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
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.
Cool! Can you also store and remember the sequence of actions in a multi-agent system?
We’ve been noticing that “memory” for agents usually breaks once workflows become long-running or tool-heavy. The agent trace angle here is interesting because conversation history alone definitely isn’t enough anymore.
How are you guys handling memory cleanup / forgetting over time?
The trace-based memory model is architecturally clever. Capturing tool calls, decisions, and outcomes from execution rather than compressing conversation history preserves the why behind agent behavior, not just the what. We've hit this ceiling building stateful agent workflows; chat summaries lose causal context fast. How do you handle storage and retrieval at scale when agent runs produce deeply nested execution graphs?
This is the right direction for agent memory. I like that you're deriving it from traces and tool outcomes instead of only chat history. The real test I'd be curious about is how teams inspect and prune memories when workflows change.
How does Memori handle memory updates when the agent learns something new that contradicts an older trace?
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?
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.
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.
“Persistent memory from agent trace, not just conversation”
Memori launched on Product Hunt on May 28th, 2026 and earned 123 upvotes and 17 comments, placing #8 on the daily leaderboard. 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
Memori was featured in Open Source (68.4k followers), Developer Tools (513.1k followers) and Artificial Intelligence (469.5k followers) on Product Hunt. Together, these topics include over 177.9k products, making this a competitive space to launch in.
Who hunted Memori?
Memori was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Memori stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
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.