Initial commit: llm-router — smart OpenAI/Anthropic/Ollama router

A stdlib pre-router in front of Ollama with LiteLLM backend:
- auto model selection by content/tools/modality, with fallbacks
- OpenAI /v1, Anthropic /v1/messages, and Ollama-native /api/* endpoints
- Whisper-shaped /v1/audio/transcriptions + in-chat audio
- key-based fleet policies (e.g. force a client onto uncensored models)
- optional Bearer auth; launchd/systemd service install
- benchmark harnesses (speed, quality, agentic tool use) with sample results

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

benchmarks/results/combined_report.md

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+# Local fleet — combined benchmark (quality × agentic × speed)
+
+Date: 2026-07-03. Hardware: Apple M4 Max, 36 GB. All models 100% GPU.
+
+- **quality** = auto-graded mean of code (executed unit tests), math (GSM8K-style),
+  MC (knowledge/reasoning), instruction-following. think disabled for a fast baseline.
+- **agentic** = tool-use via Ollama tools API: invoke / args / select / multi-arg /
+  use-result / abstain (6 checks).
+- **tok/s** = generation speed from Ollama eval metrics.
+- \* = corrected after a think/budget artifact in the first quality pass.
+
+| Model | Quality | Agentic | tok/s |
+|---|--:|--:|--:|
+| gemma4:26b | 100% | 100% | 71.6 |
+| gemma4:31b | 100% | 100% | 16.6 |
+| qwen3.6:27b | 96% | 100% | 18.3 |
+| gemma4:e4b | 96% | 100% | 66.9 |
+| gemma4:e2b | 92% | 100% | 101.3 |
+| qwen3-vl:30b-a3b-instruct | 92% | 100% | 86.1 |
+| gpt-oss:20b | 92%* | 100% | 70.9 |
+| qwen3:14b | 92% | 100% | 36.2 |
+| qwen3.6:35b-a3b | 91% | 100% | 74.5 |
+| qwen3-coder:30b | 88% | 100% | 85.5 |
+| qwen3:8b | 88% | 100% | 59.9 |
+| gemma4:12b | 87% | 100% | 36.3 |
+| glm-4.7-flash | 83% | 100% | 65.6 |
+| qwen3-vl:8b | 62%* | 100% | 58.1 |
+
+## Takeaways
+- gemma4:26b — best all-round (top quality + fast, MoE). Strong router default.
+- MoE >> dense on this Mac: gemma4:31b / qwen3.6:27b match on quality but ~4x slower.
+- gemma4:e2b — best speed/size/quality ratio (92% @ 101 tok/s, 7 GB).
+- Agentic basics are universal (all 6/6); needs harder tasks to rank.
+
+## Harnesses
+- benchmark.py  — speed + OCR + routing
+- quality_bench.py — objective quality auto-grading
+- agent_bench.py — tool-use scenarios