The best agent memory system depends on the proof you need. Choose Answer Engine for inspectable source memory, Zep for temporal graph semantics, Mem0 for hosted memory API ergonomics, Letta for a stateful agent runtime, and Cognee for open graph/vector experimentation your team is ready to operate.
Last updated: June 20, 2026.
Why compare these five systems?
Mem0, Zep, Letta, Cognee, and Answer Engine all use the word memory, but they do not package the same product. Mem0 documents a managed memory API and platform setup path (Mem0 API overview). Zep centers a temporal Context Graph (Zep concepts). Letta frames memory as editable state inside stateful agents (Letta stateful agents). Cognee documents relational, vector, and graph stores as part of its architecture (Cognee architecture).
Answer Engine enters the comparison as an inspectable memory and context layer for agents that need source-aware recall from work data.
What is the five-way matrix?
| System | Inspectable | Supersession-aware | Provable forgetting | Portable | Multi-source | Tenant-isolated |
|---|---|---|---|---|---|---|
| Answer Engine | Strong wedge: source lineage and recall evidence. | Partial now; deeper graph parity is roadmap. | Treated as a product-control requirement. | MCP-first delivery reduces client lock-in. | Designed around work sources. | Launch trust property. |
| Mem0 | Evaluate through API responses and platform tooling. | Depends on memory model and integration. | Verify deletion semantics for your use case. | Hosted API and OSS options exist. | Strong for conversational memory; source breadth is implementation-dependent. | Validate for your architecture. |
| Zep | Strong graph/context inspection story. | Strongest published temporal graph posture. | Verify deletion posture separately. | Product-specific graph model. | Strong for graph memory; source breadth depends on integration. | Validate for your architecture. |
| Letta | Strong state visibility through memory blocks. | Agent-state model, not primarily a source supersession layer. | Verify operational deletion and export needs. | Runtime-centered. | Best for agent state, not a broad source memory layer. | Validate deployment model. |
| Cognee | Good for teams that can inspect their own graph/vector pipeline. | Depends on implementation. | You own the control plane. | Open framework posture. | Strong experimentation across graph and vector stores. | You own isolation design. |
This table avoids public performance numbers. The right next step is to run a golden set over your own sources, not borrow a vendor number from a different workload.
How should you shortlist?
Start with the failure you cannot tolerate.
| Buyer problem | Start with |
|---|---|
| ”We need source evidence before we trust the rollout.” | Answer Engine |
| ”We need a temporal graph as the memory core.” | Zep |
| ”We need a hosted memory API quickly.” | Mem0 |
| ”We want memory inside a stateful agent runtime.” | Letta |
| ”We want to build and operate graph/vector memory ourselves.” | Cognee |
The shortlist should include more than one system when requirements conflict. For example, a team might evaluate Zep for temporal graph behavior and Answer Engine for source inspection. That is healthier than forcing every memory requirement into one generic label.
What should you test before buying?
Use the same test script for each vendor:
- Add source content with a current fact and an older conflicting fact.
- Ask the agent a question that requires the current fact.
- Inspect returned evidence and source metadata.
- Check how the older fact is represented.
- Ask how to delete, export, or migrate the memory.
- Confirm tenant or user scope before retrieval.
This method follows the evaluation pattern in OpenAI’s guidance: define the task, run it with test inputs, analyze results, and iterate (OpenAI evals guide). It also matches the “design your own evals” framing in OpenAI’s evaluation best practices (OpenAI evaluation best practices).
Where does Answer Engine fit in the five-way?
Answer Engine is not claiming to beat every competitor on every axis. It is claiming a specific wedge: inspectable, tenant-scoped, source-aware memory reachable through agent interfaces. That is why the comparison hub uses inspection and lifecycle columns instead of a generic “memory score.”
Use Answer Engine when your team needs to prove what an agent recalled and why. Use Zep when temporal graph semantics are decisive. Use Mem0 when hosted memory API ergonomics matter most. Use Letta when the runtime model is the purchase. Use Cognee when your team wants to own the infrastructure.
What should you read next?
Read Best agent memory framework (2026) for best-fit verdicts, Answer Engine vs Mem0 for the hosted API comparison, and Answer Engine vs Zep for the temporal graph concession.