claudomator — task automation server https://git.terst.org/claudomator.git/atom?h=fix%2Fdockersandbox-gitpush-host-security 2026-05-26T07:11:59+00:00 2026-05-26T07:11:59+00:00 Claude noreply@anthropic.com 2026-05-26T07:11:59+00:00 urn:sha1:301e7a66387f99ab76754d08bca42f4a9930d3b1 LocalRunner previously ignored the AgentChannel and produced a single fire-and-forget completion. It now declares the four agent back-channel tools (ask_user/report_summary/spawn_subtask/record_progress) as OpenAI function-calling definitions and runs a tool-use loop: each turn feeds tool results back as message history (re-feed) until the model stops calling tools, bounded by maxLocalToolTurns. ask_user converts a buffered question into a *BlockedError so the task blocks like the container runners. Adds tool-use support to the llm client (Tool/ToolCall/ToolFunction types, Tools on ChatRequest, ToolCalls on ChatResponse + wire request/response). The loop uses non-streaming Chat (tool_calls don't stream cleanly); assistant text is still written to stdout.log in the Claude stream-json envelope so summary/ changestats parsing is unchanged. Fully tested against a mock OpenAI endpoint + storeChannel: spawn/summary/ progress dispatch, ask_user blocking, token accumulation, and the llm tools round-trip. NOTE: local resume re-feeds conversation state (Decision #8) — not yet wired, so a blocked local task resumes fresh for now. https://claude.ai/code/session_01SESwn7kQ7oP62trWw6pc39 2026-04-28T09:24:43+00:00 Claude noreply@anthropic.com 2026-04-28T09:24:43+00:00 urn:sha1:0865afc43be562dbe14528e4299b9e213b54cc93 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