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GraphRAG Chat

Moderated multi-agent group chat.

  • top-level agent routes questions to sub-agents by name or speciality
  • top-level agent includes multi-memory delegation
  • all memories saved to each memory implementation
  • sub-agents can select which memory implementation to read from
  • a Neo4j graph catalog is available for read/write to multiple knowledge graphs

Agents:

graphrag_chat_agent_v1 (root agent)
│
├── cypher_agent: direct read/write access to available knowledge graphs
│
├── agent_smith: Neo4j product specialist
│   └── (memory: in-memory service. TBD migrate to simple KG memory)
│
└── new_agent: Give them a fun name and topical expertise
      └── (memory: note the memory impl)

Developer Notes

1. Initialize the Python environment with uv

  • Create a virtual environment:
    uv venv
  • Activate the virtual environment:
    source .venv/bin/activate  # select the appropriate activate.* for your shell
  • Install dependencies:
    uv sync 

2. Set up configuration files

  • Copy the environment template and edit as needed:
    cp .env.template .env
    # Edit .env to set your environment variables
  • Copy the Neo4j connections example and edit as needed:
    cp neo4j.example.json neo4j.json
    # Edit neo4j.json to configure your Neo4j connections

3. Run the agent using adk

  • Start the agent web server:
    adk web

You should now be able to access the agent locally. See below for more details or troubleshooting steps.

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