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, ask_live_memory, 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 ask_live_memory 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:

Understanding-heavy work also offloads ~93% of the premium model's codebase-reading tokens (with lower cost variance), and correctness never regressed on the hybrid tasks (12/12 passed with and without it). DeepSeek-v4-flash matched Haiku's answer accuracy (98% vs 91% over 3 reps) at ~8× lower token price; a local companion is ≈ free, so all-in ≈ the premium-model bill. Fully reproducible + audited (human + Fable). Full numbers + methodology: benchmark/results/RESULTS.md.

_{† companion re-priced at DeepSeek-v4-flash rates (~8× cheaper than Haiku); exact for the understanding case, derived from the measured cost ratio for the hybrid case.}

Lineage: Live Memory began as a feature of shofer.dev (Arkware's parallel multi-agent coding platform), where sessions share an in-sync codebase memory. This is that idea as a standalone Claude Code plugin — a fresh implementation, self-contained, with no dependency on shofer. Part of the shofer Claude Code plugin family (with slang-workflows). Design: DESIGN.md · Testing: TESTING.md · Privacy: PRIVACY.md.

Quickstart

live-memory is an HTTP MCP server you run once (a singleton that serves every Claude Code session) plus a plugin that registers ask_live_memory, the hooks, and the slash commands. Start the server first — Claude Code only connects to it (it never spawns it), so if it isn't running you'll get a connection error.

1 — Start the server (zero-config on a Claude subscription → Haiku; no API key needed):

git clone https://github.com/shofer-dev/claude-code-live-memory
cd claude-code-live-memory/deploy && ./install-service.sh   # venv + user systemd service, auto-starts on boot
# …or just run it in a terminal:
#   cd claude-code-live-memory/server && pip install -e . && python -m live_memory

2 — Install the plugin (inside a Claude Code session):

/plugin marketplace add shofer-dev/claude-code-live-memory
/plugin install live-memory@shofer-live-memory

Ask your agent a whole-repo question — it'll call ask_live_memory instead of reading files. /live-memory-stats shows accumulated knowledge + cost · /live-memory-config switches model/provider · /live-memory-empty wipes memory. Providers, systemd, workspaces, and concurrency are detailed below.

Shape

live-memory/
├── .claude-plugin/plugin.json     # plugin manifest
├── .mcp.json                      # registers the server (type:http, explicit timeout)
├── hooks/                         # PostToolUse(Read|Write|Edit|…) + FileChanged → TEE file content (passive learning)
│   ├── hooks.json
│   └── notify.py
├── skills/live-memory/SKILL.md    # tells the agent when/why to call ask_live_memory
├── commands/                      # USER-facing slash commands (not agent tools)
│   ├── live-memory-stats.md       # /live-memory-stats  → GET /stats
│   ├── live-memory-config.md      # /live-memory-config → set model/provider, hot-reload
│   ├── live-memory-empty.md       # /live-memory-empty  → wipe memory (this workspace or `all`)
│   ├── stats.py · config.py · empty.py
├── settings.json
├── deploy/                        # systemd unit + env example + install-service.sh
└── server/                        # the long-running MCP server (Python, asyncio)
    ├── pyproject.toml             # deps + mypy(strict) + pytest config
    ├── tests/                     # pytest unit suite (mocked; no network)
    └── live_memory/
        ├── __main__.py            # entrypoint: python -m live_memory
        ├── server.py              # MCP (HTTP) ask_live_memory + /health + /stats + /notify + /reload
        ├── workspace.py           # per-cwd state registry (window + queue + store); fork/commit
        ├── manager.py             # the agent loop (process one question); compaction
        ├── context_window.py      # budget; file-context evict, Q&A summarize; fork/clone
        ├── summarizer.py          # NEUTRAL, query-agnostic knowledge-ledger summarization
        ├── question_queue.py      # per-workspace admission (serial/parallel) + per-entry timeout
        ├── async_jobs.py          # opt-in fire-and-forget job registry (submit/poll)
        ├── keep_warm.py           # background KV/prompt-cache keep-warm loop
        ├── conversation_store.py  # versioned JSON snapshot (SHA-256 file validation)
        ├── llm_client.py          # provider-pluggable: Anthropic Messages | OpenAI-compatible
        ├── oauth.py               # subscription OAuth credential + auto-refresh (zero-config)
        ├── config.py              # layered config (env > config.json > defaults) + provider knowledge
        ├── constants.py           # ALL tunable magic numbers + defaults, centralized (config sources its defaults here)
        ├── models.py              # core dataclasses (ChatMessage, FileContext, QuestionResult, …)
        ├── tool_executor.py       # read-only tools (Read/Grep/Glob/find_paths/git/…), path-jailed
        ├── directory_tree.py      # workspace scan, ~10% context cap
        ├── pricing.py             # per-model USD cost (+ env overrides)
        ├── logging_setup.py       # stderr→journald + optional rotating file
        └── prompts.py             # system prompt + neutral-summary prompt

Architecture (see DESIGN.md for the full rationale)

• One externally-supervised, idempotent HTTP MCP server (singleton) serves all Claude Code sessions; state is keyed per workspace (cwd).
• Model = independent + provider-pluggable: the server runs its own cheap model (not the session's). Two adapters cover ~everything — Anthropic Messages (with cache_control) and OpenAI-compatible (DeepSeek/OpenAI/gateways). Zero-config: with no key but a Claude subscription, it uses the subscription OAuth token (auto-refreshed) on Haiku.
• Passive (organic) learning: PostToolUse/FileChanged hooks tee the content of the files your agent reads/edits into the memory, so it warms up for free from real work; ask_live_memory is the active fallback for anything unseen.
• Append-only window between compactions; compaction = batched neutral summarization with a high/low-watermark (rare, batched) — observed files + Q&A distilled into a query-agnostic knowledge ledger — never front-truncation.
• Two-tier timeout: ask_live_memory(question, cwd, timeout) — the soft timeout informs the model's budget and yields a best-effort answer before the hard .mcp.json MCP timeout.
• Human status via the /live-memory-stats slash command (→ /stats), kept off the agent's tool surface.

Installation

Prerequisites: Python ≥ 3.10; ripgrep (rg) recommended (powers Grep); git optional (powers git_search / get_changed_files).

1. Install the server:

cd server
pip install -e .          # runtime deps (mcp, anthropic, starlette, uvicorn, watchdog, httpx)
# for development/tests:  pip install -e ".[dev]"   # adds mypy, pytest, pytest-asyncio

2. Install the plugin into Claude Code so it reads .mcp.json, the hooks, the skill, and the slash commands. The repo root doubles as a single-plugin marketplace (.claude-plugin/marketplace.json); /plugin install only installs from a marketplace, never a bare directory — so add the marketplace first, then install from it. Inside a Claude Code session:

/plugin marketplace add https://github.com/shofer-dev/claude-code-live-memory
/plugin install live-memory@shofer-live-memory

shofer is the marketplace name; live-memory is the plugin name. To install from a local clone instead, point add at the checkout directory:

/plugin marketplace add /ABSOLUTE/PATH/TO/claude-code-live-memory
/plugin install live-memory@shofer-live-memory

After editing plugin files later, run /plugin marketplace update shofer then /reload-plugins (installed plugins are cached under ~/.claude/plugins/, so source edits aren't picked up live).

For local development, skip the marketplace entirely and launch Claude Code with the plugin loaded directly — this does pick up edits via /reload-plugins:

claude --plugin-dir /ABSOLUTE/PATH/TO/claude-code-live-memory

(The server in step 3 must be running before Claude Code connects — .mcp.json points at a type:http endpoint Claude Code only connects to, never spawns; if the server is down you'll see a connection error in /plugin's Errors tab.)

3. Run the server (next section). 4. Dev checks: mypy live_memory/ && pytest.

Running the server

The HTTP transport requires the server to be already running before Claude Code connects (Claude Code does not start type:http servers) — run it under an external supervisor (systemd/container/etc.).

Zero-config (no API key): if you're logged into a Claude subscription, it just works — the server reuses that credential (auto-refreshed) on Haiku.

cd server && pip install -e .
python -m live_memory
# serves MCP at http://127.0.0.1:7711/mcp  (+ /health, /stats, /notify, /reload)

The subscription path draws on your subscription's rate-limit budget (not $-metered) — a documented ToS gray area. For an always-on service prefer a key.

Pick any model/provider — env vars or the /live-memory-config slash command (writes config.json, hot-reloads, no restart):

# DeepSeek (cheap, recommended), via env:
LIVE_MEMORY_PROVIDER=openai LIVE_MEMORY_BASE_URL=https://api.deepseek.com \
  LIVE_MEMORY_API_KEY=sk-... LIVE_MEMORY_MODEL=deepseek-chat  python -m live_memory

# …or at runtime, from inside Claude Code:
/live-memory-config set provider=openai base_url=https://api.deepseek.com model=deepseek-chat api_key=sk-...
/live-memory-config show

Supported providers: anthropic (Messages API + Bedrock/Vertex/gateways, API key or subscription OAuth) and openai (any OpenAI-compatible endpoint: OpenAI, DeepSeek, local models, gateways). Then enable the plugin so Claude Code reads .mcp.json and connects.

Run as a systemd service

deploy/ has the boilerplate. One command registers and starts it:

cd deploy
./install-service.sh            # user service (recommended — see below)
./install-service.sh --system   # system-wide (best for API-key setups)

It creates a venv + installs the server, writes a config at ~/.config/live-memory/live-memory.env (from live-memory.env.example — edit it for provider/model/key), installs live-memory.service, enables lingering, and starts it. Config is supplied to the process by systemd via EnvironmentFile=.

• Persistence survives restarts automatically: per-workspace snapshots live in LIVE_MEMORY_DATA_DIR (default ~/.claude/plugins/data/live-memory), so a restart reloads each workspace's memory on its next query.
• Subscription (zero-config) auth needs a user service (the default): it runs as you, so it can read ~/.claude/.credentials.json and reuse your Claude login (auto-refreshed). A --system service can also do this, but the unit must set User= + Environment=HOME=… (the installer does this for you).

systemctl --user status live-memory
journalctl --user -u live-memory -f
systemctl --user restart live-memory     # after editing the env file

Logs

By default the server logs to stderr, which systemd captures into the journal — the idiomatic place, with rotation and unit/PID metadata:

journalctl --user -u live-memory -f          # user service
sudo journalctl -u live-memory -f            # system service

Two caveats and the escape hatch:

• A user service's journal only persists across reboots if journald has persistent storage (/var/log/journal exists). If yours is volatile, those logs vanish on reboot.
• For a durable, greppable plain-text log regardless, set LIVE_MEMORY_LOG_FILE (e.g. /var/log/live-memory/live-memory.log for a system service, or an absolute path under ~/.local/state/live-memory/ for a user one). It's a rotating handler (LIVE_MEMORY_LOG_MAX_BYTES × LIVE_MEMORY_LOG_BACKUPS) and writes in addition to journald. LIVE_MEMORY_LOG_LEVEL (default INFO) tunes verbosity.

Workspaces & cwd

Memory is keyed per workspace, from the cwd passed to ask_live_memory. cwd must be an absolute path (a relative path is rejected — the shared server can't resolve it against your session). By default the server snaps each cwd to its enclosing git repo root, so a subdirectory and the repo root share one memory:

Concurrency

Questions to the same workspace are admitted by one of two models:

(Questions to different workspaces always run concurrently, regardless of this setting.)

Async (fire-and-forget) tools — opt-in

MCP tool calls block the caller's turn until they return. If you want the agent to submit a slow query, keep working, and collect the answer later, set LIVE_MEMORY_ASYNC_TOOLS=true to additionally expose:

• ask_live_memory_submit(question, cwd, timeout) → returns a job_id immediately.
• ask_live_memory_result(job_id) → the answer when ready, or [running] to poll again.

The agent drives the polling (Claude Code can't push completion into a running turn). Off by default; ask_live_memory (synchronous) is always available.

Cache keep-warm

Off by default — opt in with LIVE_MEMORY_KEEP_WARM=true. A background loop pings each recently-active workspace's prefix (max_tokens=1, output discarded) so the provider's KV/prompt cache doesn't go cold between questions — keeping the next real query on cache-hit pricing instead of a full cold re-read. The interval is provider knowledge: ~240s for Anthropic/OpenAI (minute-scale caches), and auto-set very long for DeepSeek (its disk cache lasts hours/days, so even enabled it self-disables there). Override with LIVE_MEMORY_KEEP_WARM_INTERVAL_S, or stop warming idle workspaces sooner with LIVE_MEMORY_KEEP_WARM_MAX_IDLE_S. /live-memory-stats shows when the cache was last refreshed.