Cognee vs RelataDB
TL;DR – Cognee is an open-source data-to-knowledge-graph pipeline for LLMs (ingest → parse → graph + vectors, Pydantic-shaped datapoints). RelataDB is a governed temporal knowledge database. Both build knowledge structures for retrieval; they differ on whether the result must be governed, bi-temporal, and queryable through your existing clients.
What each one is
Cognee is an ETL-style framework: you feed it documents/databases, it runs a deterministic pipeline (the ECL — Extract, Cognify, Load), produces a knowledge graph plus vector embeddings defined as Pydantic DataPoints, and lets you retrieve against it. Its center of gravity is ingestion-to-graph, framework-first.
RelataDB is a database: you declare an ontology (ObjectType, EventType, LinkType, ActionType) and ingest rows; the engine standardizes identities (76 canonical kinds), links same-entity records into a graph deterministically, time-stamps every row twice, and notarizes every fact. Retrieval is SQL/Cypher/GQL/SPARQL/MCP, governed by cell-level ACL.
Feature matrix
| Cognee | RelataDB | |
|---|---|---|
| Center of gravity | Data-to-graph pipeline (framework) | Governed temporal database (engine) |
| Schema | Pydantic DataPoint definitions | Ontology-declared types; planner/storage derive from them |
| Graph construction | Pipeline stage extracts entities/relations | Automatic from validated identifiers + co-occurrence; you can also declare links |
| Identity resolution | LLM/pipeline-driven | Deterministic checksum parsers (76 canonical kinds) |
| Bi-temporal history | No | Yes — on every row (AS OF valid + AS OF SYSTEM TIME) |
| Provenance / audit | Weak / none | Hash-chained, tamper-evident per fact |
| Access control | App-enforced | Cell-level ACL in the scan predicate; per-tenant encryption |
| Query languages | Python retrieval API | SQL, Cypher, GQL, SPARQL, MCP, plus 8 wire-protocol doors |
| Talk to existing clients? | Cognee SDK | Postgres / S3 / Mongo / Redis / ClickHouse / Neo4j-Bolt / Flight |
| Self-host | Yes | Yes (single binary) |
When to pick Cognee
- Your core problem is "turn my documents/tables into a graph + vectors for RAG" and you like the Pydantic-datapoint pipeline model.
- You want a framework you embed in an existing app, not a database to operate.
- The output doesn't need to be governed, bi-temporal, or court-grade reproducible.
When to pick RelataDB
- The knowledge must be a defensible system of record — auditable, reproducible, access-controlled, recoverable to any past state.
- You want the graph to form itself from standardized identities, not be hand-built per pipeline run.
- You want one engine behind the clients your stack already speaks, instead of a pipeline + Postgres + vector DB + graph DB.
FAQ
Is RelataDB a Cognee replacement? They overlap on "build a graph for retrieval." If you specifically want Cognee's Pydantic-pipeline developer model, Cognee is good at that. If you want the graph to be a governed, bi-temporal, multi-protocol database, RelataDB is the fit.
Does RelataDB do ETL? Yes — relata import --from postgres|neo4j|mongo migrates existing data, and SmartIngest standardizes identifiers at ingest. But the result is a database, not a pipeline artifact.
Which is more "deterministic"? Cognee markets determinism (the ECL pipeline is repeatable). RelataDB is deterministic at the storage level — same ingest always produces the same standardized, timestamped, hash-notarized rows, independent of any model.
See also: RelataDB vs the field and SmartIngest.