Live Memory MCP server

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        always-up-to-date codebase memory for Claude Code · ask, don't re-read

Live Memory — Claude Code plugin

A cheap, always-on model that learns your repo — so your agent stops re-reading it. Live Memory runs a separate, cheap large-context model as a long-lived MCP server that accumulates knowledge of your codebase across sessions. Instead of re-reading the same files every session, your agent asks one read-only tool, asklivememory, the broad-understanding questions — "where is X, how does Y work, what calls Z" — and Live Memory answers in a way that bootstraps the primary agent to start doing productive work (e.g., edits). It learns passively from your agent's own reads and edits (teed via hooks — no extra reading) and stays current as the repo changes (modifications and deletions). Read-only and path-jailed (it can never edit, create, or run anything); zero-config on a Claude subscription (Haiku, no API key); the memory model is pluggable — point it at a local model or any OpenAI-compatible endpoint.

How it works

flowchart LR
  repo[("Your repo")]
  subgraph sessions["Claude Code sessions — premium model (many, over time)"]
    agent["Agent"]
  end
  agent -->|"reads / edits files"| repo
  agent -. "PostToolUse / FileChanged hooks<br/>tee file content — passive learning (free)" .-> lm
  agent ==>|"ask_live_memory(question, cwd)<br/>one read-only MCP tool"| lm
  lm ==>|"grounded answer — no file re-reading"| agent
  subgraph server["Live Memory — one MCP server per workspace (singleton)"]
    lm["Cheap / local large-context model<br/>+ accumulating context window<br/>append-only · neutral compaction to a ledger"]
    lm --> snap[("Local JSON snapshot<br/>persists across sessions")]
  end

Your agent reads or edits files as usual; hooks quietly tee that content to the server so it learns for free. When the agent needs to understand something, it asks asklivememory instead of re-reading — the server answers from its accumulated, per-workspace memory (or reads the code itself, read-only, if it hasn't seen it yet). One server serves every session and persists across sessions.

Benchmarks

A/B on a real repo, cost per task, run to completion. Cost is shown three ways: the premium (building) model's bill — what your expensive model spends, since the companion runs on a cheap or local model — and all-in, also counting the companion's own cost on DeepSeek-v4-flash or Haiku:

Shortened. The full README is on GitHub.