Loom is an open-source, self-hosted MCP server that gives your agents long-term, shared
memory: what one agent learns in a session, the next agent or tool can find.
Agents connect through MCP, HTTP, Python or TypeScript. Self-host with Docker or
on Kubernetes with the Helm chart; agent context and
index data live in ClickHouse, and a
deployment can choose Postgres at first boot for live
memory and knowledge state. The dashboard brings memories, repositories, tasks and runs
into one place.
What Loom is for
- Long-term memory shared across your coding agents, with plugins that load context
at session start and capture the session as it runs.
Agent memory ·
Connect a coding agent
- Memory for an agent application, per user, from the Python or TypeScript
SDK. Your first agent
- A multi-user MCP memory server with namespaces, roles and tokens.
Multi-user mode ·
Namespaces and sharing
- A code index of your repositories that coding agents query for definitions,
callers and change impact. Code index
- Lower model cost for coding agents, as measured below.
Coding-agent cost
- Document search with citations (RAG), including PDF,
.docx and .pptx files the
extractor service converts. Document search
- Shared context for multi-agent workflows: shared memory, task inboxes and
decisions. Multi-agent
- Memory, recall, audit and erasure for autonomous agents.
Autonomous agents
- A record of agent work, with searchable conversations (when recording is on),
session briefs to resume from, runs with their goals, outcomes and model costs when
supplied, and PR and tracker history.
Runs and PR history
Every use case, and when another tool fits better: use cases.
Loom is not an in-process library for one agent's short-lived memory, nor an agent
framework. When Loom does not fit
Measured results
| Evaluation | With Loom | Without Loom | Efficiency gain | Scope |
|---|
| Coding-agent answer quality | 0.84 | 0.78 | 20% lower model cost | 24 questions × 3 paired runs |
| Coding-agent file localization | 96.4% | 91.4% | 21% lower model cost | 36 questions × 10 paired runs |
| Memory vs full-history answers | 94% | 90% | 91% fewer reader prompt tokens | 100 paired LongMemEval-S questions |
Both coding setups use Claude Code (claude-sonnet-5) on the ClickHouse codebase;
cost is the whole session's model bill, excluding Loom's server-side model calls, hosting
and indexing. Answer quality
measures coverage of a verified answer's key points, with a contradiction penalty.
File localization counts whether the right file is among the up to three files the agent is
asked to name at the end of its answer.
The memory row compares retrieved memories with full conversation history using
the same gpt-5 reader. Token savings exclude memory construction; the 94% vs 90%
accuracy difference is descriptive, not statistically established.
Separately, Loom's memory.ask configuration scored 96.6% answer accuracy on all
500 LongMemEval-S questions. This is a product evaluation with its own configured
answer models, not the fixed-reader comparison above.
Results are specific to the tested workloads.
Protocols and reproducible artifacts ·
detailed results ·
evaluate on your workload
Get started
1. Choose your Docker setup
- Personal use—the default: no login, localhost-only access and a
dev/local
namespace. One person can connect multiple agents.
- Team use: authenticated accounts and namespace access controls.
Start in multi-user mode from the first boot.
Both modes support the same Loom features, including configured LLMs and embeddings.
Single-user is not a reduced-feature edition. You can also
switch an existing installation without deleting its data;
grant new accounts access to the existing namespaces.
To try it with nothing installed, open it in a codespace:
(what it runs and who can reach it)
For the default personal installation, with Docker installed (on Windows, in a WSL 2
terminal where docker works: Docker Desktop with its WSL integration on, or Docker Engine
installed in the distribution):
curl -fsSL https://github.com/ClickHouse/loom/releases/latest/download/install.sh -o loom-install.sh && sh loom-install.sh && rm loom-install.sh
以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗
