claude-code-llm-router 6.7.0

Multi-LLM router MCP server for Claude Code — smart complexity routing, Claude subscription monitoring, Codex integration, 20+ providers

Route every AI call to the cheapest model that can do the job well. 48 tools · 20+ providers · personal routing memory · budget caps, dashboards, traces.

Average savings: 60–80% vs running everything on Claude Opus.

Install

pipx install claude-code-llm-router && llm-router install

Host	Command
Claude Code	llm-router install
VS Code	llm-router install --host vscode
Cursor	llm-router install --host cursor
Codex CLI	llm-router install --host codex

What It Does

Intercepts prompts and routes them to the cheapest model that can handle the task. Most AI sessions are full of low-value work: file lookups, small edits, quick questions. Those burn through expensive models unnecessarily.

llm-router keeps cheap work on cheap/free models, escalates to premium models only when needed. No micromanagement required.

• Works in: Claude Code, Cursor, VS Code, Codex, Windsurf, Zed, claw-code, Agno
• Free-first: Ollama (local) → Codex → Gemini Flash → OpenAI → Claude (subscription)

Mental Model

Think of llm-router as a smart task dispatcher. When you ask a question:

1. Analyze — What kind of task is this? (simple lookup vs. complex reasoning)
2. Choose — Which model can handle this best and cheapest?
3. Check Constraints — Are we over budget? Is this model degraded?
4. Execute — Send to that model

The dispatcher learns over time: if a model starts performing poorly (judge scores drop), it gets demoted in future decisions. If you're running low on quota (budget pressure), it automatically uses cheaper models. You don't manage any of this—it just happens behind the scenes.

Example: "Explain this error message" → Simple task → Route to Haiku (fast, cheap) → Done. vs. "Refactor this complex architecture" → Complex task → Route to Opus (expensive but thorough) → Done.

The savings come from not using Opus for every question.

New in v6.4 — Quality Guard

• Judge-based quality feedback integrated into routing decisions
• Quality reordering — models demoted if scores drop below threshold
• Hard floor enforcement — poor-performing models automatically escalated to better tier

See CHANGELOG.md for all changes.

New in v6.3 — Three-Layer Compression

• RTK command compression — bash output filtered (60–90% reduction)
• Model-based routing — existing cost reduction (70–90%)
• Response compression — LLM outputs condensed (60–75% reduction)
• Unified dashboard — llm_gain shows all layers

How It Works

User Prompt
    ↓
[Complexity Classifier] — Haiku/Sonnet/Opus?
    ↓
[Free-First Router] — Ollama → Codex → Gemini Flash → OpenAI → Claude
    ↓
[Budget Pressure Check] — Downshift if over 85% budget
    ↓
[Quality Guard] — Demote if judge score < 0.6
    ↓
Selected Model → Execute

Configuration

Zero-config by default if you use Claude Code Pro/Max (subscription mode).

Optional env vars:

OPENAI_API_KEY=sk-...                   # GPT-4o, o3
GEMINI_API_KEY=AIza...                  # Gemini Flash (free tier)
OLLAMA_BASE_URL=http://localhost:11434  # Local Ollama (free)
LLM_ROUTER_PROFILE=balanced             # budget|balanced|premium
LLM_ROUTER_COMPRESS_RESPONSE=true       # Enable response compression

For full setup guide, see docs/SETUP.md.

MCP Tools (48 total)

Routing:

• llm_route — Route task to optimal model
• llm_classify — Classify task complexity
• llm_quality_guard — Monitor model health

Text:

• llm_query, llm_research, llm_generate, llm_analyze, llm_code

Media:

• llm_image, llm_video, llm_audio

Admin:

• llm_usage, llm_savings, llm_budget, llm_health, llm_providers

Advanced:

• llm_orchestrate — Multi-step pipelines
• llm_setup — Configure provider keys
• llm_policy — Routing policy management

Full tool reference — Complete documentation for all 48 tools

Architecture

See CLAUDE.md for:

• Design decisions
• Module organization
• Development workflow
• Release process

See docs/ARCHITECTURE.md for:

• Three-layer compression pipeline
• Judge scoring system
• Quality trend tracking
• Budget pressure algorithm

Development

uv run pytest tests/ -q          # Run tests
uv run ruff check src/ tests/    # Lint
uv run llm-router --version      # Check version

License

MIT — See LICENSE

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Releases: PyPI