Quickstart

1. Build and run

gligabao is pure Go and builds with CGO_ENABLED=0, so no C compiler is needed.

./run_build.sh                      # builds build/gligabao
export ANTHROPIC_API_KEY=...        # required for remember/query
./build/gligabao serve                # listens on :7878 by default

Prefer containers? docker compose up --build starts the server on localhost:7878 and the Python sidecar on localhost:8787, on one Docker network (the server talks only to the sidecar, which needs Modal tokens to proxy training calls to Modal). Set ANTHROPIC_API_KEY (and optionally OPENAI_API_KEY) in your shell first; data is kept in the gligabao-data volume.

Run ./run_tests.sh to run the test eval. Plain go build ./cmd/gligabao and go test ./... also work.

2. Remember something

curl -X POST localhost:7878/v1/gb/remember \
  -H 'content-type: application/json' \
  -d '{"user_id": "u1", "text": "Alice works at Acme and lives in Paris."}'

The response contains the artifact_id, the extracted entities, the edges with their temporal fields and any invalidated_edges. See Remember.

3. Query

curl -X POST localhost:7878/v1/gb/query \
  -H 'content-type: application/json' \
  -d '{"user_id": "u1", "query": "Where does Alice work?"}'

You get an answer plus the evidence (entities and edges) it relied on. See Query.

4. Inspect the graph

curl 'localhost:7878/v1/gb/datasets/main/graph?user_id=u1'

See Graph export.

Next steps

Configuration

API keys, database, vector store and prices.

Temporal facts

How contradicting facts are reconciled.

Getting started

Concepts

Guides

Evals

Training

1. Build and run

2. Remember something

3. Query

4. Inspect the graph

Next steps

Configuration

Temporal facts

​1. Build and run

​2. Remember something

​3. Query

​4. Inspect the graph

​Next steps

Configuration

Temporal facts

1. Build and run

2. Remember something

3. Query

4. Inspect the graph

Next steps