feat(learn): weight loops in Headroom Learn + RTK-loop eval#1160
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Headroom Learn ranked recommendations by a single LLM-guessed estimated_tokens_saved with a flat hardcoded confidence, and had no notion of a loop. Two consequences: - RTK re-fetch loops were invisible: RTK truncates a command's output, so the agent re-runs larger-limit variants to fetch more. Those calls SUCCEED (is_error=False), and analyze() early-returned when a session had no failures and no events — skipping the loop entirely. - Even when surfaced, a loop ranked no higher than a one-off mistake. Add headroom/learn/loops.py: - detect_loops(): group calls by a canonical signature that collapses RTK pagination/limit variants, flag >=3 repeats, classify error vs rtk-refetch loops, and compute MEASURED wasted tokens. - format_loops_for_digest(): surface loops as a highest-priority digest section so the analyzer LLM sees them. - apply_loop_weighting(): raise a matching recommendation's savings to at least the loop's measured waste and tag it as a loop guardrail, so loops outrank one-offs deterministically. Wire into analyzer.py: detect loops up front (fixes the no-failure early-return), lead the digest with them, prioritize loops in the system prompt, and re-sort after weighting. Add is_loop_guardrail / loop_occurrences to Recommendation. Eval: benchmarks/rtk_loop_learn_eval.py reproduces the loop, runs learn, scores the guardrail (produced/ranked-first/names-command/prescribes-fix/ weight-reflects-waste), then injects it and asserts a guarded session does not re-trigger it. Deterministic in CI; real-LLM via --real. Tests: tests/test_learn/test_loop_weighting.py (13) and test_rtk_loop_eval.py. Design notes in docs/rtk-loop-weighting.md. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PR governanceThis PR follows the template and is marked ready for human review. |
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Correction (resolved). Disregard my CRLF diagnosis above — I had it wrong. The The regex ( |
…igure The design doc stated the LLM 'rated the eval's loop at 150 tokens vs ~5,000 measured' as an empirical result. 150 was the deterministic stub's constant, never a real model output — removed. Doc now states the implementation is a hybrid (digest prompt hint + post-hoc fuzzy boost) and that the prompt hint is currently load-bearing while the post-hoc boost is fuzzy-match-based and does not always fire. Stub comment clarified as a simulated value. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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This PR is not merge-ready yet. The body still says the review-readiness box is intentionally unchecked pending maintainer agreement, and GitHub reports mergeStateStatus=UNSTABLE rather than clean/green. Please mark it ready, ensure the required CI surface is green, and update the description once the approach is no longer pending.
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Thanks @JerrettDavis - done:
The only remaining non-green status is that 4 workflows are awaiting maintainer approval to run (first-time contributor) — |
CI lint failed: headroom/learn/loops.py:197 no-untyped-def — the recommendations param was untyped. Recommendation lives in models.py which does not import loops, so there is no circular import; annotate it directly as list[Recommendation] (removing the stale lazy-typing comment). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Re-reviewed the current head after the main merge/type-annotation commit. The loop detection, digest surfacing, measured-waste weighting, and deterministic RTK-loop eval still look good, and the branch is clean with CI green.
…labs-ai#1160) ## Description `headroom learn` ranked recommendations by a single LLM-guessed `estimated_tokens_saved` with a flat hardcoded `confidence`, and had **no notion of a loop**. So (1) RTK re-fetch loops were invisible - RTK truncates a command's output, the agent re-runs larger-limit variants, those calls *succeed* (`is_error=False`), and `analyze()` even early-returned when a session had no failures and no events - and (2) even when surfaced, a loop ranked no higher than a one-off mistake. This adds loop-aware weighting plus the eval that reproduces an RTK loop, runs it through Learn, and checks the guardrail prevents re-triggering. Closes headroomlabs-ai#1159 ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [x] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made - New `headroom/learn/loops.py`: `detect_loops()` (canonical signature collapses RTK pagination/limit variants; classifies error vs rtk-refetch loops; **measured** wasted tokens), `format_loops_for_digest()`, `apply_loop_weighting()`. - `analyzer.py`: detect loops up front (fixes the no-failure early-return), lead the digest with them, prioritize loops in the system prompt, re-sort after weighting. - `models.py`: `Recommendation.is_loop_guardrail` / `loop_occurrences`. - `benchmarks/rtk_loop_learn_eval.py` + `headroom/learn/fixtures.py`: the two-phase RTK-loop eval and its session fixtures. - Tests, `docs/rtk-loop-weighting.md`, CHANGELOG entry. ## Testing - [x] Unit tests pass (`pytest`) - [x] Linting passes (`ruff check .`) - [ ] Type checking passes (`mypy headroom`) - not run (mypy not in my minimal env; see Not tested) - [x] New tests added for new functionality - [x] Manual testing performed ### Test Output ```text $ python -m pytest tests/test_learn/ -q 190 passed, 3 skipped, 1 warning in 5.85s $ ruff check <changed files> All checks passed! ``` ## Real Behavior Proof - Environment: macOS (Darwin 25.0), Python 3.10.18, fresh venv (`pip install -e` minus the optional `hnswlib`/proxy extras, which are unrelated to `learn`); real LLM via the analyzer's claude CLI backend (`HEADROOM_LEARN_CLI=claude`, claude-cli 2.1.158) — no API key used. - Exact command / steps: `HEADROOM_LEARN_CLI=claude python -c "from benchmarks.rtk_loop_learn_eval import run_eval; c=run_eval(use_real_llm=True); print(c.render())"` - Observed result: the analyzer shelled out to a real model and produced the "Commands" guardrail quoted below, naming the looping command. The digest reports the measured 5,005-token waste and asks the model to rank loops first, so the model emitted that figure; in this run the guardrail ranked **headroomlabs-ai#1** and the scorecard was all-PASS (below). Caveat — real-mode is run-dependent: the rule's wording, and whether the post-hoc `apply_loop_weighting` fuzzy match fires, vary across runs (in one run it did not tag the rule). The **deterministic CI eval** (stub LLM) is the stable, reproducible artifact; this real run corroborates it. - Not tested: the analyzer's API-key path (ANTHROPIC/OPENAI/GEMINI) — exercised the equivalent claude CLI backend instead; `mypy`; a live agent *obeying* the written rule end-to-end (Phase 2 is a non-recurrence check, not a live agent — called out in the doc). Real model output from this run, ranked headroomlabs-ai#1 at the measured 5,005-token weight: > **Commands** — When grepping logs (or any large file), never loop with increasing `| head -N` limits — tool output is capped at ~4 KB regardless of N, so repeated attempts return identical bytes. Instead: redirect to a temp file (`grep ... > /tmp/out.txt`) then read it, or use `grep -c` first… ```text [PASS] loop_detected (1 loop(s), ~5,005 tok wasted) [PASS] guardrail_produced [PASS] ranked_first [PASS] names_command [PASS] prescribes_fix [PASS] weight_reflects_waste [PASS] guardrail_holds RESULT: PASS ``` (One real-mode run via the claude CLI backend. The deterministic `pytest` eval above is the stable artifact; see the run-dependence caveat under Observed result.) The real run also caught an over-brittle check: an earlier `names_command` required the literal "TimeoutError"; the real model wrote a *more general* rule (grep + `head -N`) without it, so I fixed the check to verify the looping **command** is named, not an incidental literal. ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [x] I have updated the CHANGELOG.md if applicable ## Additional Notes - No new dependencies. No network, no user/assistant content dropped — operates on already-captured session digests. - Kept as one logical change. mypy not run locally (minimal env); happy to address anything CI's mypy flags. --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: JD Davis <mxjerrett@gmail.com>
🤖 I have created a release *beep* *boop* --- <details><summary>0.28.0</summary> ## [0.28.0](v0.27.0...v0.28.0) (2026-06-29) ### Features * add --disable-kompress-fallback to restore legacy PASSTHROUGH fallback ([#1185](#1185)) ([f309244](f309244)) * add first-class OpenCode support (wrap, learn, mcp install) ([#559](#559)) ([91cd210](91cd210)) * add HEADROOM_KEEPALIVE_EXPIRY to keep upstream connections warm ([#1124](#1124)) ([85786b3](85786b3)) * **azure-foundry:** derive upstream URL from ANTHROPIC_FOUNDRY_RESOURCE ([#1138](#1138)) ([e5031b0](e5031b0)) * **cache:** attribute prompt-cache misses to TTL lapse vs prefix change ([#1313](#1313)) ([#1343](#1343)) ([4658721](4658721)) * **code:** add Perl support to code-aware compressor ([#1125](#1125)) ([f39858c](f39858c)) * headroom wrap opencode / unwrap opencode CLI ([#1105](#1105)) ([b4571cc](b4571cc)) * **learn:** weight loops in Headroom Learn + RTK-loop eval ([#1160](#1160)) ([14e8dc4](14e8dc4)) * **learn:** write per-project learnings to CLAUDE.local.md by default ([#1115](#1115)) ([ced75e4](ced75e4)) * **proxy:** add request timeout config ([#738](#738)) ([c0745d4](c0745d4)) * **proxy:** pilot hardening — inbound auth, security headers, audit log, air-gap switch ([#1537](#1537)) ([546ab55](546ab55)) * **proxy:** support glob patterns in exclude_tools ([#870](#870)) ([#1259](#1259)) ([a2159c0](a2159c0)) * **read-maturation:** activity-based hold-back Read maturation (Mechanism B) ([#1068](#1068)) ([723b80c](723b80c)) * **savings:** durable savings ledger + headroom savings command ([#1127](#1127)) ([978ffa0](978ffa0)) * **wrap:** add --1m to preserve the 1M context window on wrap claude ([#1158](#1158)) ([#1351](#1351)) ([b50d9c1](b50d9c1)) * **wrap:** make tokensave the primary coding-task compressor, Serena the backup ([#1230](#1230)) ([dca9853](dca9853)) ### Bug Fixes * **agent-evals:** Phase 0 — coding-agent accuracy A/B framework ([#1037](#1037)) ([84f9871](84f9871)) * **agno:** tolerate streaming tool-call SDK objects in parser ([#1312](#1312)) ([#1336](#1336)) ([5986c22](5986c22)) * **bedrock:** add boto3 1.41 + CRT for aws login credentials ([#1486](#1486)) ([4db3bc9](4db3bc9)) * bump codebase-memory-mcp to v0.8.1 ([#1284](#1284)) ([530318b](530318b)) * **ccr:** make headroom_retrieve a hash-only full-content lookup ([#1532](#1532)) ([c2fc4d3](c2fc4d3)) * **ccr:** propagate --no-ccr-marker flag to all compressors ([#1022](#1022)) ([#1197](#1197)) ([0c9b42a](0c9b42a)) * **ccr:** skip Anthropic marker emission when tool injection is deferred ([#1273](#1273)) ([2cae13d](2cae13d)) * **ci:** extend gitleaks allowlist to cover test fixtures + verified examples ([#1539](#1539)) ([d2565a6](d2565a6)) * **ci:** guarantee model present in test shards to end cache-miss flakiness ([#1399](#1399)) ([2e29c72](2e29c72)) * **ci:** normalize Windows CRLF line endings in PR governance script ([#1012](#1012)) ([5194388](5194388)) * **cli:** add explicit UTF-8 encoding to file I/O in wrap commands ([#1126](#1126)) ([#1164](#1164)) ([a0cb798](a0cb798)) * **cli:** fall back gracefully when embedding-server sidecar is absent ([#1206](#1206)) ([38f1404](38f1404)) * **cli:** harden all CLI surfaces + fix docs accuracy ([#1491](#1491)) ([bd76235](bd76235)) * **cli:** wire --http2/--no-http2 (HEADROOM_HTTP2) into proxy command ([#1373](#1373)) ([e06b616](e06b616)) * **cli:** wire --rpm/--tpm and HEADROOM_RPM/HEADROOM_TPM to the Click proxy command ([#1375](#1375)) ([8aab8f2](8aab8f2)) * **code:** slice tree-sitter byte offsets as UTF-8 ([#1332](#1332)) ([8238402](8238402)) * **code:** validate Python compressed syntax ([#1302](#1302)) ([cbd361d](cbd361d)) * **code:** verify a real parse in tree-sitter availability check ([#1231](#1231)) ([#1299](#1299)) ([5e0bb69](5e0bb69)) * **codex:** retag threads on init so Codex Desktop history stays visible ([#961](#961)) ([#1349](#1349)) ([e6bbc40](e6bbc40)) * **codex:** stop pinning Codex memory MCP to one project db ([#1269](#1269)) ([ad7993b](ad7993b)) * **dashboard:** include RTK stats in the historical tab ([#1324](#1324)) ([35939c3](35939c3)) * **deps:** remediate dependency CVEs and publish SBOM ([#1509](#1509)) ([5771a80](5771a80)) * **docker:** persist session history across container revisions ([#1118](#1118)) ([5912d65](5912d65)) * **gemini:** offload compression to the executor ([#1382](#1382)) ([615848e](615848e)) * **gemini:** resolve Google model capabilities through ModelRegistry ([#1276](#1276)) ([17ecad9](17ecad9)) * **install:** guard install_agent_ensure against duplicate runtime spawns ([#1301](#1301)) ([8da0b4e](8da0b4e)) * **install:** repair macOS launchd restart/start lifecycle ([#1290](#1290)) ([da1a397](da1a397)) * **install:** stop duplicating ENTRYPOINT in persistent-docker runtime command ([#833](#833)) ([#1348](#1348)) ([feedead](feedead)) * **io:** use UTF-8 with locale fallback and preserve line endings on config/text I/O ([#1498](#1498)) ([1baa04e](1baa04e)) * **kompress:** hard override keeps must-keep tokens regardless of model score ([#1400](#1400)) ([42612c8](42612c8)) * **langchain:** disable streaming on wrapped model during ainvoke() ([#1287](#1287)) ([3590046](3590046)) * **mcp:** register managed installs with a resolvable headroom command ([#1386](#1386)) ([22def93](22def93)) * **mcp:** report correct savings_percent in headroom_compress ([#1106](#1106)) ([f216e43](f216e43)) * **opencode:** write local MCP config ([#1381](#1381)) ([6c83790](6c83790)) * **packaging:** move hnswlib to optional [vector] extra so [all] needs no C++ toolchain ([#1499](#1499)) ([80fa086](80fa086)) * patch rtk hook script to use absolute path after register_claude_hooks ([#571](#571)) ([b618d2d](b618d2d)) * **perf:** surface RTK/CLI context-tool savings in perf and the session card ([#1433](#1433)) ([9362747](9362747)) * **proxy:** add --protect-tool-results to prevent lossy compression of exact-output Bash results ([#1374](#1374)) ([51d4bcf](51d4bcf)) * **proxy:** add an Anthropic buffered read-timeout override ([#1331](#1331)) ([3be2526](3be2526)) * **proxy:** add versionless Vertex AI routes for Claude Code compatibility ([#1321](#1321)) ([bb3e040](bb3e040)) * **proxy:** bind before eager preload so a hung compressor load can't block startup ([#1500](#1500)) ([d5ac07f](d5ac07f)) * **proxy:** build SSL contexts for custom CA bundles ([#1134](#1134)) ([561ba17](561ba17)) * **proxy:** forward request-id headers on the streaming path ([#1100](#1100)) ([#1258](#1258)) ([3d59df7](3d59df7)) * **proxy:** gate CCR retrieve/compress endpoints to loopback ([#1338](#1338)) ([acafb2d](acafb2d)) * **proxy:** honor force_kompress routing profile ([#996](#996)) ([b4682d6](b4682d6)) * **proxy:** keep large compression results on the critical path ([#296](#296)) ([#1352](#1352)) ([90734b6](90734b6)) * **proxy:** offload /v1/compress to the compression executor to stop blocking the loop ([#1501](#1501)) ([27e010e](27e010e)) * **proxy:** preserve Responses memory continuations with store=false ([#1103](#1103)) ([cdfeeac](cdfeeac)) * **proxy:** queue mid-turn user messages on non-Bedrock streaming path ([#1377](#1377)) ([b09f027](b09f027)) * **proxy:** register interceptor in explicit transforms list when HEADROOM_INTERCEPT_ENABLED ([#1376](#1376)) ([55c700c](55c700c)) * **proxy:** report real input tokens on streaming message_start ([#1132](#1132)) ([#1305](#1305)) ([70cc96a](70cc96a)) * **proxy:** retry upstream 429 with Retry-After on both forwarders ([#1329](#1329)) ([90bee89](90bee89)) * **proxy:** retry upstream 529 overloaded like 429 on both forwarders ([#1495](#1495)) ([547b15d](547b15d)) * **proxy:** stop re-compressing headroom_retrieve output and emitting unredeemable markers ([#1323](#1323)) ([43494ff](43494ff)) * **proxy:** strip Codex lite header from OpenAI WebSockets ([#1543](#1543)) ([5d3803a](5d3803a)) * **read-lifecycle:** persist STALE Read originals in the CCR store ([#1488](#1488)) ([9157173](9157173)) * recover persistent proxy feature checks and reject non-Copilot exchange URL ([#1465](#1465)) ([16c638b](16c638b)) * remove agents.md ([#1540](#1540)) ([a7d3360](a7d3360)) * respect COPILOT_PROVIDER_TYPE env var when provider_type is auto ([#549](#549)) ([24cf256](24cf256)) * restore token-mode compression on frozen prefixes ([#1489](#1489)) ([8e0dadf](8e0dadf)) * **router:** degrade to pure-Python detection on native panic ([#1123](#1123)) ([#1260](#1260)) ([a00fb67](a00fb67)) * **rtk:** stop hook registration timing out on a forked daemon ([#1314](#1314)) ([9758817](9758817)) * **smart-crusher:** honor enable_ccr_marker on the opaque-blob path ([#1130](#1130)) ([27d6f8e](27d6f8e)) * **subscription:** only reset 5h contribution on real rollover, not API jitter ([#1255](#1255)) ([8d6c175](8d6c175)) * **subscription:** run transcript token scan off the event loop ([#1263](#1263)) ([f03021f](f03021f)) * surface output reduction without a restart, and explain $0.00 savings on Python 3.14 ([#1296](#1296)) ([c30ec4c](c30ec4c)) * **tests:** reset whole headroom logger subtree so caplog stays deterministic ([#1117](#1117)) ([fda4670](fda4670)) * **tls:** add HEADROOM_TLS_STRICT=0 toggle for corporate SSL inspection ([#1308](#1308)) ([#1341](#1341)) ([52068dd](52068dd)) * **tokenizers:** price CJK/Kana/Hangul at ~1 token per char in EstimatingTokenCounter ([#1093](#1093)) ([a35fe86](a35fe86)) * **transforms:** gate tool string output from lossy compression ([#1307](#1307)) ([#1387](#1387)) ([c6c921a](c6c921a)) * **websocket:** harden responses websocket origin handling ([#1481](#1481)) ([c632023](c632023)) * **windows:** pin UTF-8 encoding on text-mode subprocess calls ([#1311](#1311)) ([d633e81](d633e81)) * **wrap:** add Copilot unwrap command ([#1251](#1251)) ([b4fde0c](b4fde0c)) * **wrap:** isolate proxy stdio from proxy.log on Windows ([#1191](#1191)) ([959ab0d](959ab0d)) * **wrap:** keep agent savings opt-in ([#1294](#1294)) ([b829ceb](b829ceb)) * **wrap:** show the dashboard URL when the proxy is already running ([#1313](#1313)) ([b0146c4](b0146c4)) ### Performance Improvements * **compression:** take large cold-start contexts off the synchronous kompress path ([#1171](#1171)) ([#1298](#1298)) ([6c68ff4](6c68ff4)) </details> --- This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please). Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Description
headroom learnranked recommendations by a single LLM-guessedestimated_tokens_savedwith a flat hardcodedconfidence, and had no notion of a loop. So (1) RTK re-fetch loops were invisible - RTK truncates a command's output, the agent re-runs larger-limit variants, those calls succeed (is_error=False), andanalyze()even early-returned when a session had no failures and no events - and (2) even when surfaced, a loop ranked no higher than a one-off mistake. This adds loop-aware weighting plus the eval that reproduces an RTK loop, runs it through Learn, and checks the guardrail prevents re-triggering.Closes #1159
Type of Change
Changes Made
headroom/learn/loops.py:detect_loops()(canonical signature collapses RTK pagination/limit variants; classifies error vs rtk-refetch loops; measured wasted tokens),format_loops_for_digest(),apply_loop_weighting().analyzer.py: detect loops up front (fixes the no-failure early-return), lead the digest with them, prioritize loops in the system prompt, re-sort after weighting.models.py:Recommendation.is_loop_guardrail/loop_occurrences.benchmarks/rtk_loop_learn_eval.py+headroom/learn/fixtures.py: the two-phase RTK-loop eval and its session fixtures.docs/rtk-loop-weighting.md, CHANGELOG entry.Testing
pytest)ruff check .)mypy headroom) - not run (mypy not in my minimal env; see Not tested)Test Output
Real Behavior Proof
pip install -eminus the optionalhnswlib/proxy extras, which are unrelated tolearn); real LLM via the analyzer's claude CLI backend (HEADROOM_LEARN_CLI=claude, claude-cli 2.1.158) — no API key used.HEADROOM_LEARN_CLI=claude python -c "from benchmarks.rtk_loop_learn_eval import run_eval; c=run_eval(use_real_llm=True); print(c.render())"apply_loop_weightingfuzzy match fires, vary across runs (in one run it did not tag the rule). The deterministic CI eval (stub LLM) is the stable, reproducible artifact; this real run corroborates it.mypy; a live agent obeying the written rule end-to-end (Phase 2 is a non-recurrence check, not a live agent — called out in the doc).Real model output from this run, ranked #1 at the measured 5,005-token weight:
(One real-mode run via the claude CLI backend. The deterministic
pytesteval above is the stable artifact; see the run-dependence caveat under Observed result.)The real run also caught an over-brittle check: an earlier
names_commandrequired the literal "TimeoutError"; the real model wrote a more general rule (grep +head -N) without it, so I fixed the check to verify the looping command is named, not an incidental literal.Review Readiness
Checklist
Additional Notes