by ourmem
Shared Memory That Never Forgets — persistent memory for AI agents with Space-based sharing across agents and teams. Plugins for OpenCode, Claude Code, OpenClaw, MCP Server.
# Add to your Claude Code skills
git clone https://github.com/ourmem/omemLast scanned: 5/30/2026
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}omem is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ourmem. Shared Memory That Never Forgets — persistent memory for AI agents with Space-based sharing across agents and teams. Plugins for OpenCode, Claude Code, OpenClaw, MCP Server. It has 198 GitHub stars.
Yes. omem passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/ourmem/omem" and add it to your Claude Code skills directory (see the Installation section above).
omem is primarily written in Rust. It is open-source under ourmem on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh omem against similar tools.
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Your AI agents have amnesia — and they work alone.
ourmem fixes all of this.
ourmem gives AI agents shared persistent memory — across sessions, devices, agents, and teams. One API key reconnects everything.
🌐 Website: ourmem.ai
Install the plugin for your platform. Memory works automatically — your agent recalls past context on session start and captures key info on session end.
→ Jump to Quick Start
REST API with 48+ endpoints. Docker one-liner for self-deploy. Embed persistent memory into your own agents and workflows.
→ Jump to Self-Deploy
📖 Memory Pipeline Architecture — Technical deep-dive into how ourmem stores, retrieves, and evolves memories.
🔗 Memory Sharing Architecture — How memories flow across agents and teams: sharing, provenance, versioning, and cross-space search.
| Category | Feature | Details |
|---|---|---|
| Platforms | 4 platforms | OpenCode, Claude Code, OpenClaw, MCP Server |
| Sharing | Space-based sharing | Personal / Team / Organization with provenance |
| Provenance tracking | Every shared memory carries full lineage | |
| Quality-gated auto-sharing | Rules fire on memory creation (async, non-blocking) | |
| Vector-enabled shared copies | Shared copies carry source vector embeddings for full search | |
| Idempotent sharing | Re-sharing returns existing copy (no duplicates) | |
| Version tracking | Memories track version counter, shared copies detect staleness via ?check_stale=true |
|
| Re-share stale copies | Refresh outdated shared copies with latest source content and vector | |
| Convenience sharing | One-step cross-user share (share-to-user) and bulk share (share-all-to-user) with auto-bridging |
|
| Organization management | One-step org creation (org/setup) and publish (org/publish) with auto-share rules |
|
| Cross-space search | Search across all accessible spaces at once | |
| Ingestion | Smart dedup | 7 decisions: CREATE, MERGE, SKIP, SUPERSEDE, SUPPORT, CONTEXTUALIZE, CONTRADICT |
| Noise filter | Regex + vector prototypes + feedback learning | |
| Admission control | 5-dimension scoring gate (utility, confidence, novelty, recency, type prior) | |
| Dual-stream write | Sync fast path (<50ms) + async LLM extraction | |
| Post-import intelligence | Batch import → async LLM re-extraction + relation discovery | |
| Adaptive import strategy | Auto/atomic/section/document — heuristic content type detection | |
| Content fidelity | Original text preserved, dual-path search (vector + BM25 on source text) | |
| Cross-reconcile | Discover relations between memories via vector similarity | |
| Batch self-dedup | LLM deduplicates facts within same import batch | |
| Privacy protection | <private> tag redaction before storage |
|
| Retrieval | 11-stage pipeline | Vector + BM25 → RRF → reranker → decay → importance → MMR diversity |
| User Profile | Static facts + dynamic context, <100ms | |
| Retrieval trace | Per-stage explainability (input/output/score/duration) | |
| Lifecycle | Weibull decay | Tier-specific β (Core=0.8, Working=1.0, Peripheral=1.3) |
| Three-tier promotion | Peripheral ↔ Working ↔ Core with access-based promotion | |
| Auto-forgetting | TTL detection for time-sensitive info ("tomorrow", "next week") | |
| Multi-modal | File processing | PDF, image OCR, video transcription, code AST chunking |
| GitHub connector | Real-time webhook sync for code, issues, PRs | |
| Deploy | Open source | Apache-2.0 |
| Self-hostable | Single binary, Docker one-liner, ~$5/month | |
| musl static build | Zero-dependency binary for any Linux x86_64 | |
| Object storage | AWS S3 or any S3-compatible storage, with IAM role support | |
| Hosted option | ourmem.ai — nothing to deploy |
Most AI memory systems trap knowledge in silos. ourmem's three-tier Space architecture enables knowledge flow across agents and teams — with provenance tracking and quality-gated sharing.
Research shows collaborative memory reduces redundant work by up to 61% — agents stop re-discovering what their teammates already know. — Collaborative Memory, ICLR 2026
| Personal | Team | Organization | |
|---|---|---|---|
| Scope | One user, multiple agents | Multiple users | Company-wide |
| Example | Coder + Writer share preferences | Backend team shares arch decisions | Tech standards, security policies |
| Access | Owner's agents only | Team members | All org members (read-only) |
Provenance-tracked sharing — every shared memory carries its lineage: who shared it, when, and where it came from. Shared copies include the source memory's vector embedding, so they're fully searchable in the target space.
Quality-gated auto-sharing — rules filter by importance, category, and tags. Rules fire automatically when new memories are created. Only high-value insights cross space boundaries.
┌──────────────────────────────────────────────────────────────────┐
│ Your AI Agent (OpenCode / Claude Code / OpenClaw / Cursor) │
│ │
│ Session Start → auto-recall relevant memories │
│ During Work → keyword detection triggers recall │
│ Session End → auto-capture decisions, preferences, facts │
└───────────────────────────┬──────────────────────────────────────┘
│ REST API (X-API-Key)
▼
┌──────────────────────────────────────────────────────────────────┐
│ ourmem Server │
│ │
│ ┌─ Smart Ingest ─────────────────────────────────────────────┐ │
│ │ Messages → LLM extraction → noise filter → admission │ │
│ │ → 7-decision reconciliation (CREATE / MERGE / SUPERSEDE / │ │
│ │ SUPPORT / CONTEXTUALIZE / CONTRADICT / SKIP) │ │
│ │ → cross-reconcile relations → privacy redaction │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Hybrid Search (11 stages) ────────────────────────────────┐ │
│ │ Vector + BM25 → RRF fusion → cross-encoder reranker │ │
│ │ → Weibull decay boost → importance scoring │ │
│ │ → MMR diversity → parallel cross-space aggregation │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Sharing Engine ───────────────────────────────────────────┐ │
│ │ Personal / Team / Organization spaces │ │
│ │ → provenance tracking → version-based stale detection │ │
│ │ → auto-share rules → one-step share-to-user │ │
│ └────────────────────────────────────────────────────────────┘ │
│ │
│ ┌─ Lifecycle ────────────────────────────────────────────────┐ │
│ │ Weibull decay (Core β=0.8 / Working β=1.0 / Peripheral │ │
│ │ β=1.3) → 3-tier promotion → auto-forgetting TTL │ │
│ └────────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────┘