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Skill Detail

Give agents persistent shared memory with mcp-memory-service

Run a self-hosted MCP and REST memory layer so agents can store decisions, retrieve project context, and share knowledge across sessions and runtimes.

Integrations & ConnectorsMCP
Integrations & Connectors MCP Security Reviewed
⭐ 2k GitHub stars
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill give-agents-persistent-shared-memory-with-mcp-memory-service Copy
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
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At a glance
Tools required
Python, pip, MCP-compatible client or HTTP-capable agent framework
Install & setup
Install with pip install mcp-memory-service, then run memory server for local MCP stdio use or MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http for HTTP/REST agent pipelines. Connect the documented MCP client or REST agent framework and use the memory store/search tools for durable project context.
Author
doobidoo
Publisher
Individual
Last updated
Oct 5, 2026
Quick brief

Use mcp-memory-service when an agent needs durable context instead of starting each session from a blank slate. The operator workflow is concrete: run the memory server, connect an MCP client or HTTP-capable agent framework, store decisions and project facts, tag writes by agent or conversation, and retrieve relevant memories before planning or implementation. Invoke this instead of using a normal chat or product UI when the useful work is cross-session agent continuity, shared project memory, or retrieval-backed context injection for LangGraph, CrewAI, AutoGen, Claude Code, Codex, ChatGPT, or other MCP/HTTP clients. The scope boundary is the memory and retrieval layer around agent work; it is not a generic vector database, dashboard, SDK, or broad infrastructure listing.

How it works

What this skill actually does

Inputs and prerequisites: Python, pip, MCP-compatible client or HTTP-capable agent framework.

Setup notes: Install with pip install mcp-memory-service , then run memory server for local MCP stdio use or MCP_ALLOW_ANONYMOUS_ACCESS=true memory server –http for HTTP/REST agent pipelines. Connect the documented MCP client or REST agent framework and use the memory store/search tools for durable project context.

Source and verification boundary: use https://github.com/doobidoo/mcp-memory-service as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.

Framework fit: publish this as a MCP workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.