## Summary - Removes the deprecated `OldAgentLibraryView` directory (13 files, ~2200 lines deleted) - Removes the `NEW_AGENT_RUNS` feature flag from the `Flag` enum and defaults - Removes the legacy agent library page at `library/legacy/[id]` - Moves shared `CronScheduler` components to `src/components/contextual/CronScheduler/` - Moves `agent-run-draft-view` and `agent-status-chip` to `legacy-builder/` (co-located with their only consumer) - Updates all import paths in consuming files (`AgentInfoStep`, `SaveControl`, `RunnerInputUI`, `useRunGraph`) ## Test plan - [x] `pnpm format` passes - [x] `pnpm types` passes (no TypeScript errors) - [x] No remaining references to `OldAgentLibraryView`, `NEW_AGENT_RUNS`, or `new-agent-runs` in the codebase - [x] Verify `RunnerInputUI` dialog still works in the legacy builder - [x] Verify `AgentInfoStep` cron scheduling works in the publish modal - [x] Verify `SaveControl` cron scheduling works in the legacy builder 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- greptile_comment --> <h2>Greptile Overview</h2> <details><summary><h3>Greptile Summary</h3></summary> This PR removes deprecated code from the legacy agent library view system and consolidates the codebase to use the new agent runs implementation exclusively. The refactor successfully removes ~2200 lines of code across 13 deleted files while properly relocating shared components. **Key changes:** - Removed the entire `OldAgentLibraryView` directory and its 13 component files - Removed the `NEW_AGENT_RUNS` feature flag from the `Flag` enum and defaults - Deleted the legacy agent library page route at `library/legacy/[id]` - Moved `CronScheduler` components to `src/components/contextual/CronScheduler/` for shared use across the application - Moved `agent-run-draft-view` and `agent-status-chip` to `legacy-builder/` directory, co-locating them with their only consumer - Updated `useRunGraph.ts` to import `GraphExecutionMeta` from the generated API models instead of the deleted custom type definition - Updated all import paths in consuming components (`AgentInfoStep`, `SaveControl`, `RunnerInputUI`) **Technical notes:** - The new import path for `GraphExecutionMeta` (`@/app/api/__generated__/models/graphExecutionMeta`) will be generated when running `pnpm generate:api` from the OpenAPI spec - All references to the old code have been cleanly removed from the codebase - The refactor maintains proper separation of concerns by moving shared components to contextual locations </details> <details><summary><h3>Confidence Score: 4/5</h3></summary> - This PR is safe to merge with minimal risk, pending manual verification of the UI components mentioned in the test plan - The refactor is well-structured and all code changes are correct. The score of 4 (rather than 5) reflects that the PR author has marked three manual testing items as incomplete in the test plan: verifying `RunnerInputUI` dialog, `AgentInfoStep` cron scheduling, and `SaveControl` cron scheduling. While the code changes are sound, these UI components should be manually tested before merging to ensure the moved components work correctly in their new locations. - No files require special attention. The author should complete the manual testing checklist items for `RunnerInputUI`, `AgentInfoStep`, and `SaveControl` as noted in the test plan. </details> <details><summary><h3>Sequence Diagram</h3></summary> ```mermaid sequenceDiagram participant Dev as Developer participant FE as Frontend Build participant API as Backend API participant Gen as Generated Types Note over Dev,Gen: Refactor: Remove OldAgentLibraryView & NEW_AGENT_RUNS flag Dev->>FE: Delete OldAgentLibraryView (13 files, ~2200 lines) Dev->>FE: Remove NEW_AGENT_RUNS from Flag enum Dev->>FE: Delete library/legacy/[id]/page.tsx Dev->>FE: Move CronScheduler → src/components/contextual/ Dev->>FE: Move agent-run-draft-view → legacy-builder/ Dev->>FE: Move agent-status-chip → legacy-builder/ Dev->>FE: Update RunnerInputUI import path Dev->>FE: Update SaveControl import path Dev->>FE: Update AgentInfoStep import path Dev->>FE: Update useRunGraph.ts FE->>Gen: Import GraphExecutionMeta from generated models Note over Gen: Type available after pnpm generate:api Gen-->>API: Uses OpenAPI spec schema API-->>FE: Type-safe GraphExecutionMeta model ``` </details> <!-- greptile_other_comments_section --> <!-- /greptile_comment --> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
AutoGPT Platform
Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
Getting Started
Prerequisites
- Docker
- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
Running the System
To run the AutoGPT Platform, follow these steps:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to access the AutoGPT Platform frontend.
Running Just Core services
You can now run the following to enable just the core services.
# 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
Docker Compose Commands
Here are some useful Docker Compose commands for managing your AutoGPT Platform:
docker compose up -d: Start the services in detached mode.docker compose stop: Stop the running services without removing them.docker compose rm: Remove stopped service containers.docker compose build: Build or rebuild services.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:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
-
Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
-
Checking the status of services:
docker compose psThis shows the current status of all services defined in your docker-compose.yml file.
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:
-
Open the
docker-compose.ymlfile in a text editor. -
Add volume configurations for PostgreSQL and Redis services:
services: postgres: # ... other configurations ... volumes: - postgres_data:/var/lib/postgresql/data redis: # ... other configurations ... volumes: - redis_data:/data volumes: postgres_data: redis_data: -
Save the file and run
docker compose up -dto 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 Orvalpnpm 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:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.