Setup: Hermes and Copilot CLI

Pick a driver. You only need one, but the course shows both because they teach different halves of the idea.

The lab code

git clone https://github.com/brianbaldock/graph-engineering-course
cd graph-engineering-course/labs
python3 -m venv .venv
.venv/bin/pip install pytest mcp

Check it works:

.venv/bin/python -m pytest tests/ -q

Expected output:

...............                                                          [100%]
15 passed in 0.06s

If those 15 tests pass, every lab in this course will run on your machine. Nothing else is required for Parts 1 through 3.

Driver A: GitHub Copilot CLI

Best if you want to feel the routing idea. One binary, many models, one subscription.

copilot --version
gh auth status          # Copilot inherits GitHub auth

Model selection is a flag, which is exactly the lever this course is about:

# cheap, mechanical work
copilot -p "Summarize labs/graphlab/validate.py in five bullets" \
  --model claude-haiku-4.5 --allow-all-tools

# expensive, high-judgment work
copilot -p "Critique the validation gate in labs/graphlab/validate.py. \
What class of bad extraction still gets through?" \
  --model claude-opus-4.8 --effort high --allow-all-tools

There is no copilot models subcommand. To discover what’s available, ask Copilot itself:

copilot -p "list available models" --allow-all-tools --model claude-sonnet-4.5

Driver B: Hermes Agent

Best if you want durable memory and MCP tools present in every conversation, without re-wiring per session.

Hermes has a native MCP client. Servers listed in ~/.hermes/config.yaml are connected at startup, their tools discovered, and those tools injected into every platform toolset:

mcp_servers:
  graphlab:
    command: "/absolute/path/to/labs/.venv/bin/python"
    args: ["/absolute/path/to/labs/mcp_server.py"]
    env:
      GRAPHLAB_DB: "/absolute/path/to/labs/memory.db"
    timeout: 60

Tools land in the registry as mcp_{server}_{tool}, so the four tools you’ll build become mcp_graphlab_search_entities, mcp_graphlab_get_subgraph, mcp_graphlab_add_knowledge, and mcp_graphlab_graph_stats.

Two things to know before Lesson 8:

• Restart is required. There’s no hot-reload for MCP servers. Add config, restart the agent.
• The environment is filtered. Hermes does not pass your whole shell environment to MCP subprocesses. Only PATH, HOME, USER, LANG, TERM, SHELL, TMPDIR and XDG_* are inherited. Anything else, including API keys, must be named explicitly under env:. That’s a deliberate credential-leak guard, and it’s the reason GRAPHLAB_DB appears above.

Use absolute paths everywhere

MCP servers are launched as subprocesses with a working directory you do not control. Every path in an MCP config must be absolute. This is the single most common setup failure, in both drivers.

Get yours:

cd graph-engineering-course/labs && pwd

Optional: the production track

Only needed for Lesson 11. Skip it for now.

• Docker, for Neo4j
• uv, for running the Graphiti MCP server
• An ANTHROPIC_API_KEY or OPENAI_API_KEY if you want real LLM extraction instead of the deterministic offline one

Everything in Parts 1 through 3 runs free and offline.

Next: what a knowledge graph actually buys you that a vector store doesn’t.