Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated today
Install
Commons
Overview
Description from the maker (original English)
Shared memory, work claims and handoffs so parallel AI coding sessions stop colliding.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
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: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
COMMONS_AGENT,COMMONS_TOOLS
You need
- DockerDocker installed
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"commons-memory": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"COMMONS_AGENT",
"-e",
"COMMONS_TOOLS",
"ghcr.io/neoo-blue/commons-memory:0.37.1",
"serve",
"--transport",
"stdio"
],
"env": {
"COMMONS_AGENT": "your-value",
"COMMONS_TOOLS": "your-value"
}
}
}
}Run this in your terminal
claude mcp add commons-memory --env COMMONS_AGENT=your-value --env COMMONS_TOOLS=your-value -- docker run -i --rm -e COMMONS_AGENT -e COMMONS_TOOLS ghcr.io/neoo-blue/commons-memory:0.37.1 serve --transport stdioFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"commons-memory": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"COMMONS_AGENT",
"-e",
"COMMONS_TOOLS",
"ghcr.io/neoo-blue/commons-memory:0.37.1",
"serve",
"--transport",
"stdio"
],
"env": {
"COMMONS_AGENT": "your-value",
"COMMONS_TOOLS": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"commons-memory": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"COMMONS_AGENT",
"-e",
"COMMONS_TOOLS",
"ghcr.io/neoo-blue/commons-memory:0.37.1",
"serve",
"--transport",
"stdio"
],
"env": {
"COMMONS_AGENT": "your-value",
"COMMONS_TOOLS": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
COMMONS_AGENTenvName this client goes by in CommonsCOMMONS_TOOLSenvTool listing: lite, core or all
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
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 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Neoo-Blue, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … 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
Compared with others in Search & AI memoryi
- More stars than 0% of the other 7254 servers in this category
- Published to the registry more recently than 99% of the other 7254 servers in this category
- Basic checks equal to or better than 100% of the other 7254 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 Commons safe?
It passed 1 of 1 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 –, and the latest version (0.37.1) reached the registry today.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer★ 48,148MITUpdated 20 days ago
Install
Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated last month
Install
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.
Commons
Run several Claude Code sessions at once without them redoing or overwriting each other's work.
Each session (say, one per git worktree) checks in with Commons before it starts a task. If another session already has it, it hears so and backs off. Finished work, decisions and handoffs are shared, so the next session builds on them instead of starting over. Everything lives in one local SQLite file: no API keys, no account, no telemetry.
/plugin marketplace add Neoo-Blue/commons-memory/plugin install commons@commons-memory
Needs uv. Each folder is one agent (claude-code@ ), so run parallel sessions in separate worktrees or checkouts with different folder names.
Two sessions, two worktrees, same task:
claude-code@payments-fix start_work("fix the payments retry", paths=["pay.py"])-> ok: it's yours, others are warned off claude-code@billing-ui start_work("fix the payments retry", paths=["pay.py"]) -> busy: claude-code@payments-fix has it since 02:33. msg_send them to split it, or wait for their finish_work.
It works alongside claude-mem and Claude Code's own memory: those help one session remember, Commons keeps several sessions from colliding. The plugin lists 19 tools (memory and coordination only). Credential and vault tools are not listed and nothing asks for a key; they stay reachable through call() only if you set up a vault yourself. How it stays safe: docs/threat-model.md.
Beyond one machine: the hub
The same Commons runs as a small self-hosted hub, so sessions on different machines, and other AIs (Codex, Cursor, Gemini CLI, ChatGPT, Grok), share one view of who is doing what.
Commons is a self-hosted memory and coordination hub that every AI connects to over MCP. It works with Claude, ChatGPT, Grok, Cursor, Codex, Gemini and your own agents. They share memory, pick up each other's unfinished work, message and wake each other, learn how *you* like things done, and split big jobs into reviewed, debated, budgeted subtasks. One SQLite file (or Postgres) holds it all. A dashboard shows who is doing what and what it costs.
curl -fsSL https://raw.githubusercontent.com/Neoo-Blue/commons-memory/main/install.sh | shirm https://raw.githubusercontent.com/Neoo-Blue/commons-memory/main/install.ps1 | iex # WindowsDocker: see docs/docker.md. Prefer to read it first? curl -fsSLO https://raw.githubusercontent.com/Neoo-Blue/commons-memory/main/install.sh && less install.sh && sh install.sh. No-script alternatives: uvx --from git+https://github.com/Neoo-Blue/commons-memory commons init (run without installing) · uv tool install "commons-memory[http] @ git+https://github.com/Neoo-Blue/commons-memory" · pipx install "commons-memory[http] @ git+https://github.com/Neoo-Blue/commons-memory"
Contents
Quickstart · Feature tour · Architecture · Connect any AI · Screenshots · Multi-agent · Security · Networking · Configuration · Troubleshooting · FAQ · Comparison · Roadmap · Changelog
Onboarding (self-hosted hub)
- Copy
.env.exampleto.env, create the three secret files it lists (dashboard token, Postgres password, vault key), thendocker compose up -d(docker-compose.yml; the comments explain every option). - Open the dashboard and go to Setup. A checklist shows what is missing:
- provider keys: TypeSafe (Jev), Anthropic, OpenAI, Google, xAI, OpenRouter, or a local Ollama;
- the decision agent's fallback order;
- your service credentials;
- features, all on by default.
Keys are stored encrypted in the vault and never shown again.
- Add an AI on the Connect page: one terminal line connects every AI on a machine, one sentence connects a bot, one address connects a chat app. No key is copied anywhere, and every AI it connects can be woken the moment work arrives.
- Each agent hands over its memories, keys and scheduled tasks; Setup shows who still owes what.
2-minute quickstart
# 1. install (above) — this also runs `commons init` (~/.commons/commons.db)2. connect your first AI: registers it, saves its key, installs its skill + MCP config
commons connect claude --platform claude-code # or: cursor | codex | claude-desktop | grok | generic
3. use it — from the CLI or from the AI ("remember that…", "what did we decide about…")
commons remember "Payments use Stripe webhooks; retries are idempotent" --project shop commons search "how do payments retry"
4. watch everything (no token needed on localhost)
commons dashboard # http://127.0.0.1:8766
Connect a second AI the same way and it can see the first one's memory immediately. Each client lists only the 32 core tools (6.1k tokens, against 40.4k for all 186); `tools(query)` finds the other 154 and `call(tool, args)` runs them. For other AIs (ChatGPT, Grok, Muse…), paste [the universal prompt](#connect-any-ai).Feature tour
| Area | What you get | Learn more |
|---|---|---|
| Infrastructure map | One layered map of where everything runs and how it connects: machines, LXCs and VMs, containers, services, URLs and ports, tunnels, storage and bind mounts, repos, systems (e.g. Plex: its parts and the guest each runs on) and the vault credential each uses (name only). Built from a read-only collector (commons infra collect --submit, or commons infra install to run it on a schedule) and from your memories (a small model extracts stated facts, each citing its notes), merged by vmid, IP and URL. Zoomable map, inventory and drawers on the dashboard; agents ask infra("plex") | docs/guide.md |
| Memory & knowledge | Hybrid search (BM25 + local fastembed vectors, entity/time/recency signals, optional rerank) with token budgets · context=N returns the turns around each short hit (a reply needs the turn it answers; a long turn stands alone) and relative dates resolve against each memory's own date ("yesterday (7 May 2023)") · free-text contradiction flags (TypeSafe) · auto-capture hooks for Claude Code/Codex/Gemini/Cursor that keep every message and reply verbatim plus typed observations, and a turn hook for chat apps without hooks (docs/hooks.md) · one graph linking memory, code, sessions, agents · code index (Python, JS/TS, Go, Rust, Java, Ruby, C, C++, C#) plus live definitions, references and types from each language's own server (code_lookup, LSP) · Graphiti adapter (mirror memories into a temporal knowledge graph, bring its facts back) · conflict detection · auto-built, cited knowledge base per project · librarian that tidies and answers find() | docs/tools.md |
| Project board | Every request you make, in your words, with the targets (metric, goal, current), expectations, actions and deliveries under it, layer by layer (project / ui/dashboard). Done needs evidence, dropped needs a reason, and asking twice is counted. It leads every agent's check-in, so nothing you asked for is lost to context compaction or an agent switch. Agents' questions for you wait under "Needs you" and your answer goes straight back to them | docs/tools.md |
| No duplicate work | start_work checks every task against what other agents are doing now and what was finished, so two agents never build the same thing and nobody redoes finished work; hooks run the same check on every message. Each agent gets the Commons routine on its first check-in, so you never have to explain it | PROMPT.md |
| Credential vault | Commons stores your API keys and logins encrypted and makes calls for agents, so no agent ever sees a key, and a key only ever goes to its own service. A decision agent rules on each request: sound reads are allowed, writes need your request behind them, anything destructive or costly comes to you as an approval link the agent hands you: one page showing the exact request, approved with your owner password (agents can't), and approving runs it. Or switch on "approve everything" at your own risk, or always allow one key. It is an order you choose (TypeSafe, any provider you have a key for, a live connected agent on its own subscription, then you), and a phone notification with Yes / No buttons lets you answer anywhere. Agents creating accounts get a generated username and password from the vault · an agent deposits a key the moment it gets one; the base URLs it may go to can follow, and until then it goes nowhere · each key records what it is for, what it can do at the provider, and every place it is installed or used, linked on the infrastructure map, so it can be rotated or narrowed without guessing | CHANGELOG.md |
| Team awareness | A live Team now view (who is working, blocked, done or waiting on you) · a card per agent that the hub writes from what it can check (who issued its key and when, where it runs, how it is woken, its usage headroom, its schedules, the work it finished), kept apart from what the agent claims, with who(name) to read it · agents meet a newcomer at their next check-in instead of a broadcast that would wake and bill every agent, and a newcomer gets the whole team at its first · display names only you set, so grok reads as "Grok bot" while messages still reach it, and no agent can name itself after another · introductions you ask for: one note to each, nobody else woken · every release tells all agents what changed and what to adapt · handoffs carry a commit Commons checks on GitHub · failed CI runs, new issues and new PRs (GitHub watcher or webhook) become jobs for the best live agent | CHANGELOG.md |
| Continuity | Sessions, checkpoints, handoffs that resume in another AI · layered context packs (L0 gist → L3 full) so the next AI doesn't re-pay tokens · decisions with rationale and why() · leftovers and recurring jobs | docs/guide.md |
| Teamwork | Submit a task and it is split for the team: a model plans it (Haiku by default), else a live agent does, else keyword rules, sized to who is live, what each agent is good at and its usage left. Big hand-offs are planned too, and an agent can split a subtask it holds and merge the parts. Six-stage workflow (submit_task(pattern="sdlc") or /sdlc, or one stage with /spec /plan /build /test /review /ship): you approve the spec and confirm the release from the board or your phone, stage leads hand out task cards up to 3 levels deep, each card goes to a cheap, mid or top model and effort by difficulty x criticality (on the agent's own CLI), and the Workflow page shows the stage spine, the agent tree, traceability and tokens saved. Patterns: plan → parallel parts each gated by a review → summary; work → cross-review (author ≠ reviewer); debate → judge; consensus vote. Every job has a shared workspace: each part's result, the code it changed (diff, commit checked on GitHub, also captured by the hooks) and any files, on one timeline with an agent filter. Pin a step to a named agent (retries stay with it), quality gates, budgets, leases with automatic reassignment, an escalation ladder, routing to the right agent + model + effort by strengths, past success, speed, price and usage left, multi-project scheduling, task breakdown view | docs/guide.md, docs/multitasking.md |
| Limits & schedules | Each agent's 5-hour a
Only the beginning is shown; read the rest in the repo