Skip to main content
Glama

Multi-Agent Dev Automation System — Agentic CI/CD Pipeline

An automated agentic CI/CD pipeline using FastAPI, Claude-powered agents, and Model Context Protocol (MCP) to automatically analyze GitHub Pull Requests, run unit tests, and lint files.

Project Architecture

devagent/
├── agents/             # Claude-powered agents
│   ├── base_agent.py      # Base agent with tool execution loop
│   ├── reviewer_agent.py  # Reviewer agent parsing PR diffs
│   └── prompts.py         # System prompts
├── app/                # FastAPI application
│   ├── api/routes/        # API endpoints (health)
│   ├── services/          # Claude integration service
│   ├── config.py          # Settings and environment config
│   └── main.py            # FastAPI main entrypoint
├── mcp_server/         # MCP Server
│   ├── server.py          # MCPServer registration and endpoints
│   └── tools/             # MCP tools (fetch_pr_diff, lint, run_tests, search)
├── tests/              # Test suite (pytest)
│   ├── test_agents.py
│   ├── test_health.py
│   └── test_mcp_tools.py
├── .env.example        # Environment template
├── .gitignore          # Git ignore files
└── requirements.txt    # Python dependencies

Related MCP server: code-review-mcp-server

Features & MCP Tools

  • BaseAgent: Executes a tool-calling loop using the Anthropic API (Claude 3.5 Sonnet) to recursively gather codebase information.

  • ReviewerAgent: Performs code reviews on PR diffs looking for correctness bugs, security vulnerabilities, and code quality issues.

  • MCP Tools:

    • fetch_pr_diff: Retrieves changed files and patches for a GitHub PR.

    • search_codebase: Searches local files using patterns or text search.

    • run_tests: Runs unit tests using pytest within the target repository.

    • lint_code: Runs Ruff linter on target files.

Installation & Setup

  1. Clone the repository (once pushed to GitHub).

  2. Create and activate a virtual environment:

    python -m venv .venv
    # Windows:
    .venv\Scripts\activate
    # Linux/macOS:
    source .venv/bin/activate
  3. Install dependencies:

    pip install -r requirements.txt
  4. Configure environment variables: Create a .env file from the template and fill in your keys:

    cp .env.example .env

    Add your ANTHROPIC_API_KEY and optionally GITHUB_TOKEN.

Running the Project

Running FastAPI App

uvicorn app.main:app --reload

Access the API docs at http://localhost:8000/docs.

Running MCP Server

To run the MCP server directly:

python mcp_server/server.py

Running Tests

To run all tests and verify the system works:

python -m pytest
A
license - permissive license
Not graded
quality - not tested
C
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • A
    license
    B
    quality
    C
    maintenance
    Enables code review operations on GitHub and GitLab, including fetching pull/merge requests, viewing diffs, adding comments, analyzing code quality, and creating merge requests directly from your MCP client.
    15
    34
    4
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Provides 15 MCP tools for AI-powered Git intelligence, enabling commit messages, branch creation, PR descriptions, code review, diff analysis, and push operations directly from your AI assistant.
    MIT

View all related MCP servers

Related MCP Connectors

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Hasinipachimatla/devagent'

If you have feedback or need assistance with the MCP directory API, please join our Discord server