knowledge-worker 0.6.2

Provenance-backed personal knowledge graph for local AI workflows

knowledge-worker

A personal knowledge graph that survives between AI conversations.
User-centered (not conversation-centered). Provenance-or-bust. Built on boring infrastructure.

Your AI is only as smart as what it remembers about you.

knowledge-worker is a local-first personal knowledge graph for carrying context across AI sessions. It turns notes into reviewable concepts, decisions, goals, and relationships, keeps source excerpts attached, and exports compact context you can paste into Claude, GPT, Ollama, or any other LLM workflow.

Your private graph stays on your machine, enabling you to preserve the thread of your own reasoning across AI sessions.

Why

AI conversations usually start from zero. You clarify a decision, name a constraint, sketch a goal, and then the next session forgets it. RAG can be heavy, full-note prompts are noisy, and most note apps do not plug cleanly into chat workflows.

Stop dumping context. Build memory. knowledge-worker turns chats, notes, decisions, and sources into a local provenance-backed knowledge graph, then uses graph analytics to show what matters, what connects, what is weak, and what context an AI should see.

knowledge-worker keeps the useful parts: cited claims, explicit relationships, human review, and a small context snapshot when you need continuity.

How It Compares

knowledge-worker is personal AI memory with source-backed claims, not a team chat-to-wiki system. It keeps reasoning local, reviewable, and tied to literal provenance excerpts before claims become durable graph knowledge.

See Competitive Analysis for the category matrix and Benchmarks for the offline demo-graph checks.

What It Does

• Ingests markdown notes into candidate graph nodes and edges.
• Requires provenance excerpts before claims become durable memory.
• Lets you review, accept, reject, or edit LLM proposals before merge.
• Searches by term, lists nodes by type, and finds paths between ideas.
• Exports an LLM-ready context snapshot for a fresh chat session.
• Audits memory shape with PageRank, betweenness, k-core, communities, weak claims, and provenance coverage.
• Generates an offline HTML graph viewer for exploration and demos.

Design Principles

Provenance first. Every durable claim points back to a source document and literal excerpt.

Local first. The graph is a file on your machine. No cloud sync, accounts, or telemetry.

Review before merge. The LLM proposes. You decide. Deterministic validation runs before anything enters the graph.

Boring persistence. Plain JSON until it becomes the limiting factor. The schema stays stable across storage backends.

Quick Start

Requirements: Python 3.10+ on macOS, Linux, or Windows.

Install from PyPI

The core CLI has no runtime dependencies beyond the standard library. Optional extras pull in LLM backends and RDF export only when you need them:

python -m pip install knowledge-worker               # core CLI, stdlib only (mykg / mygraph)
python -m pip install "knowledge-worker[rdf]"        # + Turtle/RDF export (rdflib)
python -m pip install "knowledge-worker[anthropic]"  # + Claude-backed ingest
python -m pip install "knowledge-worker[openai]"     # + OpenAI-backed ingest
python -m pip install "knowledge-worker[ollama]"     # + local Ollama ingest
python -m pip install "knowledge-worker[all]"        # all ingest backends + RDF

Verify the install (no clone needed — seed generates its own demo graph):

mykg --help
MYGRAPH_PATH=/tmp/knowledge-worker-demo.json mykg seed
MYGRAPH_PATH=/tmp/knowledge-worker-demo.json mykg summary

Using a virtual environment avoids Homebrew/system Python's externally-managed install errors:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install knowledge-worker

Run from a clone (no install)

The core demo CLI uses only the standard library, so you can run it straight from a checkout without installing anything:

git clone https://github.com/rahulmranga/knowledge-worker
cd knowledge-worker

# Run the public demo graph, no API key needed
MYGRAPH_PATH=examples/demo_graph.json python3 mygraph/mygraph.py summary
MYGRAPH_PATH=examples/demo_graph.json python3 mygraph/mygraph.py query "provenance"

# Generate an LLM-ready context snapshot
MYGRAPH_PATH=examples/demo_graph.json python3 mygraph/mygraph.py context

# Audit memory structure and proof coverage
MYGRAPH_PATH=examples/demo_graph.json python3 mygraph/mygraph.py audit --out /tmp/analytics.json --html /tmp/memory_audit.html

# Visualize the graph as a self-contained HTML file
python3 mygraph/mygraph.py viz --graph examples/demo_graph.json --out /tmp/demo.html

For the shorter mykg command from a clone, install it editable inside a virtual environment:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e .
MYGRAPH_PATH=examples/demo_graph.json mykg query provenance

On Windows PowerShell:

py -3 -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install knowledge-worker

$env:MYGRAPH_PATH = "$env:TEMP\knowledge-worker-demo.json"
mykg seed
mykg summary

From a clone, install editable instead:

python -m pip install -e .

$env:MYGRAPH_PATH = "examples\demo_graph.json"
mykg query provenance
mykg audit --out "$env:TEMP\analytics.json" --html "$env:TEMP\memory_audit.html"

If PowerShell blocks activation scripts, run this for the current terminal session and activate again:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Commands

Command	What it does
seed	Populate a fictional demo graph
summary	Show node and edge counts by type
query <term>	Search nodes, neighbors, and provenance
list <type>	List nodes of a given type
path <a> <b>	Find the shortest path between two nodes
ingest <file.md>	Extract, validate, review, merge, and eval candidates
check --provenance	Flag nodes with missing source citations
export --ttl	Emit Turtle/RDF
context	Print a compact LLM-ready context snapshot
viz	Generate an offline single-file HTML viewer
audit	Emit graph analytics, directed idea-flow queues, and optional Memory Audit HTML
discover	Propose derived edges and second-order insights (read-only, promotion queue)
state "<entry>"	Append a mood/state sidecar entry
dump	Print the raw graph JSON
reset	Delete the active graph file

Use Your Own Notes

You can ingest your notes with or without an API key.

Claude or Codex App, No API Key

If you are already working with Claude, Codex, or ChatGPT in an app session, you do not need an API key. Ask the assistant to produce a *.candidates.json file that follows the schema in mygraph/extractor.py, then let the local CLI validate, review, and merge it. In Claude Code, the bundled /ingest-notes skill runs this flow for you:

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

mykg ingest path/to/your/notes.md --candidates-file path/to/your/notes.candidates.json

The app subscription helps you create the candidates file. The repo still keeps graph validation and merge local.

Automated API-Backed Ingest

If you want the CLI to call an LLM directly, use a provider API key or local Ollama.

For Anthropic API:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[anthropic]"
export ANTHROPIC_API_KEY=...

mykg ingest path/to/your/notes.md

The Claude backend also auto-detects Anthropic-compatible provider env:

• Anthropic API: ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN
• Foundry: ANTHROPIC_FOUNDRY_API_KEY plus ANTHROPIC_FOUNDRY_RESOURCE or ANTHROPIC_FOUNDRY_BASE_URL
• Bedrock: AWS_BEARER_TOKEN_BEDROCK, or AWS credentials plus AWS_REGION/AWS_DEFAULT_REGION

For OpenAI API:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[openai]"
export OPENAI_API_KEY=...

mykg ingest path/to/your/notes.md --backend openai --model gpt-5.2

Graph Workflow

The public repo ships code, docs, and a fictional demo graph. Your real graph should live outside the repo or in the ignored default path, then be loaded explicitly:

MYGRAPH_PATH=~/my-private-graph/mygraph.json mykg summary
MYGRAPH_PATH=~/my-private-graph/mygraph.json mykg query "architecture"
MYGRAPH_PATH=~/my-private-graph/mygraph.json mykg context

Your private mygraph.json, generated private viewers, TTL exports, eval logs, state logs, and local env files are ignored by default.

Memory Audit

mykg audit is a read-only layer over the graph. It ranks important concepts with PageRank, bridge ideas with betweenness, structural strength with k-core, communities with deterministic graph splitting, and weak claims from confidence and provenance gaps. It also includes directed idea-flow queues: idea_attractors for concepts that many edges point into, idea_generators for ideas that branch outward, and a weak_claim_queue that asks for human review actions instead of auto-promoting conclusions.

MYGRAPH_PATH=examples/demo_graph.json mykg audit \
  --out /tmp/analytics.json \
  --html /tmp/memory_audit.html

The generated HTML puts ranked panels and legwork queues first, with the graph canvas second. This keeps the feature focused on memory governance instead of making the raw graph view the product.

Discovery Layer

Where audit ranks what the graph already says, mykg discover infers what it implies but does not yet say — and turns every inference into a reviewable proposal:

• Staleness radar: important nodes whose evidence trail has gone cold, scored by importance × days since the graph last touched them.
• Co-mention candidates: pairs that recur together across multiple sources but were never linked (CO_MENTIONED_WITH).
• Goal-alignment candidates: ideas and decisions structurally entangled with a goal they have no contribution path to (SERVES_CANDIDATE).
• Link prediction: Adamic-Adar over the semantic graph (RELATES_TO).
• Question debt: open questions ranked by age, centrality, and missing evidence; answered questions are detected via decision ABOUT edges.
• Corroboration: claims that hang on a single source (SINGLE_SOURCE).
• Bridge finder: cross-community connectors that remain after removing dominant hub "spines" that mask real bridges (BRIDGES).
• Tension detector: claims that are both supported and challenged, and goal contributions that inherit a challenge to the goal (TENSION_WITH).

MYGRAPH_PATH=examples/demo_graph.json mykg discover \
  --out /tmp/discovery.json \
  --candidates /tmp/discovery.candidates.json

Discover never mutates the graph. Derived edges land in a candidates file — a promotion queue for human review. AI proposes, provenance verifies, the owner promotes. Committed sample output: examples/demo_discovery.json.

Local LLM Support

The ollama_proxy/ package adds three local-model surfaces:

• server.py: MCP wrapper for Claude/Cowork-style tool use.
• proxy.py: Ollama-compatible logging passthrough for HTTP clients.
• extractor_adapter.py: drop-in extraction backend for mykg ingest --backend ollama.

See ollama_proxy/README.md for setup.

Repository Layout

mygraph/          Core CLI and pipeline modules
examples/         Fictional demo graph, TTL, and HTML viewer
docs/             Roadmap and public assets
ollama_proxy/     Adapter, MCP server, and proxy for local Ollama workflows
tests/            CLI smoke tests
SPEC.md           Graph model specification
DESIGN.md         Pipeline design notes

Contributing

See CONTRIBUTING.md. The core graph model is intentionally minimal; contributions that preserve that shape are preferred.

License

MIT