Launching today
Memori
Persistent memory from agent trace, not just conversation
48 followers
Persistent memory from agent trace, not just conversation
48 followers
Memori launched its new agent-native memory infrastructure, enabling agents to create structured, long-term memory directly from agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic. This allows memory to also be generated from what an agent actually does. Benchmark results: 81.95% accuracy on LoCoMo using only 1,294 tokens per query, roughly 5% of full-context cost, saving users 95%+ on inference spend. 15K GitHub stars, 200000+ downloads
See how Memori works: https://memorilabs.ai/benchmark/#demo
Select a task and watch both agents in real time: https://memorilabs.ai/agent-trace/#demo
While most customer facing AI agents are limited by short-term memory constraints, Memori brings the long-term persistent memory and unlike traditional memory systems that rely primarily on long-form natural language conversation history, Memori enables agents to automatically create structured, long-term memory directly from the agent trace — including execution paths, tool results, workflow steps, outcomes, and decision-making logic.
- Structured, persistent memory for AI agents — Memori replaces flat markdown memory files with a structured knowledge graph that captures facts, decisions, outcomes, and patterns across every session — without bloating the prompt.
- Grounded in what agents actually do, not just what they say — Memori captures tool calls, execution traces, and real-time agent decisions alongside conversation, giving agents a fuller picture of prior task execution.
- Agent-controlled intelligent recall — Agents decide when and what to retrieve, scoped precisely by project, session, entity, or time range — eliminating irrelevant context and cross-project noise.
- Automatic memory building, zero latency impact — Memory is structured and updated asynchronously after each interaction, so it never slows the agent's response.
- Smarter daily briefs — Memori generates structured daily briefings built from execution traces and structured memory — covering priorities, risks, active goals, open loops, and known failure patterns — far beyond a simple conversation recap.
- Built for multi-user, multi-project environments — Memory is fully scoped and isolated by project, process, session, and entity, preventing data bleed across users and contexts.
- Production-ready observability — Full visibility into memory creation, recall activity, retrieval performance, and quota usage via Memori Cloud.
@memorilabs Congrats on the launch guys. How do you carve out the useful data from std tool use noise?
Flowtica Scribe
The memory layer design is genuinely artistic.
Instead of treating memory as compressed chat history, @Memori turns agent execution traces into reusable state: what tools were called, what worked, what failed, what decisions were made, and what patterns should carry forward.
That is a much stronger memory primitive for agents. Real agent context lives in the execution path, not just in the conversation around it.
Kudos to @_gordee @adam_b_struck @memorilabs and the team!
15K GitHub stars and rising — well deserved!
The "memory from execution trace, not just conversation" framing is exactly the right primitive for agents that actually do things vs just talk. Quick question: when a tool call returns a large blob (say a 10MB SQL result), how does Memori decide what to keep in long-term memory vs discard? Curious about the pruning side.
Interesting one @memorilabs @adam_b_struck @_gordee ! Upvoted :)
What's the memory an agent gets from the trace, that would be useful for another session?
Like I have set up a rule in Claude.md asking the agent to keep adding new learnings (during the session) into a project level .md file. How would your tool be different from that?