🧠Memori
MemoriLabs/Memori · homepage ↗
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.
⭐ Very popular: 16k stars, gaining about 401 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Gives LLM agents persistent memory across sessions by capturing conversations, tool calls, and decisions without changes to agent code.
- Works across model providers including OpenAI, Anthropic, Bedrock, Gemini, DeepSeek, and Grok, and across frameworks such as LangChain, Pydantic AI, and Agno, so teams are not locked to one stack.
- Targets production teams that need durable agent state without rebuilding retrieval plumbing, with deployment options spanning managed cloud, single-tenant, VPC, and on-premises.
under the hood
- Primarily Python, with TypeScript and Rust components, distributed via PyPI (pip install memori) and NPM (@memorilabs/memori).
- Integrates through an SDK or the Model Context Protocol, so MCP clients like Claude Code, Cursor, Codex, and Warp can attach memory directly.
- Uses a bring your own database model with TiDB cited as a backend, and augments stored memories with facts, preferences, relationships, skills, and rules at entity, process, and session levels; the README claims 82 percent accuracy on the LoCoMo benchmark at 1,294 tokens per query, licensed Apache 2.
Radar summary, generated from the project's public sources
star history
- PR#22 5k 2025-11-26
- now 16k + 11k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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SQL native memory for LLMs and agents
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