MCP 服务器

Echo Memory

A long-horizon memory architecture for AI agents, providing a scalable, graph-based memory with causal typing and an MCP interface.

README

Echo Memory

A long-horizon memory architecture for AI agents. Echo Memory is built to remember everything an agent has ever learned, in the best possible way, and to keep fetching and writing that memory efficiently no matter how much history accumulates, for coding tools, chatbots, DevOps agents, or any other agentic system, local or deployed.

Why

Every AI agent starts from zero unless something remembers what happened last time, and remembers it well enough and fast enough to still be useful after months or years of accumulated history. Most memory tools solve short-term recall with plain vector search over stored facts. That degrades as history grows: more candidates, more noise, slower retrieval. Echo Memory is built around the read/write algorithm and the data structure that keeps working at long horizons, not just at day one:

A temporal, self-consolidating memory graph. Facts are edges between entities, not flat vector rows. Old, rarely-accessed memory doesn't just accumulate: it gets consolidated into higher-level summaries over time (never deleted, always traceable back to the original), so retrieval cost stays bounded by what's currently relevant, not by everything that's ever been written. See docs/designs/echo-memory-design.md for the actual mechanism.
Real graph structure, not just similarity. Multi-hop queries like "how did we end up here?", answerable because facts are connected, not just individually embedded.
Causal typing, not just similarity. Edges can be tagged caused_by, led_to, blocked_by, contradicts, set by the agent's own read of the conversation, not inferred statistically. Honest about what's tractable today and what isn't.
Auditable by design. Every change to memory is logged, with a plain-language reason you can read back (echo-memory why <fact_id>). Memory that consolidates and edits itself is only trustworthy if you can see why.
One storage engine, every scale. Postgres + pgvector + Apache AGE, from a single local agent up to an organization-wide shared graph spanning every agent a business runs. No forced migration later. (The novel work is the memory structure and algorithm running on top of Postgres, not a new database engine; see the design doc for why.)
Any agent, not one vendor's. The interface is MCP: any MCP-compatible agent can read and write the same memory graph, whether that's a coding assistant, a chatbot, an ops agent, or something built in-house.

Who this is for

A developer running local agents who wants Claude Code, Cursor, or anything else to stop losing context between sessions and tools.
A team or organization running agentic systems in production (support bots, DevOps agents, internal tooling) that needs a shared memory layer instead of N disconnected ones, with the tenancy model (below) to keep it scoped correctly per agent, per team, or org-wide.

Status

Early and staged. See docs/designs/ for the full architecture and the v1a → v1b build plan. The validated wedge driving v1a is specifically cross-tool coding agent memory (the founder's own daily pain, real and tested). The broader vision above is the target this architecture is built toward, not yet something v1a itself proves. v1a proves basic recall works before v1b adds causal typing and multi-hop graph retrieval, and before v1.1 adds the org-wide tenancy the broader vision depends on.

Getting started

Not yet ready for use; see docs/designs/echo-memory-design.md for the current build plan and progress, and docs/DEVELOPMENT.md for the local setup once code exists.

Architecture

Storage: PostgreSQL with the pgvector and Apache AGE extensions
Retrieval: hybrid vector + full-text search (v1a), with Personalized PageRank via networkx added in v1b for multi-hop associative retrieval
Interface: Model Context Protocol server: write_episode, query_memory, get_audit_log