agent-kb

A drop-in, local-first knowledge base for LLM coding agents. Index your repository's documentation, concept ontology, and build targets into Qdrant, and expose retrieval to any MCP-capable agent (Claude Code, Cursor, custom agents) as tools — not context stuffing.

No closed corpora, no paid APIs, no managed vector database: one Qdrant container, CPU-local embeddings via fastembed, and a small Python MCP server.

The idea

Agents lose accuracy the moment they're asked about anything outside their pre-training corpus. The usual fix is RAG, but how the agent consumes retrieval matters more than the embedding model: inline context-stuffing burns tokens and amplifies noise; tool-shaped retrieval lets the agent ground claims selectively, the same way it already uses other tools.

Grounding splits into three layers with different staleness profiles:

The KB deliberately stops at the symbol name. It never stores file paths or line numbers for code — that's the LSP's job, and indexing source into a vector store just guarantees churn.

Install

Requirements: Python 3.11+, uv, Docker (for Qdrant).

git clone https://github.com/zmij/agent-kb          # next to your repo, or as a submodule
cd agent-kb
make install        # uv venv + editable install
make up             # start Qdrant (docker compose)

Or from your own repo, if you vendor this as a submodule and include kb.mk in your Makefile (see Make integration):

make kb-install kb-up

Configure your project

Create kb.yaml at your repository's root (not in this repo). It declares the project identity and the sources to index:

project: my-project           # → collection "my_project_kb", MCP server "my-project-kb"

sources:
  user_docs:
    type: markdown            # heading-aware chunking, frontmatter lifted to payload
    root: docs/guides
    uri_prefix: "docs://guides"   # optional: preserve your internal link scheme
  arch_docs:
    type: markdown
    root: docs
    exclude: [guides, ontology]   # subtrees that have their own indexers
  ontology:
    type: ontology            # concept → code-symbol bindings (see below)
    root: docs/ontology
  make_targets:
    type: make_targets        # every documented `target: ## description`
    files: [Makefile, "scripts/make/*.mk"]

# Only needed if you use the ontology maintenance loop (verify/heal/suggest-new)
symbols:
  language: cpp
  include_root: include/myproject   # public header tree to parse
  base_classes: [Strategy]          # subclasses of these are discoverable concepts
  strip_suffixes: [Strategy, Impl]  # trimmed when deriving stub titles/slugs
  stub_subdir: strategies           # stubs land in docs/ontology/strategies/
  stub_kind: strategy               # `kind:` value written into stubs

All sources share one collection by default (cross-source retrieval in a single search); override per-source with collection: or globally with default_collection:.

Index and search

kb index --all                 # chunk → embed → upsert (incremental by default)
kb search "how do I deploy"    # semantic search across all sources
kb sources                     # what's indexed, per source
kb index user_docs --full      # re-embed one source from scratch

Incremental indexing hashes file content and only re-embeds changed files; chunks of deleted files are evicted automatically.

Expose to your agent (MCP)

kb serve-mcp                   # stdio MCP server

For Claude Code, register per project/worktree:

claude mcp add my-project-kb -e KB_REPO_ROOT=$(pwd) -- \
  /path/to/agent-kb/.venv/bin/kb serve-mcp

(the kb-register make target below does this for you, self-healing).

Tools exposed: kb_search, kb_get, kb_list_sources, kb_reindex.

The ontology layer

An ontology entry is a small markdown file binding a domain concept to the code symbols that implement it:

---
concept: x-wing
title: X-Wing
kind: technique
implements:
  - sudoku::XWingTechnique
related_concepts: [swordfish]
---

A fish pattern on two rows and two columns…

The chunk text bakes the symbol list into the embeddable body, so "which class implements X-Wing" hits the bound names, not just prose. And because bindings are curated, they need a maintenance loop:

Currently the symbol parser covers C++ headers (tree-sitter). Other languages: PRs welcome — the parser interface is one function, parse_header(path, root) -> [Symbol].

Make integration

kb.mk ships includable targets (kb-up, kb-index, kb-search, kb-register, kb-verify, …). From a consuming repo:

KB_DIR := tools/knowledge_base       # wherever the submodule/clone lives
KB_MCP_NAME := my-project-kb
include $(KB_DIR)/kb.mk

Run make kb-help for the full target list.

Per-worktree collection scoping

If you use git worktrees, each worktree writes to its own Qdrant collections, suffixed with a slug derived from the worktree directory name (my_project_kb_backend, my_project_kb_frontend, …). Concurrent indexing across worktrees never collides, while one Qdrant container serves them all.

The slug derives from KB_REPO_ROOT — kb-register bakes KB_REPO_ROOT=$(pwd) into the MCP registration so queries always land in the registering worktree's collections. kb collections --all shows every worktree's collections.

Configuration reference

Environment (infrastructure — machine-level, .env supported):

Switching embedding backends changes vector dimensions — re-index with kb index --all --full.

Layout

agent-kb/
├── kb.example.yaml        # annotated project-config template
├── kb.mk                  # includable make module
├── docker-compose.yml     # Qdrant
├── src/kb/
│   ├── config.py          # Settings (env) + KBConfig (kb.yaml)
│   ├── chunking/          # heading-aware markdown chunker
│   ├── embedding/         # provider Protocol + fastembed/ollama backends
│   ├── indexers/          # markdown / ontology / make_targets
│   ├── parsing/           # C++ header parser (tree-sitter)
│   ├── runner.py          # chunk → embed → upsert, incremental + retries
│   ├── verify.py, heal.py, discover.py   # ontology maintenance loop
│   ├── qdrant_client.py   # store wrapper, per-worktree namespacing
│   ├── mcp_server.py      # stdio MCP server
│   └── cli.py             # `kb` entrypoint
├── tests/
└── docs/WORKSHOP.md       # background: design rationale and the workshop story

For agents

If you are an LLM agent working in a repo that uses agent-kb, read AGENTS.md for when to search the KB, how to phrase queries per source, and how to run the ontology maintenance loop.

Licence

MIT.