Cognee vs Mem0
Cognee vs Mem0: side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Cognee | Mem0 | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 65 | 70 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Multi Agent Coordination |
Where they differ (13)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Cognee | Mem0 | |
|---|---|---|
| Capability composite | 65 | 70 |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Multi Agent Coordination |
| Type | Knowledge graph + ECL pipeline | Vector + graph + KV (hybrid) |
| Created | 2023-08 | 2023-06 |
| Latest release | v1.0.8 2026-05-06 | openclaw-v1.0.11 2026-04-29 |
| GitHub | 17.1k★ +152/mo Python | 54.9k★ +1.6k/mo Python |
| Funding | $10M total Seed · 2026-02 | $24M total $150M val Series A · 2025-10 |
| Backend storage | hybrid (vector + graph) | hybrid (vector + graph + KV) |
| API surface | REST, SDK: Python | REST, SDK: Python, Node.js |
| Multi-tenancy | Logical namespace per project/dataset; self-hosted OSS deployment | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation |
| MCP | native (first-party) — cognee-mcp | native (first-party) — official mem0-mcp server |
| OpenTelemetry | not documented publicly | via adapter — AgentOps integration |
| Optimised for | typed knowledge graph extraction (ECL pipeline) over RAG | developer experience + universal memory layer (model-agnostic, multi-store) |
At a glance
| Cognee | Mem0 | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Knowledge graph + ECL pipeline | Vector + graph + KV (hybrid) |
| Created | 2023-08 | 2023-06 |
| Latest release | v1.0.8 2026-05-06 | openclaw-v1.0.11 2026-04-29 |
| License | Apache-2.0 | Apache-2.0 |
| GitHub | 17.1k★ +152/mo Python | 54.9k★ +1.6k/mo Python |
| Pricing | Free + paid | Free + paid |
| Funding | $10M total Seed · 2026-02 | $24M total $150M val Series A · 2025-10 |
| Backend storage | hybrid (vector + graph) | hybrid (vector + graph + KV) |
| Deployment | Both | Both |
| API surface | REST, SDK: Python | REST, SDK: Python, Node.js |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | Logical namespace per project/dataset; self-hosted OSS deployment | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation |
| MCP | native (first-party) — cognee-mcp | native (first-party) — official mem0-mcp server |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | not documented publicly | via adapter — AgentOps integration |
| Optimised for | typed knowledge graph extraction (ECL pipeline) over RAG | developer experience + universal memory layer (model-agnostic, multi-store) |
| Anti-fit | no anti-fit explicitly stated | no anti-fit explicitly stated |
Taxonomy
| Axis | Cognee | Mem0 |
|---|---|---|
| storage | graph | vector |
| retrieval | graph-traversal | similarity |
| persistence | long-term | long-term |
| update | extraction | extraction |
| unit | fact | fact |
| governance | inspectable | opaque |
| conflict | llm-arbitrate | llm-arbitrate |
Pros & cons
Cognee
Pros: Pipeline-as-code ECL (extract/cognify/load) makes the memory build path inspectable and replayable; fully OSS.
Cons: Smaller community than Mem0/Letta; more end-to-end engineering required to deploy.
Mem0
Pros: Hybrid (vector + graph + KV) gives the most architectural flexibility of any memory layer; AWS Agent SDK exclusivity and 51k★ make it the field's de-facto reference.
Cons: LOCOMO benchmark numbers were publicly disputed by Zep in counter-analysis; LLM-extraction approach risks dropping facts that don't fit the prompt.