UniMem.

Find your next step.

EXPLORE UNIMEM

The memory loop · remember, measure, governHow UniMem connects persistent agent memory to randomized outcome measurement, review, withdrawal, promotion, and rollback.Developers · quickstart & integrationsInstall locally. Connect Codex, Claude Code, Cursor, Windsurf, Python, or HTTP.Enterprise · deployment & governanceExplore UniMem local and self-hosted deployment, scoped memory, configurable review, and a focused enterprise pilot.The next session should inherit the lesson.Use UniMem for persistent coding-agent context, reviewed project lessons, and outcome measurement across repeated engineering tasks.Shared memory needs shared boundaries.Scope shared agent memory, review conflicting changes, and use opt-in stale-generation checks in UniMem multi-agent workflows.Persistent context. Accountable change.Plan a bounded UniMem pilot for enterprise agents with local deployment, reviewed memory, access boundaries, and verified outcomes.Documentation · setup, interfaces & guaranteesA curated UniMem documentation index for local setup, measurement, promotion, team isolation, safety, and deployment guarantees.The agent memory problem.Why persistence and retrieval are only part of agent memory, and why long-lived agents need a feedback loop for remembered experience.Memory and context do different jobs.Understand context windows, persistent agent memory, retrieval, and the lifecycle controls needed between storage and agent behavior.Evaluate what memory changes.A practical guide to evaluating agent memory with stable task IDs, eligible-memory holdouts, complete outcomes, and integrity checks.Platform · one governed memory systemThe UniMem platform: lessons, bitemporal facts, knowledge graph, conversations, memory blocks and procedures; hybrid multi-hop retrieval; randomized evidence; governance; MCP, HTTP, Python and ROS 2 interfaces.Physical AI · robots & fleetsUniMem for physical AI: planner context and durable outcomes for robots, in-loop working memory, a bounded STL shield on memory-derived actions, collective site memory and living safety cases.Vision · memory for intelligent machinesWhy every agent and robot will need a governed memory, why that memory must prove it helps, and what UniMem is building toward.Benchmarks · evidence & methodologyUniMem conversation memory measured end to end on LoCoMo, LongMemEval and BEAM: retrieval, answer accuracy, serving latency, reproduction commands and honest caveats.Investor overviewUniMem is raising to build memory infrastructure for AI agents and robots. Explore the product, published benchmarks, commercial strategy, and next milestones.About UniMemUniMem’s mission: give long-lived AI agents persistent, reviewable memory connected to evidence from future work.Learn · field notesGuides to agent memory, context, and evidence.Contact · explore a pilotBring one workflow and one real memory problem.
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Experience should have a future.

We are building the memory system for agents and robots that return to the work—and need a way to learn what deserves to carry forward.

Start building Read the docs

Continuity needs judgment.

UniMem means Unified Memory. The product begins with a simple conviction: persistent agents need more than a place to put information. They need boundaries for what enters memory, evidence about what helps, and a way to reconsider what no longer belongs.

A useful memory can save repeated discovery. An outdated memory can preserve a mistake. Both should be visible to the team responsible for the agent.

Our focus is evidence-based agent memory: local-first persistence, outcome measurement, and governed change. The aim is to make remembered experience something builders can inspect and improve.

A product you can examine.

The source includes a local Python core, CLI, MCP server, HTTP gateway, and a self-hosted multi-tenant Hub. The project is under active development. Individual components differ in maturity and deployment requirements.

We prefer specific capabilities and explicit limits to blanket promises. Benchmarks, simulated examples, experimental adapters, and production guarantees are different kinds of evidence and should be labeled accordingly.

The product source is licensed AGPL-3.0-only. Review the license and optional integration terms when considering your deployment.

Product source and status

Build the next loop with us.

The most useful next conversation starts with a real workflow: an agent that repeats a mistake, rediscovers context, or needs a reviewed way to update recurring guidance.

We welcome pilot inquiries, integration discussions, and conversations with researchers, investors, and strategic partners. Bring the task, the data boundary, and what a useful result would mean for your team.

Bring one real memory problem.

Try the local loop, or bring one workflow to a pilot conversation.

Talk about a pilot Developer quickstart
KEEP EXPLORINGTest one workflow with us.Remember. Measure. Reconsider.The agent memory problem.

Technical details link to the inspected source. Check deployment requirements before enabling optional features.