grag

Local-first, LLM-first graph knowledgebase. One embedded Cypher engine (LadybugDB, the Kuzu successor), one file per database, zero daemons, nothing leaves your machine — 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.

Local-first, token-frugal

grag is built for your machine, not a server farm. Every developer runs their own gragdb, with each project's knowledge in its own .lbdb file. Your LLM queries that — locally, offline, no per-token API cost for retrieval — instead of re-reading your whole codebase every session.

The point isn't just "local storage," it's token economics:

• Stop re-reading files. An agent that greps and re-reads source every session burns thousands of tokens re-deriving structure it already knew. grag answers "what calls X / what imports Y / why did we choose Z" with a cheap Cypher or search call — tokens go to reasoning, not re-discovery.
• Structure over bodies. Code ingestion stores signatures, docstrings, and line ranges — never source bodies — so the graph stays tiny and queries resolve at near-zero body tokens. Fetch a body only when the graph points you at the exact path:line_start-line_end.
• Token budgets everywhere. search_knowledge / get_context return cited subgraphs packed to a budget you set, so grounding never floods the context window.
• Context that compounds. Decisions, conventions, and rationale the agent learns get written back (upsert_nodes/edges with provenance) and linked to the code they describe — so the next session starts from what you already established, not from scratch.
• Local means private and free. No external embedding service by default (optional local ONNX embeddings, no torch), no telemetry, no daemon. Your code and your knowledge stay on-disk, in a file you can copy, back up, or delete.

The result: an LLM that grounds its answers in your project's accumulated knowledge — with far fewer tokens, far less hallucination, and zero data leaving the box.

Why a graph

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

How grag differs

The space tends to split two ways: code-graph extractors (compile a repo into a graph artifact an assistant can traverse) and enterprise graph platforms (a server you operate, then bolt RAG on yourself). grag is the missing middle — an embedded Cypher knowledgebase agents both build and retrieve from, with hybrid search packed to a token budget. One .lbdb file per project, no daemon.

What that means in practice:

• Writable memory, not just an extract. Agents define_schema and upsert facts/decisions with _source provenance, so knowledge compounds across sessions instead of being re-derived every time.
• Hybrid GraphRAG as the product surface. BM25 + vectors → RRF → per-label diversity → k-hop expansion → cited context under a token budget (search_knowledge / get_context). Not a bag of chunks, not a bare Cypher driver.
• Structure-only code indexing. Signatures, docstrings, and line ranges — never source bodies. Fetch a file only when the graph points at the exact path:line_start-line_end.
• MCP-shaped for self-correction. Eight tools; describe_schema before Cypher so the model stops inventing labels; errors come back with hints.

Use an extractor when you want a one-shot map of a codebase. Use a graph platform when you need multi-user ops, clustering, or a shared server. Use grag when the agent should accumulate project knowledge locally and ground answers in a hybrid subgraph without standing up a database.

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 ".[code]"          # optional: tree-sitter code parsing (ts/js/cs/tf)
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

# single-process mode: UI + REST + MCP on one live .lbdb (recommended for
# dogfooding — the UI sees MCP writes the moment they land)
grag --db examples/knowledge.lbdb serve --with-mcp
#   UI  → http://127.0.0.1:8471/
#   MCP → http://127.0.0.1:8471/mcp   (streamable-http; point MCP clients here)

# 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. Click a label in the legend (bottom-left) to view just that label and its 1-hop relationships — e.g. click Decision to see only your Decisions and what they document/motivate; run a query or reload to reset the canvas.

One process, one live file. LadybugDB is single-writer, so serve and mcp can't share a .lbdb as separate processes. serve --with-mcp mounts the MCP endpoint inside the REST/UI server, so UI + REST + MCP share one registry and one write connection — the UI watches the AI's writes land live instead of reading a stale copy. Use --mcp-path to change the MCP mount path (default /mcp).

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 8 tools:

tool	purpose
describe_schema	prompt-shaped schema: tables, properties, row counts, sample keys. Call before writing Cypher — kills hallucinated labels.
define_schema	create node/rel tables (LLM designs the graph for a domain)
upsert_nodes / upsert_edges	idempotent MERGE writes; _source provenance automatic
cypher_query	read-only Cypher; errors come back with correction hints
search_knowledge	hybrid BM25 + vector seeds → RRF fusion → per-label diversity cap → k-hop expansion → cited, token-budgeted context
get_context	re-pack chosen node ids into a token budget
ingest_code	index a repo's code STRUCTURE (Repo/Module/Class/Function + CONTAINS/IMPORTS/CALLS/INHERITS) — never source bodies

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

Ingest code

Point ingest_code at a repo and structural questions become cheap Cypher instead of file-reading spelunking. Two entry points, same engine:

# CLI
grag --db knowledge.lbdb ingest-code src/ ../other-repo [--no-calls] [--max-file-kb 2048]

# MCP (8th tool) — an agent indexes a repo on demand
ingest_code(paths=["src/"], calls=true, max_file_kb=1024)

graph LR
  R[Repo] -->|CONTAINS_REPO_MODULE| M[Module]
  M -->|CONTAINS_MODULE_CLASS| C[Class]
  M -->|CONTAINS_MODULE_FUNCTION| F[Function]
  C -->|CONTAINS_CLASS_FUNCTION| F
  M -->|IMPORTS| M
  C -->|INHERITS| C
  F -->|CALLS| F

Nodes carry path, line range, signature and docstring — structure only, no source bodies — with ids like Module:repo:src/a.py and Function:repo:src/a.py#Class.method. Re-ingesting the same tree is idempotent (MERGE by key). Three recipes:

// what imports module X?
MATCH (m:Module)-[:IMPORTS]->(x:Module) WHERE x.id = 'pkg:core.py' RETURN m.id
// what calls function Y?
MATCH (f:Function)-[:CALLS]->(y:Function) WHERE y.id = 'pkg:core.py#helper' RETURN f.id
// cross-repo imports (multiple paths ingested into one db)
MATCH (r1:Repo)-[:CONTAINS_REPO_MODULE]->(a:Module)-[:IMPORTS]->(b:Module)<-[:CONTAINS_REPO_MODULE]-(r2:Repo)
WHERE r1.id <> r2.id RETURN a.id, b.id

Python parses via stdlib ast in every install. TypeScript/JavaScript/C#/Terraform (.ts/.tsx/.js/.jsx/.mjs/.cjs/.cs/.tf) parse via tree-sitter and need pip install "gragdb[code]"; without it those files raise a hint-carrying error. CALLS/INHERITS edges are Python-only for now; IMPORTS is best-effort (path/namespace-based) for the tree-sitter languages.

Multiple projects

There are two distinct ways to hold several projects, depending on whether they relate:

A. Related projects → one shared .lbdb. Ingest several repos into the same database and they become separate Repo nodes in a single queryable graph — so the LLM can trace a call or an import across repo boundaries, or link a Decision in one project to a Function in another. This is the model for a monorepo, a system split across services, or any set of codebases that reference each other.

grag --db platform.lbdb ingest-code ../api ../web ../infra   # 3 repos, one graph

// cross-repo imports, inside one db
MATCH (r1:Repo)-[:CONTAINS_REPO_MODULE]->(a:Module)-[:IMPORTS]->(b:Module)<-[:CONTAINS_REPO_MODULE]-(r2:Repo)
WHERE r1.id <> r2.id RETURN a.id, b.id

B. Unrelated projects → separate .lbdb files. One file = one isolated universe (no shared entities, no cross-db queries), so a throwaway experiment never pollutes a real project's graph. This is the default local-first pattern: one .lbdb per project, per developer, each queryable locally with zero per-token retrieval cost. To serve many of them at once, opt into multi-db mode with --db-dir:

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.

HTTP security posture. The REST layer has no accounts or sessions; the trust model is "whoever can reach the port directly is trusted." Drive-by browser access is denied by default: a Host-header allow-list (loopbacks + the bind host) blocks DNS rebinding, and CORS grants no cross-origin access at all unless you opt in via GRAG_CORS_ORIGINS (the built-in UI is served same-origin and needs none). If you bind a non-loopback address, set GRAG_API_TOKEN — every /api/* route except /api/health (and the MCP mount, when enabled) then requires Authorization: Bearer <token>.

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,ingest/code}, 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):

codec	bytes/vec (dim 64)	recall@10	note
fp32	256	0.998	baseline; native HNSW index
int8	68	0.998	4x smaller, near-zero loss
binary	8	0.476	32x, hamming scan + rescore
polar	14	0.766	experimental PolarQuant-style angular codes (sine-power-law bit allocation, training-free)

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

Two honest costs of the codec path: candidate generation for non-fp32 codecs is an O(rows) approximate scan (only pk + code bytes cross the wire; fp32 nodes are fetched for the 4·top_k rescore shortlist only) — that's the property grag bench measures, so no ANN index is involved. And the first searches after a large ingest embed lazily: at most GRAG_MAX_EMBED_PER_SEARCH (default 256) nodes per search call, with the remainder reported as pending_embeddings on the search response so agents know vector recall is still improving.

Configuration

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

GRAG_SEARCH_LABEL_CAP (default 2) is the per-label diversity cap on search_knowledge: no single node label may occupy more than this many of the fused top_k seeds before other labels get a turn (leftover slots then backfill by rank). It stops a large table — e.g. an ingested repo's Function nodes — from crowding out knowledge tables (Decision/Concept) on a general query. Set 0 to disable and get pure RRF rank order.

Performance budget

Measured — 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/          # 240+ tests, ~25s
ruff check src tests && mypy src/grag   # CI gates on both
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).