Files
AutoGPT/autogpt_platform
Ubbe 187ab04745 refactor(frontend): remove OldAgentLibraryView and NEW_AGENT_RUNS flag (#12088)
## 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>
2026-02-16 18:29:59 +08:00
..

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:

  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
    

    This command will copy the .env.default file to .env. You can modify the .env file to add your own environment variables.

  3. 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.

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:

  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:

    docker compose ps
    

    This 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:

  1. Open the docker-compose.yml file in a text editor.

  2. 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:
    
  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.