Most tools store facts. Attest stores claims — with provenance, confidence, and contradiction handling built into the engine. Here's how that changes what's possible.
This comparison is based on public documentation review. Where we've tested a system directly, we note it. Capabilities marked as "possible with custom code" mean the core engine doesn't provide it out of the box.
| Capability | Attest | Mem0 | Letta / MemGPT | Zep / Graphiti | Neo4j | PostgreSQL | Vector DBs |
|---|---|---|---|---|---|---|---|
| Provenance on every write | Required — engine rejects writes without source | No | No | Partial — conversation-level | Optional property | Optional column | No |
| Contradictions coexist | Native — both claims stored with confidence | Overwrites | Overwrites | Based on public docs: last-write-wins | Possible with custom schema | Possible with custom schema | N/A — no structured facts |
| Source retraction | One call — corroborated facts survive, cascade audit | No | No | No | Custom logic | Custom logic | Delete + re-embed |
| Multi-source corroboration | Automatic — content_id grouping + confidence boost | No | No | Based on public docs: not built-in | Custom queries | Custom queries | No |
| Confidence tracking | Per-claim, Tier-1 + Tier-2 scoring | No | No | Edge weights | Property | Column | Similarity score only |
| Impact analysis | db.impact(source_id) |
No | No | No | Custom Cypher | Custom SQL | No |
| Knowledge drift | db.drift(days=30) |
No | No | No | Custom queries | Custom queries | No |
| Time-travel queries | db.at(timestamp) |
No | No | Based on public docs: not built-in | No (needs temporal graphs extension) | Possible with temporal tables | No |
| Audit trail | db.audit(claim_id) — full chain |
No | No | Partial | Custom queries | Custom queries | No |
| Zero infrastructure | pip install attestdb — embedded |
Hosted service | Server required | Server required | Server required | Server required | Varies — some embedded |
Attest is not a general-purpose database, a vector store, or an LLM memory layer. It's a claim-native database — purpose-built for the case where knowledge comes from multiple sources, contradicts itself, and needs to be retracted or corrected over time.
If your use case is "store text and retrieve it by similarity," a vector database is simpler. If your use case is "model a fixed graph schema," Neo4j is battle-tested. If your use case is "conversational memory for a chatbot," Mem0 or Zep may be a better fit.
But if you need to know who said what, when, and how confident they were — and you need the system to handle the case where a source turns out to be wrong — that's what Attest was built for.