okl — Observed Knowledge Ledger

Keeps your coding agent true to your rules, specs and docs. Keeps them true to the code. Keeps learning as you build.
It guards against drift both ways: your agent drifting from your rules, and your rules, specs and docs drifting from the code.
Your rules, architecture decisions, past defects, documentation and diagrams: recorded once, briefed to your agent before every task (by a hook in Claude Code; any MCP agent can ask for the same briefing), each proven by a check you choose, flagged before you push (or in CI) when what it covers changes, and corrected or retired as you build. Prompts advise; the gate enforces. Use it in one repo, or share one store across every repo your team runs.
Under the hood: typed records, keyword search and one-step links. No model runs by default, okl calls no generative model unasked, and nothing is captured automatically. Three local models are optional, measured and off until you turn them on: an embedding arm, a reranker and a warm embedding model, okl warm (REPORT §4q, §4t, §4z). The scaffold's optional review step runs a generative model only if you name one, and okl review, which checks the lessons nothing re-checks, runs one only when you run it (REPORT §4ai). A lesson is written on purpose and proven by a check. The claim in full, how much of it holds today and how it has changed: docs/THESIS.md.

🚀 Quickstart · 📘 Getting started · 📥 Load your rules · 🔬 How it works · 📊 Results · 🧪 Eval report · ⚖️ How it is evaluated · 🧩 Plugin

pipx install 'observed-knowledge-ledger[mcp]'   # the command it installs is `okl`
okl init --repo my-repo                         # hooks, MCP server, push gate, starter lessons

42% → 7%. On eight tasks written to invite defects the store already knew about, 68 of 163 unbriefed attempts repeated one against 11 of 164 briefed, pooled over seven A/B runs with a different model judging than generating (the receipts, §5 finding 7). Small sample, one-shot code on bait tasks; what it does and does not show.

What goes wrong, and what catches it

One lesson all the way round, in okl's own output:

Quickstart

New here? docs/GETTING-STARTED.md walks through setup and the habit of adding lessons as you build a feature. Already have a CLAUDE.md, ADRs, standards or specs? Load your rules shows how to load them, prove the ones that matter, and keep them current. Starting from an empty folder? Set up a repository from scratch builds the CI, linters and tests okl's checks point at, for Python, .NET or TypeScript.

1. Install the CLI. Python 3.11+ (0.8.9 is the last release for 3.10), git for drift. macOS and Linux; Windows is untested (#85).¹

pipx install 'observed-knowledge-ledger[mcp]'

2. Wire your repo, from its root:

okl init --repo my-repo --dry-run   # lists every file it would write; writes nothing
okl init --repo my-repo             # config, Claude Code hooks, MCP server, pre-push drift gate, starter lessons

init detects your stack (*.csproj, package.json, pyproject.toml …, at the root or up to two folders down), sets the repo's interests from it, and fills the store with 20 starter lessons that hold on almost any codebase plus the bundled packs for your stack, so the first prompt is already briefed (--interests chooses your own subjects; --no-seed leaves the store empty). It wires Claude Code when the repo has a .claude/ directory or claude is on your PATH (--claude / --no-claude), installs a pre-push hook that blocks a push only when lessons have drifted (--no-git-hook skips it; --ci installs the GitHub workflow instead, --ci --git-hook both), and remembers both choices. Every path it writes, and what each is for: What okl init writes. With a branch rule that requires code-owner review, --owner @you makes each change to the thesis need your approval; okl doctor says whether GitHub enforces it, and the rule and the agent's own identity are yours to set up: Give your agent its own identity.

3. Add a rule of your own. The starter lessons are generic; what pays is what only your codebase knows. Tell your agent — "record an okl rule for this repo: order lookups are scoped to the signed-in customer; governs app/orders.py" — and it records the lesson with okl's okl_record MCP tool (in Claude Code, /record drafts it and asks you first). Underneath, that is one CLI call you can also run yourself:

okl record --type Rule --scope repo --id order-owner-scope \
  --title "Order lookups are scoped to the signed-in customer" \
  --symptom "an endpoint fetches an order by id with no owner filter" \
  --fix "filter by the caller's customer id in the query; return 404 on no match"

Then prove it: okl verify order-owner-scope --run "pytest -q tests/test_orders.py" --expect passed. Loading the rules, ADRs and specs you already wrote, and the bundled packs, is Seed it.

4. Check it works:

okl check --task "add an endpoint that returns an order for the logged-in user"
okl doctor                  # flags other memory tools, double wiring, where drift is gated, and what your interests hide

Which agent, which part

The hooks are Claude Code's; everything else is a command or an MCP tool and works anywhere. Setting up another agent: Getting started.

What a normal day looks like

• You prompt as usual. In Claude Code, the pre-task hook runs okl check on what you typed and puts the relevant lessons in the agent's context before it starts, and shows you one line — okl · briefed 9 lesson(s): … — so you can see it working (OKL_QUIET=1 hides it).
• When you learn something worth keeping — a decision, a convention the agent broke, a bug you fixed — say so and the agent records it (okl_record, or okl record), with the files it governs. In Claude Code the Stop hook also asks once, at the end of a session that changed files, what was learned, and names the lessons the session was briefed on and those whose files it changed, so the agent can propose correcting or retiring one (you approve).
• Using another agent? The briefing and recording are MCP tools (okl mcp); register them and add one line to your AGENTS.md: before each task call okl_check. The hooks that do this automatically are Claude Code's; see Getting started.
• When a lesson needs correcting, take its [id] from the briefing and change only what is wrong: okl update <id> --fix "..." (or the okl_update tool) keeps the rest, including its proof, unless you change its governed --files: then it needs verifying again, and okl reverify re-runs its stored check. okl show <id> prints the whole lesson, with how often its check has passed and failed: every run of okl verify and okl reverify is kept, so a check that caught a change leaves a record. Recording an existing id again is refused, because it used to replace the lesson and wipe its proof.
• When a lesson is wrong or replaced, retire it with a reason; nothing is deleted. okl retire <id> --reason "..." keeps a wrong one briefed as AVOID, so nobody restates it; --by <new-id> takes a replaced one out of briefings, which point to the new one; --obsolete is for a lesson whose subject is gone. A fixed defect is not retired: it keeps briefing against its return.
• Proving a lesson is true is a check you run, not a flag you set: okl verify <id> --run "pytest -q tests/test_orders.py" --expect "passed".
• When code a lesson governs changes, okl drift goes red until its check passes again, or the code is put back as it was when the check passed (a lesson recorded with --files is also red until its first okl verify). The briefing says so too: such a lesson is marked STALE (or UNVERIFIED, or UNPROVEN) with the file that changed, so the agent confirms it against the code instead of trusting it blindly. A lesson that governs no files here, and was never verified or carries a stamp with no okl verify run behind it, is marked UNCHECKED; drift has no verdict on it, so nothing else would say so. The agent treats it as advice, and one line under the briefing says how many there are. okl reverify re-runs each drifted lesson's stored check after you confirm. Drift notices that what a lesson watches changed; it does not find every break. Of 52 patches written to break the lessons it watched in this repo, a judge confirmed 36 as breaks, and drift caught 14 of those 36; most of the misses changed a file one step from the ones the lesson watches (REPORT §4af). So list every file a lesson's claim depends on, and read a green drift as "nothing it watches moved". With the GitHub workflow (okl init --ci), CI reads a committed snapshot, okl-drift.json, which okl verify creates the first time a lesson with --files is verified and keeps current after that: commit it after the code change it verifies. Until then CI warns "Drift not checked", which is expected. Without the workflow, okl verify writes no snapshot unless one is already committed.
• Headless runs (claude -p, scripts, CI agents) set OKL_DISABLED_HOOKS=encode, or the end-of-session question replaces the printed answer.

The problem it solves

A team, or an agent, fixes a subtle bug, learns why it happened, and writes a rule to prevent it. Weeks later, in a different file or a different repository, the same class of bug comes back, because the person or agent doing the new work never saw that rule. The knowledge existed; it was not in front of whoever needed it, at the moment they needed it. And when it was, it was often out of date: the code it described had moved on.

okl answers both. The relevant lessons are read automatically at the start of a task, not looked up if someone remembers to, and a lesson stays true or goes stale loudly: each is proven by a check you choose and re-opened when the code it covers changes.

This is a v0 starter, not production-hardened. It ships an end-to-end test suite (pytest -q shows the suite and its result in your environment). The core is stdlib-only with zero required dependencies.

What okl is

A store of your engineering lessons, and the machinery that keeps them true. Typed records (rules, architecture decisions, known defects, gates, tombstones, retractions) that are briefed into an agent's context before a task and go stale loudly when the code they describe moves on. Everything measured in evals/REPORT.md measures this.

It is worth being precise about what that store fills up with, because "lessons a codebase has learned" invites the picture of a bug database. In the 183-record corpus in seed/ it is mostly not that: 110 Rules, 20 Decisions and 7 Gates against 36 Defects — conventions the code follows and trade-offs already settled, not a ledger of things that broke. Count it yourself:

python3 -c "import json,glob,collections; c=collections.Counter(
  n['type'] for f in glob.glob('seed/*.json') for n in json.load(open(f))['nodes']); print(c)"

A second, optional thing ships in the package: okl scaffold, a starter kit for the in-repo discipline the store assumes (a lean rules file, mechanical gates, a review agent, an eval harness). It has never been measured; use it to get a new repo to the state where a store has something to attach to.

What okl is not for

okl holds lessons: what an agent would see or do (the symptom), what to do instead (the fix), why, and where it can be proven, the check. Most of a good agent setup is other things, and they work better elsewhere:

Everything with a symptom and a fix belongs here: a convention the code follows, a decision made on purpose, a defect class you have already paid for. The getting-started guide has the same split from the side of recording.

What it keeps from drifting, and how

Knowledge rots in a specific way: the code changes and everything written about the code silently stops being true. Each row below catches a different version of that, and it is worth knowing which one catches what, because they do not overlap.

Three honest limits on that table:

• Diagram content is still a human job. check-diagram-pairs.sh proves the rendered image exists; nothing proves it matches the source it was exported from, or that either matches the code. For that, name the diagram in a record's --files alongside the code it depicts, so changing the code turns the drift gate red until someone re-verifies the picture. This repo holds its README that way; its pipeline diagram is generated from the code instead, and a test fails when the committed render no longer matches.
• Comments are covered only by the tombstone gate. It finds a retired name in .md, .py, .ts, .tsx, .cs, .yaml, .yml, .json and .sh files; the retraction gate reads markdown only. Any other stale comment is not caught, and okl reverify re-stamps a governed file without reading its comments, so a check has to test the claim a comment makes for drift to mean anything.
• okl drift only watches what a record claims. A file no record governs is not watched by anything. Coverage is a curation decision, and the gap is invisible until something breaks — which is why the mechanical gates above scan the whole repo (the tombstone and retraction gates grep every committed file of their kinds) rather than only what is enrolled.

How okl differs

• From memory layers (mem0, Zep, Letta, the Claude Code memory plugins): they recall what the agent experienced; okl keeps what must stay true, and proves it with a check you chose.
• From rules files (CLAUDE.md, AGENTS.md): a briefing selects the few lessons that match the task; a rules file loads whole, every time, and nothing re-checks it.
• From instruction-file linters: they check that a path or command a rules file names still exists; okl runs your check, and blocks the push when its code moved.
• From documentation platforms: they draft and review; okl drafts nothing and blocks a push.

The long version, with the comparison table and how to run okl beside another memory tool: Where this sits. It fails closed by design: an unreachable store blocks or warns, never reports "clean".

What the research literature measured in 2026, with figures read from the papers and a "where okl stands" column beside each result: Related work.

Measured effect, and its limits

What follows was measured on authored tasks. On real work, two lessons that applied were missed by every retrieval arm that ran, and none of the measurements below could have shown it (REPORT §4ak). The standard okl is now held to, and where each claim stands against it: docs/EVALUATION.md.

One held-fixed A/B (8 authored tasks, 3 samples per arm per run; generator and blind judge are different models; method + raw receipts in evals/REPORT.md):

• Same model, briefed vs not: in the first 3-sample runs (2026-08-30), defect reproduction fell 33% → 4% (sonnet) and 38% → 12% (haiku). Later sonnet runs, as the retrieval pipeline changed, read 35–50% → 4–8% (REPORT §4b–§4i, excluding the reverted §4d and the opus-judged §4f; the latest, §4i, 43% → 8%). Pooled over the seven runs on that instrument from §4 to §4h, the reverted §4d included: 68 of 163 unbriefed attempts repeated a known defect (42%) against 11 of 164 briefed (7%); REPORT §5 finding 7 names the receipts and the other ways to cut it. The unbriefed arm alone has moved 21 points between runs with nothing changed, and 26 across the §4j boundary, so the noise floor is at least 21 points (§2b, §4m). Every "reproduced" is a defect class this store had already paid to learn — a price-tamper fallback, tokens in web storage, an unpinned CI gate, a script that trusts exit 0 without checking its output, an eval summary that does not put its failure rate first — not lint noise. Two tasks (an IDOR endpoint, a React fetch) have never reproduced without the briefing in any committed receipt (46–48 unbriefed samples each), so they cannot show a reduction; the rate limiter has reproduced 1 of 41 times without the briefing (§4m) and 2 of 44 with it (REPORT §4d, §4m, §5 finding 6).
• The result worth remembering, from those first runs: briefed haiku (12%) beat unbriefed sonnet (33%). It suggests the briefing is a cost lever, not just a quality lever — it can hold a cheaper model above a frontier model's unbriefed floor on the org's known failure modes. Two caveats: the haiku run was never repeated, and it was judged by sonnet where the sonnet runs were judged by haiku.
• Every run above predates the briefing's Decisions section: until recently a Decision that won a slot was silently dropped; it now appears under its own heading. No eval task's briefing held one, so the eight measured briefings are byte-identical either way.
• Every run above also predates the [id] each briefed lesson now carries. That one is not byte-identical: the eight eval briefings are 5% larger, with every lesson's content kept (REPORT §4j). Its run (2026-10-10) read 17% → 0%, but the unbriefed arm itself fell 26 points from §4i's, which points at the generator rather than okl, so it starts a new series and is not pooled with the runs above.

The figure is also a transfer result. The eight tasks are fresh code written from a task prompt, and the defect classes they invite were learned in other projects: each task names its lesson in evals/tasks.jsonl, and those lessons arrive through six of the twelve seed packs. A lesson paid for in one project prevented the defect in code written for another. In the pooled runs the briefing held each task's lesson with three exceptions, whose arms measure the briefing without it: exit_code_trust's ranked below the twelve-record cutoff in every run until the lesson gained a symptom on 2026-10-22 (REPORT §4b, §4o); rate_limiter's was filtered out in the reverted §4d run; and react_fetch's was filtered out in the two runs made with this repo's declared interests (§4h). Running that across an organisation, and what it asks of a lesson, is Across an org.

What this does not show: the tasks were authored to invite defect classes the store encodes, so it measures what a briefing does when a directly relevant lesson exists — not general code quality, and not retrieval at scale. n is small; treat it as a pilot with receipts, not a benchmark.

How it works

The overview at the top is rendered from docs/overview/okl-overview.html. The anatomy under "What goes wrong" follows one lesson all the way round, in okl's own output; it is rendered from docs/overview/okl-anatomy.html, and docs/overview/anatomy-story.sh runs the same story in a throwaway repo. How the project itself is built (where the code lives, what each command touches, what runs when, and a lesson's states) is drawn in docs/ARCHITECTURE.md.

How a briefing is actually built

okl check is a retrieval pipeline: it searches the task's subject words, then narrows the whole corpus to the five or fewer records an agent reads in full, and names the rest of what matched in a line each. Two stages can drop a record on what it is about, and only one of them is entitled to: the distinction that this project got wrong once and measured its way out of (REPORT §4d).

Then the decision. A record is briefed only when its title, symptom, aliases and triggers hold three of the task's subject words, or two nearly as rare as a word in one record. Triggers are phrasings of the moments a lesson applies, written when it is recorded. The line was set on 282 real prompts with no subject of their own, so that about one in twenty such prompts gets anything; at most five records clear it. Nothing else that matched is hidden: what cleared the line past the five, and what the earlier word floor keeps, follow one line each (id, title and the start of the fix, up to twelve), so the agent can read any of them with okl show <id> (REPORT §4y).

It was measured on a benchmark built to decide: 519 generated queries, 550 real prompts with no subject, and a held-out half run once after the choice was written down. Against the previous pipeline:

Real prompts are terser than the benchmark's queries. On prompts a person typed, against a store of recorded lessons (REPORT §4x), the line alone surfaced about a third of the applying lessons the earlier rule did. That is why the default names the rest in a line each (REPORT §4y, held out):

A team's own lessons need triggers to reach the first tier: okl record asks for them, okl triggers lists those that lack them, and /triggers drafts them in Claude Code. The evidence, the alternatives that lost (rerankers, graphs, query-time generation, conversation context) and that cost are in docs/RETRIEVAL.md and REPORT §4w and §4x and §4y. --decision calibrated gives the line alone, the smallest briefing; --decision floor the earlier rule (two shared subject words, every match in full, up to twelve records).

A repo can also name up to ten standing lessons (okl standing add <id>...): the ones its changes keep needing, named one line each on every task briefing whatever its words. Found by words, each competes with every other lesson on every prompt; named on every task, the ten most needed on earlier work raised the briefing's reach on later work (REPORT §4am, model-judged). They are off until a repo lists some.

With the optional semantic arm on (okl check --hybrid --decision floor), a second candidate source joins before the gates: the task as one vector, its nearest records by cosine, and a fusion of the two orders. It needs the floor: a record found by meaning alone shares no word with the task, so the calibrated line cannot brief it. Meaning measured best added to the word count instead. EmbeddingGemma-300m took held-out recall from 90.0% to 95.2% that way at full precision. The Claude Code prompt hook would pay a second or two to load it on every prompt, so okl warm keeps its int8 file loaded in a local process: on once okl warm index has embedded a local store's lessons, about 1.6 GB of memory, 114 ms added to a prompt at the median, and on real held-out prompts 46 applying lessons shown in full against 38 (REPORT §4z). The same generator draws it from the same code path, and REPORT §4q says what it buys and costs. A local cross-encoder over those candidates (okl check --rerank) was measured next (REPORT §4t): one held-out paraphrase in, one bug report out, so it ships as a flag, off.

Every count in that diagram is traced, not transcribed: docs/render_pipeline_diagram.py runs the real briefing_terms, store.search, core._in_scope, core._live_hits and decide.select against a store seeded from seed/, and test_pipeline_diagram_is_current fails if the committed render is not what the generator produces today. Change a BM25 weight or a filter predicate and the diagram goes red with it. The generator once searched the raw sentence after okl check had moved to subject words, and that test could not notice; the trace now cross-checks itself against _live_hits.

Lessons and links

A lesson is one of a few kinds:

Each carries a Symptom → Cause → Fix: when you see this, the cause is this, do this. That is what makes a lesson actionable rather than informational. Lessons link: a Gate CATCHES a Defect, a Decision SUPERSEDES an older one, so a lookup pulls in the connected context.

Two actions. check: you describe the task; okl searches the store, keeps the lessons in your scope, ranks them, and returns a briefing that leads with concrete actions (FIX: … when you see … → …), followed by the decisions already made on purpose. record: when you fix or decide something, you write it down, with the files it governs; from then on every task that resembles it is briefed, and okl update <id> refines it without losing its proof.

Scope is the one decision that matters most. repo:<name> stays with that project. org reaches any project that loads the pack it is in, and is briefed only when its applies_to, the project's interests and the task agree. Subject tags from a closed vocabulary (KNOWN_TAGS in store.py, grown with okl record --type Vocabulary) say what a lesson is about; okl init --interests says what a repo cares about (decision). Packs of org lessons across many repositories, through a ledger repo and pull requests, with each repo proving what it loads: Across an org.

The choices behind all this, and why: docs/DESIGN-CHOICES.md.

The data is one SQLite file per clone by default, .okl/okl.db, gitignored. Point several repos at a shared service when you want one store (docs/SCAFFOLD.md).

Use it

# 1. READ the relevant lessons before starting a task (the load-bearing move)
okl check --task "add an endpoint that returns an order for the logged-in user"
#   add --format actions --limit 3 for a ~400-token version (subagents, CI)

# 2. RECORD a lesson after you learn it, with an actionable symptom/cause/fix
okl record --type Defect --scope org --tags "security" \
  --title  "Trusting a client-supplied price lets the client set it to anything" \
  --symptom "a request body carries a price/amount/status/isAdmin field" \
  --body    "cause: the handler saved the client's value instead of computing it" \
  --fix     "drop those fields from the request; compute them server-side" \
  --files   "**/orders/*.py"         # prints the new record's id

# ...then prove it with a check, rather than asserting it
okl verify <id> --run "pytest -q tests/test_orders.py" --expect "passed"

# 3. SEARCH the stored lessons directly
okl search "price tampering"

# 4. LINK a check to the defect it catches (so a lookup pulls in both)
okl link <gate_id> CATCHES <defect_id>

okl record --verified is refused: it exits 2 and points at okl verify, because a stamp is evidence of a run, not a claim. Every other command, with its exit codes: docs/COMMANDS.md. Why a step never grades itself, and the four rungs okl applies to its own stamps: docs/VERIFICATION.md.

--symptom/--fix are what make check emit a leading "Do this" action list ("FIX: … — when you see: …") instead of a wall of prose. --files tells okl which source files a lesson governs, which is what drift watches (below).

What it costs

Installing okl is not free. It is worth knowing exactly what you are signing up for before you wire it into every prompt. Every number below was measured rather than estimated, with one representative task ("add an endpoint that returns an order for the logged-in user"; tokens ≈ characters ÷ 4). The sizes come from a fresh store holding every bundled seed pack, 183 records (python3 evals/briefing_size.py, receipt evals/results/briefing-size-20261102-0123.json); the latency from the 161 seed records bundled before that. Your store and your tasks will differ.

Per prompt, once the hook is installed:

Per session: in Claude Code, the Stop hook interrupts once at the end to ask what was learned. It blocks the first stop only, and answering it is the whole write side of the loop. With the question it lists up to five commands that failed during the session, read from the session's own transcript — candidates, not records: no hook runs on every tool call, and no model is called to summarise anything. Then okl recap names the lessons the session touched: up to five it was briefed on most often, and up to five whose governed files it changed, each with the command that corrects, retires or re-proves it. The agent proposes a change in one line and makes it once you agree. To read the whole session back afterwards, okl recap --report lists the briefings, the lessons recorded and retired, the checks run and failed, the commits, and what is stale now, from the store's own logs.

A full briefing costs roughly 1,580 tokens on the measurement above (~780 for the line alone, ~1,810 under the floor's twelve), fine for a main session with a large window and still a real share for a subagent working in a few thousand. That asymmetry matters because subagents are exactly where org rules get lost: a focused worker handling one subtask has the least context and the most need for "here is the mistake this codebase already made."

Measured on every bundled seed pack (183 records), one representative task (receipt): the action list alone is ~400 tokens at --limit 3, ~710 at --limit 5 and ~1,150 at --limit 12 (its one-line entries follow the limit), against ~1,580 for the full briefing. Cheap enough to call per subtask; --decision calibrated drops the one-line entries for the smallest list.

The same holds for a main session on a model with a small context window, where the briefing arrives with every prompt. Set OKL_BRIEFING_COMPACT=1 and the Claude Code prompt hook sends only the action list (okl check --compact); set OKL_BRIEFING_LIMIT=3 and it shows three records and names three more, instead of five and twelve. Together that is ~400 tokens instead of ~1,580 on the measurement above. Set them where the agent starts: hooks inherit its environment. A limit that is not a whole number above zero is ignored, and an okl too old for --compact briefs in full rather than blocking the prompt. Like --format actions, the compact briefing does not say how many records it trimmed.

okl check --format actions is the imperatives only, about half the size; --format json is the raw result for scripts; --format hook is what the Claude Code hook prints; --limit N draws on fewer records and says how many it trimmed.

Every knob, what okl init writes, how to turn parts off, and the opt-in architecture review: Costs and knobs. Subagents and small windows: docs/SUBAGENTS.md.

Questions

How do I stop my agent repeating a bug it already fixed? Record the fix as a lesson with a symptom, a fix and the files it governs, then okl verify it with the test that proves it. Any task that shares its words is briefed with it first; if the code it covers changes, the push waits until the check passes again.

What happens when the code a lesson covers changes? okl drift lists it, the pre-push hook holds the push (or CI fails), and the next briefing marks it STALE. okl reverify re-runs its stored check; a pass re-stamps it. Putting the code back as it was clears it too.

Does it work with Cursor, Codex or Copilot? The store, the commands, the push gate and the six MCP tools do. The briefing on every prompt and the session-end question are Claude Code hooks; other agents call okl_check (how).

Does it capture my sessions automatically? No. A lesson is written on purpose, by you or by the agent when you say so; in Claude Code, the Stop hook asks once. Nothing records tool calls, and no model is called in the loop.

Can I run it beside claude-mem, mem0 or another memory tool? Yes; they are different layers. okl doctor names what is installed and the three known collisions (Where this sits).

Is it production-hardened? No: v0, one person's project, with an end-to-end suite and the measurements above. What it is not for and the limits are stated on purpose.

Install as a Claude Code plugin

/plugin marketplace add emeraldleaf/okl
/plugin install okl@okl

The plugin carries the three hooks, the MCP tools and the seeding commands. It does not carry okl itself: install the CLI first, with the MCP extra, because the plugin registers the okl mcp server (pipx install 'observed-knowledge-ledger[mcp]'), then run okl init in the repo for the store and the drift gate (a pre-push hook; --ci for the GitHub workflow) — with the plugin enabled, init skips the hooks and MCP registration, because registering them twice would brief every prompt twice. okl doctor reports a repo where both the plugin and the project hooks are active. To load the plugin from a checkout for one session: claude --plugin-dir <path>.

Install

pipx install observed-knowledge-ledger   # provides the `okl` command
pip install -e .                   # or from a clone of this repo

The core (local + client + CLI) is stdlib-only — zero required dependencies. Extras are opt-in:

pip install "observed-knowledge-ledger[service]"    # FastAPI shared service
pip install "observed-knowledge-ledger[postgres]"   # Postgres backend (psycopg)
pip install "observed-knowledge-ledger[mcp]"        # MCP server for Claude Code / Cursor / Copilot
pip install "observed-knowledge-ledger[all]"

Agent integration (MCP)

pip install "observed-knowledge-ledger[mcp]"
okl mcp     # register in your coding agent's tool config

Exposes six tools to a coding agent: okl_check (read lessons before a task, or, given a path, every lesson that governs a file before it is read or edited), okl_record (a new lesson), okl_update (refine one by id, keeping its proof), okl_retire (withdraw one, with a reason), okl_get (one lesson whole) and okl_search. okl_check reports an outage loudly — if a configured shared instance is unreachable it says so rather than returning a reassuring "nothing found," because those two look identical from the agent's side and only one is safe. Unlike the hook, a tool result cannot block the agent; it can only warn it.

Layout

src/okl/
  store.py        # the database: lesson + link schema, swappable SQLite/Postgres backend
  core.py         # check / record / search / link — the logic, independent of transport
  client.py       # resolves local-file vs. shared-service; fails closed
  cli/            # the `okl` command, one module per command group (cli/__init__.py maps them)
  drift.py        # drift: lessons whose governed files changed since their check
  ownership.py    # okl-fingerprint lines: which installed files are okl's, and untouched
  coexist.py      # `okl doctor`: detects other agent-memory plugins and double wiring
  bootstrap.py    # propose starter lessons from a repo's git history + docs
  service.py      # the shared web service (the [service] extra)
  mcp_server.py   # coding-agent tools (the [mcp] extra)
  seed.py         # load a JSON seed file
  scaffold_cmd.py # the `okl scaffold` starter-files stamper
seed/             # starter lesson files (examples + genuinely useful defects)
docs/decisions/   # design decision records
tests/            # end-to-end tests

Test

pip install -e . --group dev   # from a clone of this repo (pip 25.1+; or `uv sync`)
pytest -q                 # full suite

Some tests skip rather than fail where git is unavailable (or git init is blocked) or an optional extra such as service is not installed.

License

MIT.

Contributing

See CONTRIBUTING.md for setup, the repo's own rules (mirror files, drift, evidence-based verification), and where help is most useful. docs/DEVELOPING.md walks through how okl is developed end to end: the everyday loop, CI, pull requests, releases, the evaluations and what you can observe. Security policy and the deployment threat model: SECURITY.md.