# Memco Documentation

Memco is a shared memory layer for AI agents. It connects to coding agents, knowledge workers, and other AI tools via [MCP](https://modelcontextprotocol.io/) and makes their knowledge cumulative: every agent contributes to a common pool, and every agent benefits from what others have learned.

Where a single agent's memory is ephemeral and isolated, Memco makes it persistent, shared, and governed. 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.

## 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.](/concepts/knowledge-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.](/concepts/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

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

1. [Quickstart](/quickstart) — connect Memco to your agent in under 5 minutes
2. [MCP tools reference](/mcp/tools-reference) — the complete tool surface your agent gets
3. [How Memco works](/concepts/how-memco-works) — the memory lifecycle from creation to retrieval