Getting Started¶
This tutorial walks you through installing Distill, connecting it to Claude Code, and storing your first team memory.
Prerequisites¶
- Python 3.11+
- Ollama installed and running
- Claude Code CLI
Step 1: Install Distill¶
pip install distill-mcp
Or from source:
git clone https://github.com/5queezer/distill.git
cd distill
uv sync
Step 2: Pull the required Ollama models¶
Distill needs two models — one for distillation (turning your raw text into anonymous facts) and one for embeddings (enabling semantic search):
ollama pull gemma3:4b # distillation
ollama pull nomic-embed-text # embeddings (768-dim vectors)
Step 3: Register with Claude Code¶
claude mcp add distill -- python -m distill_mcp
Or if running from source:
claude mcp add distill -- uv run python -m distill_mcp
Step 4: Store your first memory¶
Open Claude Code and say:
You: "Remember that we chose gRPC over REST for inter-service
communication because of streaming support and type safety."
Claude will:
- Send the raw text to your local Ollama for distillation
- Show you the distilled preview (e.g., "gRPC was chosen over REST for inter-service communication due to streaming support and type safety.")
- Wait for your approval
- Store the approved fact in the team database
Step 5: Search your memories¶
You: "How do our services communicate?"
Claude: [searches team memory]
Based on your team's knowledge base, inter-service communication
uses gRPC, chosen for streaming support and type safety.
What you've learned¶
- How to install Distill and its Ollama dependencies
- How to register the MCP server with Claude Code
- The two-phase remember flow: distill → preview → confirm
- How search retrieves stored team knowledge
Next steps¶
- Installation options for alternative setups
- GCP backend setup for team-shared databases
- Tools reference for all available MCP tools