Quipu

Name: Quipu
Author: scbrown

by scbrownversion 0.15.0i★ –iRuns on your computer

Registry nameio.github.scbrown/quipu

Install

Overview

Description from the maker (original English)

Local knowledge graph memory for agents. SHACL rules refuse bad facts; SPARQL and time travel.

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).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "quipu": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/scbrown/quipu:0.15.0"
      ]
    }
  }
}

Run this in your terminal

claude mcp add quipu -- docker run -i --rm ghcr.io/scbrown/quipu:0.15.0

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "quipu": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/scbrown/quipu:0.15.0"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "quipu": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/scbrown/quipu:0.15.0"
      ]
    }
  }
}

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.

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 scbrown, 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 7270 servers in this category
Published to the registry more recently than 69% of the other 7270 servers in this category
Basic checks equal to or better than 100% of the other 7270 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 Quipu 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 (0.15.0) reached the registry 17 days ago.

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

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 yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 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.

Quipu Governed knowledge your agents can read, reuse and share

Read the book →

Quipu is a governed, bitemporal knowledge store for AI agents and the people who run them. Use it from the command line, REST, MCP or a Rust application.

Why you would want it

Governed: check incoming knowledge against your vocabulary and shapes.
Bitemporal: keep a queryable history of facts and decisions.
Reusable agent knowledge: store memory agents can read, reuse and share across tools.
Time-aware entities: attach event-triggering time attributes for Chaski to react to.

Use Quipu as the central graph and base for many tools: see the stack. The full case is in Why Quipu.

Install

curl -fsSL https://raw.githubusercontent.com/intigraph/quipu/main/install.sh | sh

Linux and macOS, x86_64 and arm64; requires Python 3.9+ and curl. The installer verifies the published archive checksum and executable version. Then run quipu --version. Install, upgrade or remove Quipu. For the whole stack, use Tambo's guided onboarding.

First success: insert a fact, watch the graph change

Write one fact in plain Turtle, knot it into a local store, and ask for it back:

printf '@prefix : <urn:demo:> .\n:Ada :studies :Engine .\n' > ada.ttl

quipu knot ada.ttl --db demo.db quipu read 'SELECT ?who ?what WHERE { ?who ?what }' --db demo.db

Add a second fact and the graph grows. The first version is still there to query:

printf '@prefix : <urn:demo:> .\n:Babbage :designed :Engine .\n' > babbage.ttl

quipu knot babbage.ttl --db demo.db quipu read 'SELECT ?s ?p ?o WHERE { ?s ?p ?o }' --db demo.db quipu read 'SELECT ?s ?p ?o WHERE { ?s ?p ?o }' --tx 1 --db demo.db

After the first fact After the second fact

Transcript, tool version and reproducible capture.

On your own code

Keep project facts, decisions and relationships together instead of pasting the same context into each agent.

You want to	Start here
Load facts and apply your vocabulary	Quick Start
Ask about current or historical knowledge	SPARQL and time
Share a governed knowledge bundle	Verified sharing
Prepare domain knowledge and shapes	Camayoc

The latest release includes a repository pendant, checksum and provenance. Verify them together, then use quipu import SHARE_DIR --db repository.db; import stages knowledge and leaves ROOT unchanged. Follow the verified import workflow.

Wire it into your agent

For Claude Code, connect the local MCP server to your database:

claude mcp add quipu -- quipu mcp --db /path/to/knowledge.db

The MCP Registry identifier is mcp-name: io.github.intigraph/quipu. Other clients, HTTP MCP and credential configuration: Agent Setup. Use Tambo to configure and verify the tools together.

Before you start

The CLI runs locally; quipu-server supplies the network API and explorer.
Fresh stores support keyword search without downloading a model. quipu search "your terms" uses keyword search until an embedding provider is configured; explicit --mode semantic still requires a model and vectors. Install the pinned default model with quipu model install in a full build. Check readiness with quipu doctor search.
Configuration covers storage and server settings.

What's next

The Quipu book: tutorials, concepts and references.
Quick Start: governed local examples.
Domain shapes: producer-owned vocabulary.
Stack README format and shared hero theme.

🧺 The stack

Tambo installs these together and proves each one works; every tool also stands alone. Use each tool's README for its own installation and first success.

The Quipu stack is built around a governed, bitemporal knowledge store: shared facts, decisions and work history your agents can query. Quipu can check writes against your rules, records where each fact came from, and answers what was true when.

tool	what it gives your agents
tambo	🧰 Agent Platform Toolkit - installs and configures the quipu stack
quipu	stores shared facts with provenance and history; can check writes against your rules
camayoc	prepares knowledge and vocabulary for Quipu, with checks before it is admitted
quechua	shared vocabulary and shapes for interoperable knowledge
chaski	reacts to graph changes and delivers governed notifications
bobbin	indexes your codebase and injects context from that index and Quipu knowledge
yupana	shows code impact and can read policies from Quipu to govern agent actions
desire-path	turns agent mistakes into feature requests; works on its own without Quipu
seeds	a work item tracker backed by the Quipu knowledge base
shantytown	runs crews of AI agents on work items, triggered by hand, on a schedule or by events; can share work and memory through Quipu
rikuy	live brief: status, graphs, watches and signed decisions over Quipu
shuttle	runs signed agent workflows and keeps their records in Quipu

Contributing

See CONTRIBUTING.md. The repository's checks are:

just check

just docs check

📜 License

Apache-2.0.

Quipu

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Why you would want it

Install

First success: insert a fact, watch the graph change

On your own code

Wire it into your agent

Before you start

What's next

🧺 The stack

Contributing

📜 License