What it does Memori wraps your existing LLM client (OpenAI, Anthropic, Gemini, etc.) and silently persists conversations and agent actions to structured memory. On subsequent calls, it recalls relevant context automatically — so your agent remembers a user’s favorite color, past tool calls, and decision history without you manually stuffing context windows.

The interesting bit The “Advanced Augmentation” pipeline runs in the background and extracts typed memories — facts, preferences, relationships, rules, skills — across three scopes (entity, process, session). The LoCoMo benchmark claims 81.95% accuracy using only ~1,294 tokens per query, roughly 5% of a full-context approach. That’s the actual pitch: structured memory as a cheaper, more targeted alternative to giant prompts.

Key highlights

• Drop-in SDKs for Python and TypeScript; register your existing LLM client in two lines
• MCP server for Claude Code, Cursor, Codex, Warp — no SDK integration needed
• Plugins for OpenClaw and Hermes agents for zero-code persistent memory
• BYODB option if you don’t want the managed cloud service
• Free tier for developers; rate-limited without signup, unlimited with API key

Caveats

• Core functionality requires a Memori Cloud API key; the “zero config” quickstart is not self-hostable out of the box
• Benchmark comparisons (vs. Zep, LangMem, Mem0) are from the project’s own paper — worth reading critically
• Attribution (entity + process IDs) is mandatory; forget it and Memori silently does nothing

Verdict Worth a look if you’re building stateful agents and tired of hand-rolling RAG or context management. Skip it if you need fully offline, air-gapped memory — the cloud dependency is baked into the happy path.