.github.com"
git config --global user.name "claude[bot]"
- name: Create fix branch
id: branch
run: |
BRANCH_NAME="claude-auto-fix-ci-${{ github.event.workflow_run.head_branch }}-${{ github.run_id }}"
git checkout -b "$BRANCH_NAME"
echo "branch_name=$BRANCH_NAME" >> $GITHUB_OUTPUT
- name: Get CI failure details
id: failure_details
uses: actions/github-script@v7
with:
script: |
const run = await github.rest.actions.getWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
run_id: ${{ github.event.workflow_run.id }}
});
const jobs = await github.rest.actions.listJobsForWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
run_id: ${{ github.event.workflow_run.id }}
});
const failedJobs = jobs.data.jobs.filter(job => job.conclusion === 'failure');
let errorLogs = [];
for (const job of failedJobs) {
const logs = await github.rest.actions.downloadJobLogsForWorkflowRun({
owner: context.repo.owner,
repo: context.repo.repo,
job_id: job.id
});
errorLogs.push({
jobName: job.name,
logs: logs.data
});
}
return {
runUrl: run.data.html_url,
failedJobs: failedJobs.map(j => j.name),
errorLogs: errorLogs
};
- name: Fix CI failures with Claude
id: claude
uses: anthropics/claude-code-action@v1
with:
prompt: |
/fix-ci
Failed CI Run: ${{ fromJSON(steps.failure_details.outputs.result).runUrl }}
Failed Jobs: ${{ join(fromJSON(steps.failure_details.outputs.result).failedJobs, ', ') }}
PR Number: ${{ github.event.workflow_run.pull_requests[0].number }}
Branch Name: ${{ steps.branch.outputs.branch_name }}
Base Branch: ${{ github.event.workflow_run.head_branch }}
Repository: ${{ github.repository }}
Error logs:
${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
claude_args: "--allowedTools 'Edit,MultiEdit,Write,Read,Glob,Grep,LS,Bash(git:*),Bash(bun:*),Bash(npm:*),Bash(npx:*),Bash(gh:*)'"

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@@ -1,379 +0,0 @@
# Claude Dependabot PR Review Workflow
#
# This workflow automatically runs Claude analysis on Dependabot PRs to:
# - Identify dependency changes and their versions
# - Look up changelogs for updated packages
# - Assess breaking changes and security impacts
# - Provide actionable recommendations for the development team
#
# Triggered on: Dependabot PRs (opened, synchronize)
# Requirements: ANTHROPIC_API_KEY secret must be configured
name: Claude Dependabot PR Review
on:
pull_request:
types: [opened, synchronize]
jobs:
dependabot-review:
# Only run on Dependabot PRs
if: github.actor == 'dependabot[bot]'
runs-on: ubuntu-latest
timeout-minutes: 30
permissions:
contents: write
pull-requests: read
issues: read
id-token: write
actions: read # Required for CI access
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 1
# Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
echo "✅ AutoGPT Platform development environment setup complete!"
echo "🚀 Ready for development with Docker services running"
echo "📝 Backend server: poetry run serve (port 8000)"
echo "🌐 Frontend server: pnpm dev (port 3000)"
- name: Run Claude Dependabot Analysis
id: claude_review
uses: anthropics/claude-code-action@v1
with:
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
claude_args: |
--allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
prompt: |
You are Claude, an AI assistant specialized in reviewing Dependabot dependency update PRs.
Your primary tasks are:
1. **Analyze the dependency changes** in this Dependabot PR
2. **Look up changelogs** for all updated dependencies to understand what changed
3. **Identify breaking changes** and assess potential impact on the AutoGPT codebase
4. **Provide actionable recommendations** for the development team
## Analysis Process:
1. **Identify Changed Dependencies**:
- Use git diff to see what dependencies were updated
- Parse package.json, poetry.lock, requirements files, etc.
- List all package versions: old → new
2. **Changelog Research**:
- For each updated dependency, look up its changelog/release notes
- Use WebFetch to access GitHub releases, NPM package pages, PyPI project pages. The pr should also have some details
- Focus on versions between the old and new versions
- Identify: breaking changes, deprecations, security fixes, new features
3. **Breaking Change Assessment**:
- Categorize changes: BREAKING, MAJOR, MINOR, PATCH, SECURITY
- Assess impact on AutoGPT's usage patterns
- Check if AutoGPT uses affected APIs/features
- Look for migration guides or upgrade instructions
4. **Codebase Impact Analysis**:
- Search the AutoGPT codebase for usage of changed APIs
- Identify files that might be affected by breaking changes
- Check test files for deprecated usage patterns
- Look for configuration changes needed
## Output Format:
Provide a comprehensive review comment with:
### 🔍 Dependency Analysis Summary
- List of updated packages with version changes
- Overall risk assessment (LOW/MEDIUM/HIGH)
### 📋 Detailed Changelog Review
For each updated dependency:
- **Package**: name (old_version → new_version)
- **Changes**: Summary of key changes
- **Breaking Changes**: List any breaking changes
- **Security Fixes**: Note security improvements
- **Migration Notes**: Any upgrade steps needed
### ⚠️ Impact Assessment
- **Breaking Changes Found**: Yes/No with details
- **Affected Files**: List AutoGPT files that may need updates
- **Test Impact**: Any tests that may need updating
- **Configuration Changes**: Required config updates
### 🛠️ Recommendations
- **Action Required**: What the team should do
- **Testing Focus**: Areas to test thoroughly
- **Follow-up Tasks**: Any additional work needed
- **Merge Recommendation**: APPROVE/REVIEW_NEEDED/HOLD
### 📚 Useful Links
- Links to relevant changelogs, migration guides, documentation
Be thorough but concise. Focus on actionable insights that help the development team make informed decisions about the dependency updates.

View File

@@ -1,331 +0,0 @@
name: Claude Code
on:
issue_comment:
types: [created]
pull_request_review_comment:
types: [created]
issues:
types: [opened, assigned]
pull_request_review:
types: [submitted]
jobs:
claude:
if: |
(
(github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
(github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
) && (
github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR' ||
github.event.review.author_association == 'OWNER' ||
github.event.review.author_association == 'MEMBER' ||
github.event.review.author_association == 'COLLABORATOR' ||
github.event.issue.author_association == 'OWNER' ||
github.event.issue.author_association == 'MEMBER' ||
github.event.issue.author_association == 'COLLABORATOR'
)
runs-on: ubuntu-latest
timeout-minutes: 45
permissions:
contents: write
pull-requests: read
issues: read
id-token: write
actions: read # Required for CI access
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 1
- name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@v1.3.1
with:
large-packages: false # slow
docker-images: false # limited benefit
# Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
echo "✅ AutoGPT Platform development environment setup complete!"
echo "🚀 Ready for development with Docker services running"
echo "📝 Backend server: poetry run serve (port 8000)"
echo "🌐 Frontend server: pnpm dev (port 3000)"
- name: Run Claude Code
id: claude
uses: anthropics/claude-code-action@v1
with:
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
claude_args: |
--allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr edit:*)"
--model opus
additional_permissions: |
actions: read

View File

@@ -1,312 +0,0 @@
name: "Copilot Setup Steps"
# Automatically run the setup steps when they are changed to allow for easy validation, and
# allow manual testing through the repository's "Actions" tab
on:
workflow_dispatch:
push:
paths:
- .github/workflows/copilot-setup-steps.yml
pull_request:
paths:
- .github/workflows/copilot-setup-steps.yml
jobs:
# The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
copilot-setup-steps:
runs-on: ubuntu-latest
timeout-minutes: 45
# Set the permissions to the lowest permissions possible needed for your steps.
# Copilot will be given its own token for its operations.
permissions:
# If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete.
contents: read
# You can define any steps you want, and they will run before the agent starts.
# If you do not check out your code, Copilot will do this for you.
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
submodules: true
# Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
- name: Install Poetry
run: |
# Extract Poetry version from backend/poetry.lock (matches CI)
cd autogpt_platform/backend
HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
# Add Poetry to PATH
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Check poetry.lock
working-directory: autogpt_platform/backend
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
echo "Warning: poetry.lock not up to date, but continuing for setup"
git checkout poetry.lock # Reset for clean setup
fi
- name: Install Python dependencies
working-directory: autogpt_platform/backend
run: poetry install
- name: Generate Prisma Client
working-directory: autogpt_platform/backend
run: poetry run prisma generate && poetry run gen-prisma-stub
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable corepack
run: corepack enable
- name: Set pnpm store directory
run: |
pnpm config set store-dir ~/.pnpm-store
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install JavaScript dependencies
working-directory: autogpt_platform/frontend
run: pnpm install --frozen-lockfile
# Install Playwright browsers for frontend testing
# NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
# - name: Install Playwright browsers
# working-directory: autogpt_platform/frontend
# run: pnpm playwright install --with-deps chromium
# Docker setup for development environment
- name: Free up disk space
run: |
# Remove large unused tools to free disk space for Docker builds
sudo rm -rf /usr/share/dotnet
sudo rm -rf /usr/local/lib/android
sudo rm -rf /opt/ghc
sudo rm -rf /opt/hostedtoolcache/CodeQL
sudo docker system prune -af
df -h
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Copy default environment files
working-directory: autogpt_platform
run: |
# Copy default environment files for development
cp .env.default .env
cp backend/.env.default backend/.env
cp frontend/.env.default frontend/.env
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes
key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
restore-keys: |
docker-images-v2-${{ runner.os }}-
docker-images-v1-${{ runner.os }}-
- name: Load or pull Docker images
working-directory: autogpt_platform
run: |
mkdir -p ~/docker-cache
# Define image list for easy maintenance
IMAGES=(
"redis:latest"
"rabbitmq:management"
"clamav/clamav-debian:latest"
"busybox:latest"
"kong:2.8.1"
"supabase/gotrue:v2.170.0"
"supabase/postgres:15.8.1.049"
"supabase/postgres-meta:v0.86.1"
"supabase/studio:20250224-d10db0f"
)
# Check if any cached tar files exist (more reliable than cache-hit)
if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
echo "Docker cache found, loading images in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
if [ -f ~/docker-cache/${filename}.tar ]; then
echo "Loading $image..."
docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
fi
done
wait
echo "All cached images loaded"
else
echo "No Docker cache found, pulling images in parallel..."
# Pull all images in parallel
for image in "${IMAGES[@]}"; do
docker pull "$image" &
done
wait
# Only save cache on main branches (not PRs) to avoid cache pollution
if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
echo "Saving Docker images to cache in parallel..."
for image in "${IMAGES[@]}"; do
# Convert image name to filename (replace : and / with -)
filename=$(echo "$image" | tr ':/' '--')
echo "Saving $image..."
docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
done
wait
echo "Docker image cache saved"
else
echo "Skipping cache save for PR/feature branch"
fi
fi
echo "Docker images ready for use"
# Phase 2: Build migrate service with GitHub Actions cache
- name: Build migrate Docker image with cache
working-directory: autogpt_platform
run: |
# Build the migrate image with buildx for GHA caching
docker buildx build \
--cache-from type=gha \
--cache-to type=gha,mode=max \
--target migrate \
--tag autogpt_platform-migrate:latest \
--load \
-f backend/Dockerfile \
..
# Start services using pre-built images
- name: Start Docker services for development
working-directory: autogpt_platform
run: |
# Start essential services (migrate image already built with correct tag)
docker compose --profile local up deps --no-build --detach
echo "Waiting for services to be ready..."
# Wait for database to be ready
echo "Checking database readiness..."
timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do
echo " Waiting for database..."
sleep 2
done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
# Check migrate service status
echo "Checking migration status..."
docker compose ps migrate || echo " Migrate service not visible in ps output"
# Wait for migrate service to complete
echo "Waiting for migrations to complete..."
timeout 30 bash -c '
ATTEMPTS=0
while [ $ATTEMPTS -lt 15 ]; do
ATTEMPTS=$((ATTEMPTS + 1))
# Check using docker directly (more reliable than docker compose ps)
CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
if [ -z "$CONTAINER_STATUS" ]; then
echo " Attempt $ATTEMPTS: Migrate container not found yet..."
elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
echo "✅ Migrations completed successfully"
docker compose logs migrate --tail=5 2>/dev/null || true
exit 0
elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
echo "❌ Migrations failed with exit code: $EXIT_CODE"
echo "Migration logs:"
docker compose logs migrate --tail=20 2>/dev/null || true
exit 1
elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
echo " Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
else
echo " Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
fi
sleep 2
done
echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
echo "Final container check:"
docker ps -a --filter "label=com.docker.compose.service=migrate" || true
echo "Migration logs (if available):"
docker compose logs migrate --tail=10 2>/dev/null || echo " No logs available"
' || echo "⚠️ Migration check completed with warnings, continuing..."
# Brief wait for other services to stabilize
echo "Waiting 5 seconds for other services to stabilize..."
sleep 5
# Verify installations and provide environment info
- name: Verify setup and show environment info
run: |
echo "=== Python Setup ==="
python --version
poetry --version
echo "=== Node.js Setup ==="
node --version
pnpm --version
echo "=== Additional Tools ==="
docker --version
docker compose version
gh --version || true
echo "=== Services Status ==="
cd autogpt_platform
docker compose ps || true
echo "=== Backend Dependencies ==="
cd backend
poetry show | head -10 || true
echo "=== Frontend Dependencies ==="
cd ../frontend
pnpm list --depth=0 | head -10 || true
echo "=== Environment Files ==="
ls -la ../.env* || true
ls -la .env* || true
ls -la ../backend/.env* || true
echo "✅ AutoGPT Platform development environment setup complete!"
echo "🚀 Ready for development with Docker services running"
echo "📝 Backend server: poetry run serve (port 8000)"
echo "🌐 Frontend server: pnpm dev (port 3000)"

View File

@@ -3,7 +3,6 @@ name: AutoGPT Platform - Deploy Prod Environment
on:
release:
types: [published]
workflow_dispatch:
permissions:
contents: 'read'
@@ -18,8 +17,6 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ github.ref_name || 'master' }}
- name: Set up Python
uses: actions/setup-python@v5
@@ -37,9 +34,8 @@ jobs:
python -m prisma migrate deploy
env:
DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
trigger:
needs: migrate
runs-on: ubuntu-latest
@@ -50,5 +46,4 @@ jobs:
token: ${{ secrets.DEPLOY_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
event-type: build_deploy_prod
client-payload: |
{"ref": "${{ github.ref_name || 'master' }}", "repository": "${{ github.repository }}"}
client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

View File

@@ -5,13 +5,6 @@ on:
branches: [ dev ]
paths:
- 'autogpt_platform/**'
workflow_dispatch:
inputs:
git_ref:
description: 'Git ref (branch/tag) of AutoGPT to deploy'
required: true
default: 'master'
type: string
permissions:
contents: 'read'
@@ -26,8 +19,6 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
ref: ${{ github.event.inputs.git_ref || github.ref_name }}
- name: Set up Python
uses: actions/setup-python@v5
@@ -45,7 +36,6 @@ jobs:
python -m prisma migrate deploy
env:
DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
trigger:
needs: migrate
@@ -57,4 +47,4 @@ jobs:
token: ${{ secrets.DEPLOY_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
event-type: build_deploy_dev
client-payload: '{"ref": "${{ github.event.inputs.git_ref || github.ref }}", "repository": "${{ github.repository }}"}'
client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

View File

@@ -32,12 +32,14 @@ jobs:
strategy:
fail-fast: false
matrix:
python-version: ["3.11", "3.12", "3.13"]
python-version: ["3.10"]
runs-on: ubuntu-latest
services:
redis:
image: redis:latest
image: bitnami/redis:6.2
env:
REDIS_PASSWORD: testpassword
ports:
- 6379:6379
rabbitmq:
@@ -48,23 +50,6 @@ jobs:
env:
RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
clamav:
image: clamav/clamav-debian:latest
ports:
- 3310:3310
env:
CLAMAV_NO_FRESHCLAMD: false
CLAMD_CONF_StreamMaxLength: 50M
CLAMD_CONF_MaxFileSize: 100M
CLAMD_CONF_MaxScanSize: 100M
CLAMD_CONF_MaxThreads: 4
CLAMD_CONF_ReadTimeout: 300
options: >-
--health-cmd "clamdscan --version || exit 1"
--health-interval 30s
--health-timeout 10s
--health-retries 5
--health-start-period 180s
steps:
- name: Checkout repository
@@ -95,35 +80,18 @@ jobs:
- name: Install Poetry (Unix)
run: |
# Extract Poetry version from backend/poetry.lock
HEAD_POETRY_VERSION=$(python ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
if [ -n "$BASE_REF" ]; then
BASE_BRANCH=${BASE_REF/refs\/heads\//}
BASE_POETRY_VERSION=$((git show "origin/$BASE_BRANCH":./poetry.lock; true) | python ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry -)
echo "Found Poetry version ${BASE_POETRY_VERSION} in backend/poetry.lock on ${BASE_REF}"
POETRY_VERSION=$(printf '%s\n' "$HEAD_POETRY_VERSION" "$BASE_POETRY_VERSION" | sort -V | tail -n1)
else
POETRY_VERSION=$HEAD_POETRY_VERSION
fi
echo "Using Poetry version ${POETRY_VERSION}"
# Install Poetry
curl -sSL https://install.python-poetry.org | POETRY_VERSION=$POETRY_VERSION python3 -
curl -sSL https://install.python-poetry.org | python3 -
if [ "${{ runner.os }}" = "macOS" ]; then
PATH="$HOME/.local/bin:$PATH"
echo "$HOME/.local/bin" >> $GITHUB_PATH
fi
env:
BASE_REF: ${{ github.base_ref || github.event.merge_group.base_ref }}
- name: Check poetry.lock
run: |
poetry lock
if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
if ! git diff --quiet poetry.lock; then
echo "Error: poetry.lock not up to date."
echo
git diff poetry.lock
@@ -134,7 +102,7 @@ jobs:
run: poetry install
- name: Generate Prisma Client
run: poetry run prisma generate && poetry run gen-prisma-stub
run: poetry run prisma generate
- id: supabase
name: Start Supabase
@@ -146,40 +114,10 @@ jobs:
# outputs:
# DB_URL, API_URL, GRAPHQL_URL, ANON_KEY, SERVICE_ROLE_KEY, JWT_SECRET
- name: Wait for ClamAV to be ready
run: |
echo "Waiting for ClamAV daemon to start..."
max_attempts=60
attempt=0
until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
sleep 5
attempt=$((attempt+1))
done
if [ $attempt -eq $max_attempts ]; then
echo "ClamAV failed to start after $((max_attempts*5)) seconds"
echo "Checking ClamAV service logs..."
docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
exit 1
fi
echo "ClamAV is ready!"
# Verify ClamAV is responsive
echo "Testing ClamAV connection..."
timeout 10 bash -c 'echo "PING" | nc localhost 3310' || {
echo "ClamAV is not responding to PING"
docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
exit 1
}
- name: Run Database Migrations
run: poetry run prisma migrate dev --name updates
env:
DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
- id: lint
name: Run Linter
@@ -188,21 +126,20 @@ jobs:
- name: Run pytest with coverage
run: |
if [[ "${{ runner.debug }}" == "1" ]]; then
poetry run pytest -s -vv -o log_cli=true -o log_cli_level=DEBUG
poetry run pytest -s -vv -o log_cli=true -o log_cli_level=DEBUG test
else
poetry run pytest -s -vv
poetry run pytest -s -vv test
fi
if: success() || (failure() && steps.lint.outcome == 'failure')
env:
LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
REDIS_HOST: "localhost"
REDIS_PORT: "6379"
ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=" # DO NOT USE IN PRODUCTION!!
SUPABASE_JWT_SECRET: ${{ steps.supabase.outputs.JWT_SECRET }}
REDIS_HOST: 'localhost'
REDIS_PORT: '6379'
REDIS_PASSWORD: 'testpassword'
env:
CI: true
@@ -215,8 +152,8 @@ jobs:
# If you want to replace this, you can do so by making our entire system generate
# new credentials for each local user and update the environment variables in
# the backend service, docker composes, and examples
RABBITMQ_DEFAULT_USER: "rabbitmq_user_default"
RABBITMQ_DEFAULT_PASS: "k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7"
RABBITMQ_DEFAULT_USER: 'rabbitmq_user_default'
RABBITMQ_DEFAULT_PASS: 'k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7'
# - name: Upload coverage reports to Codecov
# uses: codecov/codecov-action@v4

View File

@@ -1,198 +0,0 @@
name: AutoGPT Platform - Dev Deploy PR Event Dispatcher
on:
pull_request:
types: [closed]
issue_comment:
types: [created]
permissions:
issues: write
pull-requests: write
jobs:
dispatch:
runs-on: ubuntu-latest
steps:
- name: Check comment permissions and deployment status
id: check_status
if: github.event_name == 'issue_comment' && github.event.issue.pull_request
uses: actions/github-script@v7
with:
script: |
const commentBody = context.payload.comment.body.trim();
const commentUser = context.payload.comment.user.login;
const prAuthor = context.payload.issue.user.login;
const authorAssociation = context.payload.comment.author_association;
// Check permissions
const hasPermission = (
authorAssociation === 'OWNER' ||
authorAssociation === 'MEMBER' ||
authorAssociation === 'COLLABORATOR'
);
core.setOutput('comment_body', commentBody);
core.setOutput('has_permission', hasPermission);
if (!hasPermission && (commentBody === '!deploy' || commentBody === '!undeploy')) {
core.setOutput('permission_denied', 'true');
return;
}
if (commentBody !== '!deploy' && commentBody !== '!undeploy') {
return;
}
// Process deploy command
if (commentBody === '!deploy') {
core.setOutput('should_deploy', 'true');
}
// Process undeploy command
else if (commentBody === '!undeploy') {
core.setOutput('should_undeploy', 'true');
}
- name: Post permission denied comment
if: steps.check_status.outputs.permission_denied == 'true'
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: `❌ **Permission denied**: Only the repository owners, members, or collaborators can use deployment commands.`
});
- name: Get PR details for deployment
id: pr_details
if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
uses: actions/github-script@v7
with:
script: |
const pr = await github.rest.pulls.get({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.issue.number
});
core.setOutput('pr_number', pr.data.number);
core.setOutput('pr_title', pr.data.title);
core.setOutput('pr_state', pr.data.state);
- name: Dispatch Deploy Event
if: steps.check_status.outputs.should_deploy == 'true'
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
event-type: pr-event
client-payload: |
{
"action": "deploy",
"pr_number": "${{ steps.pr_details.outputs.pr_number }}",
"pr_title": "${{ steps.pr_details.outputs.pr_title }}",
"pr_state": "${{ steps.pr_details.outputs.pr_state }}",
"repo": "${{ github.repository }}"
}
- name: Post deploy success comment
if: steps.check_status.outputs.should_deploy == 'true'
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: `🚀 **Deploying PR #${{ steps.pr_details.outputs.pr_number }}** to development environment...`
});
- name: Dispatch Undeploy Event (from comment)
if: steps.check_status.outputs.should_undeploy == 'true'
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
event-type: pr-event
client-payload: |
{
"action": "undeploy",
"pr_number": "${{ steps.pr_details.outputs.pr_number }}",
"pr_title": "${{ steps.pr_details.outputs.pr_title }}",
"pr_state": "${{ steps.pr_details.outputs.pr_state }}",
"repo": "${{ github.repository }}"
}
- name: Post undeploy success comment
if: steps.check_status.outputs.should_undeploy == 'true'
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: `🗑️ **Undeploying PR #${{ steps.pr_details.outputs.pr_number }}** from development environment...`
});
- name: Check deployment status on PR close
id: check_pr_close
if: github.event_name == 'pull_request' && github.event.action == 'closed'
uses: actions/github-script@v7
with:
script: |
const comments = await github.rest.issues.listComments({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number
});
let lastDeployIndex = -1;
let lastUndeployIndex = -1;
comments.data.forEach((comment, index) => {
if (comment.body.trim() === '!deploy') {
lastDeployIndex = index;
} else if (comment.body.trim() === '!undeploy') {
lastUndeployIndex = index;
}
});
// Should undeploy if there's a !deploy without a subsequent !undeploy
const shouldUndeploy = lastDeployIndex !== -1 && lastDeployIndex > lastUndeployIndex;
core.setOutput('should_undeploy', shouldUndeploy);
- name: Dispatch Undeploy Event (PR closed with active deployment)
if: >-
github.event_name == 'pull_request' &&
github.event.action == 'closed' &&
steps.check_pr_close.outputs.should_undeploy == 'true'
uses: peter-evans/repository-dispatch@v3
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
event-type: pr-event
client-payload: |
{
"action": "undeploy",
"pr_number": "${{ github.event.pull_request.number }}",
"pr_title": "${{ github.event.pull_request.title }}",
"pr_state": "${{ github.event.pull_request.state }}",
"repo": "${{ github.repository }}"
}
- name: Post PR close undeploy comment
if: >-
github.event_name == 'pull_request' &&
github.event.action == 'closed' &&
steps.check_pr_close.outputs.should_undeploy == 'true'
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: `🧹 **Auto-undeploying**: PR closed with active deployment. Cleaning up development environment for PR #${{ github.event.pull_request.number }}.`
});

View File

@@ -12,126 +12,56 @@ on:
- "autogpt_platform/frontend/**"
merge_group:
concurrency:
group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
defaults:
run:
shell: bash
working-directory: autogpt_platform/frontend
jobs:
setup:
runs-on: ubuntu-latest
outputs:
cache-key: ${{ steps.cache-key.outputs.key }}
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
- name: Enable corepack
run: corepack enable
- name: Generate cache key
id: cache-key
run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
- name: Cache dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ steps.cache-key.outputs.key }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install dependencies
run: pnpm install --frozen-lockfile
lint:
runs-on: ubuntu-latest
needs: setup
steps:
- name: Checkout repository
uses: actions/checkout@v4
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
- name: Enable corepack
run: corepack enable
- name: Restore dependencies cache
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
node-version: "21"
- name: Install dependencies
run: pnpm install --frozen-lockfile
run: |
yarn install --frozen-lockfile
- name: Run lint
run: pnpm lint
run: |
yarn lint
chromatic:
type-check:
runs-on: ubuntu-latest
needs: setup
# Only run on dev branch pushes or PRs targeting dev
if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
- name: Enable corepack
run: corepack enable
- name: Restore dependencies cache
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
node-version: "21"
- name: Install dependencies
run: pnpm install --frozen-lockfile
run: |
yarn install --frozen-lockfile
- name: Run Chromatic
uses: chromaui/action@latest
with:
projectToken: chpt_9e7c1a76478c9c8
onlyChanged: true
workingDir: autogpt_platform/frontend
token: ${{ secrets.GITHUB_TOKEN }}
exitOnceUploaded: true
- name: Run tsc check
run: |
yarn type-check
test:
runs-on: big-boi
needs: setup
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
browser: [chromium, webkit]
steps:
- name: Checkout repository
@@ -142,98 +72,50 @@ jobs:
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
node-version: "21"
- name: Enable corepack
run: corepack enable
- name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@main
with:
large-packages: false # slow
docker-images: false # limited benefit
- name: Copy default supabase .env
run: |
cp ../.env.default ../.env
cp ../supabase/docker/.env.example ../.env
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Cache Docker layers
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
restore-keys: |
${{ runner.os }}-buildx-frontend-test-
- name: Copy backend .env
run: |
cp ../backend/.env.example ../backend/.env
- name: Run docker compose
run: |
NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
env:
DOCKER_BUILDKIT: 1
BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
- name: Move cache
run: |
rm -rf /tmp/.buildx-cache
if [ -d "/tmp/.buildx-cache-new" ]; then
mv /tmp/.buildx-cache-new /tmp/.buildx-cache
fi
- name: Wait for services to be ready
run: |
echo "Waiting for rest_server to be ready..."
timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
echo "Waiting for database to be ready..."
timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
- name: Create E2E test data
run: |
echo "Creating E2E test data..."
# First try to run the script from inside the container
if docker compose -f ../docker-compose.yml exec -T rest_server test -f /app/autogpt_platform/backend/test/e2e_test_data.py; then
echo "✅ Found e2e_test_data.py in container, running it..."
docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python backend/test/e2e_test_data.py" || {
echo "❌ E2E test data creation failed!"
docker compose -f ../docker-compose.yml logs --tail=50 rest_server
exit 1
}
else
echo "⚠️ e2e_test_data.py not found in container, copying and running..."
# Copy the script into the container and run it
docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.yml ps -q rest_server):/tmp/e2e_test_data.py || {
echo "❌ Failed to copy script to container"
exit 1
}
docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
echo "❌ E2E test data creation failed!"
docker compose -f ../docker-compose.yml logs --tail=50 rest_server
exit 1
}
fi
- name: Restore dependencies cache
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
docker compose -f ../docker-compose.yml up -d
- name: Install dependencies
run: pnpm install --frozen-lockfile
run: |
yarn install --frozen-lockfile
- name: Install Browser 'chromium'
run: pnpm playwright install --with-deps chromium
- name: Setup Builder .env
run: |
cp .env.example .env
- name: Run Playwright tests
run: pnpm test:no-build
- name: Install Browser '${{ matrix.browser }}'
run: yarn playwright install --with-deps ${{ matrix.browser }}
- name: Upload Playwright artifacts
if: failure()
uses: actions/upload-artifact@v4
with:
name: playwright-report
path: playwright-report
- name: Run tests
timeout-minutes: 20
run: |
yarn test --project=${{ matrix.browser }}
- name: Print Final Docker Compose logs
if: always()
run: docker compose -f ../docker-compose.yml logs
run: |
docker compose -f ../docker-compose.yml logs
- uses: actions/upload-artifact@v4
if: ${{ !cancelled() }}
with:
name: playwright-report-${{ matrix.browser }}
path: playwright-report/
retention-days: 30

View File

@@ -1,136 +0,0 @@
name: AutoGPT Platform - Frontend CI
on:
push:
branches: [master, dev]
paths:
- ".github/workflows/platform-fullstack-ci.yml"
- "autogpt_platform/**"
pull_request:
paths:
- ".github/workflows/platform-fullstack-ci.yml"
- "autogpt_platform/**"
merge_group:
concurrency:
group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
defaults:
run:
shell: bash
working-directory: autogpt_platform/frontend
jobs:
setup:
runs-on: ubuntu-latest
outputs:
cache-key: ${{ steps.cache-key.outputs.key }}
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
- name: Enable corepack
run: corepack enable
- name: Generate cache key
id: cache-key
run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
- name: Cache dependencies
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ steps.cache-key.outputs.key }}
restore-keys: |
${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
${{ runner.os }}-pnpm-
- name: Install dependencies
run: pnpm install --frozen-lockfile
types:
runs-on: ubuntu-latest
needs: setup
strategy:
fail-fast: false
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
submodules: recursive
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
- name: Enable corepack
run: corepack enable
- name: Copy default supabase .env
run: |
cp ../.env.default ../.env
- name: Copy backend .env
run: |
cp ../backend/.env.default ../backend/.env
- name: Run docker compose
run: |
docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
- name: Restore dependencies cache
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
restore-keys: |
${{ runner.os }}-pnpm-
- name: Install dependencies
run: pnpm install --frozen-lockfile
- name: Setup .env
run: cp .env.default .env
- name: Wait for services to be ready
run: |
echo "Waiting for rest_server to be ready..."
timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
echo "Waiting for database to be ready..."
timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
- name: Generate API queries
run: pnpm generate:api:force
- name: Check for API schema changes
run: |
if ! git diff --exit-code src/app/api/openapi.json; then
echo "❌ API schema changes detected in src/app/api/openapi.json"
echo ""
echo "The openapi.json file has been modified after running 'pnpm generate:api-all'."
echo "This usually means changes have been made in the BE endpoints without updating the Frontend."
echo "The API schema is now out of sync with the Front-end queries."
echo ""
echo "To fix this:"
echo "1. Pull the backend 'docker compose pull && docker compose up -d --build --force-recreate'"
echo "2. Run 'pnpm generate:api' locally"
echo "3. Run 'pnpm types' locally"
echo "4. Fix any TypeScript errors that may have been introduced"
echo "5. Commit and push your changes"
echo ""
exit 1
else
echo "✅ No API schema changes detected"
fi
- name: Run Typescript checks
run: pnpm types

View File

@@ -11,12 +11,12 @@ jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@v10
- uses: actions/stale@v9
with:
# operations-per-run: 5000
stale-issue-message: >
This issue has automatically been marked as _stale_ because it has not had
any activity in the last 170 days. You can _unstale_ it by commenting or
any activity in the last 50 days. You can _unstale_ it by commenting or
removing the label. Otherwise, this issue will be closed in 10 days.
stale-pr-message: >
This pull request has automatically been marked as _stale_ because it has
@@ -25,7 +25,7 @@ jobs:
close-issue-message: >
This issue was closed automatically because it has been stale for 10 days
with no activity.
days-before-stale: 170
days-before-stale: 100
days-before-close: 10
# Do not touch meta issues:
exempt-issue-labels: meta,fridge,project management

View File

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

View File

@@ -1,60 +0,0 @@
#!/usr/bin/env python3
import sys
if sys.version_info < (3, 11):
print("Python version 3.11 or higher required")
sys.exit(1)
import tomllib
def get_package_version(package_name: str, lockfile_path: str) -> str | None:
"""Extract package version from poetry.lock file."""
try:
if lockfile_path == "-":
data = tomllib.load(sys.stdin.buffer)
else:
with open(lockfile_path, "rb") as f:
data = tomllib.load(f)
except FileNotFoundError:
print(f"Error: File '{lockfile_path}' not found", file=sys.stderr)
sys.exit(1)
except tomllib.TOMLDecodeError as e:
print(f"Error parsing TOML file: {e}", file=sys.stderr)
sys.exit(1)
except Exception as e:
print(f"Error reading file: {e}", file=sys.stderr)
sys.exit(1)
# Look for the package in the packages list
packages = data.get("package", [])
for package in packages:
if package.get("name", "").lower() == package_name.lower():
return package.get("version")
return None
def main():
if len(sys.argv) not in (2, 3):
print(
"Usages: python get_package_version_from_lockfile.py <package name> [poetry.lock path]\n"
" cat poetry.lock | python get_package_version_from_lockfile.py <package name> -",
file=sys.stderr,
)
sys.exit(1)
package_name = sys.argv[1]
lockfile_path = sys.argv[2] if len(sys.argv) == 3 else "poetry.lock"
version = get_package_version(package_name, lockfile_path)
if version:
print(version)
else:
print(f"Package '{package_name}' not found in {lockfile_path}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()

7
.gitignore vendored
View File

@@ -5,8 +5,6 @@ classic/original_autogpt/*.json
auto_gpt_workspace/*
*.mpeg
.env
# Root .env files
/.env
azure.yaml
.vscode
.idea/*
@@ -123,6 +121,7 @@ celerybeat.pid
# Environments
.direnv/
.env
.venv
env/
venv*/
@@ -166,7 +165,7 @@ package-lock.json
# Allow for locally private items
# private
pri*
pri*
# ignore
ig*
.github_access_token
@@ -177,5 +176,3 @@ autogpt_platform/backend/settings.py
*.ign.*
.test-contents
.claude/settings.local.json
/autogpt_platform/backend/logs

3
.gitmodules vendored
View File

@@ -1,3 +1,6 @@
[submodule "classic/forge/tests/vcr_cassettes"]
path = classic/forge/tests/vcr_cassettes
url = https://github.com/Significant-Gravitas/Auto-GPT-test-cassettes
[submodule "autogpt_platform/supabase"]
path = autogpt_platform/supabase
url = https://github.com/supabase/supabase.git

View File

@@ -1,3 +1,6 @@
[pr_reviewer]
num_code_suggestions=0
[pr_code_suggestions]
commitable_code_suggestions=false
num_code_suggestions=0

View File

@@ -17,7 +17,7 @@ repos:
name: Detect secrets
description: Detects high entropy strings that are likely to be passwords.
files: ^autogpt_platform/
stages: [pre-push]
stages: [push]
- repo: local
# For proper type checking, all dependencies need to be up-to-date.
@@ -140,7 +140,7 @@ repos:
language: system
- repo: https://github.com/psf/black
rev: 24.10.0
rev: 23.12.1
# Black has sensible defaults, doesn't need package context, and ignores
# everything in .gitignore, so it works fine without any config or arguments.
hooks:
@@ -235,44 +235,44 @@ repos:
hooks:
- id: tsc
name: Typecheck - AutoGPT Platform - Frontend
entry: bash -c 'cd autogpt_platform/frontend && pnpm types'
entry: bash -c 'cd autogpt_platform/frontend && npm run type-check'
files: ^autogpt_platform/frontend/
types: [file]
language: system
pass_filenames: false
# - repo: local
# hooks:
# - id: pytest
# name: Run tests - AutoGPT Platform - Backend
# alias: pytest-platform-backend
# entry: bash -c 'cd autogpt_platform/backend && poetry run pytest'
# # include autogpt_libs source (since it's a path dependency) but exclude *_test.py files:
# files: ^autogpt_platform/(backend/((backend|test)/|poetry\.lock$)|autogpt_libs/(autogpt_libs/.*(?<!_test)\.py|poetry\.lock)$)
# language: system
# pass_filenames: false
- repo: local
hooks:
- id: pytest
name: Run tests - AutoGPT Platform - Backend
alias: pytest-platform-backend
entry: bash -c 'cd autogpt_platform/backend && poetry run pytest'
# include autogpt_libs source (since it's a path dependency) but exclude *_test.py files:
files: ^autogpt_platform/(backend/((backend|test)/|poetry\.lock$)|autogpt_libs/(autogpt_libs/.*(?<!_test)\.py|poetry\.lock)$)
language: system
pass_filenames: false
# - id: pytest
# name: Run tests - Classic - AutoGPT (excl. slow tests)
# alias: pytest-classic-autogpt
# entry: bash -c 'cd classic/original_autogpt && poetry run pytest --cov=autogpt -m "not slow" tests/unit tests/integration'
# # include forge source (since it's a path dependency) but exclude *_test.py files:
# files: ^(classic/original_autogpt/((autogpt|tests)/|poetry\.lock$)|classic/forge/(forge/.*(?<!_test)\.py|poetry\.lock)$)
# language: system
# pass_filenames: false
- id: pytest
name: Run tests - Classic - AutoGPT (excl. slow tests)
alias: pytest-classic-autogpt
entry: bash -c 'cd classic/original_autogpt && poetry run pytest --cov=autogpt -m "not slow" tests/unit tests/integration'
# include forge source (since it's a path dependency) but exclude *_test.py files:
files: ^(classic/original_autogpt/((autogpt|tests)/|poetry\.lock$)|classic/forge/(forge/.*(?<!_test)\.py|poetry\.lock)$)
language: system
pass_filenames: false
# - id: pytest
# name: Run tests - Classic - Forge (excl. slow tests)
# alias: pytest-classic-forge
# entry: bash -c 'cd classic/forge && poetry run pytest --cov=forge -m "not slow"'
# files: ^classic/forge/(forge/|tests/|poetry\.lock$)
# language: system
# pass_filenames: false
- id: pytest
name: Run tests - Classic - Forge (excl. slow tests)
alias: pytest-classic-forge
entry: bash -c 'cd classic/forge && poetry run pytest --cov=forge -m "not slow"'
files: ^classic/forge/(forge/|tests/|poetry\.lock$)
language: system
pass_filenames: false
# - id: pytest
# name: Run tests - Classic - Benchmark
# alias: pytest-classic-benchmark
# entry: bash -c 'cd classic/benchmark && poetry run pytest --cov=benchmark'
# files: ^classic/benchmark/(agbenchmark/|tests/|poetry\.lock$)
# language: system
# pass_filenames: false
- id: pytest
name: Run tests - Classic - Benchmark
alias: pytest-classic-benchmark
entry: bash -c 'cd classic/benchmark && poetry run pytest --cov=benchmark'
files: ^classic/benchmark/(agbenchmark/|tests/|poetry\.lock$)
language: system
pass_filenames: false

12
.vscode/launch.json vendored
View File

@@ -6,7 +6,7 @@
"type": "node-terminal",
"request": "launch",
"cwd": "${workspaceFolder}/autogpt_platform/frontend",
"command": "pnpm dev"
"command": "yarn dev"
},
{
"name": "Frontend: Client Side",
@@ -19,12 +19,12 @@
"type": "node-terminal",
"request": "launch",
"command": "pnpm dev",
"command": "yarn dev",
"cwd": "${workspaceFolder}/autogpt_platform/frontend",
"serverReadyAction": {
"pattern": "- Local:.+(https?://.+)",
"uriFormat": "%s",
"action": "debugWithChrome"
"action": "debugWithEdge"
}
},
{
@@ -32,9 +32,9 @@
"type": "debugpy",
"request": "launch",
"module": "backend.app",
"env": {
"OBJC_DISABLE_INITIALIZE_FORK_SAFETY": "YES"
},
// "env": {
// "ENV": "dev"
// },
"envFile": "${workspaceFolder}/backend/.env",
"justMyCode": false,
"cwd": "${workspaceFolder}/autogpt_platform/backend"

View File

@@ -1,53 +0,0 @@
# AutoGPT Platform Contribution Guide
This guide provides context for Codex when updating the **autogpt_platform** folder.
## Directory overview
- `autogpt_platform/backend` FastAPI based backend service.
- `autogpt_platform/autogpt_libs` Shared Python libraries.
- `autogpt_platform/frontend` Next.js + Typescript frontend.
- `autogpt_platform/docker-compose.yml` development stack.
See `docs/content/platform/getting-started.md` for setup instructions.
## Code style
- Format Python code with `poetry run format`.
- Format frontend code using `pnpm format`.
## Testing
- Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).
- Frontend: `pnpm test` or `pnpm test-ui` for Playwright tests. See `docs/content/platform/contributing/tests.md` for tips.
Always run the relevant linters and tests before committing.
Use conventional commit messages for all commits (e.g. `feat(backend): add API`).
Types:
- feat
- fix
- refactor
- ci
- dx (developer experience)
Scopes:
- platform
- platform/library
- platform/marketplace
- backend
- backend/executor
- frontend
- frontend/library
- frontend/marketplace
- blocks
## Pull requests
- Use the template in `.github/PULL_REQUEST_TEMPLATE.md`.
- Rely on the pre-commit checks for linting and formatting
- Fill out the **Changes** section and the checklist.
- Use conventional commit titles with a scope (e.g. `feat(frontend): add feature`).
- Keep out-of-scope changes under 20% of the PR.
- Ensure PR descriptions are complete.
- For changes touching `data/*.py`, validate user ID checks or explain why not needed.
- If adding protected frontend routes, update `frontend/lib/supabase/middleware.ts`.
- Use the linear ticket branch structure if given codex/open-1668-resume-dropped-runs

View File

@@ -2,6 +2,9 @@
If you are reading this, you are probably looking for the full **[contribution guide]**,
which is part of our [wiki].
Also check out our [🚀 Roadmap][roadmap] for information about our priorities and associated tasks.
<!-- You can find our immediate priorities and their progress on our public [kanban board]. -->
[contribution guide]: https://github.com/Significant-Gravitas/AutoGPT/wiki/Contributing
[wiki]: https://github.com/Significant-Gravitas/AutoGPT/wiki
[roadmap]: https://github.com/Significant-Gravitas/AutoGPT/discussions/6971

195
LICENSE
View File

@@ -1,197 +1,6 @@
All portions of this repository are under one of two licenses.
All portions of this repository are under one of two licenses. The majority of the AutoGPT repository is under the MIT License below. The autogpt_platform folder is under the
Polyform Shield License.
- Everything inside the autogpt_platform folder is under the Polyform Shield License.
- Everything outside the autogpt_platform folder is under the MIT License.
More info:
**Polyform Shield License:**
Code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.
Read more about this effort here: https://agpt.co/blog/introducing-the-autogpt-platform
**MIT License:**
All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes:
- The Original, stand-alone AutoGPT Agent
- Forge: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge
- AG Benchmark: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark
- AutoGPT Classic GUI: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend.
We also publish additional work under the MIT Licence in other repositories, such as GravitasML (https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform, and our [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
Both licences are available to read below:
=====================================================
-----------------------------------------------------
=====================================================
# PolyForm Shield License 1.0.0
<https://polyformproject.org/licenses/shield/1.0.0>
## Acceptance
In order to get any license under these terms, you must agree
to them as both strict obligations and conditions to all
your licenses.
## Copyright License
The licensor grants you a copyright license for the
software to do everything you might do with the software
that would otherwise infringe the licensor's copyright
in it for any permitted purpose. However, you may
only distribute the software according to [Distribution
License](#distribution-license) and make changes or new works
based on the software according to [Changes and New Works
License](#changes-and-new-works-license).
## Distribution License
The licensor grants you an additional copyright license
to distribute copies of the software. Your license
to distribute covers distributing the software with
changes and new works permitted by [Changes and New Works
License](#changes-and-new-works-license).
## Notices
You must ensure that anyone who gets a copy of any part of
the software from you also gets a copy of these terms or the
URL for them above, as well as copies of any plain-text lines
beginning with `Required Notice:` that the licensor provided
with the software. For example:
> Required Notice: Copyright Yoyodyne, Inc. (http://example.com)
## Changes and New Works License
The licensor grants you an additional copyright license to
make changes and new works based on the software for any
permitted purpose.
## Patent License
The licensor grants you a patent license for the software that
covers patent claims the licensor can license, or becomes able
to license, that you would infringe by using the software.
## Noncompete
Any purpose is a permitted purpose, except for providing any
product that competes with the software or any product the
licensor or any of its affiliates provides using the software.
## Competition
Goods and services compete even when they provide functionality
through different kinds of interfaces or for different technical
platforms. Applications can compete with services, libraries
with plugins, frameworks with development tools, and so on,
even if they're written in different programming languages
or for different computer architectures. Goods and services
compete even when provided free of charge. If you market a
product as a practical substitute for the software or another
product, it definitely competes.
## New Products
If you are using the software to provide a product that does
not compete, but the licensor or any of its affiliates brings
your product into competition by providing a new version of
the software or another product using the software, you may
continue using versions of the software available under these
terms beforehand to provide your competing product, but not
any later versions.
## Discontinued Products
You may begin using the software to compete with a product
or service that the licensor or any of its affiliates has
stopped providing, unless the licensor includes a plain-text
line beginning with `Licensor Line of Business:` with the
software that mentions that line of business. For example:
> Licensor Line of Business: YoyodyneCMS Content Management
System (http://example.com/cms)
## Sales of Business
If the licensor or any of its affiliates sells a line of
business developing the software or using the software
to provide a product, the buyer can also enforce
[Noncompete](#noncompete) for that product.
## Fair Use
You may have "fair use" rights for the software under the
law. These terms do not limit them.
## No Other Rights
These terms do not allow you to sublicense or transfer any of
your licenses to anyone else, or prevent the licensor from
granting licenses to anyone else. These terms do not imply
any other licenses.
## Patent Defense
If you make any written claim that the software infringes or
contributes to infringement of any patent, your patent license
for the software granted under these terms ends immediately. If
your company makes such a claim, your patent license ends
immediately for work on behalf of your company.
## Violations
The first time you are notified in writing that you have
violated any of these terms, or done anything with the software
not covered by your licenses, your licenses can nonetheless
continue if you come into full compliance with these terms,
and take practical steps to correct past violations, within
32 days of receiving notice. Otherwise, all your licenses
end immediately.
## No Liability
***As far as the law allows, the software comes as is, without
any warranty or condition, and the licensor will not be liable
to you for any damages arising out of these terms or the use
or nature of the software, under any kind of legal claim.***
## Definitions
The **licensor** is the individual or entity offering these
terms, and the **software** is the software the licensor makes
available under these terms.
A **product** can be a good or service, or a combination
of them.
**You** refers to the individual or entity agreeing to these
terms.
**Your company** is any legal entity, sole proprietorship,
or other kind of organization that you work for, plus all
its affiliates.
**Affiliates** means the other organizations than an
organization has control over, is under the control of, or is
under common control with.
**Control** means ownership of substantially all the assets of
an entity, or the power to direct its management and policies
by vote, contract, or otherwise. Control can be direct or
indirect.
**Your licenses** are all the licenses granted to you for the
software under these terms.
**Use** means anything you do with the software requiring one
of your licenses.
=====================================================
-----------------------------------------------------
=====================================================
MIT License

View File

@@ -1,82 +1,24 @@
# AutoGPT: Build, Deploy, and Run AI Agents
[![Discord Follow](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fautogpt%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&label=total%20members&logo=discord&logoColor=white&color=7289da)](https://discord.gg/autogpt) &ensp;
[![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](https://discord.gg/autogpt) &ensp;
[![Twitter Follow](https://img.shields.io/twitter/follow/Auto_GPT?style=social)](https://twitter.com/Auto_GPT) &ensp;
<!-- Keep these links. Translations will automatically update with the README. -->
[Deutsch](https://zdoc.app/de/Significant-Gravitas/AutoGPT) |
[Español](https://zdoc.app/es/Significant-Gravitas/AutoGPT) |
[français](https://zdoc.app/fr/Significant-Gravitas/AutoGPT) |
[日本語](https://zdoc.app/ja/Significant-Gravitas/AutoGPT) |
[한국어](https://zdoc.app/ko/Significant-Gravitas/AutoGPT) |
[Português](https://zdoc.app/pt/Significant-Gravitas/AutoGPT) |
[Русский](https://zdoc.app/ru/Significant-Gravitas/AutoGPT) |
[中文](https://zdoc.app/zh/Significant-Gravitas/AutoGPT)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
**AutoGPT** is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows.
## Hosting Options
- Download to self-host (Free!)
- [Join the Waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta (Closed Beta - Public release Coming Soon!)
- Download to self-host
- [Join the Waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta
## How to Self-Host the AutoGPT Platform
## How to Setup for Self-Hosting
> [!NOTE]
> Setting up and hosting the AutoGPT Platform yourself is a technical process.
> If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.
### System Requirements
Before proceeding with the installation, ensure your system meets the following requirements:
#### Hardware Requirements
- CPU: 4+ cores recommended
- RAM: Minimum 8GB, 16GB recommended
- Storage: At least 10GB of free space
#### Software Requirements
- Operating Systems:
- Linux (Ubuntu 20.04 or newer recommended)
- macOS (10.15 or newer)
- Windows 10/11 with WSL2
- Required Software (with minimum versions):
- Docker Engine (20.10.0 or newer)
- Docker Compose (2.0.0 or newer)
- Git (2.30 or newer)
- Node.js (16.x or newer)
- npm (8.x or newer)
- VSCode (1.60 or newer) or any modern code editor
#### Network Requirements
- Stable internet connection
- Access to required ports (will be configured in Docker)
- Ability to make outbound HTTPS connections
### Updated Setup Instructions:
We've moved to a fully maintained and regularly updated documentation site.
👉 [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/)
https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603
This tutorial assumes you have Docker, VSCode, git and npm installed.
---
#### ⚡ Quick Setup with One-Line Script (Recommended for Local Hosting)
Skip the manual steps and get started in minutes using our automatic setup script.
For macOS/Linux:
```
curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.sh
```
For Windows (PowerShell):
```
powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"
```
This will install dependencies, configure Docker, and launch your local instance — all in one go.
### 🧱 AutoGPT Frontend
The AutoGPT frontend is where users interact with our powerful AI automation platform. It offers multiple ways to engage with and leverage our AI agents. This is the interface where you'll bring your AI automation ideas to life:
@@ -123,17 +65,7 @@ Here are two examples of what you can do with AutoGPT:
These examples show just a glimpse of what you can achieve with AutoGPT! You can create customized workflows to build agents for any use case.
---
### **License Overview:**
🛡️ **Polyform Shield License:**
All code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.</br>_[Read more about this effort](https://agpt.co/blog/introducing-the-autogpt-platform)_
🦉 **MIT License:**
All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as [Forge](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge), [agbenchmark](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark) and the [AutoGPT Classic GUI](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend).</br>We also publish additional work under the MIT Licence in other repositories, such as [GravitasML](https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform. See also our MIT Licenced [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
---
### Mission
### Mission and Licencing
Our mission is to provide the tools, so that you can focus on what matters:
- 🏗️ **Building** - Lay the foundation for something amazing.
@@ -146,6 +78,14 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
&ensp;|&ensp;
**🚀 [Contributing](CONTRIBUTING.md)**
**Licensing:**
MIT License: The majority of the AutoGPT repository is under the MIT License.
Polyform Shield License: This license applies to the autogpt_platform folder.
For more information, see https://agpt.co/blog/introducing-the-autogpt-platform
---
## 🤖 AutoGPT Classic
> Below is information about the classic version of AutoGPT.
@@ -208,7 +148,7 @@ Just clone the repo, install dependencies with `./run setup`, and you should be
[![Join us on Discord](https://invidget.switchblade.xyz/autogpt)](https://discord.gg/autogpt)
To report a bug or request a feature, create a [GitHub Issue](https://github.com/Significant-Gravitas/AutoGPT/issues/new/choose). Please ensure someone else hasn't created an issue for the same topic.
To report a bug or request a feature, create a [GitHub Issue](https://github.com/Significant-Gravitas/AutoGPT/issues/new/choose). Please ensure someone else hasnt created an issue for the same topic.
## 🤝 Sister projects

View File

@@ -1,123 +0,0 @@
############
# Secrets
# YOU MUST CHANGE THESE BEFORE GOING INTO PRODUCTION
############
POSTGRES_PASSWORD=your-super-secret-and-long-postgres-password
JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
ANON_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
DASHBOARD_USERNAME=supabase
DASHBOARD_PASSWORD=this_password_is_insecure_and_should_be_updated
SECRET_KEY_BASE=UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq
VAULT_ENC_KEY=your-encryption-key-32-chars-min
############
# Database - You can change these to any PostgreSQL database that has logical replication enabled.
############
POSTGRES_HOST=db
POSTGRES_DB=postgres
POSTGRES_PORT=5432
# default user is postgres
############
# Supavisor -- Database pooler
############
POOLER_PROXY_PORT_TRANSACTION=6543
POOLER_DEFAULT_POOL_SIZE=20
POOLER_MAX_CLIENT_CONN=100
POOLER_TENANT_ID=your-tenant-id
############
# API Proxy - Configuration for the Kong Reverse proxy.
############
KONG_HTTP_PORT=8000
KONG_HTTPS_PORT=8443
############
# API - Configuration for PostgREST.
############
PGRST_DB_SCHEMAS=public,storage,graphql_public
############
# Auth - Configuration for the GoTrue authentication server.
############
## General
SITE_URL=http://localhost:3000
ADDITIONAL_REDIRECT_URLS=
JWT_EXPIRY=3600
DISABLE_SIGNUP=false
API_EXTERNAL_URL=http://localhost:8000
## Mailer Config
MAILER_URLPATHS_CONFIRMATION="/auth/v1/verify"
MAILER_URLPATHS_INVITE="/auth/v1/verify"
MAILER_URLPATHS_RECOVERY="/auth/v1/verify"
MAILER_URLPATHS_EMAIL_CHANGE="/auth/v1/verify"
## Email auth
ENABLE_EMAIL_SIGNUP=true
ENABLE_EMAIL_AUTOCONFIRM=false
SMTP_ADMIN_EMAIL=admin@example.com
SMTP_HOST=supabase-mail
SMTP_PORT=2500
SMTP_USER=fake_mail_user
SMTP_PASS=fake_mail_password
SMTP_SENDER_NAME=fake_sender
ENABLE_ANONYMOUS_USERS=false
## Phone auth
ENABLE_PHONE_SIGNUP=true
ENABLE_PHONE_AUTOCONFIRM=true
############
# Studio - Configuration for the Dashboard
############
STUDIO_DEFAULT_ORGANIZATION=Default Organization
STUDIO_DEFAULT_PROJECT=Default Project
STUDIO_PORT=3000
# replace if you intend to use Studio outside of localhost
SUPABASE_PUBLIC_URL=http://localhost:8000
# Enable webp support
IMGPROXY_ENABLE_WEBP_DETECTION=true
# Add your OpenAI API key to enable SQL Editor Assistant
OPENAI_API_KEY=
############
# Functions - Configuration for Functions
############
# NOTE: VERIFY_JWT applies to all functions. Per-function VERIFY_JWT is not supported yet.
FUNCTIONS_VERIFY_JWT=false
############
# Logs - Configuration for Logflare
# Please refer to https://supabase.com/docs/reference/self-hosting-analytics/introduction
############
LOGFLARE_LOGGER_BACKEND_API_KEY=your-super-secret-and-long-logflare-key
# Change vector.toml sinks to reflect this change
LOGFLARE_API_KEY=your-super-secret-and-long-logflare-key
# Docker socket location - this value will differ depending on your OS
DOCKER_SOCKET_LOCATION=/var/run/docker.sock
# Google Cloud Project details
GOOGLE_PROJECT_ID=GOOGLE_PROJECT_ID
GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER

View File

@@ -1,275 +0,0 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Repository Overview
AutoGPT Platform is a monorepo containing:
- **Backend** (`/backend`): Python FastAPI server with async support
- **Frontend** (`/frontend`): Next.js React application
- **Shared Libraries** (`/autogpt_libs`): Common Python utilities
## Essential Commands
### Backend Development
```bash
# Install dependencies
cd backend && poetry install
# Run database migrations
poetry run prisma migrate dev
# Start all services (database, redis, rabbitmq, clamav)
docker compose up -d
# Run the backend server
poetry run serve
# Run tests
poetry run test
# Run specific test
poetry run pytest path/to/test_file.py::test_function_name
# Run block tests (tests that validate all blocks work correctly)
poetry run pytest backend/blocks/test/test_block.py -xvs
# Run tests for a specific block (e.g., GetCurrentTimeBlock)
poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
# Lint and format
# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
poetry run format # Black + isort
poetry run lint # ruff
```
More details can be found in TESTING.md
#### Creating/Updating Snapshots
When you first write a test or when the expected output changes:
```bash
poetry run pytest path/to/test.py --snapshot-update
```
⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
### Frontend Development
```bash
# Install dependencies
cd frontend && pnpm i
# Generate API client from OpenAPI spec
pnpm generate:api
# Start development server
pnpm dev
# Run E2E tests
pnpm test
# Run Storybook for component development
pnpm storybook
# Build production
pnpm build
# Format and lint
pnpm format
# Type checking
pnpm types
```
**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
**Key Frontend Conventions:**
- Separate render logic from data/behavior in components
- Use generated API hooks from `@/app/api/__generated__/endpoints/`
- Use function declarations (not arrow functions) for components/handlers
- Use design system components from `src/components/` (atoms, molecules, organisms)
- Only use Phosphor Icons
- Never use `src/components/__legacy__/*` or deprecated `BackendAPI`
## Architecture Overview
### Backend Architecture
- **API Layer**: FastAPI with REST and WebSocket endpoints
- **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
- **Queue System**: RabbitMQ for async task processing
- **Execution Engine**: Separate executor service processes agent workflows
- **Authentication**: JWT-based with Supabase integration
- **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
### Frontend Architecture
- **Framework**: Next.js 15 App Router (client-first approach)
- **Data Fetching**: Type-safe generated API hooks via Orval + React Query
- **State Management**: React Query for server state, co-located UI state in components/hooks
- **Component Structure**: Separate render logic (`.tsx`) from business logic (`use*.ts` hooks)
- **Workflow Builder**: Visual graph editor using @xyflow/react
- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
- **Icons**: Phosphor Icons only
- **Feature Flags**: LaunchDarkly integration
- **Error Handling**: ErrorCard for render errors, toast for mutations, Sentry for exceptions
- **Testing**: Playwright for E2E, Storybook for component development
### Key Concepts
1. **Agent Graphs**: Workflow definitions stored as JSON, executed by the backend
2. **Blocks**: Reusable components in `/backend/blocks/` that perform specific tasks
3. **Integrations**: OAuth and API connections stored per user
4. **Store**: Marketplace for sharing agent templates
5. **Virus Scanning**: ClamAV integration for file upload security
### Testing Approach
- Backend uses pytest with snapshot testing for API responses
- Test files are colocated with source files (`*_test.py`)
- Frontend uses Playwright for E2E tests
- Component testing via Storybook
### Database Schema
Key models (defined in `/backend/schema.prisma`):
- `User`: Authentication and profile data
- `AgentGraph`: Workflow definitions with version control
- `AgentGraphExecution`: Execution history and results
- `AgentNode`: Individual nodes in a workflow
- `StoreListing`: Marketplace listings for sharing agents
### Environment Configuration
#### Configuration Files
- **Backend**: `/backend/.env.default` (defaults) → `/backend/.env` (user overrides)
- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
#### Docker Environment Loading Order
1. `.env.default` files provide base configuration (tracked in git)
2. `.env` files provide user-specific overrides (gitignored)
3. Docker Compose `environment:` sections provide service-specific overrides
4. Shell environment variables have highest precedence
#### Key Points
- All services use hardcoded defaults in docker-compose files (no `${VARIABLE}` substitutions)
- The `env_file` directive loads variables INTO containers at runtime
- Backend/Frontend services use YAML anchors for consistent configuration
- Supabase services (`db/docker/docker-compose.yml`) follow the same pattern
### Common Development Tasks
**Adding a new block:**
Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-sdk-guide.md) which covers:
- Provider configuration with `ProviderBuilder`
- Block schema definition
- Authentication (API keys, OAuth, webhooks)
- Testing and validation
- File organization
Quick steps:
1. Create new file in `/backend/backend/blocks/`
2. Configure provider using `ProviderBuilder` in `_config.py`
3. Inherit from `Block` base class
4. Define input/output schemas using `BlockSchema`
5. Implement async `run` method
6. Generate unique block ID using `uuid.uuid4()`
7. Test with `poetry run pytest backend/blocks/test/test_block.py`
Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
ex: do the inputs and outputs tie well together?
If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
**Modifying the API:**
1. Update route in `/backend/backend/server/routers/`
2. Add/update Pydantic models in same directory
3. Write tests alongside the route file
4. Run `poetry run test` to verify
**Frontend feature development:**
See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
- Add `usePageName.ts` hook for logic
- Put sub-components in local `components/` folder
2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
- Use design system components from `src/components/` (atoms, molecules, organisms)
- Never use `src/components/__legacy__/*`
3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
- Regenerate with `pnpm generate:api`
- Pattern: `use{Method}{Version}{OperationName}`
4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
5. **Testing**: Add Storybook stories for new components, Playwright for E2E
6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
### Security Implementation
**Cache Protection Middleware:**
- Located in `/backend/backend/server/middleware/security.py`
- Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
- Uses an allow list approach - only explicitly permitted paths can be cached
- Cacheable paths include: static assets (`/static/*`, `/_next/static/*`), health checks, public store pages, documentation
- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
- To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
- Applied to both main API server and external API applications
### Creating Pull Requests
- Create the PR aginst the `dev` branch of the repository.
- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
- Use conventional commit messages (see below)/
- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
- Run the github pre-commit hooks to ensure code quality.
### Reviewing/Revising Pull Requests
- When the user runs /pr-comments or tries to fetch them, also run gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews to get the reviews
- Use gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews/[review_id]/comments to get the review contents
- Use gh api /repos/Significant-Gravitas/AutoGPT/issues/9924/comments to get the pr specific comments
### Conventional Commits
Use this format for commit messages and Pull Request titles:
**Conventional Commit Types:**
- `feat`: Introduces a new feature to the codebase
- `fix`: Patches a bug in the codebase
- `refactor`: Code change that neither fixes a bug nor adds a feature; also applies to removing features
- `ci`: Changes to CI configuration
- `docs`: Documentation-only changes
- `dx`: Improvements to the developer experience
**Recommended Base Scopes:**
- `platform`: Changes affecting both frontend and backend
- `frontend`
- `backend`
- `infra`
- `blocks`: Modifications/additions of individual blocks
**Subscope Examples:**
- `backend/executor`
- `backend/db`
- `frontend/builder` (includes changes to the block UI component)
- `infra/prod`
Use these scopes and subscopes for clarity and consistency in commit messages.

View File

@@ -1,63 +0,0 @@
.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
# Run just Supabase + Redis + RabbitMQ
start-core:
docker compose up -d deps
# Stop core services
stop-core:
docker compose stop deps
reset-db:
rm -rf db/docker/volumes/db/data
cd backend && poetry run prisma migrate deploy
cd backend && poetry run prisma generate
cd backend && poetry run gen-prisma-stub
# View logs for core services
logs-core:
docker compose logs -f deps
# Run formatting and linting for backend and frontend
format:
cd backend && poetry run format
cd frontend && pnpm format
cd frontend && pnpm lint
init-env:
cp -n .env.default .env || true
cd backend && cp -n .env.default .env || true
cd frontend && cp -n .env.default .env || true
# Run migrations for backend
migrate:
cd backend && poetry run prisma migrate deploy
cd backend && poetry run prisma generate
cd backend && poetry run gen-prisma-stub
run-backend:
cd backend && poetry run app
run-frontend:
cd frontend && pnpm dev
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:"
@echo " start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
@echo " stop-core - Stop the core services"
@echo " reset-db - Reset the database by deleting the volume"
@echo " logs-core - Tail the logs for core services"
@echo " format - Format & lint backend (Python) and frontend (TypeScript) code"
@echo " migrate - Run backend database migrations"
@echo " run-backend - Run the backend FastAPI server"
@echo " run-frontend - Run the frontend Next.js development server"
@echo " test-data - Run the test data creator"
@echo " load-store-agents - Load store agents from agents/ folder into test database"

View File

@@ -8,66 +8,60 @@ Welcome to the AutoGPT Platform - a powerful system for creating and running AI
- Docker
- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
- Node.js & NPM (for running the frontend application)
### Running the System
To run the AutoGPT Platform, follow these steps:
1. Clone this repository to your local machine and navigate to the `autogpt_platform` directory within the repository:
```
git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git>
cd AutoGPT/autogpt_platform
```
2. Run the following command:
```
cp .env.default .env
git submodule update --init --recursive --progress
```
This command will copy the `.env.default` file to `.env`. You can modify the `.env` file to add your own environment variables.
This command will initialize and update the submodules in the repository. The `supabase` folder will be cloned to the root directory.
3. Run the following command:
```
cp supabase/docker/.env.example .env
```
This command will copy the `.env.example` file to `.env` in the `supabase/docker` directory. You can modify the `.env` file to add your own environment variables.
4. Run the following command:
```
docker compose up -d
```
This command will start all the necessary backend services defined in the `docker-compose.yml` file in detached mode.
4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
5. Navigate to `frontend` within the `autogpt_platform` directory:
```
cd frontend
```
You will need to run your frontend application separately on your local machine.
### Running Just Core services
6. Run the following command:
```
cp .env.example .env.local
```
This command will copy the `.env.example` file to `.env.local` in the `frontend` directory. You can modify the `.env.local` within this folder to add your own environment variables for the frontend application.
You can now run the following to enable just the core services.
7. Run the following command:
```
npm install
npm run dev
```
This command will install the necessary dependencies and start the frontend application in development mode.
If you are using Yarn, you can run the following commands instead:
```
yarn install && yarn dev
```
```
# For help
make help
# Run just Supabase + Redis + RabbitMQ
make start-core
# Stop core services
make stop-core
# View logs from core services
make logs-core
# Run formatting and linting for backend and frontend
make format
# Run migrations for backend database
make migrate
# Run backend server
make run-backend
# Run frontend development server
make run-frontend
```
8. Open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
### Docker Compose Commands
@@ -80,52 +74,43 @@ Here are some useful Docker Compose commands for managing your AutoGPT Platform:
- `docker compose down`: Stop and remove containers, networks, and volumes.
- `docker compose watch`: Watch for changes in your services and automatically update them.
### Sample Scenarios
Here are some common scenarios where you might use multiple Docker Compose commands:
1. Updating and restarting a specific service:
```
docker compose build api_srv
docker compose up -d --no-deps api_srv
```
This rebuilds the `api_srv` service and restarts it without affecting other services.
2. Viewing logs for troubleshooting:
```
docker compose logs -f api_srv ws_srv
```
This shows and follows the logs for both `api_srv` and `ws_srv` services.
3. Scaling a service for increased load:
```
docker compose up -d --scale executor=3
```
This scales the `executor` service to 3 instances to handle increased load.
4. Stopping the entire system for maintenance:
```
docker compose stop
docker compose rm -f
docker compose pull
docker compose up -d
```
This stops all services, removes containers, pulls the latest images, and restarts the system.
5. Developing with live updates:
```
docker compose watch
```
This watches for changes in your code and automatically updates the relevant services.
6. Checking the status of services:
@@ -136,6 +121,7 @@ Here are some common scenarios where you might use multiple Docker Compose comma
These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
### Persisting Data
To persist data for PostgreSQL and Redis, you can modify the `docker-compose.yml` file to add volumes. Here's how:
@@ -163,28 +149,3 @@ To persist data for PostgreSQL and Redis, you can modify the `docker-compose.yml
3. Save the file and run `docker compose up -d` to apply the changes.
This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
### API Client Generation
The platform includes scripts for generating and managing the API client:
- `pnpm fetch:openapi`: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)
- `pnpm generate:api-client`: Generates the TypeScript API client from the OpenAPI specification using Orval
- `pnpm generate:api`: Runs both fetch and generate commands in sequence
#### Manual API Client Updates
If you need to update the API client after making changes to the backend API:
1. Ensure the backend services are running:
```
docker compose up -d
```
2. Generate the updated API client:
```
pnpm generate:api
```
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.

View File

@@ -1,3 +1,3 @@
# AutoGPT Libs
This is a new project to store shared functionality across different services in the AutoGPT Platform (e.g. authentication)
This is a new project to store shared functionality across different services in NextGen AutoGPT (e.g. authentication)

View File

@@ -0,0 +1,34 @@
import hashlib
import secrets
from typing import NamedTuple
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_api_key(self) -> APIKeyContainer:
"""Generate a new API key with all its parts."""
raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
return APIKeyContainer(
raw=raw_key,
prefix=raw_key[: self.PREFIX_LENGTH],
postfix=raw_key[-self.POSTFIX_LENGTH :],
hash=hashlib.sha256(raw_key.encode()).hexdigest(),
)
def verify_api_key(self, provided_key: str, stored_hash: str) -> bool:
"""Verify if a provided API key matches the stored hash."""
if not provided_key.startswith(self.PREFIX):
return False
return hashlib.sha256(provided_key.encode()).hexdigest() == stored_hash

View File

@@ -1,81 +0,0 @@
import hashlib
import secrets
from typing import NamedTuple
from cryptography.hazmat.primitives.kdf.scrypt import Scrypt
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
key: str
head: str
tail: str
hash: str
salt: str
class APIKeySmith:
PREFIX: str = "agpt_"
HEAD_LENGTH: int = 8
TAIL_LENGTH: int = 8
def generate_key(self) -> APIKeyContainer:
"""Generate a new API key with secure hashing."""
raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
hash, salt = self.hash_key(raw_key)
return APIKeyContainer(
key=raw_key,
head=raw_key[: self.HEAD_LENGTH],
tail=raw_key[-self.TAIL_LENGTH :],
hash=hash,
salt=salt,
)
def verify_key(
self, provided_key: str, known_hash: str, known_salt: str | None = None
) -> bool:
"""
Verify an API key against a known hash (+ salt).
Supports verifying both legacy SHA256 and secure Scrypt hashes.
"""
if not provided_key.startswith(self.PREFIX):
return False
# Handle legacy SHA256 hashes (migration support)
if known_salt is None:
legacy_hash = hashlib.sha256(provided_key.encode()).hexdigest()
return secrets.compare_digest(legacy_hash, known_hash)
try:
salt_bytes = bytes.fromhex(known_salt)
provided_hash = self._hash_key_with_salt(provided_key, salt_bytes)
return secrets.compare_digest(provided_hash, known_hash)
except (ValueError, TypeError):
return False
def hash_key(self, raw_key: str) -> tuple[str, str]:
"""Migrate a legacy hash to secure hash format."""
if not raw_key.startswith(self.PREFIX):
raise ValueError("Key without 'agpt_' prefix would fail validation")
salt = self._generate_salt()
hash = self._hash_key_with_salt(raw_key, salt)
return hash, salt.hex()
def _generate_salt(self) -> bytes:
"""Generate a random salt for hashing."""
return secrets.token_bytes(32)
def _hash_key_with_salt(self, raw_key: str, salt: bytes) -> str:
"""Hash API key using Scrypt with salt."""
kdf = Scrypt(
length=32,
salt=salt,
n=2**14, # CPU/memory cost parameter
r=8, # Block size parameter
p=1, # Parallelization parameter
)
key_hash = kdf.derive(raw_key.encode())
return key_hash.hex()

View File

@@ -1,79 +0,0 @@
import hashlib
from autogpt_libs.api_key.keysmith import APIKeySmith
def test_generate_api_key():
keysmith = APIKeySmith()
key = keysmith.generate_key()
assert key.key.startswith(keysmith.PREFIX)
assert key.head == key.key[: keysmith.HEAD_LENGTH]
assert key.tail == key.key[-keysmith.TAIL_LENGTH :]
assert len(key.hash) == 64 # 32 bytes hex encoded
assert len(key.salt) == 64 # 32 bytes hex encoded
def test_verify_new_secure_key():
keysmith = APIKeySmith()
key = keysmith.generate_key()
# Test correct key validates
assert keysmith.verify_key(key.key, key.hash, key.salt) is True
# Test wrong key fails
wrong_key = f"{keysmith.PREFIX}wrongkey123"
assert keysmith.verify_key(wrong_key, key.hash, key.salt) is False
def test_verify_legacy_key():
keysmith = APIKeySmith()
legacy_key = f"{keysmith.PREFIX}legacykey123"
legacy_hash = hashlib.sha256(legacy_key.encode()).hexdigest()
# Test legacy key validates without salt
assert keysmith.verify_key(legacy_key, legacy_hash) is True
# Test wrong legacy key fails
wrong_key = f"{keysmith.PREFIX}wronglegacy"
assert keysmith.verify_key(wrong_key, legacy_hash) is False
def test_rehash_existing_key():
keysmith = APIKeySmith()
legacy_key = f"{keysmith.PREFIX}migratekey123"
# Migrate the legacy key
new_hash, new_salt = keysmith.hash_key(legacy_key)
# Verify migrated key works
assert keysmith.verify_key(legacy_key, new_hash, new_salt) is True
# Verify different key fails with migrated hash
wrong_key = f"{keysmith.PREFIX}wrongkey"
assert keysmith.verify_key(wrong_key, new_hash, new_salt) is False
def test_invalid_key_prefix():
keysmith = APIKeySmith()
key = keysmith.generate_key()
# Test key without proper prefix fails
invalid_key = "invalid_prefix_key"
assert keysmith.verify_key(invalid_key, key.hash, key.salt) is False
def test_secure_hash_requires_salt():
keysmith = APIKeySmith()
key = keysmith.generate_key()
# Secure hash without salt should fail
assert keysmith.verify_key(key.key, key.hash) is False
def test_invalid_salt_format():
keysmith = APIKeySmith()
key = keysmith.generate_key()
# Invalid salt format should fail gracefully
assert keysmith.verify_key(key.key, key.hash, "invalid_hex") is False

View File

@@ -1,19 +1,15 @@
from .config import verify_settings
from .dependencies import (
get_optional_user_id,
get_user_id,
requires_admin_user,
requires_user,
)
from .helpers import add_auth_responses_to_openapi
from .config import Settings
from .depends import requires_admin_user, requires_user
from .jwt_utils import parse_jwt_token
from .middleware import APIKeyValidator, auth_middleware
from .models import User
__all__ = [
"verify_settings",
"get_user_id",
"requires_admin_user",
"Settings",
"parse_jwt_token",
"requires_user",
"get_optional_user_id",
"add_auth_responses_to_openapi",
"requires_admin_user",
"APIKeyValidator",
"auth_middleware",
"User",
]

View File

@@ -1,90 +1,18 @@
import logging
import os
from jwt.algorithms import get_default_algorithms, has_crypto
from dotenv import load_dotenv
logger = logging.getLogger(__name__)
class AuthConfigError(ValueError):
"""Raised when authentication configuration is invalid."""
pass
ALGO_RECOMMENDATION = (
"We highly recommend using an asymmetric algorithm such as ES256, "
"because when leaked, a shared secret would allow anyone to "
"forge valid tokens and impersonate users. "
"More info: https://supabase.com/docs/guides/auth/signing-keys#choosing-the-right-signing-algorithm" # noqa
)
load_dotenv()
class Settings:
def __init__(self):
self.JWT_VERIFY_KEY: str = os.getenv(
"JWT_VERIFY_KEY", os.getenv("SUPABASE_JWT_SECRET", "")
).strip()
self.JWT_ALGORITHM: str = os.getenv("JWT_SIGN_ALGORITHM", "HS256").strip()
JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
JWT_ALGORITHM: str = "HS256"
self.validate()
def validate(self):
if not self.JWT_VERIFY_KEY:
raise AuthConfigError(
"JWT_VERIFY_KEY must be set. "
"An empty JWT secret would allow anyone to forge valid tokens."
)
if len(self.JWT_VERIFY_KEY) < 32:
logger.warning(
"⚠️ JWT_VERIFY_KEY appears weak (less than 32 characters). "
"Consider using a longer, cryptographically secure secret."
)
supported_algorithms = get_default_algorithms().keys()
if not has_crypto:
logger.warning(
"⚠️ Asymmetric JWT verification is not available "
"because the 'cryptography' package is not installed. "
+ ALGO_RECOMMENDATION
)
if (
self.JWT_ALGORITHM not in supported_algorithms
or self.JWT_ALGORITHM == "none"
):
raise AuthConfigError(
f"Invalid JWT_SIGN_ALGORITHM: '{self.JWT_ALGORITHM}'. "
"Supported algorithms are listed on "
"https://pyjwt.readthedocs.io/en/stable/algorithms.html"
)
if self.JWT_ALGORITHM.startswith("HS"):
logger.warning(
f"⚠️ JWT_SIGN_ALGORITHM is set to '{self.JWT_ALGORITHM}', "
"a symmetric shared-key signature algorithm. " + ALGO_RECOMMENDATION
)
@property
def is_configured(self) -> bool:
return bool(self.JWT_SECRET_KEY)
_settings: Settings = None # type: ignore
def get_settings() -> Settings:
global _settings
if not _settings:
_settings = Settings()
return _settings
def verify_settings() -> None:
global _settings
if not _settings:
_settings = Settings() # calls validation indirectly
return
_settings.validate()
settings = Settings()

View File

@@ -1,306 +0,0 @@
"""
Comprehensive tests for auth configuration to ensure 100% line and branch coverage.
These tests verify critical security checks preventing JWT token forgery.
"""
import logging
import os
import pytest
from pytest_mock import MockerFixture
from autogpt_libs.auth.config import AuthConfigError, Settings
def test_environment_variable_precedence(mocker: MockerFixture):
"""Test that environment variables take precedence over defaults."""
secret = "environment-secret-key-with-proper-length-123456"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_environment_variable_backwards_compatible(mocker: MockerFixture):
"""Test that SUPABASE_JWT_SECRET is read if JWT_VERIFY_KEY is not set."""
secret = "environment-secret-key-with-proper-length-123456"
mocker.patch.dict(os.environ, {"SUPABASE_JWT_SECRET": secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_auth_config_error_inheritance():
"""Test that AuthConfigError is properly defined as an Exception."""
assert issubclass(AuthConfigError, Exception)
error = AuthConfigError("test message")
assert str(error) == "test message"
def test_settings_static_after_creation(mocker: MockerFixture):
"""Test that settings maintain their values after creation."""
secret = "immutable-secret-key-with-proper-length-12345"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
settings = Settings()
original_secret = settings.JWT_VERIFY_KEY
# Changing environment after creation shouldn't affect settings
os.environ["JWT_VERIFY_KEY"] = "different-secret"
assert settings.JWT_VERIFY_KEY == original_secret
def test_settings_load_with_valid_secret(mocker: MockerFixture):
"""Test auth enabled with a valid JWT secret."""
valid_secret = "a" * 32 # 32 character secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": valid_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == valid_secret
def test_settings_load_with_strong_secret(mocker: MockerFixture):
"""Test auth enabled with a cryptographically strong secret."""
strong_secret = "super-secret-jwt-token-with-at-least-32-characters-long"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": strong_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == strong_secret
assert len(settings.JWT_VERIFY_KEY) >= 32
def test_secret_empty_raises_error(mocker: MockerFixture):
"""Test that auth enabled with empty secret raises AuthConfigError."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": ""}, clear=True)
with pytest.raises(Exception) as exc_info:
Settings()
assert "JWT_VERIFY_KEY" in str(exc_info.value)
def test_secret_missing_raises_error(mocker: MockerFixture):
"""Test that auth enabled without secret env var raises AuthConfigError."""
mocker.patch.dict(os.environ, {}, clear=True)
with pytest.raises(Exception) as exc_info:
Settings()
assert "JWT_VERIFY_KEY" in str(exc_info.value)
@pytest.mark.parametrize("secret", [" ", " ", "\t", "\n", " \t\n "])
def test_secret_only_whitespace_raises_error(mocker: MockerFixture, secret: str):
"""Test that auth enabled with whitespace-only secret raises error."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
with pytest.raises(ValueError):
Settings()
def test_secret_weak_logs_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that weak JWT secret triggers warning log."""
weak_secret = "short" # Less than 32 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": weak_secret}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert settings.JWT_VERIFY_KEY == weak_secret
assert "key appears weak" in caplog.text.lower()
assert "less than 32 characters" in caplog.text
def test_secret_31_char_logs_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that 31-character secret triggers warning (boundary test)."""
secret_31 = "a" * 31 # Exactly 31 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_31}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert len(settings.JWT_VERIFY_KEY) == 31
assert "key appears weak" in caplog.text.lower()
def test_secret_32_char_no_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture
):
"""Test that 32-character secret does not trigger warning (boundary test)."""
secret_32 = "a" * 32 # Exactly 32 characters
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_32}, clear=True)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert len(settings.JWT_VERIFY_KEY) == 32
assert "JWT secret appears weak" not in caplog.text
def test_secret_whitespace_stripped(mocker: MockerFixture):
"""Test that JWT secret whitespace is stripped."""
secret = "a" * 32
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": f" {secret} "}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == secret
def test_secret_with_special_characters(mocker: MockerFixture):
"""Test JWT secret with special characters."""
special_secret = "!@#$%^&*()_+-=[]{}|;:,.<>?`~" + "a" * 10 # 40 chars total
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": special_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == special_secret
def test_secret_with_unicode(mocker: MockerFixture):
"""Test JWT secret with unicode characters."""
unicode_secret = "秘密🔐キー" + "a" * 25 # Ensure >32 bytes
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": unicode_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == unicode_secret
def test_secret_very_long(mocker: MockerFixture):
"""Test JWT secret with excessive length."""
long_secret = "a" * 1000 # 1000 character secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": long_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == long_secret
assert len(settings.JWT_VERIFY_KEY) == 1000
def test_secret_with_newline(mocker: MockerFixture):
"""Test JWT secret containing newlines."""
multiline_secret = "secret\nwith\nnewlines" + "a" * 20
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": multiline_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == multiline_secret
def test_secret_base64_encoded(mocker: MockerFixture):
"""Test JWT secret that looks like base64."""
base64_secret = "dGhpc19pc19hX3NlY3JldF9rZXlfd2l0aF9wcm9wZXJfbGVuZ3Ro"
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": base64_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == base64_secret
def test_secret_numeric_only(mocker: MockerFixture):
"""Test JWT secret with only numbers."""
numeric_secret = "1234567890" * 4 # 40 character numeric secret
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": numeric_secret}, clear=True)
settings = Settings()
assert settings.JWT_VERIFY_KEY == numeric_secret
def test_algorithm_default_hs256(mocker: MockerFixture):
"""Test that JWT algorithm defaults to HS256."""
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": "a" * 32}, clear=True)
settings = Settings()
assert settings.JWT_ALGORITHM == "HS256"
def test_algorithm_whitespace_stripped(mocker: MockerFixture):
"""Test that JWT algorithm whitespace is stripped."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": " HS256 "},
clear=True,
)
settings = Settings()
assert settings.JWT_ALGORITHM == "HS256"
def test_no_crypto_warning(mocker: MockerFixture, caplog: pytest.LogCaptureFixture):
"""Test warning when crypto package is not available."""
secret = "a" * 32
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
# Mock has_crypto to return False
mocker.patch("autogpt_libs.auth.config.has_crypto", False)
with caplog.at_level(logging.WARNING):
Settings()
assert "Asymmetric JWT verification is not available" in caplog.text
assert "cryptography" in caplog.text
def test_algorithm_invalid_raises_error(mocker: MockerFixture):
"""Test that invalid JWT algorithm raises AuthConfigError."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "INVALID_ALG"},
clear=True,
)
with pytest.raises(AuthConfigError) as exc_info:
Settings()
assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
assert "INVALID_ALG" in str(exc_info.value)
def test_algorithm_none_raises_error(mocker: MockerFixture):
"""Test that 'none' algorithm raises AuthConfigError."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "none"},
clear=True,
)
with pytest.raises(AuthConfigError) as exc_info:
Settings()
assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
@pytest.mark.parametrize("algorithm", ["HS256", "HS384", "HS512"])
def test_algorithm_symmetric_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
):
"""Test warning for symmetric algorithms (HS256, HS384, HS512)."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
clear=True,
)
with caplog.at_level(logging.WARNING):
settings = Settings()
assert algorithm in caplog.text
assert "symmetric shared-key signature algorithm" in caplog.text
assert settings.JWT_ALGORITHM == algorithm
@pytest.mark.parametrize(
"algorithm",
["ES256", "ES384", "ES512", "RS256", "RS384", "RS512", "PS256", "PS384", "PS512"],
)
def test_algorithm_asymmetric_no_warning(
mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
):
"""Test that asymmetric algorithms do not trigger warning."""
secret = "a" * 32
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
clear=True,
)
with caplog.at_level(logging.WARNING):
settings = Settings()
# Should not contain the symmetric algorithm warning
assert "symmetric shared-key signature algorithm" not in caplog.text
assert settings.JWT_ALGORITHM == algorithm

View File

@@ -1,117 +0,0 @@
"""
FastAPI dependency functions for JWT-based authentication and authorization.
These are the high-level dependency functions used in route definitions.
"""
import logging
import fastapi
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
from .jwt_utils import get_jwt_payload, verify_user
from .models import User
optional_bearer = HTTPBearer(auto_error=False)
# Header name for admin impersonation
IMPERSONATION_HEADER_NAME = "X-Act-As-User-Id"
logger = logging.getLogger(__name__)
def get_optional_user_id(
credentials: HTTPAuthorizationCredentials | None = fastapi.Security(
optional_bearer
),
) -> str | None:
"""
Attempts to extract the user ID ("sub" claim) from a Bearer JWT if provided.
This dependency allows for both authenticated and anonymous access. If a valid bearer token is
supplied, it parses the JWT and extracts the user ID. If the token is missing or invalid, it returns None,
treating the request as anonymous.
Args:
credentials: Optional HTTPAuthorizationCredentials object from FastAPI Security dependency.
Returns:
The user ID (str) extracted from the JWT "sub" claim, or None if no valid token is present.
"""
if not credentials:
return None
try:
# Parse JWT token to get user ID
from autogpt_libs.auth.jwt_utils import parse_jwt_token
payload = parse_jwt_token(credentials.credentials)
return payload.get("sub")
except Exception as e:
logger.debug(f"Auth token validation failed (anonymous access): {e}")
return None
async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
"""
FastAPI dependency that requires a valid authenticated user.
Raises:
HTTPException: 401 for authentication failures
"""
return verify_user(jwt_payload, admin_only=False)
async def requires_admin_user(
jwt_payload: dict = fastapi.Security(get_jwt_payload),
) -> User:
"""
FastAPI dependency that requires a valid admin user.
Raises:
HTTPException: 401 for authentication failures, 403 for insufficient permissions
"""
return verify_user(jwt_payload, admin_only=True)
async def get_user_id(
request: fastapi.Request, jwt_payload: dict = fastapi.Security(get_jwt_payload)
) -> str:
"""
FastAPI dependency that returns the ID of the authenticated user.
Supports admin impersonation via X-Act-As-User-Id header:
- If the header is present and user is admin, returns the impersonated user ID
- Otherwise returns the authenticated user's own ID
- Logs all impersonation actions for audit trail
Raises:
HTTPException: 401 for authentication failures or missing user ID
HTTPException: 403 if non-admin tries to use impersonation
"""
# Get the authenticated user's ID from JWT
user_id = jwt_payload.get("sub")
if not user_id:
raise fastapi.HTTPException(
status_code=401, detail="User ID not found in token"
)
# Check for admin impersonation header
impersonate_header = request.headers.get(IMPERSONATION_HEADER_NAME, "").strip()
if impersonate_header:
# Verify the authenticated user is an admin
authenticated_user = verify_user(jwt_payload, admin_only=False)
if authenticated_user.role != "admin":
raise fastapi.HTTPException(
status_code=403, detail="Only admin users can impersonate other users"
)
# Log the impersonation for audit trail
logger.info(
f"Admin impersonation: {authenticated_user.user_id} ({authenticated_user.email}) "
f"acting as user {impersonate_header} for requesting {request.method} {request.url}"
)
return impersonate_header
return user_id

View File

@@ -1,554 +0,0 @@
"""
Comprehensive integration tests for authentication dependencies.
Tests the full authentication flow from HTTP requests to user validation.
"""
import os
from unittest.mock import Mock
import pytest
from fastapi import FastAPI, HTTPException, Request, Security
from fastapi.testclient import TestClient
from pytest_mock import MockerFixture
from autogpt_libs.auth.dependencies import (
get_user_id,
requires_admin_user,
requires_user,
)
from autogpt_libs.auth.models import User
class TestAuthDependencies:
"""Test suite for authentication dependency functions."""
@pytest.fixture
def app(self):
"""Create a test FastAPI application."""
app = FastAPI()
@app.get("/user")
def get_user_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id, "role": user.role}
@app.get("/admin")
def get_admin_endpoint(user: User = Security(requires_admin_user)):
return {"user_id": user.user_id, "role": user.role}
@app.get("/user-id")
def get_user_id_endpoint(user_id: str = Security(get_user_id)):
return {"user_id": user_id}
return app
@pytest.fixture
def client(self, app):
"""Create a test client."""
return TestClient(app)
@pytest.mark.asyncio
async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
"""Test requires_user with valid JWT payload."""
jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
# Mock get_jwt_payload to return our test payload
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user = await requires_user(jwt_payload)
assert isinstance(user, User)
assert user.user_id == "user-123"
assert user.role == "user"
@pytest.mark.asyncio
async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
"""Test requires_user accepts admin users."""
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user = await requires_user(jwt_payload)
assert user.user_id == "admin-456"
assert user.role == "admin"
@pytest.mark.asyncio
async def test_requires_user_missing_sub(self):
"""Test requires_user with missing user ID."""
jwt_payload = {"role": "user", "email": "user@example.com"}
with pytest.raises(HTTPException) as exc_info:
await requires_user(jwt_payload)
assert exc_info.value.status_code == 401
assert "User ID not found" in exc_info.value.detail
@pytest.mark.asyncio
async def test_requires_user_empty_sub(self):
"""Test requires_user with empty user ID."""
jwt_payload = {"sub": "", "role": "user"}
with pytest.raises(HTTPException) as exc_info:
await requires_user(jwt_payload)
assert exc_info.value.status_code == 401
@pytest.mark.asyncio
async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
"""Test requires_admin_user with admin role."""
jwt_payload = {
"sub": "admin-789",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user = await requires_admin_user(jwt_payload)
assert user.user_id == "admin-789"
assert user.role == "admin"
@pytest.mark.asyncio
async def test_requires_admin_user_with_regular_user(self):
"""Test requires_admin_user rejects regular users."""
jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
with pytest.raises(HTTPException) as exc_info:
await requires_admin_user(jwt_payload)
assert exc_info.value.status_code == 403
assert "Admin access required" in exc_info.value.detail
@pytest.mark.asyncio
async def test_requires_admin_user_missing_role(self):
"""Test requires_admin_user with missing role."""
jwt_payload = {"sub": "user-123", "email": "user@example.com"}
with pytest.raises(KeyError):
await requires_admin_user(jwt_payload)
@pytest.mark.asyncio
async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
"""Test get_user_id extracts user ID correctly."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"sub": "user-id-xyz", "role": "user"}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
assert user_id == "user-id-xyz"
@pytest.mark.asyncio
async def test_get_user_id_missing_sub(self):
"""Test get_user_id with missing user ID."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"role": "user"}
with pytest.raises(HTTPException) as exc_info:
await get_user_id(request, jwt_payload)
assert exc_info.value.status_code == 401
assert "User ID not found" in exc_info.value.detail
@pytest.mark.asyncio
async def test_get_user_id_none_sub(self):
"""Test get_user_id with None user ID."""
request = Mock(spec=Request)
request.headers = {}
jwt_payload = {"sub": None, "role": "user"}
with pytest.raises(HTTPException) as exc_info:
await get_user_id(request, jwt_payload)
assert exc_info.value.status_code == 401
class TestAuthDependenciesIntegration:
"""Integration tests for auth dependencies with FastAPI."""
acceptable_jwt_secret = "test-secret-with-proper-length-123456"
@pytest.fixture
def create_token(self, mocker: MockerFixture):
"""Helper to create JWT tokens."""
import jwt
mocker.patch.dict(
os.environ,
{"JWT_VERIFY_KEY": self.acceptable_jwt_secret},
clear=True,
)
def _create_token(payload, secret=self.acceptable_jwt_secret):
return jwt.encode(payload, secret, algorithm="HS256")
return _create_token
@pytest.mark.asyncio
async def test_endpoint_auth_enabled_no_token(self):
"""Test endpoints require token when auth is enabled."""
app = FastAPI()
@app.get("/test")
def test_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id}
client = TestClient(app)
# Should fail without auth header
response = client.get("/test")
assert response.status_code == 401
@pytest.mark.asyncio
async def test_endpoint_with_valid_token(self, create_token):
"""Test endpoint with valid JWT token."""
app = FastAPI()
@app.get("/test")
def test_endpoint(user: User = Security(requires_user)):
return {"user_id": user.user_id, "role": user.role}
client = TestClient(app)
token = create_token(
{"sub": "test-user", "role": "user", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get("/test", headers={"Authorization": f"Bearer {token}"})
assert response.status_code == 200
assert response.json()["user_id"] == "test-user"
@pytest.mark.asyncio
async def test_admin_endpoint_requires_admin_role(self, create_token):
"""Test admin endpoint rejects non-admin users."""
app = FastAPI()
@app.get("/admin")
def admin_endpoint(user: User = Security(requires_admin_user)):
return {"user_id": user.user_id}
client = TestClient(app)
# Regular user token
user_token = create_token(
{"sub": "regular-user", "role": "user", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get(
"/admin", headers={"Authorization": f"Bearer {user_token}"}
)
assert response.status_code == 403
# Admin token
admin_token = create_token(
{"sub": "admin-user", "role": "admin", "aud": "authenticated"},
secret=self.acceptable_jwt_secret,
)
response = client.get(
"/admin", headers={"Authorization": f"Bearer {admin_token}"}
)
assert response.status_code == 200
assert response.json()["user_id"] == "admin-user"
class TestAuthDependenciesEdgeCases:
"""Edge case tests for authentication dependencies."""
@pytest.mark.asyncio
async def test_dependency_with_complex_payload(self):
"""Test dependencies handle complex JWT payloads."""
complex_payload = {
"sub": "user-123",
"role": "admin",
"email": "test@example.com",
"app_metadata": {"provider": "email", "providers": ["email"]},
"user_metadata": {
"full_name": "Test User",
"avatar_url": "https://example.com/avatar.jpg",
},
"aud": "authenticated",
"iat": 1234567890,
"exp": 9999999999,
}
user = await requires_user(complex_payload)
assert user.user_id == "user-123"
assert user.email == "test@example.com"
admin = await requires_admin_user(complex_payload)
assert admin.role == "admin"
@pytest.mark.asyncio
async def test_dependency_with_unicode_in_payload(self):
"""Test dependencies handle unicode in JWT payloads."""
unicode_payload = {
"sub": "user-😀-123",
"role": "user",
"email": "测试@example.com",
"name": "日本語",
}
user = await requires_user(unicode_payload)
assert "😀" in user.user_id
assert user.email == "测试@example.com"
@pytest.mark.asyncio
async def test_dependency_with_null_values(self):
"""Test dependencies handle null values in payload."""
null_payload = {
"sub": "user-123",
"role": "user",
"email": None,
"phone": None,
"metadata": None,
}
user = await requires_user(null_payload)
assert user.user_id == "user-123"
assert user.email is None
@pytest.mark.asyncio
async def test_concurrent_requests_isolation(self):
"""Test that concurrent requests don't interfere with each other."""
payload1 = {"sub": "user-1", "role": "user"}
payload2 = {"sub": "user-2", "role": "admin"}
# Simulate concurrent processing
user1 = await requires_user(payload1)
user2 = await requires_admin_user(payload2)
assert user1.user_id == "user-1"
assert user2.user_id == "user-2"
assert user1.role == "user"
assert user2.role == "admin"
@pytest.mark.parametrize(
"payload,expected_error,admin_only",
[
(None, "Authorization header is missing", False),
({}, "User ID not found", False),
({"sub": ""}, "User ID not found", False),
({"role": "user"}, "User ID not found", False),
({"sub": "user", "role": "user"}, "Admin access required", True),
],
)
@pytest.mark.asyncio
async def test_dependency_error_cases(
self, payload, expected_error: str, admin_only: bool
):
"""Test that errors propagate correctly through dependencies."""
# Import verify_user to test it directly since dependencies use FastAPI Security
from autogpt_libs.auth.jwt_utils import verify_user
with pytest.raises(HTTPException) as exc_info:
verify_user(payload, admin_only=admin_only)
assert expected_error in exc_info.value.detail
@pytest.mark.asyncio
async def test_dependency_valid_user(self):
"""Test valid user case for dependency."""
# Import verify_user to test it directly since dependencies use FastAPI Security
from autogpt_libs.auth.jwt_utils import verify_user
# Valid case
user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
assert user.user_id == "user"
class TestAdminImpersonation:
"""Test suite for admin user impersonation functionality."""
@pytest.mark.asyncio
async def test_admin_impersonation_success(self, mocker: MockerFixture):
"""Test admin successfully impersonating another user."""
request = Mock(spec=Request)
request.headers = {"X-Act-As-User-Id": "target-user-123"}
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
# Mock verify_user to return admin user data
mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
mock_verify_user.return_value = Mock(
user_id="admin-456", email="admin@example.com", role="admin"
)
# Mock logger to verify audit logging
mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Should return the impersonated user ID
assert user_id == "target-user-123"
# Should log the impersonation attempt
mock_logger.info.assert_called_once()
log_call = mock_logger.info.call_args[0][0]
assert "Admin impersonation:" in log_call
assert "admin@example.com" in log_call
assert "target-user-123" in log_call
@pytest.mark.asyncio
async def test_non_admin_impersonation_attempt(self, mocker: MockerFixture):
"""Test non-admin user attempting impersonation returns 403."""
request = Mock(spec=Request)
request.headers = {"X-Act-As-User-Id": "target-user-123"}
jwt_payload = {
"sub": "regular-user",
"role": "user",
"email": "user@example.com",
}
# Mock verify_user to return regular user data
mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
mock_verify_user.return_value = Mock(
user_id="regular-user", email="user@example.com", role="user"
)
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
with pytest.raises(HTTPException) as exc_info:
await get_user_id(request, jwt_payload)
assert exc_info.value.status_code == 403
assert "Only admin users can impersonate other users" in exc_info.value.detail
@pytest.mark.asyncio
async def test_impersonation_empty_header(self, mocker: MockerFixture):
"""Test impersonation with empty header falls back to regular user ID."""
request = Mock(spec=Request)
request.headers = {"X-Act-As-User-Id": ""}
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Should fall back to the admin's own user ID
assert user_id == "admin-456"
@pytest.mark.asyncio
async def test_impersonation_missing_header(self, mocker: MockerFixture):
"""Test normal behavior when impersonation header is missing."""
request = Mock(spec=Request)
request.headers = {} # No impersonation header
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Should return the admin's own user ID
assert user_id == "admin-456"
@pytest.mark.asyncio
async def test_impersonation_audit_logging_details(self, mocker: MockerFixture):
"""Test that impersonation audit logging includes all required details."""
request = Mock(spec=Request)
request.headers = {"X-Act-As-User-Id": "victim-user-789"}
jwt_payload = {
"sub": "admin-999",
"role": "admin",
"email": "superadmin@company.com",
}
# Mock verify_user to return admin user data
mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
mock_verify_user.return_value = Mock(
user_id="admin-999", email="superadmin@company.com", role="admin"
)
# Mock logger to capture audit trail
mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Verify all audit details are logged
assert user_id == "victim-user-789"
mock_logger.info.assert_called_once()
log_message = mock_logger.info.call_args[0][0]
assert "Admin impersonation:" in log_message
assert "superadmin@company.com" in log_message
assert "victim-user-789" in log_message
@pytest.mark.asyncio
async def test_impersonation_header_case_sensitivity(self, mocker: MockerFixture):
"""Test that impersonation header is case-sensitive."""
request = Mock(spec=Request)
# Use wrong case - should not trigger impersonation
request.headers = {"x-act-as-user-id": "target-user-123"}
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Should fall back to admin's own ID (header case mismatch)
assert user_id == "admin-456"
@pytest.mark.asyncio
async def test_impersonation_with_whitespace_header(self, mocker: MockerFixture):
"""Test impersonation with whitespace in header value."""
request = Mock(spec=Request)
request.headers = {"X-Act-As-User-Id": " target-user-123 "}
jwt_payload = {
"sub": "admin-456",
"role": "admin",
"email": "admin@example.com",
}
# Mock verify_user to return admin user data
mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
mock_verify_user.return_value = Mock(
user_id="admin-456", email="admin@example.com", role="admin"
)
# Mock logger
mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
mocker.patch(
"autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
)
user_id = await get_user_id(request, jwt_payload)
# Should strip whitespace and impersonate successfully
assert user_id == "target-user-123"
mock_logger.info.assert_called_once()

View File

@@ -0,0 +1,46 @@
import fastapi
from .config import Settings
from .middleware import auth_middleware
from .models import DEFAULT_USER_ID, User
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fastapi.Depends(auth_middleware),
) -> User:
return verify_user(payload, admin_only=True)
def verify_user(payload: dict | None, admin_only: bool) -> User:
if not payload:
if Settings.ENABLE_AUTH:
raise fastapi.HTTPException(
status_code=401, detail="Authorization header is missing"
)
# This handles the case when authentication is disabled
payload = {"sub": DEFAULT_USER_ID, "role": "admin"}
user_id = payload.get("sub")
if not user_id:
raise fastapi.HTTPException(
status_code=401, detail="User ID not found in token"
)
if admin_only and payload["role"] != "admin":
raise fastapi.HTTPException(status_code=403, detail="Admin access required")
return User.from_payload(payload)
def get_user_id(payload: dict = fastapi.Depends(auth_middleware)) -> str:
user_id = payload.get("sub")
if not user_id:
raise fastapi.HTTPException(
status_code=401, detail="User ID not found in token"
)
return user_id

View File

@@ -0,0 +1,68 @@
import pytest
from .depends import requires_admin_user, requires_user, verify_user
def test_verify_user_no_payload():
user = verify_user(None, admin_only=False)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_verify_user_no_user_id():
with pytest.raises(Exception):
verify_user({"role": "admin"}, admin_only=False)
def test_verify_user_not_admin():
with pytest.raises(Exception):
verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
admin_only=True,
)
def test_verify_user_with_admin_role():
user = verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"},
admin_only=True,
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_verify_user_with_user_role():
user = verify_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
admin_only=False,
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "user"
def test_requires_user():
user = requires_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "user"
def test_requires_user_no_user_id():
with pytest.raises(Exception):
requires_user({"role": "user"})
def test_requires_admin_user():
user = requires_admin_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"}
)
assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
assert user.role == "admin"
def test_requires_admin_user_not_admin():
with pytest.raises(Exception):
requires_admin_user(
{"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
)

View File

@@ -1,64 +0,0 @@
from fastapi import FastAPI
from .jwt_utils import bearer_jwt_auth
def add_auth_responses_to_openapi(app: FastAPI) -> None:
"""
Patch a FastAPI instance's `openapi()` method to add 401 responses
to all authenticated endpoints.
This is needed when using HTTPBearer with auto_error=False to get proper
401 responses instead of 403, but FastAPI only automatically adds security
responses when auto_error=True.
"""
# Wrap current method to allow stacking OpenAPI schema modifiers like this
wrapped_openapi = app.openapi
def custom_openapi():
if app.openapi_schema:
return app.openapi_schema
openapi_schema = wrapped_openapi()
# Add 401 response to all endpoints that have security requirements
for path, methods in openapi_schema["paths"].items():
for method, details in methods.items():
security_schemas = [
schema
for auth_option in details.get("security", [])
for schema in auth_option.keys()
]
if bearer_jwt_auth.scheme_name not in security_schemas:
continue
if "responses" not in details:
details["responses"] = {}
details["responses"]["401"] = {
"$ref": "#/components/responses/HTTP401NotAuthenticatedError"
}
# Ensure #/components/responses exists
if "components" not in openapi_schema:
openapi_schema["components"] = {}
if "responses" not in openapi_schema["components"]:
openapi_schema["components"]["responses"] = {}
# Define 401 response
openapi_schema["components"]["responses"]["HTTP401NotAuthenticatedError"] = {
"description": "Authentication required",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {"detail": {"type": "string"}},
}
}
},
}
app.openapi_schema = openapi_schema
return app.openapi_schema
app.openapi = custom_openapi

View File

@@ -1,435 +0,0 @@
"""
Comprehensive tests for auth helpers module to achieve 100% coverage.
Tests OpenAPI schema generation and authentication response handling.
"""
from unittest import mock
from fastapi import FastAPI
from fastapi.openapi.utils import get_openapi
from autogpt_libs.auth.helpers import add_auth_responses_to_openapi
from autogpt_libs.auth.jwt_utils import bearer_jwt_auth
def test_add_auth_responses_to_openapi_basic():
"""Test adding 401 responses to OpenAPI schema."""
app = FastAPI(title="Test App", version="1.0.0")
# Add some test endpoints with authentication
from fastapi import Depends
from autogpt_libs.auth.dependencies import requires_user
@app.get("/protected", dependencies=[Depends(requires_user)])
def protected_endpoint():
return {"message": "Protected"}
@app.get("/public")
def public_endpoint():
return {"message": "Public"}
# Apply the OpenAPI customization
add_auth_responses_to_openapi(app)
# Get the OpenAPI schema
schema = app.openapi()
# Verify basic schema properties
assert schema["info"]["title"] == "Test App"
assert schema["info"]["version"] == "1.0.0"
# Verify 401 response component is added
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# Verify 401 response structure
error_response = schema["components"]["responses"]["HTTP401NotAuthenticatedError"]
assert error_response["description"] == "Authentication required"
assert "application/json" in error_response["content"]
assert "schema" in error_response["content"]["application/json"]
# Verify schema properties
response_schema = error_response["content"]["application/json"]["schema"]
assert response_schema["type"] == "object"
assert "detail" in response_schema["properties"]
assert response_schema["properties"]["detail"]["type"] == "string"
def test_add_auth_responses_to_openapi_with_security():
"""Test that 401 responses are added only to secured endpoints."""
app = FastAPI()
# Mock endpoint with security
from fastapi import Security
from autogpt_libs.auth.dependencies import get_user_id
@app.get("/secured")
def secured_endpoint(user_id: str = Security(get_user_id)):
return {"user_id": user_id}
@app.post("/also-secured")
def another_secured(user_id: str = Security(get_user_id)):
return {"status": "ok"}
@app.get("/unsecured")
def unsecured_endpoint():
return {"public": True}
# Apply OpenAPI customization
add_auth_responses_to_openapi(app)
# Get schema
schema = app.openapi()
# Check that secured endpoints have 401 responses
if "/secured" in schema["paths"]:
if "get" in schema["paths"]["/secured"]:
secured_get = schema["paths"]["/secured"]["get"]
if "responses" in secured_get:
assert "401" in secured_get["responses"]
assert (
secured_get["responses"]["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
if "/also-secured" in schema["paths"]:
if "post" in schema["paths"]["/also-secured"]:
secured_post = schema["paths"]["/also-secured"]["post"]
if "responses" in secured_post:
assert "401" in secured_post["responses"]
# Check that unsecured endpoint does not have 401 response
if "/unsecured" in schema["paths"]:
if "get" in schema["paths"]["/unsecured"]:
unsecured_get = schema["paths"]["/unsecured"]["get"]
if "responses" in unsecured_get:
assert "401" not in unsecured_get.get("responses", {})
def test_add_auth_responses_to_openapi_cached_schema():
"""Test that OpenAPI schema is cached after first generation."""
app = FastAPI()
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema twice
schema1 = app.openapi()
schema2 = app.openapi()
# Should return the same cached object
assert schema1 is schema2
def test_add_auth_responses_to_openapi_existing_responses():
"""Test handling endpoints that already have responses defined."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get(
"/with-responses",
responses={
200: {"description": "Success"},
404: {"description": "Not found"},
},
)
def endpoint_with_responses(jwt: dict = Security(get_jwt_payload)):
return {"data": "test"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Check that existing responses are preserved and 401 is added
if "/with-responses" in schema["paths"]:
if "get" in schema["paths"]["/with-responses"]:
responses = schema["paths"]["/with-responses"]["get"].get("responses", {})
# Original responses should be preserved
if "200" in responses:
assert responses["200"]["description"] == "Success"
if "404" in responses:
assert responses["404"]["description"] == "Not found"
# 401 should be added
if "401" in responses:
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_add_auth_responses_to_openapi_no_security_endpoints():
"""Test with app that has no secured endpoints."""
app = FastAPI()
@app.get("/public1")
def public1():
return {"message": "public1"}
@app.post("/public2")
def public2():
return {"message": "public2"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Component should still be added for consistency
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# But no endpoints should have 401 responses
for path in schema["paths"].values():
for method in path.values():
if isinstance(method, dict) and "responses" in method:
assert "401" not in method["responses"]
def test_add_auth_responses_to_openapi_multiple_security_schemes():
"""Test endpoints with multiple security requirements."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.dependencies import requires_admin_user, requires_user
from autogpt_libs.auth.models import User
@app.get("/multi-auth")
def multi_auth(
user: User = Security(requires_user),
admin: User = Security(requires_admin_user),
):
return {"status": "super secure"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Should have 401 response
if "/multi-auth" in schema["paths"]:
if "get" in schema["paths"]["/multi-auth"]:
responses = schema["paths"]["/multi-auth"]["get"].get("responses", {})
if "401" in responses:
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_add_auth_responses_to_openapi_empty_components():
"""Test when OpenAPI schema has no components section initially."""
app = FastAPI()
# Mock get_openapi to return schema without components
original_get_openapi = get_openapi
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Remove components if it exists
if "components" in schema:
del schema["components"]
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Components should be created
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
def test_add_auth_responses_to_openapi_all_http_methods():
"""Test that all HTTP methods are handled correctly."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get("/resource")
def get_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "GET"}
@app.post("/resource")
def post_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "POST"}
@app.put("/resource")
def put_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "PUT"}
@app.patch("/resource")
def patch_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "PATCH"}
@app.delete("/resource")
def delete_resource(jwt: dict = Security(get_jwt_payload)):
return {"method": "DELETE"}
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# All methods should have 401 response
if "/resource" in schema["paths"]:
for method in ["get", "post", "put", "patch", "delete"]:
if method in schema["paths"]["/resource"]:
method_spec = schema["paths"]["/resource"][method]
if "responses" in method_spec:
assert "401" in method_spec["responses"]
def test_bearer_jwt_auth_scheme_config():
"""Test that bearer_jwt_auth is configured correctly."""
assert bearer_jwt_auth.scheme_name == "HTTPBearerJWT"
assert bearer_jwt_auth.auto_error is False
def test_add_auth_responses_with_no_routes():
"""Test OpenAPI generation with app that has no routes."""
app = FastAPI(title="Empty App")
# Apply customization to empty app
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Should still have basic structure
assert schema["info"]["title"] == "Empty App"
assert "components" in schema
assert "responses" in schema["components"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
def test_custom_openapi_function_replacement():
"""Test that the custom openapi function properly replaces the default."""
app = FastAPI()
# Store original function
original_openapi = app.openapi
# Apply customization
add_auth_responses_to_openapi(app)
# Function should be replaced
assert app.openapi != original_openapi
assert callable(app.openapi)
def test_endpoint_without_responses_section():
"""Test endpoint that has security but no responses section initially."""
app = FastAPI()
from fastapi import Security
from fastapi.openapi.utils import get_openapi as original_get_openapi
from autogpt_libs.auth.jwt_utils import get_jwt_payload
# Create endpoint
@app.get("/no-responses")
def endpoint_without_responses(jwt: dict = Security(get_jwt_payload)):
return {"data": "test"}
# Mock get_openapi to remove responses from the endpoint
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Remove responses from our endpoint to trigger line 40
if "/no-responses" in schema.get("paths", {}):
if "get" in schema["paths"]["/no-responses"]:
# Delete responses to force the code to create it
if "responses" in schema["paths"]["/no-responses"]["get"]:
del schema["paths"]["/no-responses"]["get"]["responses"]
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema and verify 401 was added
schema = app.openapi()
# The endpoint should now have 401 response
if "/no-responses" in schema["paths"]:
if "get" in schema["paths"]["/no-responses"]:
responses = schema["paths"]["/no-responses"]["get"].get("responses", {})
assert "401" in responses
assert (
responses["401"]["$ref"]
== "#/components/responses/HTTP401NotAuthenticatedError"
)
def test_components_with_existing_responses():
"""Test when components already has a responses section."""
app = FastAPI()
# Mock get_openapi to return schema with existing components/responses
from fastapi.openapi.utils import get_openapi as original_get_openapi
def mock_get_openapi(*args, **kwargs):
schema = original_get_openapi(*args, **kwargs)
# Add existing components/responses
if "components" not in schema:
schema["components"] = {}
schema["components"]["responses"] = {
"ExistingResponse": {"description": "An existing response"}
}
return schema
with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
# Apply customization
add_auth_responses_to_openapi(app)
schema = app.openapi()
# Both responses should exist
assert "ExistingResponse" in schema["components"]["responses"]
assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
# Verify our 401 response structure
error_response = schema["components"]["responses"][
"HTTP401NotAuthenticatedError"
]
assert error_response["description"] == "Authentication required"
def test_openapi_schema_persistence():
"""Test that modifications to OpenAPI schema persist correctly."""
app = FastAPI()
from fastapi import Security
from autogpt_libs.auth.jwt_utils import get_jwt_payload
@app.get("/test")
def test_endpoint(jwt: dict = Security(get_jwt_payload)):
return {"test": True}
# Apply customization
add_auth_responses_to_openapi(app)
# Get schema multiple times
schema1 = app.openapi()
# Modify the cached schema (shouldn't affect future calls)
schema1["info"]["title"] = "Modified Title"
# Clear cache and get again
app.openapi_schema = None
schema2 = app.openapi()
# Should regenerate with original title
assert schema2["info"]["title"] == app.title
assert schema2["info"]["title"] != "Modified Title"

View File

@@ -1,48 +1,11 @@
import logging
from typing import Any
from typing import Any, Dict
import jwt
from fastapi import HTTPException, Security
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
from .config import get_settings
from .models import User
logger = logging.getLogger(__name__)
# Bearer token authentication scheme
bearer_jwt_auth = HTTPBearer(
bearerFormat="jwt", scheme_name="HTTPBearerJWT", auto_error=False
)
from .config import settings
async def get_jwt_payload(
credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
) -> dict[str, Any]:
"""
Extract and validate JWT payload from HTTP Authorization header.
This is the core authentication function that handles:
- Reading the `Authorization` header to obtain the JWT token
- Verifying the JWT token's signature
- Decoding the JWT token's payload
:param credentials: HTTP Authorization credentials from bearer token
:return: JWT payload dictionary
:raises HTTPException: 401 if authentication fails
"""
if not credentials:
raise HTTPException(status_code=401, detail="Authorization header is missing")
try:
payload = parse_jwt_token(credentials.credentials)
logger.debug("Token decoded successfully")
return payload
except ValueError as e:
raise HTTPException(status_code=401, detail=str(e))
def parse_jwt_token(token: str) -> dict[str, Any]:
def parse_jwt_token(token: str) -> Dict[str, Any]:
"""
Parse and validate a JWT token.
@@ -50,11 +13,10 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
:return: The decoded payload
:raises ValueError: If the token is invalid or expired
"""
settings = get_settings()
try:
payload = jwt.decode(
token,
settings.JWT_VERIFY_KEY,
settings.JWT_SECRET_KEY,
algorithms=[settings.JWT_ALGORITHM],
audience="authenticated",
)
@@ -63,18 +25,3 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
raise ValueError("Token has expired")
except jwt.InvalidTokenError as e:
raise ValueError(f"Invalid token: {str(e)}")
def verify_user(jwt_payload: dict | None, admin_only: bool) -> User:
if jwt_payload is None:
raise HTTPException(status_code=401, detail="Authorization header is missing")
user_id = jwt_payload.get("sub")
if not user_id:
raise HTTPException(status_code=401, detail="User ID not found in token")
if admin_only and jwt_payload["role"] != "admin":
raise HTTPException(status_code=403, detail="Admin access required")
return User.from_payload(jwt_payload)

View File

@@ -1,308 +0,0 @@
"""
Comprehensive tests for JWT token parsing and validation.
Ensures 100% line and branch coverage for JWT security functions.
"""
import os
from datetime import datetime, timedelta, timezone
import jwt
import pytest
from fastapi import HTTPException
from fastapi.security import HTTPAuthorizationCredentials
from pytest_mock import MockerFixture
from autogpt_libs.auth import config, jwt_utils
from autogpt_libs.auth.config import Settings
from autogpt_libs.auth.models import User
MOCK_JWT_SECRET = "test-secret-key-with-at-least-32-characters"
TEST_USER_PAYLOAD = {
"sub": "test-user-id",
"role": "user",
"aud": "authenticated",
"email": "test@example.com",
}
TEST_ADMIN_PAYLOAD = {
"sub": "admin-user-id",
"role": "admin",
"aud": "authenticated",
"email": "admin@example.com",
}
@pytest.fixture(autouse=True)
def mock_config(mocker: MockerFixture):
mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": MOCK_JWT_SECRET}, clear=True)
mocker.patch.object(config, "_settings", Settings())
yield
def create_token(payload, secret=None, algorithm="HS256"):
"""Helper to create JWT tokens."""
if secret is None:
secret = MOCK_JWT_SECRET
return jwt.encode(payload, secret, algorithm=algorithm)
def test_parse_jwt_token_valid():
"""Test parsing a valid JWT token."""
token = create_token(TEST_USER_PAYLOAD)
result = jwt_utils.parse_jwt_token(token)
assert result["sub"] == "test-user-id"
assert result["role"] == "user"
assert result["aud"] == "authenticated"
def test_parse_jwt_token_expired():
"""Test parsing an expired JWT token."""
expired_payload = {
**TEST_USER_PAYLOAD,
"exp": datetime.now(timezone.utc) - timedelta(hours=1),
}
token = create_token(expired_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Token has expired" in str(exc_info.value)
def test_parse_jwt_token_invalid_signature():
"""Test parsing a token with invalid signature."""
# Create token with different secret
token = create_token(TEST_USER_PAYLOAD, secret="wrong-secret")
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_malformed():
"""Test parsing a malformed token."""
malformed_tokens = [
"not.a.token",
"invalid",
"",
# Header only
"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9",
# No signature
"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ0ZXN0In0",
]
for token in malformed_tokens:
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_wrong_audience():
"""Test parsing a token with wrong audience."""
wrong_aud_payload = {**TEST_USER_PAYLOAD, "aud": "wrong-audience"}
token = create_token(wrong_aud_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
def test_parse_jwt_token_missing_audience():
"""Test parsing a token without audience claim."""
no_aud_payload = {k: v for k, v in TEST_USER_PAYLOAD.items() if k != "aud"}
token = create_token(no_aud_payload)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)
async def test_get_jwt_payload_with_valid_token():
"""Test extracting JWT payload with valid bearer token."""
token = create_token(TEST_USER_PAYLOAD)
credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
result = await jwt_utils.get_jwt_payload(credentials)
assert result["sub"] == "test-user-id"
assert result["role"] == "user"
async def test_get_jwt_payload_no_credentials():
"""Test JWT payload when no credentials provided."""
with pytest.raises(HTTPException) as exc_info:
await jwt_utils.get_jwt_payload(None)
assert exc_info.value.status_code == 401
assert "Authorization header is missing" in exc_info.value.detail
async def test_get_jwt_payload_invalid_token():
"""Test JWT payload extraction with invalid token."""
credentials = HTTPAuthorizationCredentials(
scheme="Bearer", credentials="invalid.token.here"
)
with pytest.raises(HTTPException) as exc_info:
await jwt_utils.get_jwt_payload(credentials)
assert exc_info.value.status_code == 401
assert "Invalid token" in exc_info.value.detail
def test_verify_user_with_valid_user():
"""Test verifying a valid user."""
user = jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=False)
assert isinstance(user, User)
assert user.user_id == "test-user-id"
assert user.role == "user"
assert user.email == "test@example.com"
def test_verify_user_with_admin():
"""Test verifying an admin user."""
user = jwt_utils.verify_user(TEST_ADMIN_PAYLOAD, admin_only=True)
assert isinstance(user, User)
assert user.user_id == "admin-user-id"
assert user.role == "admin"
def test_verify_user_admin_only_with_regular_user():
"""Test verifying regular user when admin is required."""
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=True)
assert exc_info.value.status_code == 403
assert "Admin access required" in exc_info.value.detail
def test_verify_user_no_payload():
"""Test verifying user with no payload."""
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(None, admin_only=False)
assert exc_info.value.status_code == 401
assert "Authorization header is missing" in exc_info.value.detail
def test_verify_user_missing_sub():
"""Test verifying user with payload missing 'sub' field."""
invalid_payload = {"role": "user", "email": "test@example.com"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_empty_sub():
"""Test verifying user with empty 'sub' field."""
invalid_payload = {"sub": "", "role": "user"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_none_sub():
"""Test verifying user with None 'sub' field."""
invalid_payload = {"sub": None, "role": "user"}
with pytest.raises(HTTPException) as exc_info:
jwt_utils.verify_user(invalid_payload, admin_only=False)
assert exc_info.value.status_code == 401
assert "User ID not found in token" in exc_info.value.detail
def test_verify_user_missing_role_admin_check():
"""Test verifying admin when role field is missing."""
no_role_payload = {"sub": "user-id"}
with pytest.raises(KeyError):
# This will raise KeyError when checking payload["role"]
jwt_utils.verify_user(no_role_payload, admin_only=True)
# ======================== EDGE CASES ======================== #
def test_jwt_with_additional_claims():
"""Test JWT token with additional custom claims."""
extra_claims_payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
"custom_claim": "custom_value",
"permissions": ["read", "write"],
"metadata": {"key": "value"},
}
token = create_token(extra_claims_payload)
result = jwt_utils.parse_jwt_token(token)
assert result["sub"] == "user-id"
assert result["custom_claim"] == "custom_value"
assert result["permissions"] == ["read", "write"]
def test_jwt_with_numeric_sub():
"""Test JWT token with numeric user ID."""
payload = {
"sub": 12345, # Numeric ID
"role": "user",
"aud": "authenticated",
}
# Should convert to string internally
user = jwt_utils.verify_user(payload, admin_only=False)
assert user.user_id == 12345
def test_jwt_with_very_long_sub():
"""Test JWT token with very long user ID."""
long_id = "a" * 1000
payload = {
"sub": long_id,
"role": "user",
"aud": "authenticated",
}
user = jwt_utils.verify_user(payload, admin_only=False)
assert user.user_id == long_id
def test_jwt_with_special_characters_in_claims():
"""Test JWT token with special characters in claims."""
payload = {
"sub": "user@example.com/special-chars!@#$%",
"role": "admin",
"aud": "authenticated",
"email": "test+special@example.com",
}
user = jwt_utils.verify_user(payload, admin_only=True)
assert "special-chars!@#$%" in user.user_id
def test_jwt_with_future_iat():
"""Test JWT token with issued-at time in future."""
future_payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
"iat": datetime.now(timezone.utc) + timedelta(hours=1),
}
token = create_token(future_payload)
# PyJWT validates iat claim and should reject future tokens
with pytest.raises(ValueError, match="not yet valid"):
jwt_utils.parse_jwt_token(token)
def test_jwt_with_different_algorithms():
"""Test that only HS256 algorithm is accepted."""
payload = {
"sub": "user-id",
"role": "user",
"aud": "authenticated",
}
# Try different algorithms
algorithms = ["HS384", "HS512", "none"]
for algo in algorithms:
if algo == "none":
# Special case for 'none' algorithm (security vulnerability if accepted)
token = create_token(payload, "", algorithm="none")
else:
token = create_token(payload, algorithm=algo)
with pytest.raises(ValueError) as exc_info:
jwt_utils.parse_jwt_token(token)
assert "Invalid token" in str(exc_info.value)

View File

@@ -0,0 +1,135 @@
import inspect
import logging
from typing import Any, Callable, Optional
from fastapi import HTTPException, Request, Security
from fastapi.security import APIKeyHeader, HTTPBearer
from starlette.status import HTTP_401_UNAUTHORIZED
from .config import settings
from .jwt_utils import parse_jwt_token
security = HTTPBearer()
logger = logging.getLogger(__name__)
async def auth_middleware(request: Request):
if not settings.ENABLE_AUTH:
# If authentication is disabled, allow the request to proceed
logger.warn("Auth disabled")
return {}
security = HTTPBearer()
credentials = await security(request)
if not credentials:
raise HTTPException(status_code=401, detail="Authorization header is missing")
try:
payload = parse_jwt_token(credentials.credentials)
request.state.user = payload
logger.debug("Token decoded successfully")
except ValueError as e:
raise HTTPException(status_code=401, detail=str(e))
return payload
class APIKeyValidator:
"""
Configurable API key validator that supports custom validation functions
for FastAPI applications.
This class provides a flexible way to implement API key authentication with optional
custom validation logic. It can be used for simple token matching
or more complex validation scenarios like database lookups.
Examples:
Simple token validation:
```python
validator = APIKeyValidator(
header_name="X-API-Key",
expected_token="your-secret-token"
)
@app.get("/protected", dependencies=[Depends(validator.get_dependency())])
def protected_endpoint():
return {"message": "Access granted"}
```
Custom validation with database lookup:
```python
async def validate_with_db(api_key: str):
api_key_obj = await db.get_api_key(api_key)
return api_key_obj if api_key_obj and api_key_obj.is_active else None
validator = APIKeyValidator(
header_name="X-API-Key",
validate_fn=validate_with_db
)
```
Args:
header_name (str): The name of the header containing the API key
expected_token (Optional[str]): The expected API key value for simple token matching
validate_fn (Optional[Callable]): Custom validation function that takes an API key
string and returns a boolean or object. Can be async.
error_status (int): HTTP status code to use for validation errors
error_message (str): Error message to return when validation fails
"""
def __init__(
self,
header_name: str,
expected_token: Optional[str] = None,
validate_fn: Optional[Callable[[str], bool]] = None,
error_status: int = HTTP_401_UNAUTHORIZED,
error_message: str = "Invalid API key",
):
# Create the APIKeyHeader as a class property
self.security_scheme = APIKeyHeader(name=header_name)
self.expected_token = expected_token
self.custom_validate_fn = validate_fn
self.error_status = error_status
self.error_message = error_message
async def default_validator(self, api_key: str) -> bool:
return api_key == self.expected_token
async def __call__(
self, request: Request, api_key: str = Security(APIKeyHeader)
) -> Any:
if api_key is None:
raise HTTPException(status_code=self.error_status, detail="Missing API key")
# Use custom validation if provided, otherwise use default equality check
validator = self.custom_validate_fn or self.default_validator
result = (
await validator(api_key)
if inspect.iscoroutinefunction(validator)
else validator(api_key)
)
if not result:
raise HTTPException(
status_code=self.error_status, detail=self.error_message
)
# Store validation result in request state if it's not just a boolean
if result is not True:
request.state.api_key = result
return result
def get_dependency(self):
"""
Returns a callable dependency that FastAPI will recognize as a security scheme
"""
async def validate_api_key(
request: Request, api_key: str = Security(self.security_scheme)
) -> Any:
return await self(request, api_key)
# This helps FastAPI recognize it as a security dependency
validate_api_key.__name__ = f"validate_{self.security_scheme.model.name}"
return validate_api_key

View File

@@ -0,0 +1,166 @@
import asyncio
import contextlib
import logging
from functools import wraps
from typing import Any, Awaitable, Callable, Dict, Optional, TypeVar, Union, cast
import ldclient
from fastapi import HTTPException
from ldclient import Context, LDClient
from ldclient.config import Config
from typing_extensions import ParamSpec
from .config import SETTINGS
logger = logging.getLogger(__name__)
P = ParamSpec("P")
T = TypeVar("T")
def get_client() -> LDClient:
"""Get the LaunchDarkly client singleton."""
return ldclient.get()
def initialize_launchdarkly() -> None:
sdk_key = SETTINGS.launch_darkly_sdk_key
logger.debug(
f"Initializing LaunchDarkly with SDK key: {'present' if sdk_key else 'missing'}"
)
if not sdk_key:
logger.warning("LaunchDarkly SDK key not configured")
return
config = Config(sdk_key)
ldclient.set_config(config)
if ldclient.get().is_initialized():
logger.info("LaunchDarkly client initialized successfully")
else:
logger.error("LaunchDarkly client failed to initialize")
def shutdown_launchdarkly() -> None:
"""Shutdown the LaunchDarkly client."""
if ldclient.get().is_initialized():
ldclient.get().close()
logger.info("LaunchDarkly client closed successfully")
def create_context(
user_id: str, additional_attributes: Optional[Dict[str, Any]] = None
) -> Context:
"""Create LaunchDarkly context with optional additional attributes."""
builder = Context.builder(str(user_id)).kind("user")
if additional_attributes:
for key, value in additional_attributes.items():
builder.set(key, value)
return builder.build()
def feature_flag(
flag_key: str,
default: bool = False,
) -> Callable[
[Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
]:
"""
Decorator for feature flag protected endpoints.
"""
def decorator(
func: Callable[P, Union[T, Awaitable[T]]],
) -> Callable[P, Union[T, Awaitable[T]]]:
@wraps(func)
async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
try:
user_id = kwargs.get("user_id")
if not user_id:
raise ValueError("user_id is required")
if not get_client().is_initialized():
logger.warning(
f"LaunchDarkly not initialized, using default={default}"
)
is_enabled = default
else:
context = create_context(str(user_id))
is_enabled = get_client().variation(flag_key, context, default)
if not is_enabled:
raise HTTPException(status_code=404, detail="Feature not available")
result = func(*args, **kwargs)
if asyncio.iscoroutine(result):
return await result
return cast(T, result)
except Exception as e:
logger.error(f"Error evaluating feature flag {flag_key}: {e}")
raise
@wraps(func)
def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
try:
user_id = kwargs.get("user_id")
if not user_id:
raise ValueError("user_id is required")
if not get_client().is_initialized():
logger.warning(
f"LaunchDarkly not initialized, using default={default}"
)
is_enabled = default
else:
context = create_context(str(user_id))
is_enabled = get_client().variation(flag_key, context, default)
if not is_enabled:
raise HTTPException(status_code=404, detail="Feature not available")
return cast(T, func(*args, **kwargs))
except Exception as e:
logger.error(f"Error evaluating feature flag {flag_key}: {e}")
raise
return cast(
Callable[P, Union[T, Awaitable[T]]],
async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper,
)
return decorator
def percentage_rollout(
flag_key: str,
default: bool = False,
) -> Callable[
[Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
]:
"""Decorator for percentage-based rollouts."""
return feature_flag(flag_key, default)
def beta_feature(
flag_key: Optional[str] = None,
unauthorized_response: Any = {"message": "Not available in beta"},
) -> Callable[
[Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
]:
"""Decorator for beta features."""
actual_key = f"beta-{flag_key}" if flag_key else "beta"
return feature_flag(actual_key, False)
@contextlib.contextmanager
def mock_flag_variation(flag_key: str, return_value: Any):
"""Context manager for testing feature flags."""
original_variation = get_client().variation
get_client().variation = lambda key, context, default: (
return_value if key == flag_key else original_variation(key, context, default)
)
try:
yield
finally:
get_client().variation = original_variation

View File

@@ -0,0 +1,45 @@
import pytest
from ldclient import LDClient
from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
@pytest.fixture
def ld_client(mocker):
client = mocker.Mock(spec=LDClient)
mocker.patch("ldclient.get", return_value=client)
client.is_initialized.return_value = True
return client
@pytest.mark.asyncio
async def test_feature_flag_enabled(ld_client):
ld_client.variation.return_value = True
@feature_flag("test-flag")
async def test_function(user_id: str):
return "success"
result = test_function(user_id="test-user")
assert result == "success"
ld_client.variation.assert_called_once()
@pytest.mark.asyncio
async def test_feature_flag_unauthorized_response(ld_client):
ld_client.variation.return_value = False
@feature_flag("test-flag")
async def test_function(user_id: str):
return "success"
result = test_function(user_id="test-user")
assert result == {"error": "disabled"}
def test_mock_flag_variation(ld_client):
with mock_flag_variation("test-flag", True):
assert ld_client.variation("test-flag", None, False)
with mock_flag_variation("test-flag", False):
assert ld_client.variation("test-flag", None, False)

View File

@@ -0,0 +1,15 @@
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
launch_darkly_sdk_key: str = Field(
default="",
description="The Launch Darkly SDK key",
validation_alias="LAUNCH_DARKLY_SDK_KEY",
)
model_config = SettingsConfigDict(case_sensitive=True, extra="ignore")
SETTINGS = Settings()

View File

@@ -1,26 +1,14 @@
"""Logging module for Auto-GPT."""
import logging
import os
import socket
import sys
from logging.handlers import RotatingFileHandler
from pathlib import Path
from pydantic import Field, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
from .filters import BelowLevelFilter
from .formatters import AGPTFormatter
# Configure global socket timeout and gRPC keepalive to prevent deadlocks
# This must be done at import time before any gRPC connections are established
socket.setdefaulttimeout(30) # 30-second socket timeout
# Enable gRPC keepalive to detect dead connections faster
os.environ.setdefault("GRPC_KEEPALIVE_TIME_MS", "30000") # 30 seconds
os.environ.setdefault("GRPC_KEEPALIVE_TIMEOUT_MS", "5000") # 5 seconds
os.environ.setdefault("GRPC_KEEPALIVE_PERMIT_WITHOUT_CALLS", "true")
from .formatters import AGPTFormatter, StructuredLoggingFormatter
LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
LOG_FILE = "activity.log"
@@ -91,13 +79,29 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
Note: This function is typically called at the start of the application
to set up the logging infrastructure.
"""
config = LoggingConfig()
log_handlers: list[logging.Handler] = []
structured_logging = config.enable_cloud_logging or force_cloud_logging
# Cloud logging setup
if config.enable_cloud_logging or force_cloud_logging:
import google.cloud.logging
from google.cloud.logging.handlers import CloudLoggingHandler
from google.cloud.logging_v2.handlers.transports.sync import SyncTransport
# Console output handlers
if not structured_logging:
client = google.cloud.logging.Client()
cloud_handler = CloudLoggingHandler(
client,
name="autogpt_logs",
transport=SyncTransport,
)
cloud_handler.setLevel(config.level)
cloud_handler.setFormatter(StructuredLoggingFormatter())
log_handlers.append(cloud_handler)
print("Cloud logging enabled")
else:
# Console output handlers
stdout = logging.StreamHandler(stream=sys.stdout)
stdout.setLevel(config.level)
stdout.addFilter(BelowLevelFilter(logging.WARNING))
@@ -114,16 +118,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
log_handlers += [stdout, stderr]
# Cloud logging setup
else:
# Use Google Cloud Structured Log Handler. Log entries are printed to stdout
# in a JSON format which is automatically picked up by Google Cloud Logging.
from google.cloud.logging.handlers import StructuredLogHandler
structured_log_handler = StructuredLogHandler(stream=sys.stdout)
structured_log_handler.setLevel(config.level)
log_handlers.append(structured_log_handler)
print("Console logging enabled")
# File logging setup
if config.enable_file_logging:
@@ -134,13 +129,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
print(f"Log directory: {config.log_dir}")
# Activity log handler (INFO and above)
# Security fix: Use RotatingFileHandler with size limits to prevent disk exhaustion
activity_log_handler = RotatingFileHandler(
config.log_dir / LOG_FILE,
mode="a",
encoding="utf-8",
maxBytes=10 * 1024 * 1024, # 10MB per file
backupCount=3, # Keep 3 backup files (40MB total)
activity_log_handler = logging.FileHandler(
config.log_dir / LOG_FILE, "a", "utf-8"
)
activity_log_handler.setLevel(config.level)
activity_log_handler.setFormatter(
@@ -150,13 +140,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
if config.level == logging.DEBUG:
# Debug log handler (all levels)
# Security fix: Use RotatingFileHandler with size limits
debug_log_handler = RotatingFileHandler(
config.log_dir / DEBUG_LOG_FILE,
mode="a",
encoding="utf-8",
maxBytes=10 * 1024 * 1024, # 10MB per file
backupCount=3, # Keep 3 backup files (40MB total)
debug_log_handler = logging.FileHandler(
config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
)
debug_log_handler.setLevel(logging.DEBUG)
debug_log_handler.setFormatter(
@@ -165,27 +150,17 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
log_handlers.append(debug_log_handler)
# Error log handler (ERROR and above)
# Security fix: Use RotatingFileHandler with size limits
error_log_handler = RotatingFileHandler(
config.log_dir / ERROR_LOG_FILE,
mode="a",
encoding="utf-8",
maxBytes=10 * 1024 * 1024, # 10MB per file
backupCount=3, # Keep 3 backup files (40MB total)
error_log_handler = logging.FileHandler(
config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
)
error_log_handler.setLevel(logging.ERROR)
error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))
log_handlers.append(error_log_handler)
print("File logging enabled")
# Configure the root logger
logging.basicConfig(
format=(
"%(levelname)s %(message)s"
if structured_logging
else (
DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT
)
),
format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
level=config.level,
handlers=log_handlers,
)

View File

@@ -1,6 +1,7 @@
import logging
from colorama import Fore, Style
from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
from .utils import remove_color_codes
@@ -79,3 +80,16 @@ class AGPTFormatter(FancyConsoleFormatter):
return remove_color_codes(super().format(record))
else:
return super().format(record)
class StructuredLoggingFormatter(StructuredLogHandler, logging.Formatter):
def __init__(self):
# Set up CloudLoggingFilter to add diagnostic info to the log records
self.cloud_logging_filter = CloudLoggingFilter()
# Init StructuredLogHandler
super().__init__()
def format(self, record: logging.LogRecord) -> str:
self.cloud_logging_filter.filter(record)
return super().format(record)

View File

@@ -1,5 +1,27 @@
import logging
import re
from typing import Any
from colorama import Fore
def remove_color_codes(s: str) -> str:
return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
def fmt_kwargs(kwargs: dict) -> str:
return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
def print_attribute(
title: str, value: Any, title_color: str = Fore.GREEN, value_color: str = ""
) -> None:
logger = logging.getLogger()
logger.info(
str(value),
extra={
"title": f"{title.rstrip(':')}:",
"title_color": title_color,
"color": value_color,
},
)

View File

@@ -1,5 +1,3 @@
from typing import Optional
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
@@ -15,8 +13,8 @@ class RateLimitSettings(BaseSettings):
default="6379", description="Redis port", validation_alias="REDIS_PORT"
)
redis_password: Optional[str] = Field(
default=None,
redis_password: str = Field(
default="password",
description="Redis password",
validation_alias="REDIS_PASSWORD",
)

View File

@@ -11,7 +11,7 @@ class RateLimiter:
self,
redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
redis_password: str | None = RATE_LIMIT_SETTINGS.redis_password,
redis_password: str = RATE_LIMIT_SETTINGS.redis_password,
requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
):
self.redis = Redis(

View File

@@ -0,0 +1,20 @@
import threading
from typing import Callable, ParamSpec, TypeVar
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
if cache is None:
cache = thread_local.cache = {}
key = (args, tuple(sorted(kwargs.items())))
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
return wrapper

View File

@@ -1,15 +1,15 @@
import asyncio
from contextlib import asynccontextmanager
from contextlib import contextmanager
from threading import Lock
from typing import TYPE_CHECKING, Any
from expiringdict import ExpiringDict
if TYPE_CHECKING:
from redis.asyncio import Redis as AsyncRedis
from redis.asyncio.lock import Lock as AsyncRedisLock
from redis import Redis
from redis.lock import Lock as RedisLock
class AsyncRedisKeyedMutex:
class RedisKeyedMutex:
"""
This class provides a mutex that can be locked and unlocked by a specific key,
using Redis as a distributed locking provider.
@@ -17,45 +17,41 @@ class AsyncRedisKeyedMutex:
in case the key is not unlocked for a specified duration, to prevent memory leaks.
"""
def __init__(self, redis: "AsyncRedis", timeout: int | None = 60):
def __init__(self, redis: "Redis", timeout: int | None = 60):
self.redis = redis
self.timeout = timeout
self.locks: dict[Any, "AsyncRedisLock"] = ExpiringDict(
self.locks: dict[Any, "RedisLock"] = ExpiringDict(
max_len=6000, max_age_seconds=self.timeout
)
self.locks_lock = asyncio.Lock()
self.locks_lock = Lock()
@asynccontextmanager
async def locked(self, key: Any):
lock = await self.acquire(key)
@contextmanager
def locked(self, key: Any):
lock = self.acquire(key)
try:
yield
finally:
if (await lock.locked()) and (await lock.owned()):
await lock.release()
if lock.locked():
lock.release()
async def acquire(self, key: Any) -> "AsyncRedisLock":
def acquire(self, key: Any) -> "RedisLock":
"""Acquires and returns a lock with the given key"""
async with self.locks_lock:
with self.locks_lock:
if key not in self.locks:
self.locks[key] = self.redis.lock(
str(key), self.timeout, thread_local=False
)
lock = self.locks[key]
await lock.acquire()
lock.acquire()
return lock
async def release(self, key: Any):
if (
(lock := self.locks.get(key))
and (await lock.locked())
and (await lock.owned())
):
await lock.release()
def release(self, key: Any):
if (lock := self.locks.get(key)) and lock.locked() and lock.owned():
lock.release()
async def release_all_locks(self):
def release_all_locks(self):
"""Call this on process termination to ensure all locks are released"""
async with self.locks_lock:
for lock in self.locks.values():
if (await lock.locked()) and (await lock.owned()):
await lock.release()
self.locks_lock.acquire(blocking=False)
for lock in self.locks.values():
if lock.locked() and lock.owned():
lock.release()

File diff suppressed because it is too large Load Diff

View File

@@ -1,33 +1,27 @@
[tool.poetry]
name = "autogpt-libs"
version = "0.2.0"
description = "Shared libraries across AutoGPT Platform"
authors = ["AutoGPT team <info@agpt.co>"]
description = "Shared libraries across NextGen AutoGPT"
authors = ["Aarushi <aarushik93@gmail.com>"]
readme = "README.md"
packages = [{ include = "autogpt_libs" }]
[tool.poetry.dependencies]
python = ">=3.10,<4.0"
colorama = "^0.4.6"
cryptography = "^45.0"
expiringdict = "^1.2.2"
fastapi = "^0.116.1"
google-cloud-logging = "^3.12.1"
launchdarkly-server-sdk = "^9.12.0"
pydantic = "^2.11.7"
pydantic-settings = "^2.10.1"
pyjwt = { version = "^2.10.1", extras = ["crypto"] }
redis = "^6.2.0"
supabase = "^2.16.0"
uvicorn = "^0.35.0"
google-cloud-logging = "^3.11.4"
pydantic = "^2.10.6"
pydantic-settings = "^2.7.1"
pyjwt = "^2.10.1"
pytest-asyncio = "^0.25.3"
pytest-mock = "^3.14.0"
python = ">=3.10,<4.0"
python-dotenv = "^1.0.1"
supabase = "^2.13.0"
[tool.poetry.group.dev.dependencies]
pyright = "^1.1.404"
pytest = "^8.4.1"
pytest-asyncio = "^1.1.0"
pytest-mock = "^3.14.1"
pytest-cov = "^6.2.1"
ruff = "^0.12.11"
redis = "^5.2.1"
ruff = "^0.9.6"
[build-system]
requires = ["poetry-core"]

View File

@@ -1,52 +0,0 @@
# Development and testing files
**/__pycache__
**/*.pyc
**/*.pyo
**/*.pyd
**/.Python
**/env/
**/venv/
**/.venv/
**/pip-log.txt
**/.pytest_cache/
**/test-results/
**/snapshots/
**/test/
# IDE and editor files
**/.vscode/
**/.idea/
**/*.swp
**/*.swo
*~
# OS files
.DS_Store
Thumbs.db
# Logs
**/*.log
**/logs/
# Git
.git/
.gitignore
# Documentation
**/*.md
!README.md
# Local development files
.env
.env.local
**/.env.test
# Build artifacts
**/dist/
**/build/
**/target/
# Docker files (avoid recursion)
Dockerfile*
docker-compose*
.dockerignore

View File

@@ -1,9 +1,3 @@
# Backend Configuration
# This file contains environment variables that MUST be set for the AutoGPT platform
# Variables with working defaults in settings.py are not included here
## ===== REQUIRED DATABASE CONFIGURATION ===== ##
# PostgreSQL Database Connection
DB_USER=postgres
DB_PASS=your-super-secret-and-long-postgres-password
DB_NAME=postgres
@@ -14,51 +8,67 @@ DB_CONNECT_TIMEOUT=60
DB_POOL_TIMEOUT=300
DB_SCHEMA=platform
DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
PRISMA_SCHEMA="postgres/schema.prisma"
## ===== REQUIRED SERVICE CREDENTIALS ===== ##
# Redis Configuration
# EXECUTOR
NUM_GRAPH_WORKERS=10
NUM_NODE_WORKERS=3
BACKEND_CORS_ALLOW_ORIGINS=["http://localhost:3000"]
# generate using `from cryptography.fernet import Fernet;Fernet.generate_key().decode()`
ENCRYPTION_KEY='dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw='
UNSUBSCRIBE_SECRET_KEY = 'HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio='
REDIS_HOST=localhost
REDIS_PORT=6379
# REDIS_PASSWORD=
REDIS_PASSWORD=password
# RabbitMQ Credentials
ENABLE_CREDIT=false
STRIPE_API_KEY=
STRIPE_WEBHOOK_SECRET=
# What environment things should be logged under: local dev or prod
APP_ENV=local
# What environment to behave as: "local" or "cloud"
BEHAVE_AS=local
PYRO_HOST=localhost
SENTRY_DSN=
# Email For Postmark so we can send emails
POSTMARK_SERVER_API_TOKEN=
POSTMARK_SENDER_EMAIL=invalid@invalid.com
POSTMARK_WEBHOOK_TOKEN=
## User auth with Supabase is required for any of the 3rd party integrations with auth to work.
ENABLE_AUTH=true
SUPABASE_URL=http://localhost:8000
SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
SUPABASE_JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
# RabbitMQ credentials -- Used for communication between services
RABBITMQ_HOST=localhost
RABBITMQ_PORT=5672
RABBITMQ_DEFAULT_USER=rabbitmq_user_default
RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
# Supabase Authentication
SUPABASE_URL=http://localhost:8000
SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
JWT_VERIFY_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
## ===== REQUIRED SECURITY KEYS ===== ##
# Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()
ENCRYPTION_KEY=dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=
UNSUBSCRIBE_SECRET_KEY=HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio=
## ===== IMPORTANT OPTIONAL CONFIGURATION ===== ##
# Platform URLs (set these for webhooks and OAuth to work)
PLATFORM_BASE_URL=http://localhost:8000
FRONTEND_BASE_URL=http://localhost:3000
# Media Storage (required for marketplace and library functionality)
## GCS bucket is required for marketplace and library functionality
MEDIA_GCS_BUCKET_NAME=
## ===== API KEYS AND OAUTH CREDENTIALS ===== ##
# All API keys below are optional - only add what you need
## For local development, you may need to set FRONTEND_BASE_URL for the OAuth flow
## for integrations to work. Defaults to the value of PLATFORM_BASE_URL if not set.
# FRONTEND_BASE_URL=http://localhost:3000
# AI/LLM Services
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GROQ_API_KEY=
LLAMA_API_KEY=
AIML_API_KEY=
V0_API_KEY=
OPEN_ROUTER_API_KEY=
NVIDIA_API_KEY=
## PLATFORM_BASE_URL must be set to a *publicly accessible* URL pointing to your backend
## to use the platform's webhook-related functionality.
## If you are developing locally, you can use something like ngrok to get a publc URL
## and tunnel it to your locally running backend.
PLATFORM_BASE_URL=http://localhost:3000
## == INTEGRATION CREDENTIALS == ##
# Each set of server side credentials is required for the corresponding 3rd party
# integration to work.
# OAuth Credentials
# For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
# e.g. http://localhost:3000/auth/integrations/oauth_callback
@@ -66,13 +76,9 @@ NVIDIA_API_KEY=
GITHUB_CLIENT_ID=
GITHUB_CLIENT_SECRET=
# Notion OAuth App server credentials - https://developers.notion.com/docs/authorization
# Configure a public integration
NOTION_CLIENT_ID=
NOTION_CLIENT_SECRET=
# Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
# https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
# You'll need to add/enable the following scopes (minimum):
# https://console.developers.google.com/apis/api/gmail.googleapis.com/overview ?project=<your_project_id>
# https://console.cloud.google.com/apis/library/sheets.googleapis.com/ ?project=<your_project_id>
@@ -108,68 +114,86 @@ LINEAR_CLIENT_SECRET=
TODOIST_CLIENT_ID=
TODOIST_CLIENT_SECRET=
NOTION_CLIENT_ID=
NOTION_CLIENT_SECRET=
## ===== OPTIONAL API KEYS ===== ##
# Discord OAuth App credentials
# 1. Go to https://discord.com/developers/applications
# 2. Create a new application
# 3. Go to OAuth2 section and add redirect URI: http://localhost:3000/auth/integrations/oauth_callback
# 4. Copy Client ID and Client Secret below
DISCORD_CLIENT_ID=
DISCORD_CLIENT_SECRET=
# LLM
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GROQ_API_KEY=
OPEN_ROUTER_API_KEY=
# Reddit
# Go to https://www.reddit.com/prefs/apps and create a new app
# Choose "script" for the type
# Fill in the redirect uri as <your_frontend_url>/auth/integrations/oauth_callback, e.g. http://localhost:3000/auth/integrations/oauth_callback
REDDIT_CLIENT_ID=
REDDIT_CLIENT_SECRET=
REDDIT_USER_AGENT="AutoGPT:1.0 (by /u/autogpt)"
# Payment Processing
STRIPE_API_KEY=
STRIPE_WEBHOOK_SECRET=
# Email Service (for sending notifications and confirmations)
POSTMARK_SERVER_API_TOKEN=
POSTMARK_SENDER_EMAIL=invalid@invalid.com
POSTMARK_WEBHOOK_TOKEN=
# Error Tracking
SENTRY_DSN=
# Feature Flags
LAUNCH_DARKLY_SDK_KEY=
# Content Generation & Media
DID_API_KEY=
FAL_API_KEY=
IDEOGRAM_API_KEY=
REPLICATE_API_KEY=
REVID_API_KEY=
SCREENSHOTONE_API_KEY=
UNREAL_SPEECH_API_KEY=
# Data & Search Services
E2B_API_KEY=
EXA_API_KEY=
JINA_API_KEY=
MEM0_API_KEY=
OPENWEATHERMAP_API_KEY=
GOOGLE_MAPS_API_KEY=
# Communication Services
# Discord
DISCORD_BOT_TOKEN=
MEDIUM_API_KEY=
MEDIUM_AUTHOR_ID=
# SMTP/Email
SMTP_SERVER=
SMTP_PORT=
SMTP_USERNAME=
SMTP_PASSWORD=
# Business & Marketing Tools
# D-ID
DID_API_KEY=
# Open Weather Map
OPENWEATHERMAP_API_KEY=
# SMTP
SMTP_SERVER=
SMTP_PORT=
SMTP_USERNAME=
SMTP_PASSWORD=
# Medium
MEDIUM_API_KEY=
MEDIUM_AUTHOR_ID=
# Google Maps
GOOGLE_MAPS_API_KEY=
# Replicate
REPLICATE_API_KEY=
# Ideogram
IDEOGRAM_API_KEY=
# Fal
FAL_API_KEY=
# Exa
EXA_API_KEY=
# E2B
E2B_API_KEY=
# Example API Key
EXAMPLE_API_KEY=
# Mem0
MEM0_API_KEY=
# Nvidia
NVIDIA_API_KEY=
# Apollo
APOLLO_API_KEY=
ENRICHLAYER_API_KEY=
AYRSHARE_API_KEY=
AYRSHARE_JWT_KEY=
# SmartLead
SMARTLEAD_API_KEY=
# ZeroBounce
ZEROBOUNCE_API_KEY=
# Other Services
AUTOMOD_API_KEY=
# Logging Configuration
LOG_LEVEL=INFO
ENABLE_CLOUD_LOGGING=false
ENABLE_FILE_LOGGING=false
# Use to manually set the log directory
# LOG_DIR=./logs

View File

@@ -1,4 +1,3 @@
.env
database.db
database.db-journal
dev.db
@@ -9,12 +8,4 @@ secrets/*
!secrets/.gitkeep
*.ignore.*
*.ign.*
# Load test results and reports
load-tests/*_RESULTS.md
load-tests/*_REPORT.md
load-tests/results/
load-tests/*.json
load-tests/*.log
load-tests/node_modules/*
*.ign.*

View File

@@ -1,43 +1,31 @@
FROM debian:13-slim AS builder
FROM python:3.11.10-slim-bookworm AS builder
# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
WORKDIR /app
RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
# Install Node.js repository key and setup
RUN apt-get update --allow-releaseinfo-change --fix-missing \
&& apt-get install -y curl ca-certificates gnupg \
&& mkdir -p /etc/apt/keyrings \
&& curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
&& echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list
RUN apt-get update --allow-releaseinfo-change --fix-missing
# Update package list and install Python, Node.js, and build dependencies
RUN apt-get update \
&& apt-get install -y \
python3.13 \
python3.13-dev \
python3.13-venv \
python3-pip \
build-essential \
libpq5 \
libz-dev \
libssl-dev \
postgresql-client \
nodejs \
&& rm -rf /var/lib/apt/lists/*
# Install build dependencies
RUN apt-get install -y build-essential
RUN apt-get install -y libpq5
RUN apt-get install -y libz-dev
RUN apt-get install -y libssl-dev
RUN apt-get install -y postgresql-client
ENV POETRY_HOME=/opt/poetry
ENV POETRY_NO_INTERACTION=1
ENV POETRY_VIRTUALENVS_CREATE=true
ENV POETRY_VIRTUALENVS_IN_PROJECT=true
ENV POETRY_VIRTUALENVS_CREATE=false
ENV PATH=/opt/poetry/bin:$PATH
RUN pip3 install poetry --break-system-packages
# Upgrade pip and setuptools to fix security vulnerabilities
RUN pip3 install --upgrade pip setuptools
RUN pip3 install poetry
# Copy and install dependencies
COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
@@ -47,39 +35,29 @@ RUN poetry install --no-ansi --no-root
# Generate Prisma client
COPY autogpt_platform/backend/schema.prisma ./
COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
RUN poetry run prisma generate && poetry run gen-prisma-stub
RUN poetry run prisma generate
FROM debian:13-slim AS server_dependencies
FROM python:3.11.10-slim-bookworm AS server_dependencies
WORKDIR /app
ENV POETRY_HOME=/opt/poetry \
POETRY_NO_INTERACTION=1 \
POETRY_VIRTUALENVS_CREATE=true \
POETRY_VIRTUALENVS_IN_PROJECT=true \
DEBIAN_FRONTEND=noninteractive
POETRY_VIRTUALENVS_CREATE=false
ENV PATH=/opt/poetry/bin:$PATH
# Install Python without upgrading system-managed packages
RUN apt-get update && apt-get install -y \
python3.13 \
python3-pip \
&& rm -rf /var/lib/apt/lists/*
# Upgrade pip and setuptools to fix security vulnerabilities
RUN pip3 install --upgrade pip setuptools
# Copy only necessary files from builder
COPY --from=builder /app /app
COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
# Copy Node.js installation for Prisma
COPY --from=builder /usr/bin/node /usr/bin/node
COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
COPY --from=builder /usr/bin/npm /usr/bin/npm
COPY --from=builder /usr/bin/npx /usr/bin/npx
COPY --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
COPY --from=builder /usr/local/bin /usr/local/bin
# Copy Prisma binaries
COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
ENV PATH="/app/.venv/bin:$PATH"
RUN mkdir -p /app/autogpt_platform/autogpt_libs
RUN mkdir -p /app/autogpt_platform/backend
@@ -90,18 +68,12 @@ COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.tom
WORKDIR /app/autogpt_platform/backend
FROM server_dependencies AS migrate
# Migration stage only needs schema and migrations - much lighter than full backend
COPY autogpt_platform/backend/schema.prisma /app/autogpt_platform/backend/
COPY autogpt_platform/backend/backend/data/partial_types.py /app/autogpt_platform/backend/backend/data/partial_types.py
COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migrations
FROM server_dependencies AS server
COPY autogpt_platform/backend /app/autogpt_platform/backend
RUN poetry install --no-ansi --only-root
ENV DATABASE_URL=""
ENV PORT=8000
CMD ["poetry", "run", "rest"]

View File

@@ -1,301 +0,0 @@
# Backend Testing Guide
This guide covers testing practices for the AutoGPT Platform backend, with a focus on snapshot testing for API endpoints.
## Table of Contents
- [Overview](#overview)
- [Running Tests](#running-tests)
- [Snapshot Testing](#snapshot-testing)
- [Writing Tests for API Routes](#writing-tests-for-api-routes)
- [Best Practices](#best-practices)
## Overview
The backend uses pytest for testing with the following key libraries:
- `pytest` - Test framework
- `pytest-asyncio` - Async test support
- `pytest-mock` - Mocking support
- `pytest-snapshot` - Snapshot testing for API responses
## Running Tests
### Run all tests
```bash
poetry run test
```
### Run specific test file
```bash
poetry run pytest path/to/test_file.py
```
### Run with verbose output
```bash
poetry run pytest -v
```
### Run with coverage
```bash
poetry run pytest --cov=backend
```
## Snapshot Testing
Snapshot testing captures the output of your code and compares it against previously saved snapshots. This is particularly useful for testing API responses.
### How Snapshot Testing Works
1. First run: Creates snapshot files in `snapshots/` directories
2. Subsequent runs: Compares output against saved snapshots
3. Changes detected: Test fails if output differs from snapshot
### Creating/Updating Snapshots
When you first write a test or when the expected output changes:
```bash
poetry run pytest path/to/test.py --snapshot-update
```
⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
### Snapshot Test Example
```python
import json
from pytest_snapshot.plugin import Snapshot
def test_api_endpoint(snapshot: Snapshot):
response = client.get("/api/endpoint")
# Snapshot the response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response.json(), indent=2, sort_keys=True),
"endpoint_response"
)
```
### Best Practices for Snapshots
1. **Use descriptive names**: `"user_list_response"` not `"response1"`
2. **Sort JSON keys**: Ensures consistent snapshots
3. **Format JSON**: Use `indent=2` for readable diffs
4. **Exclude dynamic data**: Remove timestamps, IDs, etc. that change between runs
Example of excluding dynamic data:
```python
response_data = response.json()
# Remove dynamic fields for snapshot
response_data.pop("created_at", None)
response_data.pop("id", None)
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"static_response_data"
)
```
## Writing Tests for API Routes
### Basic Structure
```python
import json
import fastapi
import fastapi.testclient
import pytest
from pytest_snapshot.plugin import Snapshot
from backend.api.features.myroute import router
app = fastapi.FastAPI()
app.include_router(router)
client = fastapi.testclient.TestClient(app)
def test_endpoint_success(snapshot: Snapshot):
response = client.get("/endpoint")
assert response.status_code == 200
# Test specific fields
data = response.json()
assert data["status"] == "success"
# Snapshot the full response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(data, indent=2, sort_keys=True),
"endpoint_success_response"
)
```
### Testing with Authentication
For the main API routes that use JWT authentication, auth is provided by the `autogpt_libs.auth` module. If the test actually uses the `user_id`, the recommended approach for testing is to mock the `get_jwt_payload` function, which underpins all higher-level auth functions used in the API (`requires_user`, `requires_admin_user`, `get_user_id`).
If the test doesn't need the `user_id` specifically, mocking is not necessary as during tests auth is disabled anyway (see `conftest.py`).
#### Using Global Auth Fixtures
Two global auth fixtures are provided by `backend/server/conftest.py`:
- `mock_jwt_user` - Regular user with `test_user_id` ("test-user-id")
- `mock_jwt_admin` - Admin user with `admin_user_id` ("admin-user-id")
These provide the easiest way to set up authentication mocking in test modules:
```python
import fastapi
import fastapi.testclient
import pytest
from backend.api.features.myroute import router
app = fastapi.FastAPI()
app.include_router(router)
client = fastapi.testclient.TestClient(app)
@pytest.fixture(autouse=True)
def setup_app_auth(mock_jwt_user):
"""Setup auth overrides for all tests in this module"""
from autogpt_libs.auth.jwt_utils import get_jwt_payload
app.dependency_overrides[get_jwt_payload] = mock_jwt_user['get_jwt_payload']
yield
app.dependency_overrides.clear()
```
For admin-only endpoints, use `mock_jwt_admin` instead:
```python
@pytest.fixture(autouse=True)
def setup_app_auth(mock_jwt_admin):
"""Setup auth overrides for admin tests"""
from autogpt_libs.auth.jwt_utils import get_jwt_payload
app.dependency_overrides[get_jwt_payload] = mock_jwt_admin['get_jwt_payload']
yield
app.dependency_overrides.clear()
```
The IDs are also available separately as fixtures:
- `test_user_id`
- `admin_user_id`
- `target_user_id` (for admin <-> user operations)
### Mocking External Services
```python
def test_external_api_call(mocker, snapshot):
# Mock external service
mock_response = {"external": "data"}
mocker.patch(
"backend.services.external_api.call",
return_value=mock_response
)
response = client.post("/api/process")
assert response.status_code == 200
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response.json(), indent=2, sort_keys=True),
"process_with_external_response"
)
```
## Best Practices
### 1. Test Organization
- Place tests next to the code: `routes.py``routes_test.py`
- Use descriptive test names: `test_create_user_with_invalid_email`
- Group related tests in classes when appropriate
### 2. Test Coverage
- Test happy path and error cases
- Test edge cases (empty data, invalid formats)
- Test authentication and authorization
### 3. Snapshot Testing Guidelines
- Review all snapshot changes carefully
- Don't snapshot sensitive data
- Keep snapshots focused and minimal
- Update snapshots intentionally, not accidentally
### 4. Async Testing
- Use regular `def` for FastAPI TestClient tests
- Use `async def` with `@pytest.mark.asyncio` for testing async functions directly
### 5. Fixtures
#### Global Fixtures (conftest.py)
Authentication fixtures are available globally from `conftest.py`:
- `mock_jwt_user` - Standard user authentication
- `mock_jwt_admin` - Admin user authentication
- `configured_snapshot` - Pre-configured snapshot fixture
#### Custom Fixtures
Create reusable fixtures for common test data:
```python
@pytest.fixture
def sample_user():
return {
"email": "test@example.com",
"name": "Test User"
}
def test_create_user(sample_user, snapshot):
response = client.post("/users", json=sample_user)
# ... test implementation
```
#### Test Isolation
All tests must use fixtures that ensure proper isolation:
- Authentication overrides are automatically cleaned up after each test
- Database connections are properly managed with cleanup
- Mock objects are reset between tests
## CI/CD Integration
The GitHub Actions workflow automatically runs tests on:
- Pull requests
- Pushes to main branch
Snapshot tests work in CI by:
1. Committing snapshot files to the repository
2. CI compares against committed snapshots
3. Fails if snapshots don't match
## Troubleshooting
### Snapshot Mismatches
- Review the diff carefully
- If changes are expected: `poetry run pytest --snapshot-update`
- If changes are unexpected: Fix the code causing the difference
### Async Test Issues
- Ensure async functions use `@pytest.mark.asyncio`
- Use `AsyncMock` for mocking async functions
- FastAPI TestClient handles async automatically
### Import Errors
- Check that all dependencies are in `pyproject.toml`
- Run `poetry install` to ensure dependencies are installed
- Verify import paths are correct
## Summary
Snapshot testing provides a powerful way to ensure API responses remain consistent. Combined with traditional assertions, it creates a robust test suite that catches regressions while remaining maintainable.
Remember: Good tests are as important as good code!

View File

@@ -1,242 +0,0 @@
listing_id,storeListingVersionId,slug,agent_name,agent_video,agent_image,featured,sub_heading,description,categories,useForOnboarding,is_available
6e60a900-9d7d-490e-9af2-a194827ed632,d85882b8-633f-44ce-a315-c20a8c123d19,flux-ai-image-generator,Flux AI Image Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg""]",false,Transform ideas into breathtaking images,"Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!","[""creative""]",false,true
f11fc6e9-6166-4676-ac5d-f07127b270c1,c775f60d-b99f-418b-8fe0-53172258c3ce,youtube-transcription-scraper,YouTube Transcription Scraper,https://youtu.be/H8S3pU68lGE,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg""]",false,Fetch the transcriptions from the most popular YouTube videos in your chosen topic,"Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content.","[""writing""]",false,true
17908889-b599-4010-8e4f-bed19b8f3446,6e16e65a-ad34-4108-b4fd-4a23fced5ea2,business-ownerceo-finder,Decision Maker Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg""]",false,Contact CEOs today,"Find the key decision-makers you need, fast.
This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses youre looking for and where, and it will:
* Search the area and gather public information
* Return names, roles, and contact details when available
* Provide smart Google search suggestions if details arent found
Perfect for:
* B2B sales teams seeking verified leads
* Recruiters sourcing local talent
* Researchers looking to connect with business leaders
Save hours of manual searching and get straight to the people who matter most.","[""business""]",true,true
72beca1d-45ea-4403-a7ce-e2af168ee428,415b7352-0dc6-4214-9d87-0ad3751b711d,smart-meeting-brief,Smart Meeting Prep,https://youtu.be/9ydZR2hkxaY,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png""]",true,Business meeting briefings delivered daily,"Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls.
How It Works
1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day.
2. It reviews recent email threads with each participant to surface key relationship history and communication context.
3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data.
4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points.","[""personal""]",true,true
9fa5697a-617b-4fae-aea0-7dbbed279976,b8ceb480-a7a2-4c90-8513-181a49f7071f,automated-support-ai,Automated Support Agent,https://youtu.be/nBMfu_5sgDA,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png""]",true,Automate up to 80 percent of inbound support emails,"Overview:
Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution.
How it Works:
New support emails are routed to the agent.
The agent checks internal documentation for answers.
It measures confidence in the answer found and either replies directly or escalates to a human.
Business Value:
Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times.","[""business""]",false,true
2bdac92b-a12c-4131-bb46-0e3b89f61413,31daf49d-31d3-476b-aa4c-099abc59b458,unspirational-poster-maker,Unspirational Poster Maker,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg""]",false,Because adulting is hard,"This witty AI agent generates hilariously relatable ""motivational"" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to ""get it together."" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.","[""creative""]",false,true
9adf005e-2854-4cc7-98cf-f7103b92a7b7,a03b0d8c-4751-43d6-a54e-c3b7856ba4e3,ai-shortform-video-generator-create-viral-ready-content,AI Video Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png""]",false,Create Viral-Ready Shorts Content in Seconds,"OVERVIEW
Transform any trending headline or broad topic into a polished, vertical short-form video in a single run.
The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags.
HOW IT WORKS
1. Input a topic or an exact news headline.
2. The agent fetches live search results and selects the most engaging related story.
3. Key facts are summarised into concise research notes.
4. Claude writes a 3035 second script with visual cues, a three-second hook, tension loops, and a call-to-action.
5. GPT-4o generates an eye-catching title and one or two discoverability hashtags.
6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”).
All voice, style and resolution settings can be adjusted in the Builder before you press ""Run"".
7. Output delivered: Title, Script, Hashtags, Video URL.
KEY USE CASES
- Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”).
- Real-time newsjacking with a specific breaking headline.
- Product-launch spotlights and quick event recaps while interest is high.
BUSINESS VALUE
- One-click speed: from idea to finished video in minutes.
- Consistent brand look: Revid presets keep voice, style and aspect ratio on spec.
- No-code workflow: marketers create social video without design or development queues.
- Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys.
Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once.
IMPORTANT NOTES
- The agent outputs exactly one video per execution. Run it again for additional shorts.
- Video rendering time varies; AI-generated footage may take several minutes.","[""writing""]",false,true
864e48ef-fee5-42c1-b6a4-2ae139db9fc1,55d40473-0f31-4ada-9e40-d3a7139fcbd4,automated-blog-writer,Automated SEO Blog Writer,https://youtu.be/nKcDCbDVobs,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg""]",true,"Automate research, writing, and publishing for high-ranking blog posts","Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility.
How it works:
1. Share your pitch, website, and values.
2. The agent studies your site and uncovers proven SEO opportunities.
3. It spends two hours researching and drafting each post.
4. You set the cadence—publishing runs on autopilot.
Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most.
Use cases:
• Founders: Keep your blog active with no time drain.
• Agencies: Deliver scalable SEO content for clients.
• Strategists: Automate execution, focus on strategy.
• Marketers: Drive steady organic growth.
• Local businesses: Capture nearby search traffic.","[""writing""]",false,true
6046f42e-eb84-406f-bae0-8e052064a4fa,a548e507-09a7-4b30-909c-f63fcda10fff,lead-finder-local-businesses,Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp""]",false,Auto-Prospect Like a Pro,"Turbo-charge your local lead generation with the AutoGPT Marketplaces top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching.
**WHAT IT DOES**
• Searches Google Maps via the official API (no scraping)
• Prompts like “dentists in Chicago” or “coffee shops near me”
• Returns: Name, Website, Rating, Reviews, **Phone & Address**
• Exports instantly to your CRM, sheet, or outreach workflow
**WHY YOULL LOVE IT**
✓ Hyper-targeted leads in minutes
✓ Unlimited searches & locations
✓ Zero CAPTCHAs or IP blocks
✓ Works on AutoGPT Cloud or self-hosted (with your API key)
✓ Cut prospecting time by 90%
**PERFECT FOR**
— Marketers & PPC agencies
— SEO consultants & designers
— SaaS founders & sales teams
Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit.
→ Click *Add to Library* and own your market today.","[""business""]",true,true
f623c862-24e9-44fc-8ce8-d8282bb51ad2,eafa21d3-bf14-4f63-a97f-a5ee41df83b3,linkedin-post-generator,LinkedIn Post Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png""]",false,Autocraft LinkedIn gold,"Create researchdriven, highimpact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed.
FEATURES
• Automated YouTube research discovers and analyses topranked videos so you dont have to
• AIcurated synthesis combines multiple transcripts into one authoritative narrative
• Full creative control adjust style, tone, objective, opinion, clarity, target word count and number of videos
• LinkedInoptimised output hook, 23 key points, CTA, strategic line breaks, 35 hashtags, no markdown
• Oneclick publish returns a readytopost text block (≤1 300 characters)
HOW IT WORKS
1. Enter a topic and your preferred writing parameters.
2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs.
3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet.
4. The model writes a concise, engaging post designed for maximum LinkedIn engagement.
USE CASES
• Thoughtleadership updates backed by fresh video research
• Rapid industry summaries after major events, webinars, or conferences
• Consistent LinkedIn content for busy founders, marketers, and creators
WHY YOULL LOVE IT
Save hours of manual research, avoid surfacelevel hottakes, and publish posts that showcase real expertise—without the heavy lift.","[""writing""]",true,true
7d4120ad-b6b3-4419-8bdb-7dd7d350ef32,e7bb29a1-23c7-4fee-aa3b-5426174b8c52,youtube-to-linkedin-post-converter,YouTube to LinkedIn Post Converter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png""]",false,Transform Your YouTube Videos into Engaging LinkedIn Posts with AI,"WHAT IT DOES:
This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn.
HOW IT WORKS:
- You provide the URL to the YouTube video (required)
- You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.)
- You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.)
- The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model
- The models extract key insights, memorable quotes, and the main points from the video
- Youll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement
INPUTS:
- Source YouTube Video Provide the URL to the YouTube video
- Structure Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.)
- Content Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.)
- Tone Select the tone for the post (e.g., Conversational, Inspirational, etc.)
OUTPUT:
- LinkedIn Post A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences
Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding.","[""writing""]",false,true
c61d6a83-ea48-4df8-b447-3da2d9fe5814,00fdd42c-a14c-4d19-a567-65374ea0e87f,personalized-morning-coffee-newsletter,Personal Newsletter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png""]",false,Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood.,"This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained.
How It Works
1. Enter your favorite topics, industries, or areas of interest.
2. Choose your tone—professional, casual, or humorous.
3. Set your preferred delivery cadence: daily or weekly.
4. The agent scans top sources and compiles 35 engaging stories, insights, and fun facts into a conversational newsletter.
Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day.
Use Cases
• Executives: Get a daily digest of market updates and leadership insights.
• Marketers: Receive curated creative trends and campaign inspiration.
• Entrepreneurs: Stay updated on your industry without information overload.","[""research""]",true,true
e2e49cfc-4a39-4d62-a6b3-c095f6d025ff,fc2c9976-0962-4625-a27b-d316573a9e7f,email-address-finder,Email Scout - Contact Finder Assistant,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg""]",false,Find contact details from name and location using AI search,"Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information.
Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like ""Tim Cook, USA"" or ""Sarah Smith, London"" and let the AI assistant do the work of finding potential contact details.
Key Features:
- Quick search from just name and location
- Scans multiple public sources
- Automated AI-powered search process
- Easy to use with simple inputs
Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact.
Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible.","[""""]",false,true
81bcc372-0922-4a36-bc35-f7b1e51d6939,e437cc95-e671-489d-b915-76561fba8c7f,ai-youtube-to-blog-converter,YouTube Video to SEO Blog Writer,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png""]",false,One link. One click. One powerful blog post.,"Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts.
Your videos deserve a second life—in writing.
Make your content work twice as hard by repurposing it into engaging, searchable articles.
Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest.
FEATURES
• CONTENT ANALYSIS
Extracts key points from the video while preserving your message and intent.
• CUSTOMIZABLE OUTPUT
Select a tone that fits your audience: casual, professional, educational, or formal.
• SEO OPTIMIZATION
Automatically creates engaging titles and structured subheadings for better search visibility.
• USER-FRIENDLY
Repurpose your videos into written content to expand your reach and improve accessibility.
Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless.
","[""writing""]",true,true
5c3510d2-fc8b-4053-8e19-67f53c86eb1a,f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9,ai-webpage-copy-improver,AI Webpage Copy Improver,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg""]",false,Boost Your Website's Search Engine Performance,"Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver.","[""marketing""]",true,true
94d03bd3-7d44-4d47-b60c-edb2f89508d6,b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0,domain-name-finder,Domain Name Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg""]",false,Instantly generate brand-ready domain names that are actually available,"Overview:
Finding a domain name that fits your brand shouldnt take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable.
How It Works
1. Input your product pitch, company name, or core keywords.
2. The agent analyzes brand tone, audience, and industry context.
3. It generates a list of unique, memorable domains that match your criteria.
4. All names are pre-filtered for real-time availability, so you can register immediately.
Business Value
Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains.
Key Use Cases
• Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands.
• Marketers: Test name options across campaigns with instant availability data.
• Entrepreneurs: Validate ideas faster with instant domain options.","[""business""]",false,true
7a831906-daab-426f-9d66-bcf98d869426,516d813b-d1bc-470f-add7-c63a4b2c2bad,ai-function,AI Function,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png""]",false,Never Code Again,"AI FUNCTION MAGIC
Your AIpowered assistant for turning plainEnglish descriptions into working Python functions.
HOW IT WORKS
1. Describe what the function should do.
2. Specify the inputs it needs.
3. Receive the generated Python code.
FEATURES
- Effortless Function Generation: convert naturallanguage specs into complete functions.
- Customizable Inputs: define the parameters that matter to you.
- Versatile Use Cases: simulate data, automate tasks, prototype ideas.
- Seamless Integration: add the generated function directly to your codebase.
EXAMPLE
Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.”
Input parameter: number_of_people (default 20)
Result: a list of dictionaries such as
[
{ ""name"": ""Emma Martinez"", ""date_of_birth"": ""19921103"", ""job_title"": ""Data Analyst"", ""age"": 32 },
{ ""name"": ""Liam OConnor"", ""date_of_birth"": ""19850719"", ""job_title"": ""Marketing Manager"", ""age"": 39 },
…18 more entries…
]","[""development""]",false,true
1 listing_id storeListingVersionId slug agent_name agent_video agent_image featured sub_heading description categories useForOnboarding is_available
2 6e60a900-9d7d-490e-9af2-a194827ed632 d85882b8-633f-44ce-a315-c20a8c123d19 flux-ai-image-generator Flux AI Image Generator ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg"] false Transform ideas into breathtaking images Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today! ["creative"] false true
3 f11fc6e9-6166-4676-ac5d-f07127b270c1 c775f60d-b99f-418b-8fe0-53172258c3ce youtube-transcription-scraper YouTube Transcription Scraper https://youtu.be/H8S3pU68lGE ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg"] false Fetch the transcriptions from the most popular YouTube videos in your chosen topic Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content. ["writing"] false true
4 17908889-b599-4010-8e4f-bed19b8f3446 6e16e65a-ad34-4108-b4fd-4a23fced5ea2 business-ownerceo-finder Decision Maker Lead Finder ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg"] false Contact CEOs today Find the key decision-makers you need, fast. This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses you’re looking for and where, and it will: * Search the area and gather public information * Return names, roles, and contact details when available * Provide smart Google search suggestions if details aren’t found Perfect for: * B2B sales teams seeking verified leads * Recruiters sourcing local talent * Researchers looking to connect with business leaders Save hours of manual searching and get straight to the people who matter most. ["business"] true true
5 72beca1d-45ea-4403-a7ce-e2af168ee428 415b7352-0dc6-4214-9d87-0ad3751b711d smart-meeting-brief Smart Meeting Prep https://youtu.be/9ydZR2hkxaY ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png"] true Business meeting briefings delivered daily Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls. How It Works 1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day. 2. It reviews recent email threads with each participant to surface key relationship history and communication context. 3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data. 4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points. ["personal"] true true
6 9fa5697a-617b-4fae-aea0-7dbbed279976 b8ceb480-a7a2-4c90-8513-181a49f7071f automated-support-ai Automated Support Agent https://youtu.be/nBMfu_5sgDA ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png"] true Automate up to 80 percent of inbound support emails Overview: Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution. How it Works: New support emails are routed to the agent. The agent checks internal documentation for answers. It measures confidence in the answer found and either replies directly or escalates to a human. Business Value: Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times. ["business"] false true
7 2bdac92b-a12c-4131-bb46-0e3b89f61413 31daf49d-31d3-476b-aa4c-099abc59b458 unspirational-poster-maker Unspirational Poster Maker ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg"] false Because adulting is hard This witty AI agent generates hilariously relatable "motivational" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to "get it together." Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos. ["creative"] false true
8 9adf005e-2854-4cc7-98cf-f7103b92a7b7 a03b0d8c-4751-43d6-a54e-c3b7856ba4e3 ai-shortform-video-generator-create-viral-ready-content AI Video Generator ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png"] false Create Viral-Ready Shorts Content in Seconds OVERVIEW Transform any trending headline or broad topic into a polished, vertical short-form video in a single run. The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags. HOW IT WORKS 1. Input a topic or an exact news headline. 2. The agent fetches live search results and selects the most engaging related story. 3. Key facts are summarised into concise research notes. 4. Claude writes a 30–35 second script with visual cues, a three-second hook, tension loops, and a call-to-action. 5. GPT-4o generates an eye-catching title and one or two discoverability hashtags. 6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”). – All voice, style and resolution settings can be adjusted in the Builder before you press "Run". 7. Output delivered: Title, Script, Hashtags, Video URL. KEY USE CASES - Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”). - Real-time newsjacking with a specific breaking headline. - Product-launch spotlights and quick event recaps while interest is high. BUSINESS VALUE - One-click speed: from idea to finished video in minutes. - Consistent brand look: Revid presets keep voice, style and aspect ratio on spec. - No-code workflow: marketers create social video without design or development queues. - Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys. Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once. IMPORTANT NOTES - The agent outputs exactly one video per execution. Run it again for additional shorts. - Video rendering time varies; AI-generated footage may take several minutes. ["writing"] false true
9 864e48ef-fee5-42c1-b6a4-2ae139db9fc1 55d40473-0f31-4ada-9e40-d3a7139fcbd4 automated-blog-writer Automated SEO Blog Writer https://youtu.be/nKcDCbDVobs ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg"] true Automate research, writing, and publishing for high-ranking blog posts Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility. How it works: 1. Share your pitch, website, and values. 2. The agent studies your site and uncovers proven SEO opportunities. 3. It spends two hours researching and drafting each post. 4. You set the cadence—publishing runs on autopilot. Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most. Use cases: • Founders: Keep your blog active with no time drain. • Agencies: Deliver scalable SEO content for clients. • Strategists: Automate execution, focus on strategy. • Marketers: Drive steady organic growth. • Local businesses: Capture nearby search traffic. ["writing"] false true
10 6046f42e-eb84-406f-bae0-8e052064a4fa a548e507-09a7-4b30-909c-f63fcda10fff lead-finder-local-businesses Lead Finder ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp"] false Auto-Prospect Like a Pro Turbo-charge your local lead generation with the AutoGPT Marketplace’s top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching. **WHAT IT DOES** • Searches Google Maps via the official API (no scraping) • Prompts like “dentists in Chicago” or “coffee shops near me” • Returns: Name, Website, Rating, Reviews, **Phone & Address** • Exports instantly to your CRM, sheet, or outreach workflow **WHY YOU’LL LOVE IT** ✓ Hyper-targeted leads in minutes ✓ Unlimited searches & locations ✓ Zero CAPTCHAs or IP blocks ✓ Works on AutoGPT Cloud or self-hosted (with your API key) ✓ Cut prospecting time by 90% **PERFECT FOR** — Marketers & PPC agencies — SEO consultants & designers — SaaS founders & sales teams Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit. → Click *Add to Library* and own your market today. ["business"] true true
11 f623c862-24e9-44fc-8ce8-d8282bb51ad2 eafa21d3-bf14-4f63-a97f-a5ee41df83b3 linkedin-post-generator LinkedIn Post Generator ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png"] false Auto‑craft LinkedIn gold Create research‑driven, high‑impact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed. FEATURES • Automated YouTube research – discovers and analyses top‑ranked videos so you don’t have to • AI‑curated synthesis – combines multiple transcripts into one authoritative narrative • Full creative control – adjust style, tone, objective, opinion, clarity, target word count and number of videos • LinkedIn‑optimised output – hook, 2‑3 key points, CTA, strategic line breaks, 3‑5 hashtags, no markdown • One‑click publish – returns a ready‑to‑post text block (≤1 300 characters) HOW IT WORKS 1. Enter a topic and your preferred writing parameters. 2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs. 3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet. 4. The model writes a concise, engaging post designed for maximum LinkedIn engagement. USE CASES • Thought‑leadership updates backed by fresh video research • Rapid industry summaries after major events, webinars, or conferences • Consistent LinkedIn content for busy founders, marketers, and creators WHY YOU’LL LOVE IT Save hours of manual research, avoid surface‑level hot‑takes, and publish posts that showcase real expertise—without the heavy lift. ["writing"] true true
12 7d4120ad-b6b3-4419-8bdb-7dd7d350ef32 e7bb29a1-23c7-4fee-aa3b-5426174b8c52 youtube-to-linkedin-post-converter YouTube to LinkedIn Post Converter ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png"] false Transform Your YouTube Videos into Engaging LinkedIn Posts with AI WHAT IT DOES: This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn. HOW IT WORKS: - You provide the URL to the YouTube video (required) - You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.) - You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.) - The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model - The models extract key insights, memorable quotes, and the main points from the video - You’ll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement INPUTS: - Source YouTube Video – Provide the URL to the YouTube video - Structure – Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.) - Content – Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.) - Tone – Select the tone for the post (e.g., Conversational, Inspirational, etc.) OUTPUT: - LinkedIn Post – A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding. ["writing"] false true
13 c61d6a83-ea48-4df8-b447-3da2d9fe5814 00fdd42c-a14c-4d19-a567-65374ea0e87f personalized-morning-coffee-newsletter Personal Newsletter ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png"] false Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood. This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained. How It Works 1. Enter your favorite topics, industries, or areas of interest. 2. Choose your tone—professional, casual, or humorous. 3. Set your preferred delivery cadence: daily or weekly. 4. The agent scans top sources and compiles 3–5 engaging stories, insights, and fun facts into a conversational newsletter. Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day. Use Cases • Executives: Get a daily digest of market updates and leadership insights. • Marketers: Receive curated creative trends and campaign inspiration. • Entrepreneurs: Stay updated on your industry without information overload. ["research"] true true
14 e2e49cfc-4a39-4d62-a6b3-c095f6d025ff fc2c9976-0962-4625-a27b-d316573a9e7f email-address-finder Email Scout - Contact Finder Assistant ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg"] false Find contact details from name and location using AI search Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information. Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like "Tim Cook, USA" or "Sarah Smith, London" and let the AI assistant do the work of finding potential contact details. Key Features: - Quick search from just name and location - Scans multiple public sources - Automated AI-powered search process - Easy to use with simple inputs Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact. Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible. [""] false true
15 81bcc372-0922-4a36-bc35-f7b1e51d6939 e437cc95-e671-489d-b915-76561fba8c7f ai-youtube-to-blog-converter YouTube Video to SEO Blog Writer ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png"] false One link. One click. One powerful blog post. Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts. Your videos deserve a second life—in writing. Make your content work twice as hard by repurposing it into engaging, searchable articles. Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest. FEATURES • CONTENT ANALYSIS Extracts key points from the video while preserving your message and intent. • CUSTOMIZABLE OUTPUT Select a tone that fits your audience: casual, professional, educational, or formal. • SEO OPTIMIZATION Automatically creates engaging titles and structured subheadings for better search visibility. • USER-FRIENDLY Repurpose your videos into written content to expand your reach and improve accessibility. Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless. ["writing"] true true
16 5c3510d2-fc8b-4053-8e19-67f53c86eb1a f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9 ai-webpage-copy-improver AI Webpage Copy Improver ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg"] false Boost Your Website's Search Engine Performance Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver. ["marketing"] true true
17 94d03bd3-7d44-4d47-b60c-edb2f89508d6 b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0 domain-name-finder Domain Name Finder ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg"] false Instantly generate brand-ready domain names that are actually available Overview: Finding a domain name that fits your brand shouldn’t take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable. How It Works 1. Input your product pitch, company name, or core keywords. 2. The agent analyzes brand tone, audience, and industry context. 3. It generates a list of unique, memorable domains that match your criteria. 4. All names are pre-filtered for real-time availability, so you can register immediately. Business Value Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains. Key Use Cases • Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands. • Marketers: Test name options across campaigns with instant availability data. • Entrepreneurs: Validate ideas faster with instant domain options. ["business"] false true
18 7a831906-daab-426f-9d66-bcf98d869426 516d813b-d1bc-470f-add7-c63a4b2c2bad ai-function AI Function ["https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp","https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png"] false Never Code Again AI FUNCTION MAGIC Your AI‑powered assistant for turning plain‑English descriptions into working Python functions. HOW IT WORKS 1. Describe what the function should do. 2. Specify the inputs it needs. 3. Receive the generated Python code. FEATURES - Effortless Function Generation: convert natural‑language specs into complete functions. - Customizable Inputs: define the parameters that matter to you. - Versatile Use Cases: simulate data, automate tasks, prototype ideas. - Seamless Integration: add the generated function directly to your codebase. EXAMPLE Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.” Input parameter: number_of_people (default 20) Result: a list of dictionaries such as [ { "name": "Emma Martinez", "date_of_birth": "1992‑11‑03", "job_title": "Data Analyst", "age": 32 }, { "name": "Liam O’Connor", "date_of_birth": "1985‑07‑19", "job_title": "Marketing Manager", "age": 39 }, …18 more entries… ] ["development"] false true

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View File

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"is_static": true
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
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"webhook_id": null,
"webhook": null
},
{
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"block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
"input_default": {
"group": 1,
"pattern": "<email>(.*?)<\\/email>"
},
"metadata": {
"position": {
"x": 3381.2821481740634,
"y": 246.091098184158
}
},
"input_links": [
{
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"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
}
],
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{
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"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
},
{
"id": "23591872-3c6b-4562-87d3-5b6ade698e48",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
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"block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
"input_default": {
"name": "Email"
},
"metadata": {
"position": {
"x": 4525.4246310882,
"y": 246.36913665010354
}
},
"input_links": [
{
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"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
},
{
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"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
}
],
"output_links": [],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
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"webhook_id": null,
"webhook": null
},
{
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"block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
"input_default": {},
"metadata": {
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"y": 242.00001144409185
}
},
"input_links": [
{
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"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
}
],
"output_links": [
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
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"block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
"input_default": {
"name": "Business Name",
"value": "Tim Cook"
},
"metadata": {
"position": {
"x": 1049.9704155272595,
"y": 244.49931152418344
}
},
"input_links": [],
"output_links": [
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
"input_default": {
"format": "Email Address of {{NAME}}, {{ADDRESS}}",
"values": {}
},
"metadata": {
"position": {
"x": 1625.25,
"y": 243.25001144409185
}
},
"input_links": [
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
"source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_ADDRESS",
"is_static": true
}
],
"output_links": [
{
"id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
"input_default": {
"format": "Failed to find email. \nResult:\n{{RESULT}}",
"values": {}
},
"metadata": {
"position": {
"x": 3949.7493830805934,
"y": 705.209819698647
}
},
"input_links": [
{
"id": "b15b5143-27b7-486e-a166-4095e72e5235",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
}
],
"output_links": [
{
"id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
"source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
},
{
"id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
"input_default": {
"model": "claude-sonnet-4-5-20250929",
"prompt": "<business_website>\n{{WEBSITE_CONTENT}}\n</business_website>\n\nExtract the Contact Email of {{BUSINESS_NAME}}.\n\nIf no email that can be used to contact {{BUSINESS_NAME}} is present, output `N/A`.\nDo not share any emails other than the email for this specific entity.\n\nIf multiple present pick the likely best one.\n\nRespond with the email (or N/A) inside <email></email> tags.\n\nExample Response:\n\n<thoughts_or_comments>\nThere were many emails present, but luckily one was for {{BUSINESS_NAME}} which I have included below.\n</thoughts_or_comments>\n<email>\nexample@email.com\n</email>",
"prompt_values": {}
},
"metadata": {
"position": {
"x": 2774.879259081777,
"y": 243.3102035752969
}
},
"input_links": [
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
},
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"output_links": [
{
"id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
"source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
}
],
"graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
"graph_version": 29,
"webhook_id": null,
"webhook": null
}
],
"links": [
{
"id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
"source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"source_name": "response",
"sink_name": "text",
"is_static": false
},
{
"id": "b15b5143-27b7-486e-a166-4095e72e5235",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"source_name": "negative",
"sink_name": "values_#_Result",
"is_static": false
},
{
"id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
"source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "output",
"sink_name": "value",
"is_static": false
},
{
"id": "946b522c-365f-4ee0-96f9-28863d9882ea",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_NAME",
"is_static": true
},
{
"id": "23591872-3c6b-4562-87d3-5b6ade698e48",
"source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
"sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
"source_name": "positive",
"sink_name": "value",
"is_static": false
},
{
"id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
"source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "result",
"sink_name": "prompt_values_#_BUSINESS_NAME",
"is_static": true
},
{
"id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
"source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"source_name": "output",
"sink_name": "query",
"is_static": false
},
{
"id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
"source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
"sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
"source_name": "result",
"sink_name": "values_#_ADDRESS",
"is_static": true
},
{
"id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
"source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
"sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
"source_name": "results",
"sink_name": "prompt_values_#_WEBSITE_CONTENT",
"is_static": false
}
],
"forked_from_id": null,
"forked_from_version": null,
"sub_graphs": [],
"user_id": "",
"created_at": "2025-01-03T00:46:30.244Z",
"input_schema": {
"type": "object",
"properties": {
"Address": {
"advanced": false,
"secret": false,
"title": "Address",
"default": "USA"
},
"Business Name": {
"advanced": false,
"secret": false,
"title": "Business Name",
"default": "Tim Cook"
}
},
"required": []
},
"output_schema": {
"type": "object",
"properties": {
"Email": {
"advanced": false,
"secret": false,
"title": "Email"
}
},
"required": [
"Email"
]
},
"has_external_trigger": false,
"has_human_in_the_loop": false,
"trigger_setup_info": null,
"credentials_input_schema": {
"properties": {
"jina_api_key_credentials": {
"credentials_provider": [
"jina"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "jina",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], Literal['api_key']]",
"type": "object",
"discriminator_values": []
},
"anthropic_api_key_credentials": {
"credentials_provider": [
"anthropic"
],
"credentials_types": [
"api_key"
],
"properties": {
"id": {
"title": "Id",
"type": "string"
},
"title": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Title"
},
"provider": {
"const": "anthropic",
"title": "Provider",
"type": "string"
},
"type": {
"const": "api_key",
"title": "Type",
"type": "string"
}
},
"required": [
"id",
"provider",
"type"
],
"title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], Literal['api_key']]",
"type": "object",
"discriminator": "model",
"discriminator_mapping": {
"Llama-3.3-70B-Instruct": "llama_api",
"Llama-3.3-8B-Instruct": "llama_api",
"Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
"Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
"Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
"amazon/nova-lite-v1": "open_router",
"amazon/nova-micro-v1": "open_router",
"amazon/nova-pro-v1": "open_router",
"claude-3-7-sonnet-20250219": "anthropic",
"claude-3-haiku-20240307": "anthropic",
"claude-haiku-4-5-20251001": "anthropic",
"claude-opus-4-1-20250805": "anthropic",
"claude-opus-4-20250514": "anthropic",
"claude-opus-4-5-20251101": "anthropic",
"claude-sonnet-4-20250514": "anthropic",
"claude-sonnet-4-5-20250929": "anthropic",
"cohere/command-r-08-2024": "open_router",
"cohere/command-r-plus-08-2024": "open_router",
"deepseek/deepseek-chat": "open_router",
"deepseek/deepseek-r1-0528": "open_router",
"dolphin-mistral:latest": "ollama",
"google/gemini-2.0-flash-001": "open_router",
"google/gemini-2.0-flash-lite-001": "open_router",
"google/gemini-2.5-flash": "open_router",
"google/gemini-2.5-flash-lite-preview-06-17": "open_router",
"google/gemini-2.5-pro-preview-03-25": "open_router",
"google/gemini-3-pro-preview": "open_router",
"gpt-3.5-turbo": "openai",
"gpt-4-turbo": "openai",
"gpt-4.1-2025-04-14": "openai",
"gpt-4.1-mini-2025-04-14": "openai",
"gpt-4o": "openai",
"gpt-4o-mini": "openai",
"gpt-5-2025-08-07": "openai",
"gpt-5-chat-latest": "openai",
"gpt-5-mini-2025-08-07": "openai",
"gpt-5-nano-2025-08-07": "openai",
"gpt-5.1-2025-11-13": "openai",
"gryphe/mythomax-l2-13b": "open_router",
"llama-3.1-8b-instant": "groq",
"llama-3.3-70b-versatile": "groq",
"llama3": "ollama",
"llama3.1:405b": "ollama",
"llama3.2": "ollama",
"llama3.3": "ollama",
"meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
"meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
"meta-llama/llama-4-maverick": "open_router",
"meta-llama/llama-4-scout": "open_router",
"microsoft/wizardlm-2-8x22b": "open_router",
"mistralai/mistral-nemo": "open_router",
"moonshotai/kimi-k2": "open_router",
"nousresearch/hermes-3-llama-3.1-405b": "open_router",
"nousresearch/hermes-3-llama-3.1-70b": "open_router",
"nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
"o1": "openai",
"o1-mini": "openai",
"o3-2025-04-16": "openai",
"o3-mini": "openai",
"openai/gpt-oss-120b": "open_router",
"openai/gpt-oss-20b": "open_router",
"perplexity/sonar": "open_router",
"perplexity/sonar-deep-research": "open_router",
"perplexity/sonar-pro": "open_router",
"qwen/qwen3-235b-a22b-thinking-2507": "open_router",
"qwen/qwen3-coder": "open_router",
"v0-1.0-md": "v0",
"v0-1.5-lg": "v0",
"v0-1.5-md": "v0",
"x-ai/grok-4": "open_router",
"x-ai/grok-4-fast": "open_router",
"x-ai/grok-4.1-fast": "open_router",
"x-ai/grok-code-fast-1": "open_router"
},
"discriminator_values": [
"claude-sonnet-4-5-20250929"
]
}
},
"required": [
"jina_api_key_credentials",
"anthropic_api_key_credentials"
],
"title": "EmailAddressFinderCredentialsInputSchema",
"type": "object"
}
}

View File

@@ -1,150 +0,0 @@
# Test Data Scripts
This directory contains scripts for creating and updating test data in the AutoGPT Platform database, specifically designed to test the materialized views for the store functionality.
## Scripts
### test_data_creator.py
Creates a comprehensive set of test data including:
- Users with profiles
- Agent graphs, nodes, and executions
- Store listings with multiple versions
- Reviews and ratings
- Library agents
- Integration webhooks
- Onboarding data
- Credit transactions
**Image/Video Domains Used:**
- Images: `picsum.photos` (for all image URLs)
- Videos: `youtube.com` (for store listing videos)
### test_data_updater.py
Updates existing test data to simulate real-world changes:
- Adds new agent graph executions
- Creates new store listing reviews
- Updates store listing versions
- Adds credit transactions
- Refreshes materialized views
### check_db.py
Tests and verifies materialized views functionality:
- Checks pg_cron job status (for automatic refresh)
- Displays current materialized view counts
- Adds test data (executions and reviews)
- Creates store listings if none exist
- Manually refreshes materialized views
- Compares before/after counts to verify updates
- Provides a summary of test results
## Materialized Views
The scripts test three key database views:
1. **mv_agent_run_counts**: Tracks execution counts by agent
2. **mv_review_stats**: Tracks review statistics (count, average rating) by store listing
3. **StoreAgent**: A view that combines store listing data with execution counts and ratings for display
The materialized views (mv_agent_run_counts and mv_review_stats) are automatically refreshed every 15 minutes via pg_cron, or can be manually refreshed using the `refresh_store_materialized_views()` function.
## Usage
### Prerequisites
1. Ensure the database is running:
```bash
docker compose up -d
# or for test database:
docker compose -f docker-compose.test.yaml --env-file ../.env up -d
```
2. Run database migrations:
```bash
poetry run prisma migrate deploy
```
### Running the Scripts
#### Option 1: Use the helper script (from backend directory)
```bash
poetry run python run_test_data.py
```
#### Option 2: Run individually
```bash
# From backend/test directory:
# Create initial test data
poetry run python test_data_creator.py
# Update data to test materialized view changes
poetry run python test_data_updater.py
# From backend directory:
# Test materialized views functionality
poetry run python check_db.py
# Check store data status
poetry run python check_store_data.py
```
#### Option 3: Use the shell script (from backend directory)
```bash
./run_test_data_scripts.sh
```
### Manual Materialized View Refresh
To manually refresh the materialized views:
```sql
SELECT refresh_store_materialized_views();
```
## Configuration
The scripts use the database configuration from your `.env` file:
- `DATABASE_URL`: PostgreSQL connection string
- Database should have the platform schema
## Data Generation Limits
Configured in `test_data_creator.py`:
- 100 users
- 100 agent blocks
- 1-5 graphs per user
- 2-5 nodes per graph
- 1-5 presets per user
- 1-10 library agents per user
- 1-20 executions per graph
- 1-5 reviews per store listing version
## Notes
- All image URLs use `picsum.photos` for consistency with Next.js image configuration
- The scripts create realistic relationships between entities
- Materialized views are refreshed at the end of each script
- Data is designed to test both happy paths and edge cases
## Troubleshooting
### Reviews and StoreAgent view showing 0
If `check_db.py` shows that reviews remain at 0 and StoreAgent view shows 0 store agents:
1. **No store listings exist**: The script will automatically create test store listings if none exist
2. **No approved versions**: Store listings need approved versions to appear in the StoreAgent view
3. **Check with `check_store_data.py`**: This script provides detailed information about:
- Total store listings
- Store listing versions by status
- Existing reviews
- StoreAgent view contents
- Agent graph executions
### pg_cron not installed
The warning "pg_cron extension is not installed" is normal in local development environments. The materialized views can still be refreshed manually using the `refresh_store_materialized_views()` function, which all scripts do automatically.
### Common Issues
- **Type errors with None values**: Fixed in the latest version of check_db.py by using `or 0` for nullable numeric fields
- **Missing relations**: Ensure you're using the correct field names (e.g., `StoreListing` not `storeListing` in includes)
- **Column name mismatches**: The database uses camelCase for column names (e.g., `agentGraphId` not `agent_graph_id`)

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"""Common test fixtures for server tests."""
import pytest
from pytest_snapshot.plugin import Snapshot
@pytest.fixture
def configured_snapshot(snapshot: Snapshot) -> Snapshot:
"""Pre-configured snapshot fixture with standard settings."""
snapshot.snapshot_dir = "snapshots"
return snapshot
@pytest.fixture
def test_user_id() -> str:
"""Test user ID fixture."""
return "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
@pytest.fixture
def admin_user_id() -> str:
"""Admin user ID fixture."""
return "4e53486c-cf57-477e-ba2a-cb02dc828e1b"
@pytest.fixture
def target_user_id() -> str:
"""Target user ID fixture."""
return "5e53486c-cf57-477e-ba2a-cb02dc828e1c"
@pytest.fixture
async def setup_test_user(test_user_id):
"""Create test user in database before tests."""
from backend.data.user import get_or_create_user
# Create the test user in the database using JWT token format
user_data = {
"sub": test_user_id,
"email": "test@example.com",
"user_metadata": {"name": "Test User"},
}
await get_or_create_user(user_data)
return test_user_id
@pytest.fixture
async def setup_admin_user(admin_user_id):
"""Create admin user in database before tests."""
from backend.data.user import get_or_create_user
# Create the admin user in the database using JWT token format
user_data = {
"sub": admin_user_id,
"email": "test-admin@example.com",
"user_metadata": {"name": "Test Admin"},
}
await get_or_create_user(user_data)
return admin_user_id
@pytest.fixture
def mock_jwt_user(test_user_id):
"""Provide mock JWT payload for regular user testing."""
import fastapi
def override_get_jwt_payload(request: fastapi.Request) -> dict[str, str]:
return {"sub": test_user_id, "role": "user", "email": "test@example.com"}
return {"get_jwt_payload": override_get_jwt_payload, "user_id": test_user_id}
@pytest.fixture
def mock_jwt_admin(admin_user_id):
"""Provide mock JWT payload for admin user testing."""
import fastapi
def override_get_jwt_payload(request: fastapi.Request) -> dict[str, str]:
return {
"sub": admin_user_id,
"role": "admin",
"email": "test-admin@example.com",
}
return {"get_jwt_payload": override_get_jwt_payload, "user_id": admin_user_id}

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import asyncio
from typing import Dict, Set
from fastapi import WebSocket
from backend.api.model import NotificationPayload, WSMessage, WSMethod
from backend.data.execution import (
ExecutionEventType,
GraphExecutionEvent,
NodeExecutionEvent,
)
_EVENT_TYPE_TO_METHOD_MAP: dict[ExecutionEventType, WSMethod] = {
ExecutionEventType.GRAPH_EXEC_UPDATE: WSMethod.GRAPH_EXECUTION_EVENT,
ExecutionEventType.NODE_EXEC_UPDATE: WSMethod.NODE_EXECUTION_EVENT,
}
class ConnectionManager:
def __init__(self):
self.active_connections: Set[WebSocket] = set()
self.subscriptions: Dict[str, Set[WebSocket]] = {}
self.user_connections: Dict[str, Set[WebSocket]] = {}
async def connect_socket(self, websocket: WebSocket, *, user_id: str):
await websocket.accept()
self.active_connections.add(websocket)
if user_id not in self.user_connections:
self.user_connections[user_id] = set()
self.user_connections[user_id].add(websocket)
def disconnect_socket(self, websocket: WebSocket, *, user_id: str):
self.active_connections.discard(websocket)
for subscribers in self.subscriptions.values():
subscribers.discard(websocket)
user_conns = self.user_connections.get(user_id)
if user_conns is not None:
user_conns.discard(websocket)
if not user_conns:
self.user_connections.pop(user_id, None)
async def subscribe_graph_exec(
self, *, user_id: str, graph_exec_id: str, websocket: WebSocket
) -> str:
return await self._subscribe(
_graph_exec_channel_key(user_id, graph_exec_id=graph_exec_id), websocket
)
async def subscribe_graph_execs(
self, *, user_id: str, graph_id: str, websocket: WebSocket
) -> str:
return await self._subscribe(
_graph_execs_channel_key(user_id, graph_id=graph_id), websocket
)
async def unsubscribe_graph_exec(
self, *, user_id: str, graph_exec_id: str, websocket: WebSocket
) -> str | None:
return await self._unsubscribe(
_graph_exec_channel_key(user_id, graph_exec_id=graph_exec_id), websocket
)
async def unsubscribe_graph_execs(
self, *, user_id: str, graph_id: str, websocket: WebSocket
) -> str | None:
return await self._unsubscribe(
_graph_execs_channel_key(user_id, graph_id=graph_id), websocket
)
async def send_execution_update(
self, exec_event: GraphExecutionEvent | NodeExecutionEvent
) -> int:
graph_exec_id = (
exec_event.id
if isinstance(exec_event, GraphExecutionEvent)
else exec_event.graph_exec_id
)
n_sent = 0
channels: set[str] = {
# Send update to listeners for this graph execution
_graph_exec_channel_key(exec_event.user_id, graph_exec_id=graph_exec_id)
}
if isinstance(exec_event, GraphExecutionEvent):
# Send update to listeners for all executions of this graph
channels.add(
_graph_execs_channel_key(
exec_event.user_id, graph_id=exec_event.graph_id
)
)
for channel in channels.intersection(self.subscriptions.keys()):
message = WSMessage(
method=_EVENT_TYPE_TO_METHOD_MAP[exec_event.event_type],
channel=channel,
data=exec_event.model_dump(),
).model_dump_json()
for connection in self.subscriptions[channel]:
await connection.send_text(message)
n_sent += 1
return n_sent
async def send_notification(
self, *, user_id: str, payload: NotificationPayload
) -> int:
"""Send a notification to all websocket connections belonging to a user."""
message = WSMessage(
method=WSMethod.NOTIFICATION,
data=payload.model_dump(),
).model_dump_json()
connections = tuple(self.user_connections.get(user_id, set()))
if not connections:
return 0
await asyncio.gather(
*(connection.send_text(message) for connection in connections),
return_exceptions=True,
)
return len(connections)
async def _subscribe(self, channel_key: str, websocket: WebSocket) -> str:
if channel_key not in self.subscriptions:
self.subscriptions[channel_key] = set()
self.subscriptions[channel_key].add(websocket)
return channel_key
async def _unsubscribe(self, channel_key: str, websocket: WebSocket) -> str | None:
if channel_key in self.subscriptions:
self.subscriptions[channel_key].discard(websocket)
if not self.subscriptions[channel_key]:
del self.subscriptions[channel_key]
return channel_key
return None
def _graph_exec_channel_key(user_id: str, *, graph_exec_id: str) -> str:
return f"{user_id}|graph_exec#{graph_exec_id}"
def _graph_execs_channel_key(user_id: str, *, graph_id: str) -> str:
return f"{user_id}|graph#{graph_id}|executions"

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from datetime import datetime, timezone
from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.api.conn_manager import ConnectionManager
from backend.api.model import NotificationPayload, WSMessage, WSMethod
from backend.data.execution import (
ExecutionStatus,
GraphExecutionEvent,
NodeExecutionEvent,
)
@pytest.fixture
def connection_manager() -> ConnectionManager:
return ConnectionManager()
@pytest.fixture
def mock_websocket() -> AsyncMock:
websocket: AsyncMock = AsyncMock(spec=WebSocket)
websocket.send_text = AsyncMock()
return websocket
@pytest.mark.asyncio
async def test_connect(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
await connection_manager.connect_socket(mock_websocket, user_id="user-1")
assert mock_websocket in connection_manager.active_connections
assert mock_websocket in connection_manager.user_connections["user-1"]
mock_websocket.accept.assert_called_once()
def test_disconnect(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
connection_manager.active_connections.add(mock_websocket)
connection_manager.subscriptions["test_channel_42"] = {mock_websocket}
connection_manager.user_connections["user-1"] = {mock_websocket}
connection_manager.disconnect_socket(mock_websocket, user_id="user-1")
assert mock_websocket not in connection_manager.active_connections
assert mock_websocket not in connection_manager.subscriptions["test_channel_42"]
assert "user-1" not in connection_manager.user_connections
@pytest.mark.asyncio
async def test_subscribe(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
await connection_manager.subscribe_graph_exec(
user_id="user-1",
graph_exec_id="graph-exec-1",
websocket=mock_websocket,
)
assert (
mock_websocket
in connection_manager.subscriptions["user-1|graph_exec#graph-exec-1"]
)
@pytest.mark.asyncio
async def test_unsubscribe(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
channel_key = "user-1|graph_exec#graph-exec-1"
connection_manager.subscriptions[channel_key] = {mock_websocket}
await connection_manager.unsubscribe_graph_exec(
user_id="user-1",
graph_exec_id="graph-exec-1",
websocket=mock_websocket,
)
assert "test_graph" not in connection_manager.subscriptions
@pytest.mark.asyncio
async def test_send_graph_execution_result(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
channel_key = "user-1|graph_exec#graph-exec-1"
connection_manager.subscriptions[channel_key] = {mock_websocket}
result = GraphExecutionEvent(
id="graph-exec-1",
user_id="user-1",
graph_id="test_graph",
graph_version=1,
preset_id=None,
status=ExecutionStatus.COMPLETED,
started_at=datetime.now(tz=timezone.utc),
ended_at=datetime.now(tz=timezone.utc),
stats=GraphExecutionEvent.Stats(
cost=0,
duration=1.2,
node_exec_time=0.5,
node_exec_count=2,
),
inputs={
"input_1": "some input value :)",
"input_2": "some *other* input value",
},
credential_inputs=None,
nodes_input_masks=None,
outputs={
"the_output": ["some output value"],
"other_output": ["sike there was another output"],
},
)
await connection_manager.send_execution_update(result)
mock_websocket.send_text.assert_called_once_with(
WSMessage(
method=WSMethod.GRAPH_EXECUTION_EVENT,
channel="user-1|graph_exec#graph-exec-1",
data=result.model_dump(),
).model_dump_json()
)
@pytest.mark.asyncio
async def test_send_node_execution_result(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
channel_key = "user-1|graph_exec#graph-exec-1"
connection_manager.subscriptions[channel_key] = {mock_websocket}
result = NodeExecutionEvent(
user_id="user-1",
graph_id="test_graph",
graph_version=1,
graph_exec_id="graph-exec-1",
node_exec_id="test_node_exec_id",
node_id="test_node_id",
block_id="test_block_id",
status=ExecutionStatus.COMPLETED,
input_data={"input1": "value1"},
output_data={"output1": ["result1"]},
add_time=datetime.now(tz=timezone.utc),
queue_time=None,
start_time=datetime.now(tz=timezone.utc),
end_time=datetime.now(tz=timezone.utc),
)
await connection_manager.send_execution_update(result)
mock_websocket.send_text.assert_called_once_with(
WSMessage(
method=WSMethod.NODE_EXECUTION_EVENT,
channel="user-1|graph_exec#graph-exec-1",
data=result.model_dump(),
).model_dump_json()
)
@pytest.mark.asyncio
async def test_send_execution_result_user_mismatch(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
channel_key = "user-1|graph_exec#graph-exec-1"
connection_manager.subscriptions[channel_key] = {mock_websocket}
result = NodeExecutionEvent(
user_id="user-2",
graph_id="test_graph",
graph_version=1,
graph_exec_id="graph-exec-1",
node_exec_id="test_node_exec_id",
node_id="test_node_id",
block_id="test_block_id",
status=ExecutionStatus.COMPLETED,
input_data={"input1": "value1"},
output_data={"output1": ["result1"]},
add_time=datetime.now(tz=timezone.utc),
queue_time=None,
start_time=datetime.now(tz=timezone.utc),
end_time=datetime.now(tz=timezone.utc),
)
await connection_manager.send_execution_update(result)
mock_websocket.send_text.assert_not_called()
@pytest.mark.asyncio
async def test_send_execution_result_no_subscribers(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
result = NodeExecutionEvent(
user_id="user-1",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec_id",
node_exec_id="test_node_exec_id",
node_id="test_node_id",
block_id="test_block_id",
status=ExecutionStatus.COMPLETED,
input_data={"input1": "value1"},
output_data={"output1": ["result1"]},
add_time=datetime.now(),
queue_time=None,
start_time=datetime.now(),
end_time=datetime.now(),
)
await connection_manager.send_execution_update(result)
mock_websocket.send_text.assert_not_called()
@pytest.mark.asyncio
async def test_send_notification(
connection_manager: ConnectionManager, mock_websocket: AsyncMock
) -> None:
connection_manager.user_connections["user-1"] = {mock_websocket}
await connection_manager.send_notification(
user_id="user-1", payload=NotificationPayload(type="info", event="hey")
)
mock_websocket.send_text.assert_called_once()
sent_message = mock_websocket.send_text.call_args[0][0]
expected_message = WSMessage(
method=WSMethod.NOTIFICATION,
data={"type": "info", "event": "hey"},
).model_dump_json()
assert sent_message == expected_message

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

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

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

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@@ -1,328 +0,0 @@
import logging
import urllib.parse
from collections import defaultdict
from typing import Annotated, Any, Literal, Optional, Sequence
from fastapi import APIRouter, Body, HTTPException, Security
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
import backend.api.features.store.cache as store_cache
import backend.api.features.store.model as store_model
import backend.data.block
from backend.api.external.middleware import require_permission
from backend.data import execution as execution_db
from backend.data import graph as graph_db
from backend.data import user as user_db
from backend.data.auth.base import APIAuthorizationInfo
from backend.data.block import BlockInput, CompletedBlockOutput
from backend.executor.utils import add_graph_execution
from backend.util.settings import Settings
from .integrations import integrations_router
from .tools import tools_router
settings = Settings()
logger = logging.getLogger(__name__)
v1_router = APIRouter()
v1_router.include_router(integrations_router)
v1_router.include_router(tools_router)
class UserInfoResponse(BaseModel):
id: str
name: Optional[str]
email: str
timezone: str = Field(
description="The user's last known timezone (e.g. 'Europe/Amsterdam'), "
"or 'not-set' if not set"
)
@v1_router.get(
path="/me",
tags=["user", "meta"],
)
async def get_user_info(
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.IDENTITY)
),
) -> UserInfoResponse:
user = await user_db.get_user_by_id(auth.user_id)
return UserInfoResponse(
id=user.id,
name=user.name,
email=user.email,
timezone=user.timezone,
)
@v1_router.get(
path="/blocks",
tags=["blocks"],
dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
)
async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
blocks = [block() for block in backend.data.block.get_blocks().values()]
return [b.to_dict() for b in blocks if not b.disabled]
@v1_router.post(
path="/blocks/{block_id}/execute",
tags=["blocks"],
dependencies=[Security(require_permission(APIKeyPermission.EXECUTE_BLOCK))],
)
async def execute_graph_block(
block_id: str,
data: BlockInput,
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.EXECUTE_BLOCK)
),
) -> CompletedBlockOutput:
obj = backend.data.block.get_block(block_id)
if not obj:
raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
output = defaultdict(list)
async for name, data in obj.execute(data):
output[name].append(data)
return output
@v1_router.post(
path="/graphs/{graph_id}/execute/{graph_version}",
tags=["graphs"],
)
async def execute_graph(
graph_id: str,
graph_version: int,
node_input: Annotated[dict[str, Any], Body(..., embed=True, default_factory=dict)],
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.EXECUTE_GRAPH)
),
) -> dict[str, Any]:
try:
graph_exec = await add_graph_execution(
graph_id=graph_id,
user_id=auth.user_id,
inputs=node_input,
graph_version=graph_version,
)
return {"id": graph_exec.id}
except Exception as e:
msg = str(e).encode().decode("unicode_escape")
raise HTTPException(status_code=400, detail=msg)
class ExecutionNode(TypedDict):
node_id: str
input: Any
output: dict[str, Any]
class GraphExecutionResult(TypedDict):
execution_id: str
status: str
nodes: list[ExecutionNode]
output: Optional[list[dict[str, str]]]
@v1_router.get(
path="/graphs/{graph_id}/executions/{graph_exec_id}/results",
tags=["graphs"],
)
async def get_graph_execution_results(
graph_id: str,
graph_exec_id: str,
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.READ_GRAPH)
),
) -> GraphExecutionResult:
graph_exec = await execution_db.get_graph_execution(
user_id=auth.user_id,
execution_id=graph_exec_id,
include_node_executions=True,
)
if not graph_exec:
raise HTTPException(
status_code=404, detail=f"Graph execution #{graph_exec_id} not found."
)
if not await graph_db.get_graph(
graph_id=graph_exec.graph_id,
version=graph_exec.graph_version,
user_id=auth.user_id,
):
raise HTTPException(status_code=404, detail=f"Graph #{graph_id} not found.")
return GraphExecutionResult(
execution_id=graph_exec_id,
status=graph_exec.status.value,
nodes=[
ExecutionNode(
node_id=node_exec.node_id,
input=node_exec.input_data.get("value", node_exec.input_data),
output={k: v for k, v in node_exec.output_data.items()},
)
for node_exec in graph_exec.node_executions
],
output=(
[
{name: value}
for name, values in graph_exec.outputs.items()
for value in values
]
if graph_exec.status == AgentExecutionStatus.COMPLETED
else None
),
)
##############################################
############### Store Endpoints ##############
##############################################
@v1_router.get(
path="/store/agents",
tags=["store"],
dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
response_model=store_model.StoreAgentsResponse,
)
async def get_store_agents(
featured: bool = False,
creator: str | None = None,
sorted_by: Literal["rating", "runs", "name", "updated_at"] | None = None,
search_query: str | None = None,
category: str | None = None,
page: int = 1,
page_size: int = 20,
) -> store_model.StoreAgentsResponse:
"""
Get a paginated list of agents from the store with optional filtering and sorting.
Args:
featured: Filter to only show featured agents
creator: Filter agents by creator username
sorted_by: Sort agents by "runs", "rating", "name", or "updated_at"
search_query: Search agents by name, subheading and description
category: Filter agents by category
page: Page number for pagination (default 1)
page_size: Number of agents per page (default 20)
Returns:
StoreAgentsResponse: Paginated list of agents matching the filters
"""
if page < 1:
raise HTTPException(status_code=422, detail="Page must be greater than 0")
if page_size < 1:
raise HTTPException(status_code=422, detail="Page size must be greater than 0")
agents = await store_cache._get_cached_store_agents(
featured=featured,
creator=creator,
sorted_by=sorted_by,
search_query=search_query,
category=category,
page=page,
page_size=page_size,
)
return agents
@v1_router.get(
path="/store/agents/{username}/{agent_name}",
tags=["store"],
dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
response_model=store_model.StoreAgentDetails,
)
async def get_store_agent(
username: str,
agent_name: str,
) -> store_model.StoreAgentDetails:
"""
Get details of a specific store agent by username and agent name.
Args:
username: Creator's username
agent_name: Name/slug of the agent
Returns:
StoreAgentDetails: Detailed information about the agent
"""
username = urllib.parse.unquote(username).lower()
agent_name = urllib.parse.unquote(agent_name).lower()
agent = await store_cache._get_cached_agent_details(
username=username, agent_name=agent_name
)
return agent
@v1_router.get(
path="/store/creators",
tags=["store"],
dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
response_model=store_model.CreatorsResponse,
)
async def get_store_creators(
featured: bool = False,
search_query: str | None = None,
sorted_by: Literal["agent_rating", "agent_runs", "num_agents"] | None = None,
page: int = 1,
page_size: int = 20,
) -> store_model.CreatorsResponse:
"""
Get a paginated list of store creators with optional filtering and sorting.
Args:
featured: Filter to only show featured creators
search_query: Search creators by profile description
sorted_by: Sort by "agent_rating", "agent_runs", or "num_agents"
page: Page number for pagination (default 1)
page_size: Number of creators per page (default 20)
Returns:
CreatorsResponse: Paginated list of creators matching the filters
"""
if page < 1:
raise HTTPException(status_code=422, detail="Page must be greater than 0")
if page_size < 1:
raise HTTPException(status_code=422, detail="Page size must be greater than 0")
creators = await store_cache._get_cached_store_creators(
featured=featured,
search_query=search_query,
sorted_by=sorted_by,
page=page,
page_size=page_size,
)
return creators
@v1_router.get(
path="/store/creators/{username}",
tags=["store"],
dependencies=[Security(require_permission(APIKeyPermission.READ_STORE))],
response_model=store_model.CreatorDetails,
)
async def get_store_creator(
username: str,
) -> store_model.CreatorDetails:
"""
Get details of a specific store creator by username.
Args:
username: Creator's username
Returns:
CreatorDetails: Detailed information about the creator
"""
username = urllib.parse.unquote(username).lower()
creator = await store_cache._get_cached_creator_details(username=username)
return creator

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

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@@ -1,74 +0,0 @@
import logging
import typing
from autogpt_libs.auth import get_user_id, requires_admin_user
from fastapi import APIRouter, Body, Security
from prisma.enums import CreditTransactionType
from backend.data.credit import admin_get_user_history, get_user_credit_model
from backend.util.json import SafeJson
from .model import AddUserCreditsResponse, UserHistoryResponse
logger = logging.getLogger(__name__)
router = APIRouter(
prefix="/admin",
tags=["credits", "admin"],
dependencies=[Security(requires_admin_user)],
)
@router.post(
"/add_credits", response_model=AddUserCreditsResponse, summary="Add Credits to User"
)
async def add_user_credits(
user_id: typing.Annotated[str, Body()],
amount: typing.Annotated[int, Body()],
comments: typing.Annotated[str, Body()],
admin_user_id: str = Security(get_user_id),
):
logger.info(
f"Admin user {admin_user_id} is adding {amount} credits to user {user_id}"
)
user_credit_model = await get_user_credit_model(user_id)
new_balance, transaction_key = await user_credit_model._add_transaction(
user_id,
amount,
transaction_type=CreditTransactionType.GRANT,
metadata=SafeJson({"admin_id": admin_user_id, "reason": comments}),
)
return {
"new_balance": new_balance,
"transaction_key": transaction_key,
}
@router.get(
"/users_history",
response_model=UserHistoryResponse,
summary="Get All Users History",
)
async def admin_get_all_user_history(
admin_user_id: str = Security(get_user_id),
search: typing.Optional[str] = None,
page: int = 1,
page_size: int = 20,
transaction_filter: typing.Optional[CreditTransactionType] = None,
):
""" """
logger.info(f"Admin user {admin_user_id} is getting grant history")
try:
resp = await admin_get_user_history(
page=page,
page_size=page_size,
search=search,
transaction_filter=transaction_filter,
)
logger.info(f"Admin user {admin_user_id} got {len(resp.history)} grant history")
return resp
except Exception as e:
logger.exception(f"Error getting grant history: {e}")
raise e

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@@ -1,315 +0,0 @@
import json
from unittest.mock import AsyncMock, Mock
import fastapi
import fastapi.testclient
import prisma.enums
import pytest
import pytest_mock
from autogpt_libs.auth.jwt_utils import get_jwt_payload
from pytest_snapshot.plugin import Snapshot
from backend.data.model import UserTransaction
from backend.util.json import SafeJson
from backend.util.models import Pagination
from .credit_admin_routes import router as credit_admin_router
from .model import UserHistoryResponse
app = fastapi.FastAPI()
app.include_router(credit_admin_router)
client = fastapi.testclient.TestClient(app)
@pytest.fixture(autouse=True)
def setup_app_admin_auth(mock_jwt_admin):
"""Setup admin auth overrides for all tests in this module"""
app.dependency_overrides[get_jwt_payload] = mock_jwt_admin["get_jwt_payload"]
yield
app.dependency_overrides.clear()
def test_add_user_credits_success(
mocker: pytest_mock.MockerFixture,
configured_snapshot: Snapshot,
admin_user_id: str,
target_user_id: str,
) -> None:
"""Test successful credit addition by admin"""
# Mock the credit model
mock_credit_model = Mock()
mock_credit_model._add_transaction = AsyncMock(
return_value=(1500, "transaction-123-uuid")
)
mocker.patch(
"backend.api.features.admin.credit_admin_routes.get_user_credit_model",
return_value=mock_credit_model,
)
request_data = {
"user_id": target_user_id,
"amount": 500,
"comments": "Test credit grant for debugging",
}
response = client.post("/admin/add_credits", json=request_data)
assert response.status_code == 200
response_data = response.json()
assert response_data["new_balance"] == 1500
assert response_data["transaction_key"] == "transaction-123-uuid"
# Verify the function was called with correct parameters
mock_credit_model._add_transaction.assert_called_once()
call_args = mock_credit_model._add_transaction.call_args
assert call_args[0] == (target_user_id, 500)
assert call_args[1]["transaction_type"] == prisma.enums.CreditTransactionType.GRANT
# Check that metadata is a SafeJson object with the expected content
assert isinstance(call_args[1]["metadata"], SafeJson)
actual_metadata = call_args[1]["metadata"]
expected_data = {
"admin_id": admin_user_id,
"reason": "Test credit grant for debugging",
}
# SafeJson inherits from Json which stores parsed data in the .data attribute
assert actual_metadata.data["admin_id"] == expected_data["admin_id"]
assert actual_metadata.data["reason"] == expected_data["reason"]
# Snapshot test the response
configured_snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"admin_add_credits_success",
)
def test_add_user_credits_negative_amount(
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test credit deduction by admin (negative amount)"""
# Mock the credit model
mock_credit_model = Mock()
mock_credit_model._add_transaction = AsyncMock(
return_value=(200, "transaction-456-uuid")
)
mocker.patch(
"backend.api.features.admin.credit_admin_routes.get_user_credit_model",
return_value=mock_credit_model,
)
request_data = {
"user_id": "target-user-id",
"amount": -100,
"comments": "Refund adjustment",
}
response = client.post("/admin/add_credits", json=request_data)
assert response.status_code == 200
response_data = response.json()
assert response_data["new_balance"] == 200
# Snapshot test the response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"adm_add_cred_neg",
)
def test_get_user_history_success(
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test successful retrieval of user credit history"""
# Mock the admin_get_user_history function
mock_history_response = UserHistoryResponse(
history=[
UserTransaction(
user_id="user-1",
user_email="user1@example.com",
amount=1000,
reason="Initial grant",
transaction_type=prisma.enums.CreditTransactionType.GRANT,
),
UserTransaction(
user_id="user-2",
user_email="user2@example.com",
amount=-50,
reason="Usage",
transaction_type=prisma.enums.CreditTransactionType.USAGE,
),
],
pagination=Pagination(
total_items=2,
total_pages=1,
current_page=1,
page_size=20,
),
)
mocker.patch(
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)
response = client.get("/admin/users_history")
assert response.status_code == 200
response_data = response.json()
assert len(response_data["history"]) == 2
assert response_data["pagination"]["total_items"] == 2
# Snapshot test the response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"adm_usr_hist_ok",
)
def test_get_user_history_with_filters(
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test user credit history with search and filter parameters"""
# Mock the admin_get_user_history function
mock_history_response = UserHistoryResponse(
history=[
UserTransaction(
user_id="user-3",
user_email="test@example.com",
amount=500,
reason="Top up",
transaction_type=prisma.enums.CreditTransactionType.TOP_UP,
),
],
pagination=Pagination(
total_items=1,
total_pages=1,
current_page=1,
page_size=10,
),
)
mock_get_history = mocker.patch(
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)
response = client.get(
"/admin/users_history",
params={
"search": "test@example.com",
"page": 1,
"page_size": 10,
"transaction_filter": "TOP_UP",
},
)
assert response.status_code == 200
response_data = response.json()
assert len(response_data["history"]) == 1
assert response_data["history"][0]["transaction_type"] == "TOP_UP"
# Verify the function was called with correct parameters
mock_get_history.assert_called_once_with(
page=1,
page_size=10,
search="test@example.com",
transaction_filter=prisma.enums.CreditTransactionType.TOP_UP,
)
# Snapshot test the response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"adm_usr_hist_filt",
)
def test_get_user_history_empty_results(
mocker: pytest_mock.MockerFixture,
snapshot: Snapshot,
) -> None:
"""Test user credit history with no results"""
# Mock empty history response
mock_history_response = UserHistoryResponse(
history=[],
pagination=Pagination(
total_items=0,
total_pages=0,
current_page=1,
page_size=20,
),
)
mocker.patch(
"backend.api.features.admin.credit_admin_routes.admin_get_user_history",
return_value=mock_history_response,
)
response = client.get("/admin/users_history", params={"search": "nonexistent"})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["history"]) == 0
assert response_data["pagination"]["total_items"] == 0
# Snapshot test the response
snapshot.snapshot_dir = "snapshots"
snapshot.assert_match(
json.dumps(response_data, indent=2, sort_keys=True),
"adm_usr_hist_empty",
)
def test_add_credits_invalid_request() -> None:
"""Test credit addition with invalid request data"""
# Missing required fields
response = client.post("/admin/add_credits", json={})
assert response.status_code == 422
# Invalid amount type
response = client.post(
"/admin/add_credits",
json={
"user_id": "test",
"amount": "not_a_number",
"comments": "test",
},
)
assert response.status_code == 422
# Missing comments
response = client.post(
"/admin/add_credits",
json={
"user_id": "test",
"amount": 100,
},
)
assert response.status_code == 422
def test_admin_endpoints_require_admin_role(mock_jwt_user) -> None:
"""Test that admin endpoints require admin role"""
# Simulate regular non-admin user
app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
# Test add_credits endpoint
response = client.post(
"/admin/add_credits",
json={
"user_id": "test",
"amount": 100,
"comments": "test",
},
)
assert response.status_code == 403
# Test users_history endpoint
response = client.get("/admin/users_history")
assert response.status_code == 403

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@@ -1,474 +0,0 @@
import asyncio
import logging
from datetime import datetime
from typing import Optional
from autogpt_libs.auth import get_user_id, requires_admin_user
from fastapi import APIRouter, HTTPException, Security
from pydantic import BaseModel, Field
from backend.blocks.llm import LlmModel
from backend.data.analytics import (
AccuracyTrendsResponse,
get_accuracy_trends_and_alerts,
)
from backend.data.execution import (
ExecutionStatus,
GraphExecutionMeta,
get_graph_executions,
update_graph_execution_stats,
)
from backend.data.model import GraphExecutionStats
from backend.executor.activity_status_generator import (
DEFAULT_SYSTEM_PROMPT,
DEFAULT_USER_PROMPT,
generate_activity_status_for_execution,
)
from backend.executor.manager import get_db_async_client
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ExecutionAnalyticsRequest(BaseModel):
graph_id: str = Field(..., description="Graph ID to analyze")
graph_version: Optional[int] = Field(None, description="Optional graph version")
user_id: Optional[str] = Field(None, description="Optional user ID filter")
created_after: Optional[datetime] = Field(
None, description="Optional created date lower bound"
)
model_name: str = Field("gpt-4o-mini", description="Model to use for generation")
batch_size: int = Field(
10, description="Batch size for concurrent processing", le=25, ge=1
)
system_prompt: Optional[str] = Field(
None, description="Custom system prompt (default: built-in prompt)"
)
user_prompt: Optional[str] = Field(
None,
description="Custom user prompt with {{GRAPH_NAME}} and {{EXECUTION_DATA}} placeholders (default: built-in prompt)",
)
skip_existing: bool = Field(
True,
description="Whether to skip executions that already have activity status and correctness score",
)
class ExecutionAnalyticsResult(BaseModel):
agent_id: str
version_id: int
user_id: str
exec_id: str
summary_text: Optional[str]
score: Optional[float]
status: str # "success", "failed", "skipped"
error_message: Optional[str] = None
class ExecutionAnalyticsResponse(BaseModel):
total_executions: int
processed_executions: int
successful_analytics: int
failed_analytics: int
skipped_executions: int
results: list[ExecutionAnalyticsResult]
class ModelInfo(BaseModel):
value: str
label: str
provider: str
class ExecutionAnalyticsConfig(BaseModel):
available_models: list[ModelInfo]
default_system_prompt: str
default_user_prompt: str
recommended_model: str
class AccuracyTrendsRequest(BaseModel):
graph_id: str = Field(..., description="Graph ID to analyze", min_length=1)
user_id: Optional[str] = Field(None, description="Optional user ID filter")
days_back: int = Field(30, description="Number of days to look back", ge=7, le=90)
drop_threshold: float = Field(
10.0, description="Alert threshold percentage", ge=1.0, le=50.0
)
include_historical: bool = Field(
False, description="Include historical data for charts"
)
router = APIRouter(
prefix="/admin",
tags=["admin", "execution_analytics"],
dependencies=[Security(requires_admin_user)],
)
@router.get(
"/execution_analytics/config",
response_model=ExecutionAnalyticsConfig,
summary="Get Execution Analytics Configuration",
)
async def get_execution_analytics_config(
admin_user_id: str = Security(get_user_id),
):
"""
Get the configuration for execution analytics including:
- Available AI models with metadata
- Default system and user prompts
- Recommended model selection
"""
logger.info(f"Admin user {admin_user_id} requesting execution analytics config")
# Generate model list from LlmModel enum with provider information
available_models = []
# Function to generate friendly display names from model values
def generate_model_label(model: LlmModel) -> str:
"""Generate a user-friendly label from the model enum value."""
value = model.value
# For all models, convert underscores/hyphens to spaces and title case
# e.g., "gpt-4-turbo" -> "GPT 4 Turbo", "claude-3-haiku-20240307" -> "Claude 3 Haiku"
parts = value.replace("_", "-").split("-")
# Handle provider prefixes (e.g., "google/", "x-ai/")
if "/" in value:
_, model_name = value.split("/", 1)
parts = model_name.replace("_", "-").split("-")
# Capitalize and format parts
formatted_parts = []
for part in parts:
# Skip date-like patterns - check for various date formats:
# - Long dates like "20240307" (8 digits)
# - Year components like "2024", "2025" (4 digit years >= 2020)
# - Month/day components like "04", "16" when they appear to be dates
if part.isdigit():
if len(part) >= 8: # Long date format like "20240307"
continue
elif len(part) == 4 and int(part) >= 2020: # Year like "2024", "2025"
continue
elif len(part) <= 2 and int(part) <= 31: # Month/day like "04", "16"
# Skip if this looks like a date component (basic heuristic)
continue
# Keep version numbers as-is
if part.replace(".", "").isdigit():
formatted_parts.append(part)
# Capitalize normal words
else:
formatted_parts.append(
part.upper()
if part.upper() in ["GPT", "LLM", "API", "V0"]
else part.capitalize()
)
model_name = " ".join(formatted_parts)
# Format provider name for better display
provider_name = model.provider.replace("_", " ").title()
# Return with provider prefix for clarity
return f"{provider_name}: {model_name}"
# Include all LlmModel values (no more filtering by hardcoded list)
recommended_model = LlmModel.GPT4O_MINI.value
for model in LlmModel:
label = generate_model_label(model)
# Add "(Recommended)" suffix to the recommended model
if model.value == recommended_model:
label += " (Recommended)"
available_models.append(
ModelInfo(
value=model.value,
label=label,
provider=model.provider,
)
)
# Sort models by provider and name for better UX
available_models.sort(key=lambda x: (x.provider, x.label))
return ExecutionAnalyticsConfig(
available_models=available_models,
default_system_prompt=DEFAULT_SYSTEM_PROMPT,
default_user_prompt=DEFAULT_USER_PROMPT,
recommended_model=recommended_model,
)
@router.post(
"/execution_analytics",
response_model=ExecutionAnalyticsResponse,
summary="Generate Execution Analytics",
)
async def generate_execution_analytics(
request: ExecutionAnalyticsRequest,
admin_user_id: str = Security(get_user_id),
):
"""
Generate activity summaries and correctness scores for graph executions.
This endpoint:
1. Fetches all completed executions matching the criteria
2. Identifies executions missing activity_status or correctness_score
3. Generates missing data using AI in batches
4. Updates the database with new stats
5. Returns a detailed report of the analytics operation
"""
logger.info(
f"Admin user {admin_user_id} starting execution analytics generation for graph {request.graph_id}"
)
try:
# Validate model configuration
settings = Settings()
if not settings.secrets.openai_internal_api_key:
raise HTTPException(status_code=500, detail="OpenAI API key not configured")
# Get database client
db_client = get_db_async_client()
# Fetch executions to process
executions = await get_graph_executions(
graph_id=request.graph_id,
graph_version=request.graph_version,
user_id=request.user_id,
created_time_gte=request.created_after,
statuses=[
ExecutionStatus.COMPLETED,
ExecutionStatus.FAILED,
ExecutionStatus.TERMINATED,
], # Only process finished executions
)
logger.info(
f"Found {len(executions)} total executions for graph {request.graph_id}"
)
# Filter executions that need analytics generation
executions_to_process = []
for execution in executions:
# Skip if we should skip existing analytics and both activity_status and correctness_score exist
if (
request.skip_existing
and execution.stats
and execution.stats.activity_status
and execution.stats.correctness_score is not None
):
continue
# Add execution to processing list
executions_to_process.append(execution)
logger.info(
f"Found {len(executions_to_process)} executions needing analytics generation"
)
# Create results for ALL executions - processed and skipped
results = []
successful_count = 0
failed_count = 0
# Process executions that need analytics generation
if executions_to_process:
total_batches = len(
range(0, len(executions_to_process), request.batch_size)
)
for batch_idx, i in enumerate(
range(0, len(executions_to_process), request.batch_size)
):
batch = executions_to_process[i : i + request.batch_size]
logger.info(
f"Processing batch {batch_idx + 1}/{total_batches} with {len(batch)} executions"
)
batch_results = await _process_batch(batch, request, db_client)
for result in batch_results:
results.append(result)
if result.status == "success":
successful_count += 1
elif result.status == "failed":
failed_count += 1
# Small delay between batches to avoid overwhelming the LLM API
if batch_idx < total_batches - 1: # Don't delay after the last batch
await asyncio.sleep(2)
# Add ALL executions to results (both processed and skipped)
for execution in executions:
# Skip if already processed (added to results above)
if execution in executions_to_process:
continue
results.append(
ExecutionAnalyticsResult(
agent_id=execution.graph_id,
version_id=execution.graph_version,
user_id=execution.user_id,
exec_id=execution.id,
summary_text=(
execution.stats.activity_status if execution.stats else None
),
score=(
execution.stats.correctness_score if execution.stats else None
),
status="skipped",
error_message=None, # Not an error - just already processed
)
)
response = ExecutionAnalyticsResponse(
total_executions=len(executions),
processed_executions=len(executions_to_process),
successful_analytics=successful_count,
failed_analytics=failed_count,
skipped_executions=len(executions) - len(executions_to_process),
results=results,
)
logger.info(
f"Analytics generation completed: {successful_count} successful, {failed_count} failed, "
f"{response.skipped_executions} skipped"
)
return response
except Exception as e:
logger.exception(f"Error during execution analytics generation: {e}")
raise HTTPException(status_code=500, detail=str(e))
async def _process_batch(
executions, request: ExecutionAnalyticsRequest, db_client
) -> list[ExecutionAnalyticsResult]:
"""Process a batch of executions concurrently."""
async def process_single_execution(execution) -> ExecutionAnalyticsResult:
try:
# Generate activity status and score using the specified model
# Convert stats to GraphExecutionStats if needed
if execution.stats:
if isinstance(execution.stats, GraphExecutionMeta.Stats):
stats_for_generation = execution.stats.to_db()
else:
# Already GraphExecutionStats
stats_for_generation = execution.stats
else:
stats_for_generation = GraphExecutionStats()
activity_response = await generate_activity_status_for_execution(
graph_exec_id=execution.id,
graph_id=execution.graph_id,
graph_version=execution.graph_version,
execution_stats=stats_for_generation,
db_client=db_client,
user_id=execution.user_id,
execution_status=execution.status,
model_name=request.model_name,
skip_feature_flag=True, # Admin endpoint bypasses feature flags
system_prompt=request.system_prompt or DEFAULT_SYSTEM_PROMPT,
user_prompt=request.user_prompt or DEFAULT_USER_PROMPT,
skip_existing=request.skip_existing,
)
if not activity_response:
return ExecutionAnalyticsResult(
agent_id=execution.graph_id,
version_id=execution.graph_version,
user_id=execution.user_id,
exec_id=execution.id,
summary_text=None,
score=None,
status="skipped",
error_message="Activity generation returned None",
)
# Update the execution stats
# Convert GraphExecutionMeta.Stats to GraphExecutionStats for DB compatibility
if execution.stats:
if isinstance(execution.stats, GraphExecutionMeta.Stats):
updated_stats = execution.stats.to_db()
else:
# Already GraphExecutionStats
updated_stats = execution.stats
else:
updated_stats = GraphExecutionStats()
updated_stats.activity_status = activity_response["activity_status"]
updated_stats.correctness_score = activity_response["correctness_score"]
# Save to database with correct stats type
await update_graph_execution_stats(
graph_exec_id=execution.id, stats=updated_stats
)
return ExecutionAnalyticsResult(
agent_id=execution.graph_id,
version_id=execution.graph_version,
user_id=execution.user_id,
exec_id=execution.id,
summary_text=activity_response["activity_status"],
score=activity_response["correctness_score"],
status="success",
)
except Exception as e:
logger.exception(f"Error processing execution {execution.id}: {e}")
return ExecutionAnalyticsResult(
agent_id=execution.graph_id,
version_id=execution.graph_version,
user_id=execution.user_id,
exec_id=execution.id,
summary_text=None,
score=None,
status="failed",
error_message=str(e),
)
# Process all executions in the batch concurrently
return await asyncio.gather(
*[process_single_execution(execution) for execution in executions]
)
@router.get(
"/execution_accuracy_trends",
response_model=AccuracyTrendsResponse,
summary="Get Execution Accuracy Trends and Alerts",
)
async def get_execution_accuracy_trends(
graph_id: str,
user_id: Optional[str] = None,
days_back: int = 30,
drop_threshold: float = 10.0,
include_historical: bool = False,
admin_user_id: str = Security(get_user_id),
) -> AccuracyTrendsResponse:
"""
Get execution accuracy trends with moving averages and alert detection.
Simple single-query approach.
"""
logger.info(
f"Admin user {admin_user_id} requesting accuracy trends for graph {graph_id}"
)
try:
result = await get_accuracy_trends_and_alerts(
graph_id=graph_id,
days_back=days_back,
user_id=user_id,
drop_threshold=drop_threshold,
include_historical=include_historical,
)
return result
except Exception as e:
logger.exception(f"Error getting accuracy trends for graph {graph_id}: {e}")
raise HTTPException(status_code=500, detail=str(e))

View File

@@ -1,16 +0,0 @@
from pydantic import BaseModel
from backend.data.model import UserTransaction
from backend.util.models import Pagination
class UserHistoryResponse(BaseModel):
"""Response model for listings with version history"""
history: list[UserTransaction]
pagination: Pagination
class AddUserCreditsResponse(BaseModel):
new_balance: int
transaction_key: str

View File

@@ -1,152 +0,0 @@
import logging
import tempfile
import typing
import autogpt_libs.auth
import fastapi
import fastapi.responses
import prisma.enums
import backend.api.features.store.cache as store_cache
import backend.api.features.store.db as store_db
import backend.api.features.store.model as store_model
import backend.util.json
logger = logging.getLogger(__name__)
router = fastapi.APIRouter(
prefix="/admin",
tags=["store", "admin"],
dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
)
@router.get(
"/listings",
summary="Get Admin Listings History",
response_model=store_model.StoreListingsWithVersionsResponse,
)
async def get_admin_listings_with_versions(
status: typing.Optional[prisma.enums.SubmissionStatus] = None,
search: typing.Optional[str] = None,
page: int = 1,
page_size: int = 20,
):
"""
Get store listings with their version history for admins.
This provides a consolidated view of listings with their versions,
allowing for an expandable UI in the admin dashboard.
Args:
status: Filter by submission status (PENDING, APPROVED, REJECTED)
search: Search by name, description, or user email
page: Page number for pagination
page_size: Number of items per page
Returns:
StoreListingsWithVersionsResponse with listings and their versions
"""
try:
listings = await store_db.get_admin_listings_with_versions(
status=status,
search_query=search,
page=page,
page_size=page_size,
)
return listings
except Exception as e:
logger.exception("Error getting admin listings with versions: %s", e)
return fastapi.responses.JSONResponse(
status_code=500,
content={
"detail": "An error occurred while retrieving listings with versions"
},
)
@router.post(
"/submissions/{store_listing_version_id}/review",
summary="Review Store Submission",
response_model=store_model.StoreSubmission,
)
async def review_submission(
store_listing_version_id: str,
request: store_model.ReviewSubmissionRequest,
user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
):
"""
Review a store listing submission.
Args:
store_listing_version_id: ID of the submission to review
request: Review details including approval status and comments
user_id: Authenticated admin user performing the review
Returns:
StoreSubmission with updated review information
"""
try:
already_approved = await store_db.check_submission_already_approved(
store_listing_version_id=store_listing_version_id,
)
submission = await store_db.review_store_submission(
store_listing_version_id=store_listing_version_id,
is_approved=request.is_approved,
external_comments=request.comments,
internal_comments=request.internal_comments or "",
reviewer_id=user_id,
)
state_changed = already_approved != request.is_approved
# Clear caches when the request is approved as it updates what is shown on the store
if state_changed:
store_cache.clear_all_caches()
return submission
except Exception as e:
logger.exception("Error reviewing submission: %s", e)
return fastapi.responses.JSONResponse(
status_code=500,
content={"detail": "An error occurred while reviewing the submission"},
)
@router.get(
"/submissions/download/{store_listing_version_id}",
summary="Admin Download Agent File",
tags=["store", "admin"],
)
async def admin_download_agent_file(
user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
store_listing_version_id: str = fastapi.Path(
..., description="The ID of the agent to download"
),
) -> fastapi.responses.FileResponse:
"""
Download the agent file by streaming its content.
Args:
store_listing_version_id (str): The ID of the agent to download
Returns:
StreamingResponse: A streaming response containing the agent's graph data.
Raises:
HTTPException: If the agent is not found or an unexpected error occurs.
"""
graph_data = await store_db.get_agent_as_admin(
user_id=user_id,
store_listing_version_id=store_listing_version_id,
)
file_name = f"agent_{graph_data.id}_v{graph_data.version or 'latest'}.json"
# Sending graph as a stream (similar to marketplace v1)
with tempfile.NamedTemporaryFile(
mode="w", suffix=".json", delete=False
) as tmp_file:
tmp_file.write(backend.util.json.dumps(graph_data))
tmp_file.flush()
return fastapi.responses.FileResponse(
tmp_file.name, filename=file_name, media_type="application/json"
)

View File

@@ -1,76 +0,0 @@
"""Analytics API"""
import logging
from typing import Annotated
import fastapi
import pydantic
from autogpt_libs.auth import get_user_id
from autogpt_libs.auth.dependencies import requires_user
import backend.data.analytics
router = fastapi.APIRouter(dependencies=[fastapi.Security(requires_user)])
logger = logging.getLogger(__name__)
class LogRawMetricRequest(pydantic.BaseModel):
metric_name: str = pydantic.Field(..., min_length=1)
metric_value: float = pydantic.Field(..., allow_inf_nan=False)
data_string: str = pydantic.Field(..., min_length=1)
@router.post(path="/log_raw_metric")
async def log_raw_metric(
user_id: Annotated[str, fastapi.Security(get_user_id)],
request: LogRawMetricRequest,
):
try:
result = await backend.data.analytics.log_raw_metric(
user_id=user_id,
metric_name=request.metric_name,
metric_value=request.metric_value,
data_string=request.data_string,
)
return result.id
except Exception as e:
logger.exception(
"Failed to log metric %s for user %s: %s", request.metric_name, user_id, e
)
raise fastapi.HTTPException(
status_code=500,
detail={
"message": str(e),
"hint": "Check analytics service connection and retry.",
},
)
@router.post("/log_raw_analytics")
async def log_raw_analytics(
user_id: Annotated[str, fastapi.Security(get_user_id)],
type: Annotated[str, fastapi.Body(..., embed=True)],
data: Annotated[
dict,
fastapi.Body(..., embed=True, description="The data to log"),
],
data_index: Annotated[
str,
fastapi.Body(
...,
embed=True,
description="Indexable field for any count based analytical measures like page order clicking, tutorial step completion, etc.",
),
],
):
try:
result = await backend.data.analytics.log_raw_analytics(
user_id, type, data, data_index
)
return result.id
except Exception as e:
logger.exception("Failed to log analytics for user %s: %s", user_id, e)
raise fastapi.HTTPException(
status_code=500,
detail={"message": str(e), "hint": "Ensure analytics DB is reachable."},
)

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