Long-term memory for your AI application
gligabao is a small, self-hosted HTTP service. You send it free-form text; it turns that text into a knowledge graph. Later you ask questions in natural language and it answers from what it stored, citing the facts it used.Quickstart
Build the binary, start the server and remember your first fact in minutes.
How it works
Remember, query and the bi-temporal graph underneath.
API reference
Every endpoint: remember, query, datasets, graph export, ontologies, timeseries.
Embed in Go
Use gligabao as a library inside your own Go program.
The two core calls
1
Remember
An LLM extracts entities and edges (relations such as
works_at) from the text. Entities are deduplicated, and each fact is anchored to the artifact it came from and records when it was true.2
Query
The query finds relevant entities, the surrounding subgraph is expanded by one hop, and an LLM answers using only that context. The response includes the evidence behind the answer.
3
Store
SQLite (a single file, the default) or PostgreSQL, plus an embedded or hosted vector store.
Explore
Temporal facts
Bi-temporal edges keep history instead of deleting it.
Datasets
Isolated, per-user containers for memory.
Sessions
Trace every fact back to the run it came from.
Images
Read text and themes from images.
Timeseries
Anchor values over time to entities and facts.
Evals
End-to-end checks recorded as ATIF trajectories.