polycoding 1.0.1

Coding agent that lives in your project management tool

Polycode

Multi-agent software development automation using CrewAI. GitHub App integration for webhook-driven workflows across multiple repositories.

Table of Contents

• Overview
• Tech Stack
• Prerequisites
• Getting Started
• Architecture
• Environment Variables
• Available Scripts
• CLI Commands
• Testing
• Workflows
• GitHub App Integration
• Docker Deployment
• Troubleshooting

---

Overview

Polycode automates software development tasks using AI-powered multi-agent workflows. It integrates as a GitHub App to provide seamless automation triggered by issue labels.

Key Features

• Multi-agent CrewAI workflows - Plan, implement, review, test, and verify code
• GitHub App integration - Webhook-driven automation for multi-repo support
• Label-to-flow mapping - Trigger workflows by adding labels to issues
• Iterative development - Ralph Loop with automatic verification and retry
• Context+ integration - Semantic code intelligence via MCP/Ollama

Available Workflows

Workflow	Label	Description
Ralph	ralph	Fast iterative implementation with automated verification
Specify	specify	Feature specification and planning workflow

---

Tech Stack

• Language: Python 3.13+
• Framework: CrewAI 1.10+ (multi-agent orchestration)
• Web Server: FastAPI with Uvicorn
• Database: PostgreSQL 16+ with SQLAlchemy/SQLModel
• Queue: Celery with Redis
• Package Manager: uv
• CLI: Typer with Rich
• Real-time: Socket.IO for streaming events
• Deployment: Docker (includes Node.js 22, Bun, Context+)

---

Prerequisites

• Python 3.13+ (via pyenv, mise, or asdf)
• uv package manager - curl -LsSf https://astral.sh/uv/install.sh | sh
• PostgreSQL 16+ (or Docker)
• Redis (for Celery background jobs)
• GitHub App credentials (for webhook integration)
• Ollama (optional, for Context+ semantic search)

---

Getting Started

1. Clone and Install

git clone https://github.com/your-org/polycode.git
cd polycode
uv sync

2. Environment Setup

Copy the example environment file:

cp .env.example .env

Configure required variables (see Environment Variables).

3. Database Setup

Using Docker:

docker run --name postgres \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=polycode \
  -p 5432:5432 \
  -d postgres:16

Using local PostgreSQL:

# macOS
brew install postgresql@16
brew services start postgresql@16
createdb polycode

# Ubuntu
sudo apt install postgresql-16
sudo -u postgres createdb polycode

The database tables are auto-created on first run via SQLAlchemy.

4. Redis Setup

docker run --name redis -p 6379:6379 -d redis:7

Or locally:

# macOS
brew install redis
brew services start redis

# Ubuntu
sudo apt install redis-server
sudo systemctl start redis

5. Start Services

Development (all services):

# Terminal 1: FastAPI webhook server
uv run polycode server webhook --reload

# Terminal 2: Socket.IO streaming server
uv run polycode server socketio --reload

# Terminal 3: Celery worker
uv run polycode worker start --queues celery,default --loglevel=info

# Terminal 4: Flower (Celery monitoring, optional)
uv run polycode worker flower

Quick test:

uv run polycode --help

---

Architecture

Directory Structure

polycode/
├── src/
│   ├── cli/                    # Typer CLI commands
│   │   ├── main.py             # Entry point
│   │   ├── server.py           # Server management
│   │   ├── worker.py           # Celery worker commands
│   │   ├── flow.py             # Flow execution
│   │   ├── project.py          # GitHub project management
│   │   └── db.py               # Database utilities
│   ├── crews/                  # CrewAI crew definitions
│   │   ├── base.py             # PolycodeCrewMixin with hooks
│   │   ├── plan_crew/          # Story planning
│   │   ├── implement_crew/     # Code implementation
│   │   ├── review_crew/        # Code review
│   │   ├── test_crew/          # Test generation
│   │   ├── verify_crew/        # Build verification
│   │   ├── ralph_crew/         # Ralph loop implementation
│   │   ├── conversation_crew/  # Conversational agent
│   │   └── streaming/          # Real-time event publishing
│   ├── flows/                  # CrewAI Flow orchestration
│   │   ├── base.py             # FlowIssueManagement base
│   │   ├── protocol.py         # Flow protocol definitions
│   │   ├── ralph/              # Ralph Loop flow
│   │   └── specify/            # Specify flow
│   ├── channels/               # Real-time communication
│   │   ├── base.py             # Channel base class
│   │   ├── dispatcher.py       # Multi-channel dispatcher
│   │   ├── redis/              # Redis pub/sub channel
│   │   └── stream/             # Socket.IO streaming
│   ├── github_app/             # GitHub App integration
│   │   ├── app.py              # FastAPI webhook server
│   │   ├── auth.py             # JWT authentication
│   │   ├── webhook_handler.py  # Event processing
│   │   ├── installation_manager.py
│   │   └── label_mapper.py     # Label-to-flow mapping
│   ├── project_manager/        # GitHub project management
│   │   ├── github.py           # GitHub API client
│   │   ├── github_projects_client.py
│   │   ├── git_utils.py        # Git operations
│   │   ├── flow_runner.py      # Flow execution
│   │   └── types.py            # Pydantic models
│   ├── persistence/            # Database layer
│   │   ├── postgres.py         # SQLAlchemy models
│   │   └── config.py           # Database settings
│   ├── gitcore/                # Git worktree operations
│   ├── modules/                # Plugin hook system
│   ├── tools/                  # Custom CrewAI tools
│   │   ├── file_read_tool.py
│   │   ├── directory_read_tool.py
│   │   ├── exec_tool.py
│   │   └── agents_md_loader.py
│   ├── tasks/                  # Task templates
│   ├── glm.py                  # GLM JSON LLM wrapper
│   └── bootstrap.py            # Application bootstrap
├── task_templates/             # Custom task templates
│   ├── custom_implement.md
│   └── custom_generate_result.md
├── tests/                      # Test files
├── docs/                       # Documentation
├── Dockerfile
├── entrypoint.sh
├── Makefile
└── pyproject.toml

Request Lifecycle

GitHub Webhook → FastAPI /webhook/github → LabelMapper → Celery Task
                                                        ↓
                                             FlowRunner.kickoff()
                                                        ↓
                                             RalphLoopFlow (CrewAI Flow)
                                                        ↓
                                             PlanCrew → RalphCrew → VerifyCrew
                                                        ↓
                                             Hook Events (git commit, PR create)

Flow Event System

The hook system enables modular event handling:

Event	Emitted By	Hook Handler
FLOW_STARTED	Flow setup	Initialize resources
STORIES_PLANNED	PlanCrew	Create checklist items
STORY_COMPLETED	Implement step	Commit + push code
CREW_FINISHED	Any crew	Log, metrics
FLOW_FINISHED	Flow complete	Create PR, cleanup
CLEANUP	Flow end	Remove worktree

Database Schema

-- Flow state persistence
flow_states (
    id              SERIAL PRIMARY KEY,
    flow_uuid       VARCHAR(255) NOT NULL,
    method_name     VARCHAR(255) NOT NULL,
    timestamp       TIMESTAMPTZ NOT NULL,
    state_json      JSONB NOT NULL
)

-- Async human-in-the-loop
pending_feedback (
    id              SERIAL PRIMARY KEY,
    flow_uuid       VARCHAR(255) UNIQUE NOT NULL,
    context_json    JSONB NOT NULL,
    state_json      JSONB NOT NULL,
    created_at      TIMESTAMPTZ NOT NULL
)

-- Issue tracking
requests (
    id              SERIAL PRIMARY KEY,
    issue_number    INTEGER NOT NULL,
    request_text    TEXT NOT NULL,
    status          VARCHAR(50) NOT NULL,
    commit          VARCHAR(255),
    created_at      TIMESTAMPTZ
)

-- Payment tracking (manual mode)
payments (
    id              SERIAL PRIMARY KEY,
    issue_number    INTEGER NOT NULL,
    payment_id      VARCHAR(255),
    amount          INTEGER,
    currency        VARCHAR(10),
    payment_method  VARCHAR(50),
    status          VARCHAR(50),
    created_at      TIMESTAMPTZ,
    verified_at     TIMESTAMPTZ
)

---

Environment Variables

Required

Variable	Description	Example
DATABASE_URL	PostgreSQL connection string	postgresql://user:pass@localhost:5432/polycode
REDIS_HOST	Redis hostname	localhost
REDIS_PORT	Redis port	6379
GITHUB_APP_ID	GitHub App ID	123456
GITHUB_APP_PRIVATE_KEY	GitHub App private key (with \n)	-----BEGIN RSA PRIVATE KEY-----\n...
GITHUB_APP_WEBHOOK_SECRET	Webhook secret for signature validation	your-webhook-secret

LLM Configuration

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key	-
OPENAI_URL_BASE	Custom OpenAI-compatible URL	-
MODEL	Default model identifier	gpt-4o

Context+ (Optional)

Variable	Description	Default
OLLAMA_HOST	Ollama server URL	http://localhost:11434
OLLAMA_EMBED_MODEL	Embedding model	nomic-embed-text
OLLAMA_CHAT_MODEL	Chat model for Context+	gemma2:27b

Application

Variable	Description	Default
APP_RUN	Default run mode	api
APP_HOST	Server host	0.0.0.0
APP_PORT	Server port	5000
LOG_LEVEL	Logging verbosity	INFO
DATA_PATH	Worktree storage path	/data
SSH_KEY	SSH private key for git push	-

---

Available Scripts

Package Management

uv sync                   # Install all dependencies
uv add <package>          # Add a new dependency
uv add --dev <package>    # Add development dependency
uv lock                   # Update lock file

Linting & Formatting

uv run ruff check .                    # Check lint errors
uv run ruff check --fix .              # Auto-fix lint errors
uv run ruff format .                   # Format code
uv run pyright                         # Type checking

Database

uv run polycode db init                # Initialize database tables
uv run polycode db reset               # Reset database (warning: destructive)

---

CLI Commands

# Show help
uv run polycode --help
uv run polycode <command> --help

# Server management
uv run polycode server webhook --host 0.0.0.0 --port 5000 --reload
uv run polycode server socketio --host 0.0.0.0 --port 5001

# Celery worker
uv run polycode worker start --queues celery,default --loglevel=info
uv run polycode worker flower --port=5555

# Flow execution
uv run polycode flow run ralph --issue 42 --repo owner/repo

# GitHub project management
uv run polycode project list --installation 12345
uv run polycode project status --issue 42

# Database
uv run polycode db init
uv run polycode db status

---

Testing

Run Tests

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_flows.py

# Run single test
uv run pytest tests/test_flows.py::test_ralph_flow_setup

# Run tests matching pattern
uv run pytest -k "ralph"

# Verbose output
uv run pytest -v

# Stop after first failure
uv run pytest --maxfail=1

# With coverage
uv run pytest --cov=src

Test Structure

tests/
├── __init__.py
├── test_flows.py              # Flow tests
├── test_gitcore.py            # Git operations tests
├── test_github_manager.py     # GitHub API tests
├── test_agents_md_loader.py   # AGENTS.md loader tests
├── test_project_manager_plugin.py
└── test_retro.py              # Retrospective tests

Writing Tests

"""Test module docstring."""

from unittest.mock import MagicMock, patch

from project_manager.github import GitHubProjectManager
from project_manager.types import ProjectConfig


def test_has_label_returns_true_when_label_exists():
    """Test that has_label returns True when the label is present."""
    # Arrange
    config = ProjectConfig(
        repo_owner="testowner",
        repo_name="testrepo",
        token="fake_token",
    )
    manager = GitHubProjectManager(config)
    mock_repo = MagicMock()
    mock_pr = MagicMock()
    mock_pr.labels = [MagicMock(name="approved")]
    mock_repo.get_pull.return_value = mock_pr
    manager.github_client.get_repo = MagicMock(return_value=mock_repo)

    # Act
    result = manager.has_label(123, "approved")

    # Assert
    assert result is True
    mock_repo.get_pull.assert_called_once_with(123)

---

Workflows

Ralph Flow (ralph)

Iterative development with automatic verification loops.

Flow Steps:

1. Setup - Initialize git worktree, load issue context
2. Plan - Decompose task into ordered user stories via PlanCrew
3. Implement - Execute each story via RalphCrew:
   • Implement code changes
   • Run tests
   • If tests fail: retry with error context (max 3 attempts)
   • If tests pass: commit and proceed to next story
4. Verify - Final build and test verification
5. Cleanup - Push changes, create PR, remove worktree

Tools Available to Ralph Agents:

Tool	Description
FileReadTool	Read file contents
FileWriterTool	Write/modify files
DirectoryReadTool	Explore directory structure
ExecTool	Execute shell commands (build, test)
AgentsMDLoaderTool	Load AGENTS.md context files

Safety Features:

• Per-story commits (atomic changes)
• Maximum 3 retry iterations per story
• Error context included in retries
• Automatic worktree cleanup

Specify Flow (specify)

Feature specification and planning workflow for larger features.

---

GitHub App Integration

Setup

1. Create GitHub App

   Go to https://github.com/settings/apps/new

   Configure:

   • App name: Your app name
   • Webhook URL: Your server URL + /webhook/github
   • Webhook secret: Generate a secure secret
   • Permissions:
     • Issues: Read & Write
     • Pull requests: Read & Write
     • Contents: Read & Write
     • Projects: Read & Write
     • Metadata: Read-only

2. Generate Private Key

   After creating the app, generate a private key and add to .env:

   GITHUB_APP_PRIVATE_KEY=$(cat private-key.pem | awk '{printf "%s\\n", $0}' | tr -d '\n')
   

3. Install App

   Install the app on target repositories from the app settings page.

4. Create Label Mappings

   curl -X POST http://localhost:5000/mappings \
     -H "Content-Type: application/json" \
     -d '{
       "installation_id": 12345,
       "label_name": "ralph",
       "flow_name": "ralph_flow",
       "priority": 0
     }'
   

Available Endpoints

Endpoint	Method	Description
/	GET	App info and status
/health	GET	Health check
/webhook/github	POST	GitHub webhook handler
/installations	GET	List active installations
/mappings	GET	List label mappings
/mappings	POST	Create label mapping

Triggering Flows

1. Create an issue in a connected repository
2. Add the mapped label (e.g., ralph)
3. Webhook triggers flow execution automatically

---

Docker Deployment

Build Image

make docker_build
# or
docker build -t ghcr.io/xeroc/polycode:latest .

Run Container

# API server
docker run -p 5000:5000 \
  -e DATABASE_URL=postgresql://... \
  -e REDIS_HOST=redis \
  -e GITHUB_APP_ID=123456 \
  -e GITHUB_APP_PRIVATE_KEY="-----BEGIN..." \
  -e GITHUB_APP_WEBHOOK_SECRET=secret \
  -v /data:/data \
  ghcr.io/xeroc/polycode:latest api

# Celery worker
docker run \
  -e DATABASE_URL=postgresql://... \
  -e REDIS_HOST=redis \
  -e OLLAMA_HOST=http://ollama:11434 \
  -e SSH_KEY="$(cat ~/.ssh/id_rsa)" \
  ghcr.io/xeroc/polycode:latest worker

# Socket.IO server
docker run -p 5001:5000 \
  ghcr.io/xeroc/polycode:latest socketio

Docker Compose

services:
  api:
    image: ghcr.io/xeroc/polycode:latest
    command: api
    ports:
      - "5000:5000"
    environment:
      - DATABASE_URL=postgresql://postgres:postgres@db:5432/polycode
      - REDIS_HOST=redis
      - GITHUB_APP_ID=${GITHUB_APP_ID}
      - GITHUB_APP_PRIVATE_KEY=${GITHUB_APP_PRIVATE_KEY}
      - GITHUB_APP_WEBHOOK_SECRET=${GITHUB_APP_WEBHOOK_SECRET}
    depends_on:
      - db
      - redis

  worker:
    image: ghcr.io/xeroc/polycode:latest
    command: worker
    environment:
      - DATABASE_URL=postgresql://postgres:postgres@db:5432/polycode
      - REDIS_HOST=redis
      - OLLAMA_HOST=http://ollama:11434
      - SSH_KEY=${SSH_KEY}
    volumes:
      - data:/data
    depends_on:
      - db
      - redis
      - ollama

  socketio:
    image: ghcr.io/xeroc/polycode:latest
    command: socketio
    ports:
      - "5001:5000"
    environment:
      - REDIS_HOST=redis
    depends_on:
      - redis

  db:
    image: postgres:16
    environment:
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=polycode
    volumes:
      - pgdata:/var/lib/postgresql/data

  redis:
    image: redis:7
    volumes:
      - redisdata:/data

  ollama:
    image: ollama/ollama
    volumes:
      - ollamadata:/root/.ollama

volumes:
  pgdata:
  redisdata:
  ollamadata:
  data:

---

Troubleshooting

Database Connection Issues

Error: could not connect to server: Connection refused

Solution:

1. Verify PostgreSQL is running: docker ps or pg_isready
2. Check DATABASE_URL format: postgresql://USER:PASSWORD@HOST:PORT/DATABASE
3. Ensure database exists: createdb polycode

Redis Connection Issues

Error: Error connecting to Redis

Solution:

1. Verify Redis is running: redis-cli ping
2. Check REDIS_HOST and REDIS_PORT values
3. For Docker: ensure containers are on same network

Celery Worker Not Processing Tasks

Error: Tasks queued but not executing

Solution:

# Check worker is running
uv run polycode worker start --queues celery,default --loglevel=debug

# Check Flower dashboard
uv run polycode worker flower
# Open http://localhost:5555

GitHub App Authentication Failures

Error: JWT authentication failed

Solution:

1. Verify GITHUB_APP_ID matches your app

2. Ensure private key has proper newlines:

   echo "$GITHUB_APP_PRIVATE_KEY" | head -1
   # Should show: -----BEGIN RSA PRIVATE KEY-----
   

3. Check app is installed on the repository

LLM API Errors

Error: OpenAI API error

Solution:

1. Verify OPENAI_API_KEY is set
2. Check rate limits
3. For custom endpoints, verify OPENAI_URL_BASE

Context+ Not Working

Error: Context+ MCP connection failed

Solution:

1. Ensure Ollama is running: ollama serve

2. Pull required models:

   ollama pull nomic-embed-text
   ollama pull gemma2:27b
   

3. Verify Context+ is installed: bun install -g contextplus

4. Test MCP server: bunx contextplus skeleton .

Git Operations Failing

Error: Permission denied (publickey)

Solution:

1. For Docker: ensure SSH_KEY environment variable is set

2. For local: verify SSH key has access to repository

3. Add GitHub to known hosts:

   ssh-keyscan github.com >> ~/.ssh/known_hosts
   

Import Errors

Error: ModuleNotFoundError: No module named 'xxx'

Solution:

# Ensure virtual environment is active
uv sync

# Run with uv
uv run polycode --help

---

Resources

• CrewAI Documentation
• GitHub Apps Documentation
• FastAPI Documentation
• Celery Documentation

License

MIT