Caura, formerly MemClaw, provides governed shared memory for multi-agent fleets, with permissions and audit trails.
Open source
24 alternativesMCP Memory Service
MCP Memory Service provides persistent memory for AI agent pipelines through an API and knowledge graph.
About MCP Memory Service
MCP Memory Service is an open-source persistent memory backend for AI agent pipelines, including LangGraph, CrewAI, AutoGen, and Claude. It provides a REST API, knowledge graph with typed causal edges, and autonomous memory consolidation to let agents retain project context, architecture decisions, and code patterns across sessions. The service runs self-hosted with local ONNX embeddings so memory stays on user infrastructure, supports real-time SSE notifications for memory changes, and uses X-Agent-ID and conversation_id headers for scoped retrieval and incremental conversation storage.
Key features
- Framework-agnostic REST API for agent memory operations
- Knowledge graph with typed causal edges for shared context
- Local ONNX embeddings to keep memory on user infrastructure
- Autonomous consolidation to compress old memories
- Real-time SSE notifications for memory store/delete events
- Scoped retrieval via X-Agent-ID and conversation_id headers
Best for: Developers building AI agent pipelines with supported frameworks who need persistent, shared memory without cloud lock-in or external API costs.
Good for
- Self-hosted deployment avoids cloud lock-in and extra API costs
- Local ONNX embeddings keep memory data on user infrastructure
- Shared knowledge graph enables causal context access across agent runs
MCP Memory Service at a glance
- Category
- AI & Machine Learning › AI Agent Frameworks
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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MCP Memory Service FAQ
Which AI agent frameworks are compatible with MCP Memory Service?
It supports LangGraph, CrewAI, AutoGen, Claude Desktop, Claude Code, OpenCode, and any HTTP client.
Where are memory embeddings processed?
Embeddings run locally via ONNX, so memory data stays on user infrastructure.
How can memory retrieval be scoped to specific agents or conversations?
Use the X-Agent-ID header to tag memories by agent, or conversation_id to bypass deduplication for incremental storage.
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