Quipu

io.github.scbrown由 GitHub 帳號 scbrown 發布v0.15.0更新於 Oct 24, 2026

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

概覽

AI 產生的概覽

Quipu 是給 AI 代理使用的本機受治理知識圖譜記憶,讓助理儲存附來源的事實,並查詢目前或歷史狀態。

可以做什麼
  • 把以 Turtle 撰寫的事實寫入本機圖形資料庫,寫入時依你的詞彙與形狀進行檢核
  • 以 SPARQL 查詢圖譜,並可查詢較早版本的資料以回答時間回溯問題
  • 對已存知識做關鍵字檢索,設定嵌入模型後可做語意檢索
  • 匯入經驗證的知識包,先暫存新事實而不更動現有根資料
  • 在多個代理與工具之間共用專案事實、決策與關係,不必重複貼上相同脈絡
適用情境
適合希望助理把持久、可稽核的專案知識(事實、決策及其歷史)集中保存在單一本機儲存中,供多個代理和工具讀取重用的情境,而不是每次工作階段都重複貼上同樣的脈絡。
執行需求
以 stdio 本機程序方式執行,啟動時指定資料庫路徑,可透過 Docker 映像或命令列工具啟動。命令列工具支援 Linux 與 macOS 的 x86_64 與 arm64,安裝需要 Python 3.9+ 與 curl。未宣告任何環境變數、標頭或身分驗證。語意檢索需安裝嵌入模型;全新儲存可直接使用關鍵字檢索。
安裝前請注意
伺服器會讀寫本機資料庫檔案,請指向預期的儲存並做好備份。匯入與寫入會改變已存知識,儘管匯入被描述為先暫存而不更動現有根資料。語意檢索需要下載模型。README 的安裝步驟把遠端腳本直接交給 shell 執行,執行前應先審閱。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Quipu,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

[Quipu logo: knotted strings forming a knowledge graph]

Quipu

Governed knowledge your agents can read, reuse and share

[License: Apache-2.0] [CI] [crates.io: quipu-ai] [Docs: the Quipu book] [DOI: 10.5281/zenodo.21878428] [arXiv: 2608.16813] [Part of the Tambo stack]

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.

[Animated: one Quipu store connects the CLI, MCP clients, HTTP server, Rust library, offline use and WebAssembly]

Install

sh
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:

sh
printf '@prefix : <urn:demo:> .\n:Ada :studies :Engine .\n' > ada.ttlquipu knot ada.ttl --db demo.dbquipu read 'SELECT ?who ?what WHERE { ?who <urn:demo:studies> ?what }' --db demo.db

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

sh
printf '@prefix : <urn:demo:> .\n:Babbage :designed :Engine .\n' > babbage.ttlquipu knot babbage.ttl --db demo.dbquipu read 'SELECT ?s ?p ?o WHERE { ?s ?p ?o }' --db demo.dbquipu read 'SELECT ?s ?p ?o WHERE { ?s ?p ?o }' --tx 1 --db demo.db

[Animated terminal: two plain facts are inserted and queried, then the earlier version is read back]

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 toStart here
Load facts and apply your vocabularyQuick Start
Ask about current or historical knowledgeSPARQL and time
Share a governed knowledge bundleVerified sharing
Prepare domain knowledge and shapesCamayoc

[Animated: export a share, verify it in quarantine, then explicitly promote admitted knowledge; conflicts are not silently merged]

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:

sh
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 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.

toolwhat it gives your agents
tambo🧰 Agent Platform Toolkit - installs and configures the quipu stack
quipustores shared facts with provenance and history; can check writes against your rules
camayocprepares knowledge and vocabulary for Quipu, with checks before it is admitted
quechuashared vocabulary and shapes for interoperable knowledge
chaskireacts to graph changes and delivers governed notifications
bobbinindexes your codebase and injects context from that index and Quipu knowledge
yupanashows code impact and can read policies from Quipu to govern agent actions
desire-pathturns agent mistakes into feature requests; works on its own without Quipu
seedsa work item tracker backed by the Quipu knowledge base
shantytownruns crews of AI agents on work items, triggered by hand, on a schedule or by events; can share work and memory through Quipu
rikuylive brief: status, graphs, watches and signed decisions over Quipu
shuttleruns signed agent workflows and keeps their records in Quipu

Contributing

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

sh
just checkjust docs check

📜 License

Apache-2.0.

來源:README.md,提交 2289024

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

10
  1. v0.15.0最新Oct 20, 2026
  2. v0.14.1Oct 15, 2026
  3. v0.14.0Oct 15, 2026
  4. v0.13.0Oct 14, 2026
  5. v0.12.0Oct 10, 2026
  6. v0.11.1Oct 8, 2026
  7. v0.11.0Oct 3, 2026
  8. v0.10.0Oct 2, 2026
  9. v0.9.1Sep 30, 2026
  10. v0.9.0Sep 26, 2026

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