claude-code-llm-router 2.6.0

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

LLM Router

One MCP server. Every AI model. Smart routing.

Route text, code, image, video, and audio tasks to 20+ providers — automatically picking the right model based on task complexity and your budget. Works in Claude Code, Cursor, Windsurf, Zed, claw-code, and Agno.

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Why

Not every task needs the same model. Without a router, everything goes to the same expensive frontier model — like hiring a surgeon to change a lightbulb.

Task	Without router	With router	Savings
Simple queries (60% of work)	Opus — $0.015	Haiku / Gemini Flash — $0.0001	99%
Moderate tasks (30% of work)	Opus — $0.015	Sonnet — $0.003	80%
Complex tasks (10% of work)	Opus — $0.015	Opus — $0.015	0%
Blended monthly	~$50/mo	~$8–15/mo	70–85%

With Ollama: simple tasks route to a free local model — those 60% of queries cost $0.

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Quick Start

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

That's it. The installer registers the MCP server and installs hooks into ~/.claude/ so every prompt is evaluated automatically.

Zero API keys required if you have a Claude Code Pro/Max subscription. Add GEMINI_API_KEY for a free external fallback (1M tokens/day free tier).

# Optional: add providers in .env
GEMINI_API_KEY=AIza...      # free tier
OPENAI_API_KEY=sk-proj-...
PERPLEXITY_API_KEY=pplx-...

# If you use Claude Code subscription
LLM_ROUTER_CLAUDE_SUBSCRIPTION=true

Enforcement modes — control how strictly routing is applied:

Mode	Behaviour	Set via
smart (default)	Hard block for Q&A tasks (query/research/generate/analyze), soft for code tasks	LLM_ROUTER_ENFORCE=smart
soft	Route hints in context, never blocks — lowest friction	LLM_ROUTER_ENFORCE=soft
hard	Blocks all Bash/Edit/Write until llm_* tool called — maximum savings	LLM_ROUTER_ENFORCE=hard
off	Enforcement disabled entirely	LLM_ROUTER_ENFORCE=off

Switch mode instantly with the CLI:

llm-router set-enforce smart   # (default) smart balance
llm-router set-enforce hard    # maximum cost savings
llm-router set-enforce soft    # no blocking

Set permanently in your .env or ~/.llm-router/routing.yaml:

# ~/.llm-router/routing.yaml
enforce: smart   # smart | soft | hard | off

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How It Works

Every prompt is intercepted by a UserPromptSubmit hook before your top-tier model sees it:

0. Context inherit      instant, free    "yes/ok/go ahead" reuse prior turn's route
1. Heuristic scoring    instant, free    high-confidence patterns route immediately
2. Ollama local LLM     free, ~1s        catches what heuristics miss
3. Cheap API            ~$0.0001         Gemini Flash / GPT-4o-mini fallback

Prompt	Classified as	Routed to
"What does os.path.join do?"	query/simple	Gemini Flash ($0.000001)
"Fix the bug in auth.py"	code/moderate	Haiku / Sonnet
"Design the full auth system"	code/complex	Sonnet / Opus
"Research latest AI funding"	research	Perplexity Sonar Pro
"Generate a hero image"	image	Flux Pro via fal.ai

Free-first chain (subscription mode): Ollama → Codex (free via OpenAI sub) → paid API

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MCP Tools

34 tools across 6 categories:

Smart Routing

Tool	What it does
llm_route	Auto-classify prompt → route to best model
llm_classify	Classify complexity + recommend model
llm_select_agent	Pick agent CLI (claude_code / codex) + model for a session
llm_stream	Stream LLM response for long-running tasks

Text & Code

Tool	What it does
llm_query	General questions — routed to cheapest capable model
llm_research	Web-grounded answers via Perplexity Sonar
llm_generate	Creative writing, summaries, brainstorming
llm_analyze	Deep reasoning — analysis, debugging, design review
llm_code	Code generation, refactoring, algorithms
llm_edit	Route edit reasoning to cheap model → returns {file, old, new} patch pairs

Filesystem

Tool	What it does
llm_fs_find	Describe files to find → cheap model returns glob/grep commands
llm_fs_rename	Describe a rename → returns mv/git mv commands (dry_run by default)
llm_fs_edit_many	Bulk edits across files → returns all patch pairs

Media

Tool	What it does
llm_image	Image generation — Flux, DALL-E, Gemini Imagen
llm_video	Video generation — Runway, Kling, Veo 2
llm_audio	TTS/voice — ElevenLabs, OpenAI

Orchestration

Tool	What it does
llm_orchestrate	Multi-step pipeline across multiple models
llm_pipeline_templates	List available pipeline templates

Monitoring & Admin

Tool	What it does
llm_usage	Unified dashboard — Claude sub, Codex, APIs, savings
llm_check_usage	Live Claude subscription usage (session %, weekly %)
llm_health	Provider availability + circuit breaker status
llm_providers	List all configured providers and models
llm_set_profile	Switch profile: budget / balanced / premium
llm_setup	Interactive provider wizard — add keys, validate, install hooks
llm_quality_report	Routing accuracy, savings metrics, classifier stats
llm_rate	Rate last response 👍/👎 — logged for quality tracking
llm_codex	Route task to local Codex desktop agent (free)
llm_save_session	Persist session summary for cross-session context
llm_cache_stats	Cache hit rate, entries, evictions
llm_cache_clear	Clear classification cache
llm_refresh_claude_usage	Force-refresh subscription data via OAuth
llm_update_usage	Feed usage data from claude.ai into the router
llm_track_usage	Report Claude Code token usage for budget tracking
llm_dashboard	Open web dashboard at localhost:7337

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Routing Profiles

Three profiles — switch anytime with llm_set_profile:

Profile	Use case	Chain
budget	Dev, drafts, exploration	Ollama → Haiku → Gemini Flash
balanced	Production work (default)	Codex → Sonnet → GPT-4o
premium	Critical tasks, max quality	Codex → Opus → o3

Profile is overridden by complexity: simple prompts always use the budget chain, complex ones escalate to premium, regardless of the active profile setting.

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Providers

Provider	Models	Free tier	Best for
Ollama	Any local model	Yes (forever)	Privacy, zero cost, offline
Google Gemini	2.5 Flash, 2.5 Pro	Yes (1M tokens/day)	Generation, long context
Groq	Llama 3.3, Mixtral	Yes	Ultra-fast inference
OpenAI	GPT-4o, o3, DALL-E	No	Code, reasoning, images
Perplexity	Sonar, Sonar Pro	No	Research, current events
Anthropic	Haiku, Sonnet, Opus	No	Writing, analysis, safety
DeepSeek	V3, Reasoner	Limited	Cost-effective reasoning
Mistral	Large, Small	Limited	Multilingual
fal.ai	Flux, Kling, Veo	No	Images, video, audio
ElevenLabs	Voice models	Limited	High-quality TTS
Runway	Gen-3	No	Professional video

Full setup guides: docs/PROVIDERS.md

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Works With

Claude Code

Auto-installed by llm-router install. Hooks intercept every prompt — you never need to call tools manually unless you want explicit control.

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

Live status bar shows routing stats before every prompt and in the persistent bottom statusline:

📊  CC 13%s · 24%w  │  sub:0 · free:305 · paid:27  │  $1.59 saved (35%)

claw-code

Add to ~/.claw-code/mcp.json:

{
  "mcpServers": {
    "llm-router": { "command": "llm-router", "args": [] }
  }
}

Every API call in claw-code is paid — the free-first chain (Ollama → Codex → Gemini Flash) saves more here than in Claude Code.

Cursor / Windsurf / Zed

Add to your IDE's MCP config:

{
  "mcpServers": {
    "llm-router": { "command": "llm-router", "args": [] }
  }
}

Agno (multi-agent)

Two integration modes:

Option 1 — RouteredModel (v2.0+): use llm-router as a first-class Agno model. Every agent call is automatically routed to the cheapest capable provider.

pip install "claude-code-llm-router[agno]"

from agno.agent import Agent
from llm_router.integrations.agno import RouteredModel, RouteredTeam

# Single agent — routes each call intelligently
coder = Agent(
    model=RouteredModel(task_type="code", profile="balanced"),
    instructions="You are a coding assistant.",
)
coder.print_response("Write a Python quicksort.")

# Multi-agent team with shared $20/month budget cap
# Automatically downshifts to 'budget' profile at 80% spend
team = RouteredTeam(
    members=[coder, researcher],
    monthly_budget_usd=20.0,
    downshift_at=0.80,
)

Option 2 — MCP tools: use llm-router's 34 tools in any Agno agent:

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools

agent = Agent(
    model=Claude(id="claude-sonnet-4-6"),
    tools=[MCPTools(command="llm-router")],
    instructions="Use llm_research for web searches, llm_code for coding tasks.",
)

Docker / CI

RUN pip install claude-code-llm-router && llm-router install --headless
# Pass keys at runtime: docker run -e GEMINI_API_KEY=... your-image

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Configuration

# API keys — at least one required
GEMINI_API_KEY=AIza...              # free tier at aistudio.google.com
OPENAI_API_KEY=sk-proj-...
PERPLEXITY_API_KEY=pplx-...
ANTHROPIC_API_KEY=sk-ant-...        # skip if using Claude Code subscription
DEEPSEEK_API_KEY=...
GROQ_API_KEY=gsk_...
FAL_KEY=...                         # images, video, audio via fal.ai
ELEVENLABS_API_KEY=...

# Router
LLM_ROUTER_PROFILE=balanced         # budget | balanced | premium
LLM_ROUTER_MONTHLY_BUDGET=0         # USD, 0 = unlimited
LLM_ROUTER_CLAUDE_SUBSCRIPTION=false  # true = Claude Code Pro/Max
LLM_ROUTER_ENFORCE=enforce          # shadow | suggest | enforce (default: enforce)

# Ollama (local models)
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_BUDGET_MODELS=gemma4:latest,qwen3.5:latest

# Spend limits
LLM_ROUTER_DAILY_SPEND_LIMIT=5.00   # USD, 0 = disabled

Repo-level config (.llm-router.yml)

Commit a routing policy alongside your code — no env vars required:

profile: balanced
enforce: suggest          # shadow | suggest | enforce
block_providers:
  - openai                # never use OpenAI in this repo

routing:
  code:
    model: ollama/qwen3.5:latest   # always use local model for code tasks
  research:
    provider: perplexity           # always use Perplexity for research

daily_caps:
  _total: 2.00            # global $2/day cap
  code: 0.50              # code tasks capped at $0.50/day

User-level overrides live in ~/.llm-router/routing.yaml (same schema). Repo config wins.

Full reference: .env.example

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Budget Control

LLM_ROUTER_MONTHLY_BUDGET=50   # raises BudgetExceededError when exceeded

llm_usage("month")
→ Calls: 142 | Tokens: 320k | Cost: $3.42 | Budget: 6.8% of $50

The router tracks spend in SQLite across all providers and blocks calls when the monthly cap is reached.

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Dashboard

llm-router dashboard   # opens localhost:7337

Live view of routing decisions, cost trends, model distribution, and subscription pressure. Auto-refreshes every 30s.

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Session Summary

At session end the router prints a breakdown:

  Free models  305 calls  ·  $0.52 saved  (Ollama / Codex)
  External       27 calls  ·  $0.006       (Gemini Flash, GPT-4o)
  💡 Saved ~$0.53 this session

Share your savings:

llm-router share   # copies savings card to clipboard + opens tweet

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Roadmap

Positioning: Claude Code's cost autopilot. Stop paying Opus prices for Haiku work.

Phase 1 — Trust & Proof (Apr–Jun 2026)

Version	Headline	Status
v1.3–v2.0	Foundation, dashboard, enforcement, Agno adapter	✅ Done
v2.1	Route Simulator — llm-router test "<prompt>" dry-run + llm_savings dashboard	✅ Done
v2.2	Explainable Routing — LLM_ROUTER_EXPLAIN=1, "why not Opus?", per-decision reasoning	✅ Done
v2.3	Zero-Friction Activation — onboarding wizard, shadow/suggest/enforce modes, yearly savings projection	✅ Done

Phase 2 — Smarter Routing (Jun–Aug 2026)

Version	Headline	Status
v2.4	Repo-Aware YAML Config — .llm-router.yml committed with the codebase, block_providers, model pins	✅ Done
v2.5	Context-Aware Routing — "yes/ok/go ahead" inherits prior turn's route, zero classifier latency	✅ Done
v2.6	Latency + Personalized Routing — p95 latency scoring, per-user acceptance signals	📅 Aug 2026

Phase 3 — Team Infrastructure (Sep–Nov 2026)

Version	Headline	Status
v3.0	Team Dashboard — shared savings across the whole team	📅 Sep 2026
v3.1	Policy Engine — org/project/user routing policy, spend caps, audit log	📅 Oct 2026
v3.2	Slack Digests — weekly savings summary, spend-spike alerts	📅 Nov 2026

Phase 4 — Category Leadership (Jan–Apr 2027)

Version	Headline	Status
v3.3	Community Benchmarks — opt-in anonymous routing quality leaderboard	📅 Jan 2027
v3.5	Claude Desktop + Co-Work — tool-based delegation, per-user savings attribution	📅 Mar 2027
v4.0	VS Code + Cursor GA — cross-editor routing, shared config and analytics	📅 Apr 2027

Full details: ROADMAP.md

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Development

uv sync --extra dev
uv run pytest tests/ -q --ignore=tests/test_integration.py
uv run ruff check src/ tests/

See CLAUDE.md for architecture and module layout.

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Contributing

See CONTRIBUTING.md. Key areas: new provider integrations, routing intelligence, MCP client testing.

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License

MIT

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