Open Memory Protocol Review 2026 — Portable AI Memory Across Every Tool

The Problem OMP Solves

Every AI tool remembers you differently — and only within its own walls:

• Claude knows your code style. Cursor doesn’t.
• ChatGPT learned your preferences. Your custom AI agent hasn’t.
• Copilot saw your project structure. Your terminal AI starts from zero.

This is the AI memory silo problem. Every time you switch tools, your AI forgets who you are. You repeat context. You rewrite prompts. The AI that was finally understanding you resets to a stranger.

Open Memory Protocol (OMP) is the first serious attempt to solve this with an open, vendor-neutral standard.

What Is OMP?

Launched June 29, 2026 (66 GitHub stars and growing), OMP is:

• A specification (v0.4) — precise definitions for memory objects, storage format, and HTTP API
• A reference server — self-hostable, open-source, Docker-ready
• SDKs — TypeScript and Python libraries for building OMP-compatible tools
• Adapters — plug-ins for Claude (MCP), with OpenAI, Cursor, and more in development

The core idea: any AI tool that implements OMP can instantly share memory with any other OMP-compatible tool.

Getting Started

Hosted Server (Zero Setup)

The OMP reference server is publicly hosted. Point your tools at it:

curl https://omp-server-production.up.railway.app/v1/health
# {"status":"ok","version":"0.1","compliance":"OMP-Core","memories_count":0}

Self-Hosted

npx omp-server

Or with Docker:

docker run -p 3456:3456 -v omp-data:/data ghcr.io/smjai/omp-server

Connect Claude (via MCP)

Add to your Claude Desktop config:

{
  "mcpServers": {
    "omp": {
      "command": "npx",
      "args": ["omp-mcp"],
      "env": {
        "OMP_SERVER": "http://localhost:3456",
        "OMP_API_KEY": "your-omp-key"
      }
    }
  }
}

For AI-powered memory compression, also set:

OMP_AI_PROVIDER=anthropic   # or "openai"
OMP_AI_API_KEY=sk-ant-...   # your API key

How OMP Works

The protocol defines:

1. Memory objects — structured entities containing user context, preferences, and interaction history
2. Storage format — standardized JSON schema for portability
3. HTTP API — RESTful endpoints for CRUD operations on memory
4. Vectors — optional embedding-based semantic search across memories

The Claude MCP adapter automatically captures context during conversations and stores it in OMP format. When you switch to a different OMP-compatible tool, it retrieves that context and starts with knowledge of who you are.

Hands-On Testing

Setting up the hosted server was instantaneous — the Railway endpoint responded immediately with a clean health check. The Claude MCP adapter took about 2 minutes to configure (generating an API key, editing the config file, restarting Claude Desktop).

After configuration, Claude began storing memories during conversations. The killer feature: switching to a Claude Code session and querying the same OMP server showed Claude remembering project context from the Desktop session.

Memory queries work with both exact match and semantic search:

# Exact match
curl -X GET "https://omp-server-production.up.railway.app/v1/memories?q=project%20preferences"

# Semantic search (requires AI provider configured)
curl -X GET "https://omp-server-production.up.railway.app/v1/memories/search?q=What%20framework%20do%20I%20prefer%3F"

Community Reception

OMP has generated significant interest in the AI developer community. The 66 stars in under a week reflect a real appetite for cross-tool memory. Discussion on GitHub issues has focused on:

• Security concerns — how to prevent memory leakage between users
• Compression strategies — how to distill long conversations into useful memory without losing important context
• Adapter coverage — demand for VSCode/Cursor and OpenAI adapters

The project maintains a clear spec document and health dashboard, suggesting good governance for an early-stage protocol.

Limitations

OMP is promising but early:

• v0.4 spec means breaking changes are likely before 1.0
• Adapter coverage is currently limited to Claude (the MCP adapter ecosystem must grow)
• Memory quality depends heavily on the LLM used for compression — a weaker model produces noisier memories
• No real-time sync — memories are written per-session, not streamed continuously

Verdict

OMP addresses a real pain point with a clean, open approach. The hosted server makes it trivially easy to try, and the Docker self-hosting option addresses privacy concerns. If the adapter ecosystem grows (and early momentum suggests it will), OMP could become the standard for cross-tool AI memory.

For anyone using multiple AI tools daily, OMP is worth installing today — even if the full ecosystem hasn’t arrived yet. The Claude adapter alone provides enough value to justify the setup time.

Rating: 8.2/10 — A promising standard with early but tangible utility.

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Tested with Claude Desktop, Claude Code, and custom MCP integration. OMP is Apache 2.0 licensed at github.com/SMJAI/open-memory-protocol.