ProjectFlow

A workflow management system for AI-assisted development, similar to Jira or Azure DevOps. Supports both API-driven interactions and Model Context Protocol for seamless AI agent integration.

- Hierarchical task management (Epics, Stories, Subtasks)
- 🚀 NEW: Natural Language Chat Interface- Interact with ProjectFlow using conversational commands
- REST API for programmatic access
- Model Context Protocol (MCP) support for AI agents
- Web interface for human users
- Flexible storage: File system (JSON) or PostgreSQL database
- Clean, modern UI with accessibility features
- Containerized deployment

- Backend: Go 1.24
- Storage: File system (JSON) or PostgreSQL database
- Frontend: HTML templates, CSS, JavaScript
- Containerization: Docker/Podman
- Protocols: HTTP REST API + Model Context Protocol

- Go 1.24 or later
- Docker/Podman (for containerized deployment)

git clone https://github.com/aykay76/projectflow.git cd projectflow

Open your browser and navigate tohttp://localhost:16191

ProjectFlow now features an AI-powered chat interface that allows you to manage tasks and projects using natural language commands. Simply click the chat button (💬) in the header or use the keyboard shortcut⌘+/(Mac) orCtrl+/(Windows/Linux) to get started.

Create a high priority task to fix the login bug List all tasks in the PF project Mark task PF-123 as done Show me overdue tasks Create a new project called "Website Redesign"

- Open the chat interface: Click the 💬 button in the header or press⌘+/
- Type your request: Use natural language to describe what you want to do
- Get instant results: The AI will interpret your request and perform the action

For detailed chat commands and examples, see theChat Interface Guide.

# Install Ollama brew install ollama # macOS # or curl -fsSL https://ollama.com/install.sh | sh # Linux # Start Ollama and install a model ollama serve & ollama pull llama3.2 # Configure ProjectFlow export LLM_PROVIDER=ollama export LLM_OLLAMA_MODEL=llama3.2 ./projectflow

See theOllama Quick Start Guidefor detailed setup instructions.

# For OpenAI export LLM_PROVIDER=openai export LLM_API_KEY=your-openai-key export LLM_MODEL=gpt-4 # For Groq export LLM_PROVIDER=groq export LLM_API_KEY=your-groq-key export LLM_MODEL=llama-3.1-8b-instant

- LLM_OLLAMA_HOST: Ollama server URL (default:http://localhost:11434)
- LLM_OLLAMA_MODEL: Ollama model name (default: llama3.2)

For detailed PostgreSQL setup, seePostgreSQL Storage Documentation.

podman run -p 16191:16191 -v $(pwd)/data:/app/data projectflow

POST /api/chat { "message": "Create a high priority task to fix the login bug", "conversation_id": "optional-uuid" }

{ "response": "I've created task PF-123: 'Fix login bug' with high priority.", "actions_taken": ["create_task"], "task_ids": ["PF-123"], "conversation_id": "uuid", "confidence": 0.95, "intent": "create_task" }

GET /api/chat/history?conversation_id=uuid { "id": "uuid", "messages": [ { "id": "msg-uuid", "role": "user", "content": "Create a task...", "timestamp": "2025-06-22T15:17:44.334579Z" } ], "created": "2025-06-22T15:17:44.334574Z", "updated": "2025-06-22T15:17:44.334574Z" }

GET /api/llm/info { "enabled": true, "provider": "ollama", "model": "llama3.2", "status": "healthy", "timestamp": "2025-06-23T08:56:47.927Z", "metadata": { "host": "http://localhost:11434", "version": "0.1.17" } }

GET /api/llm/health { "healthy": true, "status": "healthy", "provider": "ollama", "timestamp": "2025-06-23T08:56:47.927Z", "duration_ms": 45, "suggestions": [] }

POST /api/llm/chat { "messages": [ {"role": "user", "content": "Hello!"} ], "max_tokens": 1000, "temperature": 0.7 } Response: { "response": { "choices": [ { "message": { "role": "assistant", "content": "Hello! How can I help you today?" }, "finish_reason": "stop" } ] }, "provider": "ollama", "model": "llama3.2" }

- GET /api/tasks- List all tasks
- POST /api/tasks- Create a new task
- GET /api/tasks/{id}- Get task by ID
- PUT /api/tasks/{id}- Update task
- DELETE /api/tasks/{id}- Delete task
- GET /api/hierarchy- Get tasks in hierarchical structure

{ "id": "string", "title": "string", "description": "string", "status": "string", "priority": "string", "parent_id": "string", "children": ["string"], "created_at": "timestamp", "updated_at": "timestamp" }

The/api/hierarchyendpoint returns tasks in a nested structure:

[ { "task": { "id": "string", "title": "string", "description": "string", "status": "string", "priority": "string", "type": "string", "parent_id": "string", "children": ["string"], "created_at": "timestamp", "updated_at": "timestamp" }, "child_tasks": [ { "task": { / nested task / }, "child_tasks": [ / recursively nested / ] } ] } ]

├── cmd/server/ # Application entry point ├── internal/ │ ├── handlers/ # HTTP handlers │ ├── models/ # Data models │ └── storage/ # Storage implementations ├── pkg/api/ # Public API definitions ├── web/ │ ├── templates/ # HTML templates │ └── static/ # CSS, JS, images ├── data/ # Local data storage └── Dockerfile # Container definition

go build -o bin/projectflow cmd/server/main.go

ProjectFlow includes a Model Context Protocol (MCP) server that enables AI agents to interact with tasks programmatically. This allows AI assistants to create, read, update, and delete tasks as part of their workflow.

The MCP server runs on port 3001 by default.

Configure your MCP client:Use the providedmcp-config.jsonfile or configure manually:

{ "mcpServers": { "projectflow": { "command": "go", "args": ["run", "cmd/mcp-server/main.go"], "cwd": "/path/to/projectflow" } } }

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