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Getting Data Into Your Brain

ModusBrain is the retrieval layer. But retrieval is only as good as what you put in. This directory covers how to get data flowing into your brain automatically.

How Data Flows In

Available Integrations

Self-Installing Recipes

These are integration recipes your agent can set up for you. Run modusbrain integrations to see what’s available and their status.

Manual Integration Guides

These require manual setup (no self-installing recipe yet):

How to Read a Recipe

Integration recipes are markdown files with YAML frontmatter. Your agent reads the recipe and walks you through setup.
The recipe IS the installer. Your agent (OpenClaw, Hermes, Claude Code) reads the markdown body and executes the setup steps. It asks you for API keys, validates each one, configures the integration, and runs a smoke test.

Recipe trust boundary

Only recipes shipped inside the modusbrain package itself (the recipes/ directory in a source install, or the global install copy) are trusted. Recipes discovered at runtime from $MODUSBRAIN_RECIPES_DIR or a cwd-local ./recipes/ are marked untrusted: they cannot run command health checks, cannot run http health checks (SSRF defense), and cannot use the deprecated string health_check form. Untrusted recipes can still use env_exists and any_of compositions. To ship a recipe that runs live checks, contribute it upstream so it becomes package-bundled.

The Deterministic Collector Pattern

When an LLM keeps failing at a mechanical task despite repeated prompt fixes, stop fighting the LLM. Move the mechanical work to code. Code for data. LLMs for judgment.
  • Email collection: code pulls emails with baked-in links (100% reliable). LLM reads the digest, classifies, enriches brain entries (judgment).
  • Tweet collection: code pulls timeline, detects deletions, tracks engagement (deterministic). LLM extracts entities, writes brain updates (judgment).
  • Calendar sync: code pulls events and attendees (deterministic). LLM enriches attendee brain pages (judgment).
This pattern prevents the “LLM forgot the links” failure mode. Mechanical work must be 100% reliable. Judgment work is where LLMs shine. See Deterministic Collectors for the full pattern.

Architecture

For details on the shared infrastructure that all integrations build on (import pipeline, chunking, embedding, search), see the Infrastructure Layer. For the philosophy behind thin harness + fat skills, see Thin Harness, Fat Skills.