LLM Router

A local control plane for AI coding tools.
Routes tasks to the cheapest model that can do the job well.
Protects quota. Enforces policy. Tracks spend. Falls back on failure.

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Why This Exists

AI coding assistants route every task — simple questions, complex architecture — to the same expensive model. You pay full price for work that a cheaper model handles equally well.

llm-router sits between your AI tool and the LLM providers. It classifies each task by complexity, picks the cheapest capable model, and falls back through a provider chain on failure. You don't change your workflow. The router handles model selection automatically.

Use this if:

• You use Claude Code, Codex CLI, Gemini CLI, or Pi and want to reduce spend
• You want automatic fallback when a provider is down or rate-limited
• You want local Ollama models tried first (free) before paid APIs
• You want visibility into token spend across providers

Don't use this if:

• You always want the best possible model regardless of cost
• You don't use MCP-compatible tools
• You need guaranteed latency (routing adds classification overhead)

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Quick Start

1. Install

pip install llm-routing
llm-router install

Package name: llm-routing on PyPI. CLI command: llm-router.

2. Add providers (optional)

export OPENAI_API_KEY="sk-..."      # GPT-4o, o3
export GEMINI_API_KEY="AIza..."     # Gemini Flash/Pro (free tier available)
export OLLAMA_BASE_URL="http://localhost:11434"  # Local models (free)

Works with zero API keys on Claude Code Pro/Max subscriptions — routing uses MCP tools that call external models only when beneficial.

3. Verify

llm-router install --check   # Preview what will be installed
llm-router health            # Check provider connectivity

In Claude Code, ask a simple question. The session-end summary shows routing decisions and savings.

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How It Works

User prompt
    │
    ▼
┌──────────────────────┐
│ Complexity Classifier │  ← Heuristic (free, instant) or Ollama/Flash ($0.0001)
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│  Free-First Router   │  ← Tries cheapest model first, walks up the chain
│                      │
│  Ollama (free)       │
│  → Codex (prepaid)   │
│  → Gemini Flash      │
│  → GPT-4o / Claude   │
└──────────┬───────────┘
           │
           ▼
┌──────────────────────┐
│  Guards (parallel)   │  ← Circuit breaker, budget pressure, quality check
└──────────┬───────────┘
           │
           ▼
      Response + cost logged to local SQLite

Routing examples

Task	Complexity	Chain
"What does this error mean?"	Simple	Ollama → Codex → Gemini Flash → Groq
"Implement OAuth"	Moderate	Ollama → Codex → GPT-4o → Gemini Pro
"Design distributed tracing"	Complex	Ollama → Codex → o3 → Claude Opus

Classification is free (regex heuristics catch ~70% of tasks) or near-free (local Ollama / Gemini Flash for ambiguous cases).

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Host Support

Host	Auto-Routing	MCP Tools	Savings Potential
Claude Code	Full (hooks)	60 tools	60–80%
Codex CLI	Full (hooks)	60 tools	60–80%
Gemini CLI	Full (hooks)	60 tools	50–70%
VS Code / Cursor	Manual	60 tools	30–50%
Any MCP client	Manual	60 tools	Varies

Full = hooks intercept prompts and route automatically. No workflow change needed. Manual = MCP tools are available; you invoke them explicitly (e.g., call llm_query).

llm-router install                    # Claude Code (default)
llm-router install --host codex       # Codex CLI
llm-router install --host gemini-cli  # Gemini CLI
llm-router install --host vscode      # VS Code
llm-router install --host cursor      # Cursor

See docs/HOST_SUPPORT_MATRIX.md for full details on each host.

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What You Can Do

Use case	How
Route simple questions to free local models	Auto (hooks) or llm_query
Protect Claude subscription quota	Budget pressure monitoring + auto-downgrade
Fall back across providers on failure	Automatic chain with circuit breakers
Track token spend and savings	llm_usage, llm_savings, session-end reports
Enforce routing policy for your team	LLM_ROUTER_POLICY=aggressive
Generate images/video/audio	llm_image, llm_video, llm_audio
Run multi-step research pipelines	llm_orchestrate with templates
Bulk-edit files with cheap models	llm_fs_edit_many

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Providers

Routing chains are built from your configured providers. You only need one.

Provider	Models	Cost	Setup
Ollama	gemma4, qwen3.5, llama3, etc.	Free (local)	OLLAMA_BASE_URL
OpenAI	GPT-4o, o3, GPT-4o-mini	Paid API	OPENAI_API_KEY
Google	Gemini Flash, Pro	Free tier + paid	GEMINI_API_KEY
Anthropic	Claude Sonnet, Opus, Haiku	Paid API or subscription	ANTHROPIC_API_KEY or subscription
Perplexity	Web-grounded research	Paid API	PERPLEXITY_API_KEY
Groq	Fast inference (Llama, Mixtral)	Free tier	GROQ_API_KEY
Codex	GPT-5.4, o3 (prepaid desktop)	Included with Codex CLI	Auto-detected

See docs/PROVIDERS.md for setup instructions and model recommendations.

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Routing Policies

Control how aggressively the router offloads to cheap models.

Policy	Confidence Threshold	Typical Savings	Best For
Aggressive	2	60–75%	Maximum cost reduction
Balanced (default)	4	35–45%	Cost/quality tradeoff
Conservative	6	10–15%	Quality over cost

export LLM_ROUTER_POLICY=aggressive     # Or: balanced, conservative
export LLM_ROUTER_ENFORCE=smart          # smart | hard | soft | off
export LLM_ROUTER_PROFILE=balanced       # budget | balanced | premium

LLM_ROUTER_ENFORCE controls how strictly the auto-route hook blocks direct model use:

• smart — route when confident, pass through when uncertain
• hard — always route, block unrouted tool calls
• soft — suggest routing, never block
• off — disable hook enforcement

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MCP Tools (60)

llm-router exposes 60 MCP tools organized by function:

Category	Tools	Examples
Routing & classification	7	llm_route, llm_classify, llm_auto, llm_stream
Text generation	6	llm_query, llm_code, llm_analyze, llm_research
Media generation	3	llm_image, llm_video, llm_audio
Pipeline orchestration	2	llm_orchestrate, llm_pipeline_templates
Admin & monitoring	20+	llm_usage, llm_budget, llm_health, llm_savings
Filesystem operations	4	llm_fs_find, llm_fs_edit_many
Subscription tracking	3	llm_check_usage, llm_refresh_claude_usage

Slim mode (LLM_ROUTER_SLIM=routing or core) reduces registered tools to save context tokens in constrained environments.

Full Tool Reference

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Savings: How It Works

Savings are calculated by comparing actual spend against a baseline of routing every task to Claude Sonnet/Opus.

Methodology:

1. Each routed task logs: model used, tokens consumed, estimated cost
2. A baseline cost is computed as if the same tokens were processed by the most expensive model in the chain
3. Savings = (baseline - actual) / baseline

Assumptions and limitations:

• Baseline assumes you would have used Opus/Sonnet for everything (worst case)
• Token estimates use len(text) / 4 approximation, not exact tokenizer counts
• Cost data comes from LiteLLM's pricing tables (may lag provider price changes)
• Savings vary significantly by workload — code-heavy sessions route more to cheap models
• The router itself adds small overhead (classification costs ~$0.0001 per ambiguous task)

Observed range: 35–80% savings depending on policy and task mix. The "87%" figure in some docs represents a single-user peak over a specific development period, not a guaranteed outcome.

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Trust, Privacy, and Local-First Design

llm-router runs entirely on your machine. There is no hosted proxy, no telemetry, no account required.

What	Where	Details
Your prompts	Sent to configured providers	Exactly like using those providers directly
API keys	.env or ~/.llm-router/config.yaml	Local files, never transmitted
Usage logs	~/.llm-router/usage.db	Unencrypted SQLite (filesystem permissions)
Classification cache	In-memory	Cleared on process restart
Hook scripts	~/.claude/hooks/	Local shell scripts, inspectable

What we do:

• Scrub API keys from structured logs
• Detect hook deadlocks before installation
• Store all data locally in ~/.llm-router/
• Respect provider rate limits and TOS

What you should know:

• Prompts are sent to whichever provider the router selects — review your provider's privacy policy
• Usage logs (SQLite) are not encrypted at rest — use full-disk encryption if needed
• The router cannot prevent model jailbreaks or prompt injection at the provider level

See SECURITY.md for responsible disclosure policy and docs/SECURITY_DESIGN.md for the full threat model.

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Configuration

Minimal setup — only configure what you have:

# Provider keys (set any combination)
export OPENAI_API_KEY="sk-proj-..."
export GEMINI_API_KEY="AIza..."
export OLLAMA_BASE_URL="http://localhost:11434"
export OLLAMA_BUDGET_MODELS="gemma4:latest,qwen3.5:latest"

# Routing behavior
export LLM_ROUTER_PROFILE="balanced"       # budget | balanced | premium
export LLM_ROUTER_POLICY="balanced"        # aggressive | balanced | conservative
export LLM_ROUTER_ENFORCE="smart"          # smart | hard | soft | off

For teams or environments where .env is restricted:

# User-level config (no project .env needed)
mkdir -p ~/.llm-router && chmod 700 ~/.llm-router
cat > ~/.llm-router/config.yaml << 'EOF'
openai_api_key: "sk-proj-..."
gemini_api_key: "AIza..."
ollama_base_url: "http://localhost:11434"
llm_router_profile: "balanced"
EOF
chmod 600 ~/.llm-router/config.yaml

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Documentation

Document	Purpose
Quick Start (2 min)	Fastest path to working routing
Getting Started	Full setup walkthrough
Host Support Matrix	Per-host feature comparison
Providers	Provider setup and model recommendations
Tool Reference	All 60 MCP tools with examples
Architecture	Internal design and module structure
Troubleshooting	Common issues and fixes
Security Design	Threat model and data handling

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Contributing

Contributions welcome. See CONTRIBUTING.md for full guidelines.

git clone https://github.com/ypollak2/llm-router.git
cd llm-router
uv sync --extra dev
uv run pytest tests/ -q         # Run tests (1700+)
uv run ruff check src/ tests/   # Lint

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Package Names

Name	What it is
llm-routing	Current PyPI package (pip install llm-routing)
llm-router	CLI command and GitHub repo name
claude-code-llm-router	Deprecated legacy package (redirects to llm-routing)

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Issues · PyPI · Changelog

_{MIT License}