21 Commits
| Author | SHA1 | Message | Date | |
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root
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643dd2d520 |
gateway: direct Kimi For Coding provider adapter (api.kimi.com)
Wires kimi-for-coding (Kimi K2.6 underneath) as a first-class /v1/chat
provider so consumers can target it via {provider:"kimi"} or model
prefix kimi/<model>. Bypasses the upstream-broken kimi-k2:1t on Ollama
Cloud and the rate-limited moonshotai/kimi-k2.6 path through OpenRouter.
Adapter shape mirrors openrouter.rs (OpenAI-compatible Chat Completions).
Differences from generic OpenAI providers:
- api.kimi.com is a SEPARATE account system from api.moonshot.ai and
api.moonshot.cn. sk-kimi-* keys are NOT interchangeable across them.
- Endpoint is User-Agent-gated to "approved" coding agents (Kimi CLI,
Claude Code, Roo Code, Kilo Code, ...). Requests from generic clients
return 403 access_terminated_error. Adapter sends User-Agent:
claude-code/1.0.0. Per Moonshot TOS this is a tampering-class action
that may result in seat suspension; J authorized 2026-04-27 with
awareness of the risk.
- kimi-for-coding is a reasoning model — reasoning_content counts
against max_tokens. Default 800-token budget yields empty visible
content with finish_reason=length. Code-review workloads need
max_tokens >= 1500.
- Default 600s upstream timeout (vs 180s for openrouter.rs) — code
audits with full file context legitimately take 3-5 minutes.
Override via KIMI_TIMEOUT_SECS env.
Key handling:
- /etc/lakehouse/kimi.env (0600 root) loaded via systemd EnvironmentFile
- KIMI_API_KEY env first, then file scrape as fallback
- /etc/systemd/system/lakehouse.service NOT included in this commit
(system file outside repo); operator must add EnvironmentFile=-
/etc/lakehouse/kimi.env to the lakehouse.service unit
NOT in scrum_master_pipeline LADDER. The 9-rung ladder is for
unattended automatic recovery; placing Kimi there would hammer a
TOS-gated endpoint with hostility-policy potential. Kimi is
addressable via /v1/chat for explicit invocations only — auditor
integration in a follow-up commit.
Verification:
cargo check -p gateway --tests compiles
curl /v1/chat provider=kimi 200 OK, content="PONG"
curl /v1/chat model="kimi/kimi-for-coding" 200 OK (prefix routing)
Kimi audit on distillation last-week 7/7 grounded findings
(reports/kimi/audit-last-week-full.md)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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d77622fc6b |
distillation: fix 7 grounding bugs found by Kimi audit
Kimi For Coding (api.kimi.com, kimi-for-coding) ran a forensic audit on
distillation v1.0.0 with full file content. 7/7 flags verified real on
grep. Substrate now matches what v1.0.0 claimed: deterministic, no
schema bypasses, Rust tests compile.
Fixes:
- mode.rs:1035,1042 matrix_corpus Some/None -> vec![..]/vec![]; cargo
check --tests now compiles (was silently broken;
only bun tests were running)
- scorer.ts:30 SCORER_VERSION env override removed - identical
input now produces identical version stamp, not
env-dependent drift
- transforms.ts:181 auto_apply wall-clock fallback (new Date()) ->
deterministic recorded_at fallback
- replay.ts:378 recorded_run_id Date.now() -> sha256(recorded_at);
replay rows now reproducible given recorded_at
- receipts.ts:454,495 input_hash_match hardcoded true was misleading
telemetry; bumped DRIFT_REPORT_SCHEMA_VERSION 1->2,
field is now boolean|null with honest null when
not computed at this layer
- score_runs.ts:89-100,159 dedup keyed only on sig_hash made
scorer-version bumps invisible. Composite
sig_hash:scorer_version forces re-scoring
- export_sft.ts:126 (ev as any).contractor bypass emitted "<contractor>"
placeholder for every contract_analyses SFT row.
Added typed EvidenceRecord.metadata bucket;
transforms.ts populates metadata.contractor;
exporter reads typed value
Verification (all green):
cargo check -p gateway --tests compiles
bun test tests/distillation/ 145 pass / 0 fail
bun acceptance 22/22 invariants
bun audit-full 16/16 required checks
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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20a039c379 |
auditor: rebuild on mode runner + drop tree-split (use distillation substrate)
Some checks failed
lakehouse/auditor 13 blocking issues: cloud: claim not backed — "Invariants enforced (proven by tests + real run):"
Architectural simplification leveraging Phase 5 distillation work: the auditor no longer pre-extracts facts via per-shard summaries because lakehouse_answers_v1 (gold-standard prior PR audits + observer escalations corpus) supplies cross-PR context through the mode runner's matrix retrieval. Same signal, ~50× fewer cloud calls per audit. Per-audit cost: Before: 168 gpt-oss:120b shard summaries + 3 final inference calls After: 3 deepseek-v3.1:671b mode-runner calls (full retrieval included) Wall-clock on PR #11 (1.36MB diff): Before: ~25 minutes After: 88 seconds (3/3 consensus succeeded) Files: auditor/checks/inference.ts - Default MODEL kimi-k2:1t → deepseek-v3.1:671b. kimi-k2 is hitting sustained Ollama Cloud 500 ISE (verified via repeated trivial probes; multi-hour outage). deepseek is the proven drop-in from Phase 5 distillation acceptance testing. - Dropped treeSplitDiff invocation. Diff truncates to MAX_DIFF_CHARS and goes straight to /v1/mode/execute task_class=pr_audit; mode runner pulls cross-PR context from lakehouse_answers_v1 via matrix retrieval. SHARD_MODEL retained for legacy callCloud compatibility (default qwen3-coder:480b if it ever runs). - extractAndPersistFacts now reads from truncated diff (no scratchpad post-tree-split-removal). auditor/checks/static.ts - serde-derived struct exemption (commit 107a682 shipped this; this commit is the rest of the auditor rebuild it landed alongside) - multi-line template literal awareness in isInsideQuotedString — tracks backtick state across lines so todo!() inside docstrings doesn't trip BLOCK_PATTERNS. crates/gateway/src/v1/mode.rs - pr_audit native runner mode added to VALID_MODES + is_native_mode + flags_for_mode + framing_text. PrAudit framing produces strict JSON {claim_verdicts, unflagged_gaps} for the auditor to parse. config/modes.toml - pr_audit task class with default_model=deepseek-v3.1:671b and matrix_corpus=lakehouse_answers_v1. Documents kimi-k2 outage with link to the swap rationale. Real-data audit on PR #11 head 1b433a9 (which is the PR with all the distillation work + auditor rebuild itself): - Pipeline ran to completion (88s for inference; full audit ~3 min) - 3/3 consensus runs succeeded on deepseek-v3.1:671b - 156 findings: 12 block, 23 warn, 121 info - Block findings are legitimate signal: 12 reviewer claims like "Invariants enforced (proven by tests + real run):" that the truncated diff can't directly verify. The auditor is correctly flagging claim-vs-diff divergence — exactly its job. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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root
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d1d97a045b |
v1: fire observer /event from /v1/chat alongside Langfuse trace
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Observer at :3800 already collects scrum + scenario events into a ring
buffer that pathway-memory + KB consolidation read from. /v1/chat now
posts a lightweight {endpoint, source:"v1.chat", input_summary,
output_summary, success, duration_ms} event there too — fire-and-forget
tokio::spawn, observer-down doesn't block the chat response.
Now any tool routed through our gateway (Pi CLI, Archon, openai SDK
clients, langchain-js) shows up in the same ring buffer the scrum loop
reads, ready for the same KB-consolidation analysis. Independent of the
existing langfuse-bridge that polls Langfuse — this path is immediate.
Verified: GET /stats shows {by_source: {v1.chat: N}} grows by 1 per
chat call, both for direct curl and for Pi CLI invocations.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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540a9a27ee |
v1: accept OpenAI multimodal content shape (array-of-parts)
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Modern OpenAI clients (pi-ai, openai SDK 6.x, langchain-js, the official
agents) send `messages[].content` as an array of content parts:
`[{type:"text", text:"..."}, {type:"image_url", ...}]`. Our gateway
typed `content` as plain `String` and 422'd those calls.
Fix: `Message.content` is now `serde_json::Value` so requests
deserialize regardless of shape. `Message::text()` flattens
content-parts arrays (concat'd `text` fields, non-text parts skipped)
for places that need a plain string — Ollama prompt assembly, char
counts, the assistant's own response synthesis. `Message::new_text()`
constructs string-content messages without writing the wrapper at
each call site. Forwarders (openrouter) clone content through
verbatim so providers see exactly what the client sent.
Verified end-to-end: Pi CLI (`pi --print --provider openrouter`)
landed a clean 1902-token request through `/v1/chat/completions`,
routed to OpenRouter as `openai/gpt-oss-120b:free`, response in
1.62s, Langfuse trace `v1.chat:openrouter` recorded with provider
tag. Same path that any tool using the official openai SDK takes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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root
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3a0b37ed93 |
v1: OpenAI-compat alias + smart provider routing — gateway is now drop-in middleware
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
/v1/chat/completions route alias (same handler as /chat) lets any tool
using the official `openai` SDK adopt the gateway via OPENAI_BASE_URL
alone — no custom provider field needed.
resolve_provider() extended:
- bare `vendor/model` (slash) → openrouter (catches x-ai/grok-4.1-fast,
moonshotai/kimi-k2, deepseek/deepseek-v4-flash, openai/gpt-oss-120b:free)
- bare vendor model names (no slash, no colon) get auto-prefixed:
gpt-* / o1-* / o3-* / o4-* → openai/<name> (OpenRouter form)
claude-* → anthropic/<name>
grok-* → x-ai/<name>
Then routed to openrouter. Ollama models (with colon, no slash) keep
default routing. Tools like pi-ai validate against an OpenAI-style
catalog and send bare names — this lets them flow through cleanly.
Verified end-to-end:
- curl POST /v1/chat/completions {model: "gpt-4o-mini", ...} → 200,
routed to openrouter as openai/gpt-4o-mini
- openai SDK with baseURL=http://localhost:3100/v1 → 3 model variants all
succeed (openai/gpt-4o-mini, gpt-4o-mini, x-ai/grok-4.1-fast)
- Langfuse traces fire automatically on every call
(v1.chat:openrouter, provider tagged in metadata)
scripts/mode_pass5_variance_paid.ts gains LH_CONDITIONS env so subset
runs (e.g. just isolation vs composed) take half the latency.
Archon-on-Lakehouse integration: gateway side is done. Pi-ai's
openai-responses backend uses /v1/responses (not /chat/completions) and
its openrouter backend appears to bail in client-side validation before
sending. Patching Pi locally to override baseUrl works for arch but the
harness still rejects — needs more work in a follow-up. Direct openai
SDK path (langchain-js / agents / patched Pi) works today.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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2dbc8dbc83 |
v1/mode: model-aware enrichment downgrade + 3 corpora + variance harness
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Pass 5 (5 reps × 4 conditions × 1 file on grok-4.1-fast) showed composing matrix corpora is anti-additive on strong models — composed lakehouse_arch + symbols LOST 5/5 head-to-head vs codereview_isolation (Δ −1.8 grounded findings, p=0.031). Default flips to isolation; matrix path now auto- downgrades when the resolved model is strong. Mode runner: - matrix_corpus is Vec<String> (string OR array via deserialize_string_or_vec) - top_k=6 from each corpus, merge by score, take top 8 globally - chunk tag prefers doc_id over source so reviewer sees [adr:009] vs [lakehouse_arch] - is_weak_model() gate auto-downgrades codereview_lakehouse → codereview_isolation for strong models (default-strong; weak = :free suffix or local last-resort) - LH_FORCE_FULL_ENRICHMENT=1 bypasses for diagnostic runs - EnrichmentSources.downgraded_from records when the gate fires Three corpora indexed via /vectors/index (5849 chunks total): - lakehouse_arch_v1 — ADRs + phases + PRD + scrum spec (93 docs, 2119 chunks) - scrum_findings_v1 — past scrum_reviews.jsonl (168 docs, 1260 chunks; EXCLUDED from defaults — 24% out-of-bounds line citations from cross-file drift) - lakehouse_symbols_v1 — regex-extracted pub items + /// docs (656 docs, 2470 chunks) Experiment infra: - scripts/build_*_corpus.ts — re-runnable when source content changes - scripts/mode_pass5_variance_paid.ts — N reps × M conditions on one file - scripts/mode_pass5_summarize.ts — mean ± σ + head-to-head, parser handles numbered + path-with-line + path-with-symbol finding tables - scripts/mode_compare.ts — groups by mode|corpus when sweeps span corpora - scripts/mode_experiment.ts — default model bumped to x-ai/grok-4.1-fast, --corpus flag for per-call override Decisions + open follow-ups: docs/MODE_RUNNER_TUNING_PLAN.md Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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56bf30cfd8 |
v1/mode: override knobs + staffing native runner + pass 2/3/4 harnesses
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Setup for the corpus-tightening experiment sweep (J 2026-04-26 — "now
is the only cheap window before the corpus gets large and refactoring
costs go up").
Override params on /v1/mode/execute (additive — old callers unaffected):
force_matrix_corpus — Pass 2: try alternate corpora per call
force_relevance_threshold — Pass 2: sweep filter strictness
force_temperature — Pass 3: variance test
New native mode `staffing_inference_lakehouse` (Pass 4):
- Same composer architecture as codereview_lakehouse
- Staffing framing: coordinator producing fillable|contingent|
unfillable verdict + ranked candidate list with playbook citations
- matrix_corpus = workers_500k_v8
- Validates that modes-as-prompt-molders generalizes beyond code
- Framing explicitly says "do NOT fabricate workers" — the staffing
analog of the lakehouse mode's symbol-grounding requirement
Three sweep harnesses:
scripts/mode_pass2_corpus_sweep.ts — 4 corpora × 4 thresholds × 5 files
scripts/mode_pass3_variance.ts — 3 files × 3 temps × 5 reps
scripts/mode_pass4_staffing.ts — 5 fill requests through staffing mode
Each appends per-call rows to data/_kb/mode_experiments.jsonl which
mode_compare.ts already aggregates with grounding column.
Pass 1 (10 files × 5 modes broad sweep) currently running via the
existing scripts/mode_experiment.ts — gateway restart deferred until
it completes so the new override knobs aren't enabled mid-experiment.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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7c47734287 |
v1/mode: parameterized runner + 5 enrichment-experiment modes
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
J's directive (2026-04-26): "Create different modes so we can really
dial in the architecture before it gets further along — pinpoint the
failures and strengths equally so I know what direction to go in.
Loop theater happens when we don't pinpoint the most accurate path."
Refactored execute() to switch on mode name → EnrichmentFlags preset.
Five native modes designed as deliberate experiments — each isolates
one architectural axis so the comparison matrix reads off what's
doing work vs what's adding latency for nothing:
codereview_lakehouse — all enrichment on (ceiling)
codereview_null — raw file + generic prompt (baseline)
codereview_isolation — file + pathway only (no matrix)
codereview_matrix_only — file + matrix only (no pathway)
codereview_playbook_only — pathway only, NO file content (lossy ceiling)
Each call appends a row to data/_kb/mode_experiments.jsonl with full
sources + response. LH_MODE_LOG_OFF=1 to suppress.
scripts/mode_experiment.ts — sweeps files × modes serially, prints
live progress with per-call enrichment stats. Defaults to OpenRouter
free model so cloud quota doesn't gate experiments.
scripts/mode_compare.ts — reads the JSONL, outputs per-file matrix
+ per-mode aggregate + mode-vs-baseline win/loss with avg finding
delta. Heuristic finding-count from markdown table rows; pathway
citation count from preamble references.
scrum_master_pipeline.ts gets a mode-runner fast path gated by
LH_USE_MODE_RUNNER=1: try /v1/mode/execute first, fall through to
the existing ladder if response < LH_MODE_MIN_CHARS (default 2000)
or anything errors. Off by default until A/B-validated.
First experiment results (2 files × 5 modes via gpt-oss-120b:free):
- codereview_null produces 12.6KB response with ZERO findings
(proves adversarial framing is load-bearing)
- codereview_playbook_only produces MORE findings than lakehouse
on average (12 vs 9) at 73% the latency — pathway memory is
the dominant signal driver
- codereview_matrix_only underperforms isolation by ~0.5 findings
while costing the same latency — matrix corpus likely
underperforming for scrum_review task class
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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86f63a083d |
v1/mode: codereview_lakehouse native runner — modes are prompt-molders
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
J's framing (2026-04-26): "Modes are how you ask ONCE and get BETTER
information — they mold the data, hyperfocus the prompt on this
codebase's needs, so the model gets it right the first time without
the cascading retry ladder."
Built the first concrete native enrichment runner (codereview_lakehouse)
that composes every context primitive the gateway exposes:
1. Focus file content (read from disk OR caller-supplied)
2. Pathway memory bug_fingerprints for this file area (ADR-021
preamble — "📚 BUGS PREVIOUSLY FOUND IN THIS FILE AREA")
3. Matrix corpus search via the task_class's matrix_corpus
4. Relevance filter (observer /relevance) drops adjacency pollution
5. Assembles ONE precise prompt with system framing
6. Single call to /v1/chat with the recommended model
POST /v1/mode/execute dispatches. Native mode → runs the composer.
Non-native mode → 501 NOT_IMPLEMENTED with hint (proxy to LLM Team
/api/run is queued).
Provider hint logic auto-routes by model name shape:
- vendor/model[:tag] → openrouter
- kimi-*/qwen3-coder*/deepseek-v*/mistral-large* → ollama_cloud
- everything else → local ollama
Live test against crates/queryd/src/delta.rs (10593 bytes, 10
historical bug fingerprints, 2 matrix chunks dropped by relevance):
- enriched_chars: 12876
- response_chars: 16346 (14 findings with confidence percentages)
- Model literally cited the pathway memory preamble in finding #7
- One call to free-tier gpt-oss:120b produced what previously
required the 9-rung escalation ladder
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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d277efbfd2 |
v1/mode: task_class → mode/model router (decision-only, phase 1)
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
HANDOVER §queued (2026-04-25): "Mode router — port LLM Team multi-model
patterns. Pick the right TOOL/MODE for each task class via the matrix,
not cascade through models."
Two-stage architecture:
1. Decision (POST /v1/mode) — pure recommendation, no execution.
Returns {mode, model, decision: {source, fallbacks, matrix_corpus,
notes}} so callers see WHY this mode was picked.
2. Execution (future POST /v1/mode/execute) — proxy to LLM Team
/api/run for modes not yet ported to native Rust runners. Not
wired in this phase.
Splitting decision from execution lets us A/B-test the routing logic
without committing to running every recommendation. The decision
function is pure enough for exhaustive unit tests (3 added).
config/modes.toml — initial map for 5 task_classes (scrum_review,
contract_analysis, staffing_inference, fact_extract, doc_drift_check)
+ a default. matrix_corpus per task is reserved for the future
matrix-informed routing pass.
VALID_MODES list (24 modes) is kept in sync manually with LLM Team's
/api/run handler at /root/llm_team_ui.py:10581. Adding a mode here
without adding it upstream returns 400 from a future proxy.
GET /v1/mode/list — operator introspection so a UI can render the
registry table without re-parsing TOML.
Live-tested: 5 task classes match, unknown classes fall through to
default, force_mode override works + validates, bogus modes return
400 with the valid_modes list.
Updates reference_llm_team_modes.md memory — earlier note claiming
"only extract is registered" was wrong (all 25 are registered).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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0b3bd28cf8 |
phase-40: Gemini + Claude provider adapters
Phase 40 PRD (docs/CONTROL_PLANE_PRD.md:82-83) listed: - crates/aibridge/src/providers/gemini.rs - crates/aibridge/src/providers/claude.rs Neither existed. Landing both now, in gateway/src/v1/ (matches the existing ollama.rs + openrouter.rs sibling pattern — aibridge's providers/ is for the adapter *trait* abstractions, v1/ holds the concrete /v1/chat dispatchers that know the wire format). gemini.rs: - POST https://generativelanguage.googleapis.com/v1beta/models/ {model}:generateContent?key=<API_KEY> - Auth: query-string key (not bearer) - Maps messages → contents+parts (Gemini's wire shape), extracts from candidates[0].content.parts[0].text - 3 tests: key resolution, body serialization (camelCase generationConfig + maxOutputTokens), prefix-strip claude.rs: - POST https://api.anthropic.com/v1/messages - Auth: x-api-key header + anthropic-version: 2023-06-01 - Carries system prompt in top-level `system` field (not messages[]). Extracts from content[0].text where type=="text" - 4 tests: key resolution, body serialization with/without system field, prefix-strip v1/mod.rs: + V1State.gemini_key + claude_key Option<String> + resolve_provider() strips "gemini/" and "claude/" prefixes + /v1/chat dispatcher handles "gemini" + "claude"/"anthropic" + 2 new resolve_provider tests (prefix + strip per adapter) main.rs: + Construct both keys at startup via resolve_*_key() helpers. Missing keys log at debug (not warn) since these are optional providers — unlike OpenRouter which is the rescue rung. Every /v1/chat error path mirrors the existing pattern: - 503 SERVICE_UNAVAILABLE when key isn't configured - 502 BAD_GATEWAY with the provider's error text when the upstream call fails - Response shape always the OpenAI-compatible ChatResponse Workspace warnings still at 0. 9 new tests pass. Pre-existing test failure `executor_prompt_includes_surfaced_ candidates` at execution_loop/mod.rs:1550 is unrelated (fails on pristine HEAD too — PR fixture divergence). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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999abd6999 |
gateway/v1: model-prefix routing closes Phase 39 PRD gate
Some checks failed
lakehouse/auditor 4 blocking issues: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
Phase 39 PRD (docs/CONTROL_PLANE_PRD.md:62) promised:
"/v1/chat routes by `model` field: prefix match
(e.g. openrouter/anthropic/claude-3.5-sonnet → OpenRouter;
bare names → Ollama)"
Actual behavior required clients to pass `provider: "openrouter"`
explicitly. Bare `model: "openrouter/..."` would fall through to the
"unknown provider ''" error. PRD gate never actually passed.
Fix: resolve_provider(&ChatRequest) picks (provider, effective_model):
- explicit `req.provider` wins, model passes through unchanged
- else strip "openrouter/" prefix → provider="openrouter", model
without prefix (OpenRouter API expects "openai/gpt-4o-mini",
not "openrouter/openai/gpt-4o-mini")
- else strip "cloud/" prefix → provider="ollama_cloud"
- else default provider="ollama"
Adapter calls use Cow<ChatRequest>: borrowed when no strip needed
(zero alloc), owned when we needed to build a new model string. Keeps
the hot path allocation-free for the common case.
ChatRequest gains #[derive(Clone)] — needed for the Owned variant.
5 new tests pin the resolution semantics including the
"explicit provider + prefixed model" corner case (trust the caller,
don't double-strip).
Workspace warnings unchanged at 0.
Still not shipped from Phase 39: config/providers.toml — hardcoded
match arms work fine in practice, centralizing them is cosmetic.
Flag as a follow-up if a 4th provider lands.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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5df4d48109 |
cleanup: drop two #[allow] attributes that were hiding real dead code
Some checks failed
lakehouse/auditor 1 blocking issue: todo!() macro call in tests/real-world/scrum_master_pipeline.ts
- ingestd/src/service.rs: top-of-file `#[allow(unused_imports)]`
was masking genuinely unused `delete` and `patch` routing
constructors in an axum import block. Removed the attribute,
trimmed the imports to only `get` and `post` (what's actually
used). Any future over-import now trips the unused_imports
lint immediately instead of being silently allowed.
- gateway/src/v1/truth.rs: `truth_router()` was a 4-line stub
wrapping a single `/context` route — carried `#[allow(dead_code)]`
because v1/mod.rs wires `get(truth::context)` directly onto its
own router, bypassing this helper. Zero callers across the
workspace. Deleted the function + allow + now-unused Router
import. Left a breadcrumb comment pointing to the real wiring.
Workspace warnings: 0 (lib + tests). Each #[allow] removed raises
the bar on future code entering these modules — the linter now
catches the same classes of bugs at PR time.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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8b77d67c9c |
OpenRouter rescue ladder + tree-split reduce fix + observer→LLM Team + scrum_applier + first auto-applied patch
Some checks failed
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "journal event verified live (total_events_created 0→1 after probe)."
## Infrastructure (scrum loop hardening)
crates/gateway/src/v1/openrouter.rs — new OpenRouter provider
Direct HTTPS to openrouter.ai/api/v1/chat/completions with OpenAI-compatible shape.
Key resolution: OPENROUTER_API_KEY env → /home/profit/.env → /root/llm_team_config.json
(shares LLM Team UI's quota). Added after iter 5 hit repeated Ollama Cloud 502s on
kimi-k2:1t — different provider backbone as rescue rung. Unit tests pin the URL
stripping and OpenAI wire shape.
crates/gateway/src/v1/mod.rs + main.rs
Added `"openrouter" | "openrouter_free"` arm to /v1/chat dispatch.
V1State.openrouter_key loaded at startup via openrouter::resolve_openrouter_key()
mirroring the Ollama Cloud pattern. Startup log:
"v1: OpenRouter key loaded — /v1/chat provider=openrouter enabled"
tests/real-world/scrum_master_pipeline.ts
* 9-rung ladder — kimi-k2:1t → qwen3-coder:480b → deepseek-v3.1:671b →
mistral-large-3:675b → gpt-oss:120b → qwen3.5:397b → openrouter/gpt-oss-120b:free
→ openrouter/gemma-3-27b-it:free → local qwen3.5:latest.
Added qwen3-coder:480b as rung 2 after live probes confirmed it rescues
kimi-k2:1t 502s cleanly (0.9s latency, substantive reviews).
Dropped devstral-2 (displaced by qwen3-coder); dropped kimi-k2.6 (not available);
dropped minimax-m2.7 (returned 0 chars / 400 thinking tokens).
Local fallback promoted qwen3.5:latest per J's direction 2026-04-24.
* MAX_ATTEMPTS bumped 6 → 9 to accommodate the rescue tier.
* Tree-split scratchpad fixed — was concatenating shard markers directly
into the reviewer input, causing kimi-k2:1t to write titles like
"Forensic Audit Report – file.rs (shard 3)". Now uses internal §N§
markers during accumulation and runs a proper reduce step that
collapses per-shard digests into ONE coherent file-level synthesis
with markers stripped. Matches the Phase 21 aibridge::tree_split
map→reduce design. Fallback to stripped scratchpad if reducer returns thin.
tests/real-world/scrum_applier.ts — NEW (737 lines)
The auto-apply pipeline. Reads scrum_reviews.jsonl, filters rows where
gradient_tier ∈ {auto, dry_run} AND confidence_avg ≥ MIN_CONF (default 90),
asks the reviewer model for concrete old_string/new_string patch JSON,
applies via text replacement, runs cargo check after each file, commits
if green and reverts if red. Deny-list: /etc/, config/, ops/, auditor/,
docs/, data/, mcp-server/, ui/, sidecar/, scripts/. Hard caps: per-patch
confidence ≥ MIN_CONF, old_string must be exactly unique, max 20 lines per
patch. Never runs on main without explicit LH_APPLIER_BRANCH override.
Audit trail in data/_kb/auto_apply.jsonl.
Empirical behavior (dry-run over iter 4 reviews):
5 eligible files → 1 green commit-ready, 2 build-red reverts, 2 all-rejected
The build-green gate caught 2 bad patches before they'd have merged.
mcp-server/observer.ts — LLM Team code_review escalation
When a sig_hash accumulates ≥3 failures (ESCALATION_THRESHOLD), fire-and-forget
POST /api/run?mode=code_review at localhost:5000 with the failure cluster context.
Parses facts/entities/relationships/file_hints from the response. Writes to a
new data/_kb/observer_escalations.jsonl surface. Answers J's vision of the
observer triggering richer LLM Team calls when failures pile up.
Non-blocking: runs parallel to existing qwen2.5 analyzer, never replaces it.
Tracks escalated sig_hashes in a session-local Set to avoid re-hammering
LLM Team when a cluster persists across observer cycles.
crates/aibridge/src/context.rs
First auto-applied patch produced by scrum_applier.ts (dry-run path —
applier writes files in dry-run mode but doesn't commit; bug noted for
iter 6 fix). Adds #[deprecated] annotation to the inline estimate_tokens
helper pointing callers to the centralized shared::model_matrix::ModelMatrix
entry point (P21-002 — duplicate token-estimator surfaces). Cargo check
passes with the annotation (verified by applier's own build gate).
## Visual Control Plane (UI)
ui/server.ts — Bun.serve on :3950 with /data/* fan-out:
/data/services, /data/reviews, /data/metrics, /data/trust, /data/overrides,
/data/findings, /data/outcomes, /data/audit_facts, /data/file/:path,
/data/refactor_signals, /data/search?q=, /data/signal_classes,
/data/logs/:svc (journalctl tail per systemd unit), /data/scrum_log.
Bug fix: tryFetch always attempts JSON.parse before falling back to text
— observer's Bun.serve returns JSON without application/json content-type,
which was displaying stats as a raw string ("0 ops" on map) before.
ui/index.html + ui.css — dark neo-brutalist shell. 6 views:
MAP (D3 force-graph + overlays) / TRACE (per-file iter history) /
TRAJECTORY (signal-class cards + refactor-signals table + reverse-index
search box) / METRICS (every card has SOURCE + GOOD lines explaining
where the number comes from and what target trajectory means) /
KB (card grid with tooltips on every field) / CONSOLE (per-service
journalctl tabs).
ui/ui.js — polling client, D3 wiring, signal-class panel, refactor-signals
table, reverse-index search, per-service console tabs. Bug fix:
renderNodeContext had Object.entries() iterating string characters when
/health returned a plain string — now guards with typeof check so
"lakehouse ok" renders as one row instead of "0 l / 1 a / 2 k / ...".
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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21fd3b9c61 |
Scrum-driven fixes: P5-001 auth wired, P42-001 truth evaluator, P9-001 journal on ingest
Some checks failed
lakehouse/auditor 2 blocking issues: cloud: claim not backed — "| **P9-001** (partial) | `crates/ingestd/src/service.rs` | **3 → 6** ↑↑↑ | `journal.record_ing
Apply the highest-confidence findings from the Phase 0→42 forensic sweep
after four scrum-master iterations under the adversarial prompt. Each fix
is independently validated by a later scrum iteration scoring the same
file higher under the same bar.
Code changes
────────────
P5-001 — crates/gateway/src/auth.rs + main.rs
api_key_auth was marked #[allow(dead_code)] and never wrapped around
the router, so `[auth] enabled=true` logged a green message and
enforced nothing. Now wired via from_fn_with_state, with constant-time
header compare and /health exempted for LB probes.
P42-001 — crates/truth/src/lib.rs
TruthStore::check() ignored RuleCondition entirely — signature looked
like enforcement, body returned every action unconditionally. Added
evaluate(task_class, ctx) that actually walks FieldEquals / FieldEmpty /
FieldGreater / Always against a serde_json::Value via dot-path lookup.
check() kept for back-compat. Tests 14 → 24 (10 new exercising real
pass/fail semantics). serde_json moved to [dependencies].
P9-001 (partial) — crates/ingestd/src/service.rs
Added Optional<Journal> to IngestState + a journal.record_ingest() call
on /ingest/file success. Gateway wires it with `journal.clone()` before
the /journal nest consumes the original. First-ever internal mutation
journal event verified live (total_events_created 0→1 after probe).
Iter-4 scrum scored these files higher under same prompt:
ingestd/src/service.rs 3 → 6 (P9-001 visible)
truth/src/lib.rs 3 → 4 (P42-001 visible)
gateway/src/auth.rs 3 → 4 (P5-001 visible)
gateway/src/execution_loop 4 → 6 (indirect)
storaged/src/federation 3 → 4 (indirect)
Infrastructure additions
────────────────────────
* tests/real-world/scrum_master_pipeline.ts
- cloud-first ladder: kimi-k2:1t → deepseek-v3.1:671b → mistral-large-3:675b
→ gpt-oss:120b → devstral-2:123b → qwen3.5:397b (deep final thinker)
- LH_SCRUM_FORENSIC env: injects SCRUM_FORENSIC_PROMPT.md as adversarial preamble
- LH_SCRUM_PROPOSAL env: per-iter fix-wave doc override
- Confidence extraction (markdown + JSON), schema v4 KB rows with:
verdict, critical_failures_count, verified_components_count,
missing_components_count, output_format, gradient_tier
- Model trust profile written per file-accept to data/_kb/model_trust.jsonl
- Fire-and-forget POST to observer /event so by_source.scrum appears in /stats
* mcp-server/observer.ts — unchanged in shape, confirmed receiving scrum events
* ui/ — new Visual Control Plane on :3950
- Bun.serve with /data/{services,reviews,metrics,trust,overrides,findings,file,refactor_signals,search,logs/:svc,scrum_log}
- Views: MAP (D3 graph, 5 overlays) / TRACE (per-file iter timeline) /
TRAJECTORY (refactor signals + reverse index search) / METRICS (explainers
with SOURCE + GOOD lines) / KB (card grid with tooltips) / CONSOLE (per-service
journalctl tail, tabs for gateway/sidecar/observer/mcp/ctx7/auditor/langfuse)
- tryFetch always attempts JSON.parse (fix for observer returning JSON without content-type)
- renderNodeContext primitive-vs-object guard (fix for gateway /health string)
* docs/SCRUM_FIX_WAVE.md — iter-specific scope directing the scrum
* docs/SCRUM_FORENSIC_PROMPT.md — adversarial audit prompt (verdict/critical/verified schema)
* docs/SCRUM_LOOP_NOTES.md — iteration observations + fix-next-loop queue
* docs/SYSTEM_EVOLUTION_LAYERS.md — Layers 1-10 roadmap (trust profiling, execution DNA, drift sentinel, etc)
Measurements across iterations
──────────────────────────────
iter 1 (soft prompt, gpt-oss:120b): mean score 5.00/10
iter 3 (forensic, kimi-k2:1t): mean score 3.56/10 (−1.44 — bar raised)
iter 4 (same bar, post fixes): mean score 4.00/10 (+0.44 — fixes landed)
Score movement iter3→iter4: ↑5 ↓1 =12
21/21 first-attempt accept by kimi-k2:1t in iter 4
20/21 emitted forensic JSON (richer signal than markdown)
16 verified_components captured (proof-of-life, new metric)
Permission Gradient distribution: 0 auto · 16 dry_run · 4 sim · 1 block
Observer loop: by_source {scrum: 21, langfuse: 1985, phase24_audit: 1}
v1/usage: 224 requests, 477K tokens, all tracked
Signal classes per file (iter 3 → iter 4):
CONVERGING: 1 (ingestd/service.rs — fix clearly landed)
LOOPING: 4 (catalogd/registry, main, queryd/service, vectord/index_registry)
ORBITING: 1 (truth — novel findings surfacing as surface ones fix)
PLATEAU: 9 (scores flat with high confidence — diminishing returns)
MIXED: 6
Loop thesis status
──────────────────
A file's score rises only when the scrum confirms a real fix landed.
No false positives yet across 3 iterations. Fixes applied to 3 files all
raised their independent scores under the same adversarial prompt. Loop
is measurable, not hand-wavy.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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55f8e0fe6e |
Phase 40: Routing Engine + Policy
- RoutingEngine with RouteDecision (model_pattern → provider) - config/routing.toml: rules, fallback chain, cost gating - Per-provider Usage tracking in /v1/usage response - 12 gateway tests green |
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profit
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75a0f424ef |
Phase 40 (early): Langfuse tracing on /v1/chat — observability recovery
The lost stack J flagged was partly already present: Langfuse container has been running 2 days with the staffing project, SDK installed, mcp-server tracing gw:/* routes. What was missing was Rust-side /v1/chat emission — the new Phase 38/39 code bypassed Langfuse entirely. This commit bridges it. Fire-and-forget HTTP POST to http://localhost:3001/api/public/ingestion (batch {trace-create + generation-create}) on every chat call. Non-blocking — spawned tokio task, response latency unaffected. Trace failures log warn and drop, never propagate. Verified end-to-end after restart: - Log line "v1: Langfuse tracing enabled" at startup - /v1/chat local (qwen3.5:latest) → v1.chat:ollama trace appears with lat=0.41s, 24+6 tokens - /v1/chat cloud (gpt-oss:120b) → v1.chat:ollama_cloud trace appears with lat=1.87s, 73+87 tokens - mcp-server's existing gw:/log + gw:/intelligence/* traces continue to flow into the same project unchanged Files: - crates/gateway/src/v1/langfuse_trace.rs (new, 195 LOC) — thin client, no SDK. reqwest Basic Auth. ChatTrace payload + event serializer. from_env_or_defaults() resolver matches mcp-server/tracing.ts conventions (pk-lf-staffing / sk-lf- staffing-secret / localhost:3001) - crates/gateway/src/v1/mod.rs — V1State.langfuse field, emission after successful provider call (post-dispatch, pre-usage-update) - crates/gateway/src/main.rs — resolve + log at startup Tests: 12/12 green (9 prior + 3 for langfuse_trace: ingestion-batch serialization, uuid generator uniqueness, env resolver shape). Recovered piece #1 of 3 from the lost-stack narrative. Still open: - Langfuse → observer :3800 pipe (Phase 40 mid-deliverable) - Gitea MCP reconnect in mcp-server/index.ts (Phase 40 late) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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42a11d35cd |
Phase 39 (first slice): Ollama Cloud adapter on /v1/chat
Second provider wired. /v1/chat now routes by optional `provider` field: default "ollama" hits local via sidecar, "ollama_cloud" (or "cloud") hits ollama.com/api/generate directly with Bearer auth. Key sourced at gateway startup from OLLAMA_CLOUD_KEY env, then /root/llm_team_config.json (providers.ollama_cloud.api_key), then OLLAMA_CLOUD_API_KEY env. Config source matches LLM Team convention. Shape-identical to scenario.ts::generateCloud — same endpoint, same body, same Bearer auth. Cloud path bypasses sidecar entirely (sidecar is local-only by design, mirrors TS agent.ts). Changes: - crates/gateway/src/v1/ollama_cloud.rs (new, 130 LOC) — reqwest client, resolve_cloud_key(), chat() adapter, CloudGenerateBody / CloudGenerateResponse wire shapes - crates/gateway/src/v1/ollama.rs — flatten_messages_public() re-export so sibling adapters reuse the shape collapse - crates/gateway/src/v1/mod.rs — provider field on ChatRequest, dispatch match in chat() handler, ollama_cloud_key on V1State - crates/gateway/src/main.rs — resolves cloud key at startup, logs which source provided it - crates/gateway/Cargo.toml — reqwest 0.12 with rustls-tls Verified end-to-end after restart: - provider=ollama → qwen3.5:latest local (~400ms, Phase 38 unchanged) - provider=ollama_cloud + model=gpt-oss:120b → real 225-word technical response in 5.4s, 313 tokens Tests: 9/9 green (7 from Phase 38 + 2 new for cloud body serialization and key resolver shape). Not in this slice: trait extraction (full Phase 39 scope adds ProviderAdapter trait + OpenRouter adapter + fallback chain logic). These land next with Phase 40 routing engine on top. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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8cbbd0ef70 |
Phase 38 fix: default think=false on /v1/chat
Live-test caught the Phase 21 thinking-model trap on first call. qwen3.5 with max_tokens=50 and default think behavior burned all 50 tokens on hidden reasoning; visible content was "". completion_tokens exactly matching max_tokens was the tell. Adapter now defaults think: Some(false) matching scenario.ts hot-path discipline. Callers that want reasoning (overseers, T3+) opt in via a non-OpenAI `think: true` extension field on the request. Verified end-to-end after restart: - "Lakehouse supports ACID and raw data." (5 words, 516ms) - "tokio\nasync-std\nsmol" (3 Rust crates, 391ms) - /v1/usage accumulates across calls (2 req / 95 total tokens) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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4cb405bb42 |
Phase 38: Universal API skeleton — /v1/chat, /v1/usage, /v1/sessions
First slice of the control-plane pivot. OpenAI-compatible surface
over the existing aibridge → Ollama path. Additive — no existing
routes touched. All 7 unit tests green, release build clean.
What ships:
- crates/gateway/src/v1/mod.rs — router, V1State (ai_client + Usage
counter), ChatRequest/ChatResponse/Message/UsageBlock types, handlers
for /chat, /usage, /sessions. OpenAI-compatible field shapes:
{model, messages[{role,content}], temperature?, max_tokens?, stream?}
- crates/gateway/src/v1/ollama.rs — shape adapter. Flattens messages
into (system, prompt), calls aibridge.generate, unwraps response
back into OpenAI /v1/chat shape. Prefers sidecar-reported tokens;
falls back to chars/4 ceiling estimate matching Phase 21 convention.
- crates/gateway/src/main.rs — one new mod, one .nest("/v1", ...)
Tests (7/7):
- chat_request_parses_openai_shape
- chat_request_accepts_minimal
- usage_counter_default_is_zero
- flatten_separates_system_from_turns
- flatten_concatenates_multiple_system_messages
- flatten_with_no_system_returns_empty_system
- estimate_tokens_chars_div_4_ceiling
Not in this phase (per CONTROL_PLANE_PRD.md): streaming, tool calls,
session state, multi-provider, fallback chain, cost gating. All
land in Phases 39-44.
Next: live-test POST /v1/chat after gateway restart, then migrate
bot/propose.ts off direct sidecar calls to prove the loop end-to-end.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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