scorekeeper 0.2.0

Commitment tracking for LLM agents — a deontic scoreboard of the agent's own commitments, entitlements, and incompatibilities.

scorekeeper

Commitment tracking for LLM agents — a deontic scoreboard of the agent's own commitments, entitlements, and incompatibilities, maintained outside the agent's authority.

Long-running agents decide on Postgres at step 3 and write MongoDB code at step 47. scorekeeper treats this as a normative problem, not a memory problem: every non-trivial decision becomes a first-class commitment record with entitlement (provenance — did the user say it? did a tool show it? or did the agent just generate it?), and revisions are gated: an externally grounded revision is a legitimate SUPERSEDE; an ungrounded one is a BRANCH-CONFLICT (drift) surfaced back to the agent. A commitment with no provenance at all is what a hallucination looks like in this vocabulary — it gets a CHALLENGE.

Phase-0 evidence (paired runs, planted scenarios): the bare agent drifted against its own database decision; the scorekept twin held. False-conflict rate 0, token overhead +0.6 %. Full report in the repository.

Install

pip install scorekeeper            # core library + CLI
pip install "scorekeeper[mcp]"     # + MCP server (scorekeeper-mcp)

Claude Code (primary integration)

git clone https://github.com/michalstrnadel/scorekeeper
claude --plugin-dir ./scorekeeper/claude-code-plugin

Five hooks do the work: SessionStart injects the normative digest (and re-injects it after context compaction — the exact place summarizers drop it), PostToolUse(Edit|Write) runs a millisecond content scan against pinned choices, Stop extracts the turn's commitments (async by default — a detached worker, zero added latency), UserPromptSubmit surfaces those findings on the next turn, PreCompact backs up the board.

MCP (any harness)

SCOREKEEPER_ROOT=/path/to/project scorekeeper-mcp

Tools: get_scoreboard, get_digest, assert_commitment, check_compatibility (dry-run), supersede, challenge, retract. Writes route through the same validated operator pipeline as the hooks — the agent cannot bypass the scorer.

Library

from scorekeeper import Store
from scorekeeper.extract import ExtractedCommitment
from scorekeeper.operators import apply

store = Store("/path/to/project")
result = apply(store, [ExtractedCommitment(
    claim="The primary database is PostgreSQL 16.",
    kind="decision",
    scope=["topic:persistence", "attr:persistence.primary_db=postgresql"],
    entitlement={"source": "user_utterance"},
)])
print(store.render_digest())

Storage is transparent and git-committable: .scorekeeper/commitments/*.yaml, an append-only log.jsonl audit trail, and a generated scoreboard.md. Nothing is ever deleted — statuses transition.

Model backends

Extraction and Tier-1 detection need a small LLM; local open-source models are first-class. Auto-detection order: SCOREKEEPER_MODEL_URL (any OpenAI-compatible endpoint — Ollama, LM Studio, vLLM) → ANTHROPIC_API_KEY (Haiku) → headless claude -p.

License

Apache-2.0. Theory, benchmark, and specs: github.com/michalstrnadel/scorekeeper.