mcp-memory-service and MegaMemory
These are competitors offering overlapping persistent memory solutions for AI agents, though A targets broader agent frameworks while B specializes in coding-specific use cases with embedded semantic search.
About mcp-memory-service
doobidoo/mcp-memory-service
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowledge graph + autonomous consolidation.
Consolidates multi-agent memory using a knowledge graph with typed edges (causes, fixes, contradicts) and autonomous compression, accessible via REST API with ONNX-based embeddings that run locally. Implements Remote MCP support for browser-based claude.ai integration via Server-Sent Events, alongside traditional desktop MCP, with OAuth 2.0 authentication and self-hosted infrastructure (no cloud lock-in). Agent identity is tracked via `X-Agent-ID` headers for scoped retrieval, and conversation threading is preserved through `conversation_id` fields, enabling both shared memory across agent fleets and inter-agent messaging through semantic tag-based filtering.
About MegaMemory
0xK3vin/MegaMemory
Persistent project knowledge graph for coding agents. MCP server with semantic search, in-process embeddings, and web explorer.
Uses in-process ONNX embeddings (all-MiniLM-L6-v2) and SQLite with WAL for zero-dependency semantic search and persistence. Operates as an MCP stdio server integrated with Claude Code, OpenCode, Antigravity, and Codex, with built-in two-way merge conflict resolution for collaborative knowledge graph management across branches. The LLM itself acts as the indexer—concepts are stored in natural language rather than parsed code symbols—enabling agents to update the graph after each task and query semantic context before starting new ones.
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