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Agent-Graph is a Multi-Agent System built on the principles of Context Engineering. It integrates Sub-agent, Long-term Memory, MCP, Agent-based Workflow, and other capabilities. By integrating Context Engineering best practices into a visual development experience, Agent-Graph enables developers to rapidly build, test, and deploy complex multi-agent applications.

1. Framework
2. Deployment Guide
   • Clone Project
   • Configure and Start Docker Services
   • Deploy Backend
   • Access Application
3. Core Features
4. Future Roadmap
   • Coming Soon
   • Future Plans
5. Frontend Feature Showcase
6. Citation
7. WeChat Group

📖 Detailed Installation Documentation: docs/first-steps/install.md

git clone https://github.com/keta1930/agent-graph.git
cd agent-graph

cp .env.example .env
# Set all blank credentials and secrets before startup
python agent_graph/scripts/generate_jwt_secret.py
docker compose --env-file .env -f docker/docker-compose.yml up -d

Default Service Addresses:

• MongoDB Express (Database Management): http://localhost:20041
• MinIO Console (File Storage): http://localhost:20043

Using uv (Recommended):

uv sync
uv run --env-file .env fastapi run

Using pip:

pip install -r requirements.txt
dotenv -f .env run -- fastapi run

Run in Background:

nohup uv run --env-file .env fastapi run > app.log 2>&1 &

Open the address configured by PUBLIC_API_BASE_URL (default: http://localhost:20050).

Login Page (Admin login with username and password configured in .env):

Registration Page (New users can register with invitation code):

Other Access Endpoints:

• API Documentation: http://localhost:20050/docs
• Health Check: http://localhost:20050/health

If you need to modify frontend code:

cd frontend
npm install
npm run dev  # Uses FRONTEND_PORT from the root .env
npm run build  # Build production version

Note: The repository includes pre-built frontend files. This step is only needed when developing or customizing the frontend.

📖 Complete Roadmap: docs/roadmap/index.md

The platform continues to evolve, bringing more powerful Agent capabilities and better collaboration experiences to users.

The following features have been recently implemented and are now available:

The following features are coming soon or actively under development:

These features are under continuous exploration and planning:

Entry interface for starting conversations with Agents, supporting quick selection of preset Agents or creating new conversations.

Create, configure, and manage agents, set system prompts, tools, and model parameters.

Visual drag-and-drop workflow designer, supporting multiple node types and complex process orchestration.

Configure and manage multiple LLM models, set API Keys and model parameters.

View and configure built-in system tools, including resource creation and collaboration tools.

Manage MCP server connections, configure external tool and data source integrations.

Centrally manage reusable Prompt templates, supporting categorization and version control.

Manage uploaded files and attachments, supporting file preview and organization.

View and manage Agent's long-term memory and knowledge base.

If you find Agent-Graph helpful for your research or work, please consider citing it:

@misc{agent_graph_2025,
  title        = {agent-graph},
  author       = {Yan Yixin},
  howpublished = {\url{https://github.com/keta1930/agent-graph}},
  note         = {Accessed: 2025-04-24},
  year         = {2025}
}

For questions, suggestions, or collaboration inquiries, feel free to reach out:

📧 Email: yandeheng1@gmail.com