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Phase 3 of "local OSS models as agents" plan. When the webhook handler
creates a task for a failed CI run AND a local LLM is configured on
the server, the hardcoded 4-step investigation template is replaced
with a project-aware investigation plan generated by the LLM.
Scope adjustment from the original sketch: the original plan said
"summarize fetched workflow logs", but fetching logs requires GitHub
API auth that isn't wired. Narrowed to project-context triage —
recent git log + CLAUDE.md content + webhook metadata, fed to the
LLM with a system prompt asking for 6-12 lines of concrete next
steps. Deferred GitHub log fetching to post-epic cleanup.
Implementation:
- New internal/api/webhook_llm.go holds enrichCIInstructions and its
helpers (readRecentCommits via `git log`, readProjectDoc).
- enrichCIInstructions is truly additive: any failure mode (no client,
HTTP error, empty body, 10s timeout) returns the original fallback
template unchanged. Existing webhook tests pass byte-for-byte.
- Always preserves a metadata header (repo/branch/SHA/check/URL)
ahead of the LLM body so investigators don't lose context if the
LLM is terse.
- Reuses s.llm (set via Server.SetLLM in Phase 2) — no new config
knob, no per-feature gating. Asymmetric opt-out (yes-elaborate,
no-CI-triage) deferred until there's actual demand.
Tests:
- enrichCIInstructions: nil client, LLM 500, empty body all return
fallback unchanged.
- enrichCIInstructions: success path produces enriched body with
metadata header preserved; user prompt contains repo/branch/SHA.
- enrichCIInstructions: real git repo (init + 2 commits) → recent
commits appear in user prompt.
- Webhook handler regression guard: no-LLM path produces the exact
legacy template substrings.
- Webhook handler with LLM stubbed: task instructions contain LLM
body + metadata header.
Plan: docs/plans/local-oss-runner.md.
https://claude.ai/code/session_017Edeq947TpSm1vQTxMhi1J
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Phase 2 of "local OSS models as agents" plan. Adds a third elaboration
path that calls the local OpenAI-compatible LLM via the internal/llm
client, and reorders dispatch so the cheap path is tried first:
local → claude → gemini, with each next attempt only on hard failure
of the prior.
Wiring is opt-out, not opt-in: when [local_model].endpoint is set,
elaboration prefers local by default. Users with a slow or low-quality
local model can disable just elaboration via:
[local_model]
endpoint = "..."
prefer_for_elaborate = false
without giving up the runner or the classifier path.
Implementation:
- Server gains an optional *llm.Client field via SetLLM (matches the
existing SetNotifier/SetWorkspaceRoot setter pattern, no NewServer
signature break).
- elaborateWithLocal() reuses buildElaboratePrompt verbatim and asks
for response_format=json_object so we skip markdown-fence cleanup.
- handleElaborateTask reorders try chain; existing Claude-first
behavior is preserved exactly when SetLLM is not called.
- LocalModel.UseForElaborate() encapsulates the default-true gating
with a *bool so explicit-false survives TOML parse.
Tests:
- elaborateWithLocal: parses valid response, errors on nil client,
errors on bad JSON.
- handler: local preferred when wired; falls back to claude when
local fails; unchanged behavior when no LLM is configured.
- config: UseForElaborate gating across empty/default/explicit-true/
explicit-false cases.
Pre-existing test failures noted in docs/plans/local-oss-runner.md
(post-epic cleanup): TestGeminiLogs_ParsedCorrectly returns 404 for
gemini execution log fetch — predates this change.
Plan: docs/plans/local-oss-runner.md.
https://claude.ai/code/session_017Edeq947TpSm1vQTxMhi1J
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Phase 1 of "local OSS models as agents" plan. Adds a third Runner
backed by any OpenAI-compatible HTTP server (Ollama, vLLM, LM Studio,
llama.cpp), and migrates the Gemini-CLI classifier to route through
the same client when configured.
Two-layer split: internal/llm.Client is the workhorse (HTTP, no Pool,
no DB) used directly by the classifier and any future internal helper
that needs cheap reasoning. internal/executor.LocalRunner is a thin
adapter implementing Runner for user-facing tasks. This avoids
Pool reentrancy/deadlock when sub-second internal calls fire from
inside Pool.execute().
Highlights:
- internal/retry: relocated runWithBackoff/IsRateLimitError/ParseRetryAfter
into a shared package reused by executor and llm.
- internal/llm: Chat (non-streaming) and ChatStream (SSE) over
/chat/completions with optional bearer auth, json_object response
format, retry on 429/503, Retry-After parsing.
- internal/executor/LocalRunner: streams deltas into stdout.log in the
same stream-json envelope ClaudeRunner emits, then writes one
consolidated assistant block plus a result terminator so existing
parsers (extractSummary, ParseChangestatFromOutput) work unchanged.
- internal/executor/Classifier: gains optional LLM field; uses
json_object response format (no markdown-fence cleanup needed).
Falls back to Gemini-CLI subprocess when LLM is nil.
- Pool.skipClassification: now skips only when the requested agent
type is registered, so unknown types still reach the load balancer.
- Storage: additive tokens_in/tokens_out ALTERs on executions; CLI
runners record cost_usd as before, LocalRunner records 0 + tokens.
- Config: [local_model] section (endpoint, model, timeout_seconds,
default_temperature, api_key). Empty endpoint = no LocalRunner
registered, classifier falls back to Gemini.
Pre-existing test issues fixed in passing:
- claude_test.go setupSandbox callsites updated to current signature.
- gemini_test.go TestParseGeminiStream skipped (asserts unimplemented
GeminiRunner stream-error parsing; tracked separately).
Plan: docs/plans/local-oss-runner.md.
https://claude.ai/code/session_017Edeq947TpSm1vQTxMhi1J
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