littlebigbrain — Python SDK

The Python client for Little Big Brain — write graph facts and query one immutable published snapshot. Built on httpx + pydantic; ships sync and async clients.

pip install littlebigbrain   # imports as `lbb`

Quickstart

from lbb import LbbClient

with LbbClient(
    "https://0abc1def--production.db.eu.littlebigbrain.com",
    api_key="lbb_sk_live_...",
    graph="main",
) as lbb:
    graph = lbb.graph("main")

    # 1. Write a fact.
    graph.facts.create({
        "triplets": [{
            "source": {"type": "CONCEPT", "name": "handbook", "key": "doc:42"},
            "relation": "RELATED_TO",
            "target": {"type": "CONCEPT", "name": "vacation policy", "key": "passage:42:1"},
            "evidence": "Employees receive 25 days of annual leave.",
        }],
    }, idempotency_key="doc:42:v1")

    # 2. Publication is automatic. Inspect one coherent watermark when needed.
    published = lbb.read_snapshot_model()
    print(published.snapshot.served_at_seq, published.query_lag_commits)

    # 3. Hybrid search over the snapshot.
    results = lbb.search.hybrid(
        "how much annual leave do employees get?",
        top_k=5,
        consistency="eventual",
    )
    for hit in results.get("assertions", []):
        print(hit["relation"]["name"], hit["score"])

For hosted use, pass the exact endpoint_url shown on the stack's Connect page. Omitting base_url retains the loopback default for local/self-hosted development only; graph and branch remain ordinary client scope parameters.

Facts are graph-scoped (lbb.graph("main").facts); search and published-snapshot inspection use the client's active graph/branch scope.

Examples

Search with filters. Use the request body to filter before ranking — here, only facts an ACL principal may see:

results = lbb.graph_search({
    "query": "incident response runbook",
    "targets": ["entities"],
    "search": {
        "filters": {
            "op": "overlaps",
            "field": "acl",
            "values": ["user:rino@example.com", "group:engineering"],
        },
    },
    "top_k": 20,
})

Bulk import. Load many records as NDJSON in one call:

lbb.graph("main").facts.import_ndjson(
    [
        {"source": {"type": "DOC", "name": "handbook", "key": "doc:42"},
         "relation": "HAS_PASSAGE",
         "target": {"type": "PASSAGE", "name": "leave-policy", "key": "p:42:1"}},
        # …one record per line
    ],
    idempotency_key="handbook-batch-1",
)

For large or long-running loads, submit a streamed durable job:

accepted = lbb.submit_import_ndjson(
    records(),
    idempotency_key="hubspot:portal-42:run-2026-07-29",
)
completed = lbb.wait_for_import_job(accepted.job_id)
print(completed.state, completed.committed_commit_seq)

The async client accepts an async iterable as well. Success means all grouped commits are durable and final publication was enqueued; it does not mean published indexes have already reached committed_commit_seq. Empty iterables are rejected locally before an import POST is sent.

Time-travel read. Pin a SPARQL query to a past instant — results reflect the graph as it was then:

results = lbb.sparql(
    "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10",
    as_of_valid_time="2026-01-01T00:00:00Z",
)
print(results.vars)
for row in results:           # iterates flat {var: value} dicts
    print(row)

The async client mirrors every method — async with AsyncLbbClient(...) as lbb: and await each call.

Errors & retries

Methods return parsed dictionaries and raise LbbError (with status_code, code, param, request_id, and doc_url) on any non-2xx response. Safe reads and idempotency-keyed writes retry 429/5xx and transport failures with full-jitter backoff, bounded by a retry budget (retry_budget_ms, default 60s) rather than a fixed count, and honor Retry-After — a terminal error the server marks non-retryable surfaces immediately. Use raw_request(...) for response headers, request id, and retry/timing metadata.

More

Beyond the quickstart: entities.sample(type=..., limit=...) for a bounded published-generation sample and entities.filter_by_attributes(...) for relation-bound structured SPARQL; context.suggest(...), context.resolve(...), context.decode(...), and context.groundability(...) for vocabulary-grounded applications; and ontology/schema for ontology inspection and atomic schema publication. Model shadow evaluation and planner, preference, suggestion, and extractor datasets remain available. Typed Pydantic responses are exposed by matching *_model helpers; generated models live in lbb.models.

Full reference and guides: docs.littlebigbrain.com/sdks/python.

Develop

python3 -m venv .venv
.venv/bin/pip install -e ".[dev]"
ruff check lbb tests
mypy lbb
pytest tests

lbb/models.py is generated from the API contract — change the Rust API types and regenerate rather than editing it by hand.