Zep vs Cognee (2026)

Zep

by Zep AI

Cognee

by Topoteretes

Pros

• ✓Temporal knowledge graph automatically tracks how facts change over time: when information is updated or contradicted, Zep invalidates outdated facts while preserving historical context, making it more accurate than vector-only memory systems for complex multi-session agent workflows
• ✓Benchmark-leading retrieval with 80.32% accuracy at 189ms on LoCoMo and 63.8% on LongMemEval versus mem0's 49%, delivering measurably better temporal reasoning at lower latency using single-shot retrieval with no slow agentic tool-call loops
• ✓Graphiti MCP server gives Claude Desktop, Cursor, and other MCP clients persistent knowledge graph memory across sessions in three lines of code, with Python, TypeScript, and Go SDKs and no agent framework lock-in

Limitations

• ⚠Steep pricing jump from free tier to Flex at $125/month with no intermediate tier, so early-stage teams and lower-volume projects that outgrow free credit limits face a significant cost increase with no middle-ground option
• ⚠Credit-based pricing makes costs difficult to forecast at production scale, as usage spikes trigger auto-topup charges that require careful per-request credit monitoring to avoid unexpected billing
• ⚠Graphiti MCP server is experimental and self-hosted, requiring Docker, FalkorDB or Neo4j, and LLM API key management, meaning teams seeking a fully managed zero-infrastructure MCP memory solution face meaningful setup and operational overhead

Pros

• ✓Knowledge graph available at every pricing tier including free self-hosted -- unlike Mem0 which gates graph memory behind $249/month Pro, Cognee gives teams full graph capabilities from day one
• ✓ECL pipeline builds self-improving memory that gets sharper with use -- rated responses feed back into graph edge weights, so accuracy improves over time rather than staying static like vector-only systems
• ✓Native integrations with Claude Agent SDK, OpenAI Agents SDK, LangGraph, Google ADK, n8n, and Neo4j -- plugs into the agent frameworks teams already use without custom integration code

Limitations

• ⚠Python-only SDK limits integration options -- TypeScript, Go, or other language-based agent stacks cannot use Cognee natively, narrowing its addressable developer audience compared to Mem0's Python and JavaScript support
• ⚠Smaller ecosystem than Mem0 -- 6,000 GitHub stars versus Mem0's 48,000+ means less community content, fewer pre-built examples, and a smaller pool of developers who have solved common integration problems
• ⚠No published LongMemEval benchmark score -- Mem0 scores 49%, Hindsight scores 91.4%, but Cognee has not published equivalent temporal reasoning benchmarks, making objective capability comparison difficult