From 0865afc43be562dbe14528e4299b9e213b54cc93 Mon Sep 17 00:00:00 2001
From: Claude <noreply@anthropic.com>
Date: Tue, 28 Apr 2026 09:24:43 +0000
Subject: feat(executor): add LocalRunner and OpenAI-compat LLM client

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
---
 internal/executor/classifier.go | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

(limited to 'internal/executor/classifier.go')

diff --git a/internal/executor/classifier.go b/internal/executor/classifier.go
index 7a474b6..049dc4f 100644
--- a/internal/executor/classifier.go
+++ b/internal/executor/classifier.go
@@ -6,6 +6,8 @@ import (
 	"fmt"
 	"os/exec"
 	"strings"
+
+	"github.com/thepeterstone/claudomator/internal/llm"
 )
 
 type Classification struct {
@@ -19,7 +21,12 @@ type SystemStatus struct {
 	RateLimited map[string]bool
 }
 
+// Classifier picks a model for an incoming task. When LLM is non-nil the
+// classifier routes through the local OpenAI-compatible client (cheap,
+// private, fast). Otherwise it falls back to invoking the Gemini CLI
+// at GeminiBinaryPath.
 type Classifier struct {
+	LLM              *llm.Client
 	GeminiBinaryPath string
 }
 
@@ -62,6 +69,10 @@ func (c *Classifier) Classify(ctx context.Context, taskName, instructions string
 		agentType, taskName, instructions, agentType,
 	)
 
+	if c.LLM != nil {
+		return c.classifyViaLLM(ctx, prompt, agentType)
+	}
+
 	binary := c.GeminiBinaryPath
 	if binary == "" {
 		binary = "gemini"
@@ -123,3 +134,25 @@ func (c *Classifier) Classify(ctx context.Context, taskName, instructions string
 
 	return &cls, nil
 }
+
+// classifyViaLLM routes classification through the local OpenAI-compatible
+// client with response_format=json_object, so we get clean JSON without the
+// markdown-fence cleanup needed for the Gemini CLI fallback.
+func (c *Classifier) classifyViaLLM(ctx context.Context, prompt, agentType string) (*Classification, error) {
+	resp, err := c.LLM.Chat(ctx, llm.ChatRequest{
+		Messages:     []llm.Message{{Role: "user", Content: prompt}},
+		ResponseJSON: true,
+	})
+	if err != nil {
+		return nil, fmt.Errorf("classifier (local llm): %w", err)
+	}
+	body := strings.TrimSpace(resp.Content)
+	var cls Classification
+	if err := json.Unmarshal([]byte(body), &cls); err != nil {
+		return nil, fmt.Errorf("classifier (local llm): parse JSON: %w\nbody: %s", err, body)
+	}
+	if cls.AgentType == "" {
+		cls.AgentType = agentType
+	}
+	return &cls, nil
+}
-- 
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