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KL4A v0.0.1

This is the initial public release of KL4A.

It is being published for the first time as an open-source project, and this document is written for people encountering it that way — not as changelog entry #13 of an internal tool. Everything below has been built and exercised against an internal milestone test suite ahead of this release, so the project is not starting from a blank slate. But this is the first time it is being handed to anyone outside the team that built it.

If you're new here: KL4A is a local-first tool for turning enterprise SOPs, policies, and procedures into structured, reviewable "SOP Knowledge Bundles" — plain files on disk, readable by humans, git- friendly, and consumable by AI agents — with a human-in-the-loop review step between "the model proposed this" and "this is trusted knowledge."

What's in v0.0.1

Bundle lifecycle. sopkb-cli init creates a bundle with a manifest and the standard source/section/concept/knowledge directory layout.

sopkb-cli scan inventories markdown, DOCX, and PDF source documents with stable, checksum-backed source ids, and cleans up entries for files that have since disappeared.

sopkb-cli normalize splits normalized source text into sections. sopkb-cli validate checks manifest and bundle integrity.

Human-in-the-loop review. sopkb-cli review supports approving, rejecting, deferring, commenting on, and editing mined knowledge items.

Every action is recorded as a review event with a reviewer, a rationale, and a before/after diff when something is edited.

Once an item is approved or rejected, it's terminal — no further mutating actions apply to it — and edits are restricted to an allow-listed set of fields.

Review state shows up both in validation reports and as has_review edges in graph exports, so "what got approved and by whom" is never just implicit in a UI.

A local web workbench. Beyond the CLI, sopkb ships a local HTTP app covering:

  • sources
  • ingest
  • a document viewer
  • knowledge browsing
  • concepts
  • review
  • an agent-chat surface
  • reports
  • a graph view
  • export

— all scoped to a bundle.

Ingest works from either a source folder or direct upload. A multi-bundle index page lists everything under a workbench root. Nothing here requires a hosted backend.

A real reference bundle, not a toy. examples/glp1-healthcare is an end-to-end, buildable bundle that runs the full pipeline:

  • scan
  • normalize
  • mine
  • review (with approvals, rejections, deferrals, and edits)
  • validate
  • export to graph JSON and RDF

— over its markdown sources, including freshness metadata and conflict reporting. (DOCX and PDF ingestion is supported by scan/normalize; this checked-in bundle just doesn't include binary sources.)

It's meant to be a credible example of what a finished bundle looks like, not a synthetic demo.

An MCP server. sopkb-mcp exposes a read-only-by-default Model Context Protocol tool surface:

  • bundle.describe
  • knowledge.search
  • evidence.get
  • agent.context
  • relations.search
  • agent.guide

— plus a JSON-RPC tools/call handler.

Mutating tools like review.note stay disabled unless a host explicitly turns them on. Calling an unknown tool returns a proper JSON-RPC error rather than failing silently.

This is the project's most direct answer to "how does an AI agent actually use one of these bundles" without requiring a custom integration per agent framework.

OKF-formatted export and agent consumption. Exported bundles follow an Open Knowledge Format (OKF) directory layout:

  • sources
  • sections
  • concepts
  • knowledge
  • relations
  • rules
  • evidence
  • tasks
  • references

— with cross-linked frontmatter and a dedicated agent guide document.

CLI commands (sopkb-cli agent tasks, sopkb-cli agent context, sopkb-cli relations search, sopkb-cli relations neighborhood) and the sopkb-agent crate let a downstream agent pull task-scoped knowledge, decision rules, evidence, and relation traversals, including free-text matching from a scenario description to relevant concepts.

A worked set of queries (examples/glp1-healthcare/agent_queries.md, with captured output in sample_agent_query_results.json) shows this against the reference bundle.

Milestone numbering

For anyone who goes looking at the test suite or internal history: M4, M6, M7, and M8 were intentionally skipped in the internal roadmap. There's no missing or lost work behind those numbers — they were simply never used.

What's explicitly out of scope right now

This is a workbench for building SOP knowledge bundles, not a governed enterprise knowledgebase, and v0.0.1 does not try to be one.

Not included in v0.0.1

Specifically, this release does not provide:

  • enterprise role-based access control (RBAC),
  • tenant isolation,
  • production decision trace or interaction trace,
  • enterprise lifecycle workflows,
  • governed publication into a live knowledgebase,
  • hosted multi-tenant deployment,
  • production compliance approval workflows,
  • enterprise API access to governed knowledge,
  • proprietary graph-layer mapping into any specific enterprise product.

Those capabilities are intentionally left to a separate governed enterprise runtime that can import bundles produced here — this project's job stops at producing a well-reviewed, well-evidenced, portable bundle.

A caveat on the bundle format

Bundle format is still draft

The SOP Knowledge Bundle / OKF-based format that this tool produces is currently at spec version 0.2.0 and marked draft (see docs/OKF_BUNDLE_SPEC.md). It is versioned independently from the sopkb software itself — see docs/BUNDLE_COMPATIBILITY_POLICY.md for how that works — precisely because we expect the bundle schema to keep evolving before it reaches a 1.0 bundle-format release. If you build bundles against v0.0.1, expect that a future bundle-format change could require a documented migration step. We will call out any breaking bundle-format change explicitly in that release's notes, with a migration note, rather than letting it happen silently under a routine software version bump.

Where to discuss this release

Questions, feedback, and "here's how we're using it" reports are all welcome in GitHub Discussions on this repository.

If you're active in the knowledge-graph/OKF community, the LLM-agent ecosystem, or the MCP ecosystem, we'd appreciate a pointer to this release — the built-in MCP server, in particular, is a genuine differentiator worth a look if you're building agents that need governed, evidence-linked SOP knowledge rather than raw RAG over documents.