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Add read_file, write_file, run_bash, and glob tool definitions to
agentToolDefs() in localtools.go, with dispatch in dispatchAgentTool()
(signature now includes workDir). File and bash tools return an error
when workDir is empty.
LocalRunner.Run() clones t.Agent.ProjectDir into a temp dir when set,
passes workDir to dispatchAgentTool, pushes commits back on success,
preserves the sandbox in e.SandboxDir on BLOCKED, and cleans up on
completion.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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text output
- Retry Chat() without tools when error contains "does not support tools"
(case-insensitive); tinyllama fast-path still skips the first round-trip.
- Pass mcpEnabled=len(tools)>0 to buildAgentInstructions so tool-less models
don't receive a planning preamble they can't act on; tools must be decided
before messages are built, so reorder accordingly.
- Collect all assistant text into fullText across turns; after a non-blocked
run, if e.Summary is empty set it to the first 500 chars of fullText so
handleRunResult has something to store when report_summary is never called.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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
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Defines AgentChannel — the normalized interface by which a runner reports
agent-originated signals (AskUser, ReportSummary, SpawnSubtask,
RecordProgress) — plus a default storeChannel implementation backed by
storage. Runner.Run now takes an AgentChannel; the pool constructs one
per execution.
The file transport routes its post-exit summary detection through
ch.ReportSummary (buffered onto the execution so the pool still applies
its extract/synthesize fallbacks, no double-write). AskUser returns
ErrAgentBlocked since write-and-exit cannot answer in-session; question
persistence stays with the pool's BlockedError handling. SpawnSubtask
and RecordProgress are implemented and tested, ready for the MCP
transport in Phase 2 where the channel becomes fully load-bearing.
Store gains CreateEvent so the channel can emit agent_message events.
https://claude.ai/code/session_01SESwn7kQ7oP62trWw6pc39
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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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