grag

LLM-first graph knowledgebase. One embedded Cypher engine (LadybugDB, the Kuzu successor), one file per database, zero daemons — wrapped in the tool contract LLMs actually need: schema introspection that anchors text-to-Cypher, idempotent upserts with provenance, hybrid FTS/vector search, and token-budgeted subgraph context for grounded, low-hallucination answers.

(G(raph)RAG — retrieval-augmented generation grounded in a graph.)

Not an enterprise platform. pip install, point an MCP client at it, done.

Why

LLM answers hallucinate when retrieval returns isolated chunks. grag stores knowledge as a graph — entities, documents, and their relationships — so retrieval returns a connected, cited subgraph an LLM can reason over. The LLM can also build the graph: define_schema + upsert_nodes/edges are first-class tools, so "turn these docs into a knowledge graph" is a normal conversation, not a pipeline project.

Install

From PyPI (ships the web UI):

pip install gragdb

Python 3.10–3.14; 3.13 recommended (faster interpreter for the Python-side packing/serialization paths, and 3.10 reaches end-of-life in October 2026).

From source (for development). Build the UI first — pip install needs the built bundle at src/grag/api/static (the wheel's force-include; see pyproject.toml):

cd ui && npm ci && npm run build && cd ..   # builds the UI into src/grag/api/static/
pip install -e .            # core: engine, REST, MCP, FTS — no torch, no GPU stack
pip install -e ".[dev]"     # tests
pip install -e ".[embed-local]"   # optional: local embeddings (fastembed/ONNX, still no torch)
pip install -e ".[embed-remote]"  # optional: OpenAI-compatible remote embeddings

Without an embedder, everything works FTS-only (BM25 is native to the engine).

Enabling semantic search: install embed-local, then set GRAG_EMBED_PROVIDER=fastembed when serving. This uses ONNX Runtime — no PyTorch — so grag stays light (~50-100MB, model downloads once then works offline). Nodes are (re)embedded lazily on the next search whenever their embedding is NULL. First query downloads the model + embeds all nodes (seconds); steady state is ~300ms/query on CPU.

pip install -e ".[embed-local]"
GRAG_EMBED_PROVIDER=fastembed grag --db knowledge.lbdb serve
# optional: GRAG_EMBED_MODEL=BAAI/bge-base-en-v1.5 GRAG_EMBED_DIM=768

Quickstart

# build the demo knowledgebase (fictional company handbook, entities + relations)
python examples/build_example.py

# serve REST + the graph UI at http://127.0.0.1:8471
# (note: start it from a normal terminal — servers launched inside an agent
# sandbox get torn down and can't be reached from your browser)
grag --db examples/knowledge.lbdb serve

# or answer 3 demo questions end-to-end in the terminal
python examples/demo_e2e.py

The UI: force-graph explorer (click = inspect, double-click = expand neighbors), Cypher console (Ctrl+Enter, graph/table results), schema sidebar, and a search bar that shows the exact grounding text an LLM would receive.

Use from an LLM harness (MCP)

grag --db knowledge.lbdb mcp

Cursor / .cursor/mcp.json:

{
  "mcpServers": {
    "grag": {
      "command": "grag",
      "args": ["--db", "/absolute/path/knowledge.lbdb", "mcp"]
    }
  }
}

Any MCP client gets these 7 tools:

Errors are returned as ERROR: ... HINT: ... tool output so the model self-corrects in-loop.

Multiple projects / shared server

One .lbdb = one isolated universe — no shared entities, no cross-db queries. Per-project DBs is the default pattern; multi-db serving is opt-in via --db-dir (single-db is unchanged).

grag --db-dir ~/kb serve    # one process serves every .lbdb in ~/kb

Every /api/* endpoint accepts ?db=<name> or an x-grag-db: <name> header (query param wins). GET /api/dbs returns {"dbs": ["alpha","beta"], "default": "alpha"} ({"dbs": [], "default": null} in single-db mode). Without a selector the server prefers the file matching db_path's name, else a lone .lbdb, else 400 with a hint; unknown name → 404 listing available DBs.

For MCP, several IDE windows on one DB collide: stdio spawns a grag mcp process per client and LadybugDB allows only ONE process to write a given .lbdb ("Could not set lock"). One shared HTTP server avoids it — each window sends its project name via x-grag-db:

grag --db-dir ~/kb mcp --transport streamable-http --host 127.0.0.1 --port 8472

Cursor / .cursor/mcp.json (per window, one header per project):

{
  "mcpServers": {
    "grag": {
      "url": "http://127.0.0.1:8472/mcp",
      "headers": { "x-grag-db": "project-a" }
    }
  }
}

The server is localhost-only by default, and db names are routing hints, not auth — resolution rejects absolute paths and ... Single-db stdio (grag --db knowledge.lbdb mcp) remains the simple default.

Python API

from grag import GragConfig
from grag.service import GragService
from grag.core.types import SearchRequest

svc = GragService(GragConfig(db_path="knowledge.lbdb"))
res = svc.search_knowledge(SearchRequest(query="who owns the ingestion gateway?", hops=1))
print(res.context)        # cited subgraph text, ready for a prompt

Everything is also mirrored over REST: POST /api/{query,search,context,ingest}, GET /api/{schema,graph/sample,health}, POST /api/{schema/define,nodes/upsert,edges/upsert}.

Retrieval: hybrid + polar-split vectors

1. Text properties get a native BM25 FTS index per searchable table.
2. With an embedder configured, embeddings are written with a polar decomposition: magnitude r in one float property, direction u quantized by a swappable codec. Codes only generate candidates; final scores are exact fp32 rescore + graph rerank, so recall loss is bounded and measurable.
3. Seeds (RRF-fused FTS+vector) expand k hops through the graph — structure compensates for aggressive quantization.

Codec ladder (grag bench reproduces these numbers on a synthetic 1500-doc corpus):

Select with GRAG_VECTOR_CODEC / GragConfig.vector_codec. polar is opt-in; int8 is the sweet spot today.

Configuration

Env vars: GRAG_DB_PATH, GRAG_BUFFER_POOL_MB (default 256), GRAG_VECTOR_CODEC, GRAG_TOKEN_BUDGET, GRAG_EMBED_PROVIDER (fastembed|remote), GRAG_EMBED_MODEL, GRAG_EMBED_DIM, GRAG_EMBED_BASE_URL, GRAG_EMBED_API_KEY_ENV.

Performance budget

Measured, not assumed — tests/test_perf.py guards cold start (< 2s), search latency, and RSS; grag bench reports recall + p50/p95 + RSS per codec. Design rules: no heavy deps in the default install, one process for API+UI, lazy embedder loading, default LIMITs, hop caps, statement timeouts, token budgets everywhere.

Storage conventions

• One .lbdb file per database. Properties starting with _ are grag-internal.
• Provenance: _source, _created_at on every table created via define_schema.
• Vector columns (embedding, _emb_r, _emb_code, _emb_model) are added lazily by the retrieval layer.
• _grag_tables registry powers introspection and canonical Label:key node ids.

Develop

python -m pytest tests/          # 160+ tests, ~10s
grag bench                        # codec recall/latency/RSS table
cd ui && npm run build            # rebuilds the UI into src/grag/api/static/

See CONTRIBUTING.md for the branching model (Gitflow-lite: main + develop + feature/release/hotfix), PR rules, and how releases are cut and published to PyPI.

Known limits: embedded engine = single-writer; LadybugDB reserves a large virtual address space per open database (actual RSS stays within the buffer pool) — close Engines you create; polar codec encode is Python-speed (fine at query time, slower at write time).