Knowledge

Follow a claim back to the evidence.

Watch the 70-second film: a claim that lost its source, and how the tool puts the quote and the date back on it. The example in it is fictional.

For people connecting notes, documents and research. Knowledge turns supplied material into an inspectable graph, with sources and time attached, so you can trace relationships, question an apparent contradiction and see what changed.

Explore the site

What you get

• Keep the source within reach. Carry provenance with the claim instead of relying on an answer with no path back to its inputs.
• Explore a specific question. Use paths, evidence, gaps and timelines to navigate relationships rather than rereading an entire collection.
• Keep a history of changing claims. Close a relation’s validity window and append its replacement instead of erasing the earlier view.

Start here

git clone https://github.com/michaeljabbour/amplifier-capability-knowledge.git
cd amplifier-capability-knowledge
python3 scripts/capability.py install --dest ~/.agents/skills

This installs the portable method and its packaged references into your agent skill folder. If your host uses another folder, pass that path to --dest. Start a new session or use your host’s skill refresh so it can discover the installed skill. Optional native tools are installed separately.

Try this first task:

Use Knowledge to show how two fictional project notes support or challenge the same claim. Start with deterministic ingestion or structured additions. Trace each relationship to its source and distinguish an asserted fact from a candidate contradiction. Do not configure an external provider.

What you need

• Python 3.11 or later for the portable install helper.
• The deterministic library and CLI use Python and SQLite without a model key.
• Native package and command identity stay knowledge; optional model-backed operations need a separately configured route.
• Choose KNOWLEDGE_HOME explicitly when you want a disposable test graph.

For the optional native interface, use a project virtual environment:

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install .

Evidence and limits

Native graph, storage, analysis and interface tests passed. A passing graph operation does not establish that an extracted claim or model-written answer is correct.

The worked examples on the site are fictional. They illustrate the output and decisions; they are not user results or measured benefit. See consolidation and runtime boundaries for retained interfaces and acceptance scope.

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Retained source guide

knowledge

Ask your documents a question. See the answer as a map.

Point it at papers, specs, policies, notes, code or agent sessions. You get a graph any agent can query and add to: concepts and typed relations, each with the passage it came from, the agent that added it, a confidence and the dates it held. Ask in plain words and it answers in the form that fits: a timeline of what changed and what replaced it, an argument with the quotes for and against a claim, the steps between two ideas, a matrix of what is missing, a rhythm of when each topic was written about, or the map. When a question is unclear, it asks you instead of guessing.

It is an Amplifier Smart Tool: a Python library with a thin CLI and an optional MCP server, usable from Claude Code, Codex, Amplifier, a script or any agent. Everything except five model-backed commands runs with no model and no key.

Product page and demos · One-minute trailer · How it works

Four Claude Code agents reading Amplifier's public docs and writing to one graph at the same time, recorded live with knowledge serve (sped up).

Quick start (no model, no key)

uv tool install "amplifier-smart-tool-visualizer @ git+https://github.com/michaeljabbour/amplifier-capability-knowledge"
knowledge doctor

knowledge ingest ./notes --method cooccurrence --graph notes   # a word network, no model
knowledge analyze --graph notes                                # central ideas, bridges, topics, gaps
knowledge visualize --for "what are the main topics?" --graph notes --open   # opens on the answer

With a model, knowledge ingest ./notes extracts typed concepts and relations instead (Anthropic, OpenAI, a local Ollama model, or your own program through --complete-cmd). Inside an agent, the agent is the model: ingest --method agent hands it the chunks and the extraction prompt, and it writes back with knowledge add. No key is billed.

Examples you can open now

Real public material, each read into a graph by an agent through the tool's own agent route, each view opening on a question someone in that job would actually ask. Every relation links to its quote.

This is the beginning of the README. Read the rest at the original. Read the full README ↗