See how Cortex compares to other AI memory solutions
| Feature | ★OpenClaw Cortex | mem0 | Zep | LangChain Memory | Raw Vector DB |
|---|---|---|---|---|---|
| Multi-factor ranking | Yes | Yes | ~ | No | No |
| Graph traversal | Yes | ~ | Yes | No | No |
| Temporal versioning | Yes | No | No | No | No |
| Contradiction detection | Yes | No | No | No | No |
| Self-hosted | Yes | Yes | Yes | Yes | Yes |
| Single container | Yes | No | No | — | Yes |
| Claude integration | Yes | No | No | ~ | No |
| Auto-deduplication | Yes | Yes | Yes | No | ~ |
| Lifecycle management | Yes | ~ | ~ | No | No |
| Episodic memory | Yes | No | ~ | No | No |
Most AI memory systems store facts as isolated text fragments and retrieve them by semantic similarity. This works for simple lookups but breaks down when the answer requires traversing a chain of relationships: Alice manages project-X, project-X depends on library-Y, library-Y was deprecated last month. No semantic query captures that chain. OpenClaw Cortex stores entities and relationships as a native graph in Memgraph, then uses Reciprocal Rank Fusion to merge graph traversal results with vector similarity — surfacing structurally-connected facts that pure embedding search would miss.
When a fact changes — a team member moves projects, a preference is updated, a tool is deprecated — Cortex preserves the old version with a valid_to timestamp rather than overwriting it. Superseded memories receive a 0.3× scoring penalty, keeping them available for historical queries while ensuring current facts dominate. Contradiction detection groups conflicting facts and applies a 0.8× penalty, surfacing ambiguity to the agent rather than silently picking a winner. No other open-source memory system in this comparison offers this level of temporal precision.
Cortex v0.7.0 consolidated from Qdrant + Neo4j to a single Memgraph instance — vector search and graph traversal in one container speaking the Bolt protocol. The result is a memory backend that starts with docker compose up -d, has one health endpoint to monitor, and eliminates the partial-failure modes that come from keeping two storage backends in sync. For teams running AI agents in production, fewer moving parts is a feature.