Live Memory
Overview
Description from the maker (original English)
Persistent, read-only codebase memory for Claude Code; ask it instead of re-reading files.
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: Streamable HTTP
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Passed 2/2 checks · Runs on your computer
First: which AI do you use?
Pick your app and the steps appear below.
Works with Claude, with some setup first
Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the Claude desktop app’s settings file
SettingsDeveloperEdit Config
Paste these settings and save the file
{ "mcpServers": { "live-memory": { "command": "uvx", "args": [ "shofer-live-memory" ] } } }Quit Claude and open it again
Try it
Use Live Memory and tell me what you can do for me, then show me one thing✓ If Claude says it is using Live Memory or shows a Live Memory tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
ChatGPT cannot connect Live Memory yet
Live Memory runs on your computer, but ChatGPT can only connect servers that run on the web
What you can do instead
A · Use an app that can connect Live Memory
- Install Python (uv) on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
- Install Python (uv) on your computer first (one time)
- Install Python (uv) on your computer first (one time)
- Install Python (uv) on your computer first (one time)
B · Others in Search & AI memory that work with ChatGPT
Picked from the most used in the same category, only ones that connect by address with no key
- AIHOT✓ ChatGPT
- Sui Knowledge Base (kapa.ai)✓ ChatGPT
- Exa✓ ChatGPT
Works with Claude Code, with some setup first
Install Python (uv) on your computer first (one time)
Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open Terminal
Mac: press Command+Space and type Terminal · Windows: open PowerShell
Paste this command and press Enter
claude mcp add live-memory -- uvx shofer-live-memoryType /mcp in Claude Code
You should see Live Memory listed as connected
Try it
Use Live Memory and tell me what you can do for me, then show me one thing✓ If Claude Code says it is using Live Memory or shows a Live Memory tool in the answer, you are connected
Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with Cursor, with some setup first
Install Python (uv) on your computer first (one time)
Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the AI chat in Cursor in Agent mode
Try it
Use Live Memory and tell me what you can do for me, then show me one thing✓ If Cursor says it is using Live Memory or shows a Live Memory tool in the answer, you are connected
Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version
Works with VS Code, with some setup first
Install Python (uv) on your computer first (one time)
Install Python (uv)
One time only; it works for every server that needs Python (uv)
Open the AI chat in VS Code in Agent mode
Try it
Use Live Memory and tell me what you can do for me, then show me one thing✓ If VS Code says it is using Live Memory or shows a Live Memory tool in the answer, you are connected
Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version
For developers: Terminal commands and JSON settings
Installed on your computer
Claude Code
claude mcp add live-memory -- uvx shofer-live-memoryClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"live-memory": {
"command": "uvx",
"args": [
"shofer-live-memory"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"live-memory": {
"command": "uvx",
"args": [
"shofer-live-memory"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"live-memory": {
"type": "stdio",
"command": "uvx",
"args": [
"shofer-live-memory"
]
}
}
}These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
At a glance
Which apps can use it
Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.
Run on your computer in
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Related guides
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account shofer-dev, which owns the repo
- ✓ Pass
Still maintained
Code last changed 3 months ago
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Discussion
Ask, share a tip or report that it does not work. Sorted by what people marked helpful.
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Posts here are written by users, not the PopNx team.
User reviews
No reviews yet
Compared with others in Search & AI memoryi
- More stars than 90% of the other 5905 servers in this category
- Published to the registry more recently than 22% of the other 5905 servers in this category
- Basic checks equal to or better than 100% of the other 5905 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Live Memory safe?
It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed 3 months ago, and the latest version (0.6.0) reached the registry 4 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Ruflo, Context7, Metabase
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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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 LRrepo[("Your repo")] subgraph sessions["Claude Code sessions — premium model (many, over time)"] agent["Agent"] end agent -->|"reads / edits files"| repo agent -. "PostToolUse / FileChanged hooks tee file content — passive learning (free)" .-> lm agent ==>|"ask_live_memory(question, cwd) 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
- accumulating context window
append-only · neutral compaction to a ledger"] lm --> snap[("Local JSON snapshot 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:
| per task | premium-model bill | all-in · DeepSeek-flash† | all-in · Haiku | faster |
|---|---|---|---|---|
| Understanding-heavy (trace / comprehend) | −61% | −57% | −25% | ~22% |
| Hybrid (understand-then-edit: bug fixes + features) | −28% | −26% | −11% | ~11% |
| Pure edit / execution | ~break-even | ~break-even | ~break-even | ~0 |
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](https://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 · How it compares to Copilot/Cursor: COMPARISON.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-memorycd 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 · /live-memory-reset starts a workspace over with its configured preload docs re-loaded. 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) │ ├── live-memory-reset.md # /live-memory-reset → drop history + re-preload configured docs │ ├── stats.py · config.py · empty.py · reset.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 + /clear + /reset + /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 ├── preload.py # configured preload globs → verbatim docs on workspace load ├── 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_memoryis the active fallback for anything unseen. - Preload globs (opt-in):
preload_globs(e.g.docs/*.md) names reference docs loaded verbatim into the system prompt whenever a workspace is (re)loaded — the memory starts out knowing them. They behave like observations: compaction later distills them into the knowledge ledger under pressure./live-memory-resetdrops a workspace's history trail and re-preloads them fresh. - 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.
- Frozen volatile block + observation deltas (KV-cache preservation): the volatile system block (ledger + preloaded/observed content + manifest) is frozen between compactions and re-sent byte-identical; changes that arrive in between (a teed file, a staleness notice) are appended to the history as delta messages instead of mutating the block — so the provider's prefix cache keeps matching instead of truncating at the first changed byte.
- Two-tier timeout:
ask_live_memory(question, cwd, max_answer_tokens?)blocks up to the server-configured soft budget (default_timeout_s, default 60s — no longer a tool argument) and returns a best-effort answer before the hard.mcp.jsonMCP timeout. - Disclosed answer-length budget: the answer is hard-capped (default
LIVE_MEMORY_MAX_ANSWER_TOKENS) *and* the model is told the cap, so it self-regulates instead of being truncated mid-sentence; the optionalmax_answer_tokensarg overrides it per question. - Human status via the
/live-memory-statsslash command (→/stats), kept off the agent's tool surface.
Installation
Prerequisites: Python ≥ 3.10; ripgrep (rg) recomm