TaskFlow is a human-agent task orchestration platform that unifies work across humans and AI agents using the MCP protocol. It provides a centralized model for creating projects, assigning tasks, and delegating work to both human workers and AI agents, with a full audit trail of actions and decisions. Through the MCP interface, you can issue tasks, track progress, and enable agent-to-agent delegation to accelerate complex workflows. The platform emphasizes cross-project visibility, dynamic delegation, and a unified activity log so stakeholders can see who did what, when, and why. You can leverage the built-in task lifecycle (pending, in_progress, review, completed, blocked) and hierarchical subtasks to map real-world processes, whether coordinating human contributors or enabling autonomous agent threads to operate in concert with human teams.

Prerequisites:

• Python 3.10+ installed
• Git installed
• Access to install Python packages (pip)

1. Clone the repository git clone https://github.com/your-org/taskflow mjunaidca-taskflow cd mjunaidca-taskflow

2. Create a virtual environment and activate it python -m venv venv

   On Windows

   venv\Scripts\activate.bat

   On macOS/Linux

   source venv/bin/activate

3. Install dependencies pip install -r requirements.txt

   If a requirements file is not present, install core MCP dependencies manually

   pip install pydantic fastapi uvicorn

4. Configure environment (example) Export environment variables (or set in a .env file and load it) export MCP_PORT=8080 export DATABASE_URL=sqlite:///taskflow.db

5. Run the MCP server uvicorn taskflow_server:app --reload --port 8080

   If using the provided module runner

   python -m taskflow_server

6. Verify installation

   • Open http://localhost:8080/docs (if using FastAPI) to view API/schema
   • Interact with MCP endpoints to create projects, workers, tasks, and audits

Notes:

• If you deploy in production, bind to a proper domain, enable TLS, and configure authentication for agents and humans.
• You may switch to a docker-based deployment if preferred, see additional notes for container guidance.

Tips and common issues:

• Ensure Python version compatibility (3.10+ recommended).
• The MVP stores data locally by default (sqlite). For production, configure a persistent database and proper backups.
• Agents and humans are modeled as workers; define their capabilities and authentication keys where applicable.
• The MCP audit log is the source of truth for delegation and progress; ensure log retention and proper indexing for audits.
• If you expose endpoints publicly, implement access controls and rotate any API keys regularly.
• Typical env vars: MCP_PORT, DATABASE_URL, LOG_LEVEL. Adjust logging for debugging in development and switch to INFO/WARNING in production.