Memco Documentation

Memco is a memory platform for AI agents. It connects to coding agents, knowledge workers, and other AI tools via MCP, and to agents and products you build yourself via the Python and Node.js SDKs, and makes their knowledge persistent and portable.

Memco offers two complementary products:

Shared Memory is the core Memco product. It makes knowledge cumulative across your team: every agent contributes to a common pool, and every agent benefits from what others have learned. A pattern identified by one agent becomes available to all others without any explicit transfer step. A new agent joining a codebase or workflow immediately has access to the accumulated knowledge of every session that preceded it. Shared Memory includes trust scoring, multi-agent learning, and governed curation.

Personal Memory stores your individual preferences, working style, and context in a single place that every AI agent you use can access. It is private to you and portable across agents: switch between Claude, ChatGPT, Cursor, or any other MCP-compatible client and your agents already know how you work.

Key concepts

Knowledge domains. Memco organizes knowledge by domain. Each domain defines the semantics for how knowledge is created, searched, and tagged within a specific area of work. Memco ships with two built-in domains: coding for software development workflows and knowledge for broader knowledge work. Learn more about domains.

Trust. Every piece of knowledge in Memco carries a trust score, computed from a Bayesian evidence model called TrustDist. Trust reflects the weight of accumulated evidence — retrievals, relevance signals, enrichments — and decays naturally when a memory stops receiving signal. Learn more about trust.

Memory networks. Organizations can structure shared memory along their own boundaries — teams, departments, projects — with controlled knowledge sharing across levels. Read access flows upward through the hierarchy; write access is always local to the most specific context.

Getting started

Shared Memory

The fastest path to using Shared Memory is connecting your agent via MCP:

1. Quickstart — connect Memco to your agent in under 5 minutes
2. MCP tools reference — the complete tool surface your agent gets
3. How Memco works — the memory lifecycle from creation to retrieval

Personal Memory

1. Personal Memory overview — what it is, how to set it up, and how it relates to Shared Memory
2. Personal Memory tools reference — the complete tool surface

Building with the SDK

If you are building the agent yourself, or building memory into a product for your own customers, the SDKs give your code the same operations MCP gives an agent.

1. SDK overview — install memcoai (Python), @memco/memcoai (Node.js), or github.com/memcoai/memcoai/go/memcoai (Go), and when to use the SDK instead of MCP
2. Building an agent with the SDK — the learning loop and the toolset export for LangChain, Anthropic, and OpenAI
3. Embedding memory in your product — memory networks and the users of your product