Omem

The Problem

Your AI agents have amnesia — and they work alone.

• 🧠 Amnesia — every session starts from zero. Preferences, decisions, context — all gone.
• 🏝️ Silos — your Coder agent can't access what your Writer agent learned.
• 📁 Local lock-in — memory tied to one machine. Switch devices, lose everything.
• 🚫 No sharing — team agents can't share what they know. Every agent re-discovers the same things.
• 🔍 Dumb recall — keyword match only. No semantic understanding, no relevance ranking.
• 🧩 No collective intelligence — even when agents work on the same team, there's no shared knowledge layer.

ourmem fixes all of this.

What is ourmem

ourmem gives AI agents shared persistent memory — across sessions, devices, agents, and teams. One API key reconnects everything.

🌐 Website: ourmem.ai

🧑‍💻 I use AI coding tools

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

🔧 I'm building AI products

REST API with 48+ endpoints. Docker one-liner for self-deploy. Embed persistent memory into your own agents and workflows.

→ Jump to Self-Deploy

Core Capabilities

📖 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.

Feature Overview

From Isolated Agents to Collective Intelligence

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

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.

How It Works

┌──────────────────────────────────────────────────────────────────┐
│   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          │  │
│  └────────────────────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────────────────────┘

• Write once, recall everywhere — memories persist across sessions, devices, and agents
• Gets smarter over time — reconciliation merges, updates, and contradicts memories automatically
• Share across boundaries — Personal → Team → Organization knowledge flow with full provenance
• No manual memory management — Weibull decay handles lifecycle, auto-share rules handle distribution

Key Concepts

API Key = Tenant ID = Your Identity

When you create a tenant, the returned id is your API Key. They're the same UUID. There is no separate "tenant ID".

curl -X POST https://api.ourmem.ai/v1/tenants -d '{"name": "alice"}'
# → {"id": "a1b2c3d4-...", "api_key": "a1b2c3d4-...", "status": "active"}
#          ↑ same value ↑

API Key vs Space ID

One API Key owns multiple Spaces:

API Key "a1b2c3d4"
  │
  ├── personal/a1b2c3d4     ← auto-created, your private memories
  ├── team/e5f6g7h8          ← team space you created (you = Admin)
  ├── team/i9j0k1l2          ← team space you were invited to (you = Member)
  └── org/m3n4o5p6           ← organization you joined (you = Reader)

Sharing = Passing Your API Key

To share memories with another user, you pass their API Key as target_user:

# Share all your preferences to user Bob
curl -X POST https://api.ourmem.ai/v1/memories/share-all-to-user \
  -H "X-API-Key: your-api-key" \
  -d '{"target_user": "bobs-api-key", "filters": {"categories": ["preference"]}}'

The system automatically creates a bridging Team Space between you and Bob. No manual space management needed.

Quick Start

Agent Install (recommended)

One message to your AI agent. It handles everything — API key, plugin install, config, verification.

Hosted (ourmem.ai — nothing to deploy):

Self-hosted (your own server):

That's it. Your agent handles the rest.

Skill Install (alternative):

If you prefer CLI installation, install the ourmem skill directly:

npx skills add ourmem/omem --skill ourmem -g

This works with 44+ AI agents including Claude Code, OpenCode, Cursor, and more. See Vercel Skills CLI for details.

1. Get an API Key

Hosted:

curl -sX POST https://api.ourmem.ai/v1/tenants \
  -H "Content-Type: application/json" \
  -d '{"name": "my-workspace"}' | jq .
# → {"id": "xxx", "api_key": "xxx", "status": "active"}

Self-deploy:

docker run -d -p 8080:8080 -e OMEM_EMBED_PROVIDER=bedrock ghcr.io/ourmem/omem-server:latest
curl -sX POST http://localhost:8080/v1/tenants \
  -H "Content-Type: application/json" \
  -d '{"name": "my-workspace"}' | jq .

Save the returned api_key — this reconnects you to the same memory from any machine.

2. Install Plugin

OpenCode: Add "plugin": ["@ourmem/opencode"] to opencode.json + configure plugin_config with apiUrl and apiKey in the same file.

Claude Code: /plugin marketplace add ourmem/omem + set env vars in ~/.claude/settings.json.

OpenClaw: openclaw plugins install @ourmem/ourmem + configure openclaw.json with apiUrl and apiKey.

MCP (Cursor / VS Code / Claude Desktop):

{
  "mcpServers": {
    "ourmem": {
      "command": "npx",
      "args": ["-y", "@ourmem/mcp"],
      "env": {
        "OMEM_API_URL": "https://api.ourmem.ai",
        "OMEM_API_KEY": "your-api-key"
      }
    }
  }
}

3. Verify

curl -sX POST "$OMEM_API_URL/v1/memories" \
  -H "X-API-Key: $OMEM_API_KEY" -H "Content-Type: application/json" \
  -d '{"content": "I prefer dark mode", "tags": ["preference"]}'

curl -s "$OMEM_API_URL/v1/memories/search?q=dark+mode" -H "X-API-Key: $OMEM_API_KEY"

What Your Agent Gets

Memory Tools

Sharing Tools

Convenience APIs: share-to-user (one-step cross-user share with auto-bridging) and share-all-to-user (bulk share). MCP exposes 15 tools + 1 resource. OpenCode/OpenClaw expose 11 tools + 3 hooks.

Smart Hooks

Memory Space

Browse, search, and manage your agent's memories visually at ourmem.ai/space — see how memories connect, evolve, and decay over time.

Security & Privacy

Self-Deploy

# Minimal (BM25 search only, no embedding API needed)
docker run -d -p 8080:8080 ghcr.io/ourmem/omem-server:latest

# With Bedrock embedding (recommended, needs AWS credentials)
docker run -d -p 8080:8080 \
  -e OMEM_EMBED_PROVIDER=bedrock \
  -e AWS_REGION=us-east-1 \
  ghcr.io/ourmem/omem-server:latest

# With OpenAI-compatible embedding
docker run -d -p 8080:8080 \
  -e OMEM_EMBED_PROVIDER=openai-compatible \
  -e OMEM_EMBED_API_KEY=sk-xxx \
  ghcr.io/ourmem/omem-server:latest

Full deployment guide: docs/DEPLOY.md

Object Storage (Optional)

By default ourmem stores data on local disk. For durability and scalability, configure AWS S3 or any S3-compatible object storage:

# Add to your environment
OMEM_S3_BUCKET=your-bucket            # enables s3:// scheme
AWS_ENDPOINT_URL=https://s3.amazonaws.com
AWS_REGION=us-east-1

# Alternative: OSS-compatible storage
OMEM_OSS_BUCKET=your-bucket           # enables oss:// scheme
OSS_ENDPOINT=https://oss-xx-internal.aliyuncs.com
OSS_ACCESS_KEY_ID=your-ak             # or use ECS RAM role (auto-discovered)
OSS_ACCESS_KEY_SECRET=your-sk

If both OMEM_S3_BUCKET and OMEM_OSS_BUCKET are set, OSS takes priority.

Build from Source

Two build modes

glibc build (with Bedrock support)

cargo build --release -p omem-server
# Binary: target/release/omem-server
# Requires: same or newer glibc on target machine

musl static build (portable, zero dependencies)

Single binary that runs on any Linux x86_64 — no glibc, no libraries, nothing.

rustup target add x86_64-unknown-linux-musl

RUSTFLAGS="-C target-feature=+crt-static -C relocation-model=static" \
  cargo build --release --target x86_64-unknown-linux-musl \
  -p omem-server --no-default-features

# Binary: target/x86_64-unknown-linux-musl/release/omem-server
# Statically linked, runs anywhere

Note: The musl build uses --no-default-features which excludes AWS Bedrock support. Use OMEM_EMBED_PROVIDER=openai-compatible (e.g. DashScope, OpenAI) instead. This is because aws-lc-sys (AWS crypto library) crashes on musl static linking due to dlopen(NULL) incompatibility (aws-c-cal#213), and Rust's default static-pie output segfaults with musl-gcc (rust-lang/rust#95926).

Transfer to any server

# Compress
gzip -c target/x86_64-unknown-linux-musl/release/omem-server > omem-server.gz

# Copy to server
scp omem-server.gz user@server:/opt/

# Run (no dependencies needed)
ssh user@server "gunzip /opt/omem-server.gz && chmod +x /opt/omem-server && /opt/omem-server"

API at a Glance

Full API reference (48+ endpoints): docs/API.md

Documentation

License

Apache-2.0