claude-code-llm-router 5.3.0

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

llm-router

Route every AI call to the cheapest model that can do the job well. 46 tools · 20+ providers · budget caps, dashboards, traces, and one-command installs.

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

# One command to start saving
uvx claude-code-llm-router install

# Or: guided 5-minute setup
uvx claude-code-llm-router quickstart

Host	One-line install
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

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LLM Router is an MCP server and hook set that intercepts prompts and routes them to the cheapest model that can handle the task.

It is built for a common failure mode in AI coding tools: using your best model for everything. In Claude Code, that burns quota on simple explanations, file lookups, small edits, and repetitive prompts. In other MCP clients, it means paying premium-model prices for work that never needed them.

The goal is simple: keep cheap work on cheap or free models, keep hard work on Claude or other premium models, and remove the need to micromanage model selection. Works in Claude Code, Cursor, VS Code, Codex, Windsurf, Zed, claw-code, and Agno.

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Why

Most sessions contain a lot of low-value turns: quick questions, repo lookups, boilerplate edits, and small follow-ups. Those are exactly the prompts that quietly burn through premium models.

LLM Router offloads that work first, then escalates when the task actually needs more capability.

• Cheap work stays cheap.
• Hard work still gets the best model.
• Your workflow stays the same.

It does not try to replace Claude or force weak models onto hard tasks. It removes the waste around them.

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Quick Start

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

llm-router install registers the MCP server and installs hooks so prompt routing starts automatically.

If you use Claude Code Pro/Max, you can start with zero API keys. Otherwise add GEMINI_API_KEY for a cheap free-tier fallback.

GEMINI_API_KEY=AIza...              # optional free-tier fallback
LLM_ROUTER_CLAUDE_SUBSCRIPTION=true

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New in v5.2.0

• Safer routing under pressure: providers refresh budget pressure per attempt, fallback chains deduplicate cleanly, and hook IPC writes are atomic.
• Production-grade observability: the hot path now emits structured logs and optional OpenTelemetry spans for classify → score → build chain → provider call.
• Classifier quality you can measure: the classifier prompt is versioned in-repo and ships with a 100-example eval harness.
• Release flow you can repeat: scripts/release.py synchronizes versions, verifies metadata, builds artifacts, and drives the publish/tag workflow from one command.
• CI is stricter again: the integration suite is back in CI, with mocked coverage for end-to-end routing behavior.

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How It Works

1. Intercept the prompt before your default premium model sees it.
2. Classify the task and its complexity.
3. Try the cheapest capable route first.
4. Escalate or fall back when the task needs more capability.

Under the hood, every prompt goes through 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

Why teams keep it on

• It cuts premium-model waste without forcing people to think about models on every turn.
• It keeps a real paper trail: routing decisions, spend, budget pressure, and quality signals all stay visible.
• It degrades gracefully: stale hooks, unhealthy providers, exhausted budgets, and partial integrations fall through to safe defaults instead of deadlocking a session.
• It scales from solo use to team rollout with the same primitives: budgets, policies, dashboards, digests, and host-specific installs.

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MCP Tools

46 tools across 6 categories:

Smart Routing

Tool	What it does
llm_route	Auto-classify prompt → route to best model
llm_auto	Route + server-side savings tracking — designed for hook-less hosts (Codex CLI, Claude Desktop, Copilot)
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
llm_reroute	Correct a bad routing decision in-session and train the router

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
llm_fs_analyze_context	Summarise workspace context for smarter routing

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_savings	Cross-session savings breakdown by period, host, and task type
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
llm_team_report	Team-wide routing savings report
llm_team_push	Push local savings data to shared team store
llm_policy	Show active org/repo routing policy + last 10 policy decisions
llm_digest	Savings digest with spend-spike detection; push to Slack/Discord webhook
llm_benchmark	Per-task-type routing accuracy from llm_rate feedback
llm_session_spend	Real-time API spend breakdown for the current session
llm_approve_route	Approve or reject a pending high-cost routing call
llm_budget	Budget Oracle — real-time spend vs. cap per provider with pressure bars

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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 45 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.",
)

Supported Hosts

Host	Install command	Writes files	Hook support
Claude Code	llm-router install	✅	✅ Full auto-route
Codex CLI	llm-router install --host codex	✅	✅ PostToolUse
OpenCode	llm-router install --host opencode	✅	✅ PostToolUse
Gemini CLI	llm-router install --host gemini-cli	✅	✅ Extension hook
GitHub Copilot CLI	llm-router install --host copilot-cli	✅	—
OpenClaw	llm-router install --host openclaw	✅	—
Trae IDE	llm-router install --host trae	✅	—
Factory Droid	llm-router install --host factory	✅ manifest	— (Claude Code compat)
VS Code (MCP native)	llm-router install --host vscode	✅	—
Cursor IDE	llm-router install --host cursor	✅	—
Claude Desktop	llm-router install --host desktop	snippet	—
GitHub Copilot (VS Code)	llm-router install --host copilot	snippet	—

All installs are idempotent — run any command twice safely.

Codex CLI

llm-router install --host codex

Writes ~/.codex/config.yaml, ~/.codex/hooks.json (PostToolUse), and ~/.codex/instructions.md.

codex plugin install llm-router   # or via Codex marketplace

OpenCode

llm-router install --host opencode

Writes ~/.config/opencode/config.json (MCP block), PostToolUse hook, and routing rules.

Gemini CLI

llm-router install --host gemini-cli

Writes ~/.gemini/settings.json, creates the llm-router extension with gemini-extension.json + hooks.json, and appends routing rules.

GitHub Copilot CLI

llm-router install --host copilot-cli

Writes ~/.config/gh/copilot/mcp.json and routing rules.

OpenClaw

llm-router install --host openclaw

Writes ~/.openclaw/mcp.json and routing rules.

Trae IDE

llm-router install --host trae

Writes the platform-appropriate Trae config (~/Library/Application Support/Trae/mcp.json on macOS) and a .rules file in the current directory.

Factory Droid

Factory Droid natively supports Claude Code plugin format (.claude-plugin/) — no extra setup needed:

factory plugin install ypollak2/llm-router
# or via Factory marketplace search: llm-router

The dedicated .factory-plugin/ manifest is included for Factory marketplace discovery.

Claude Desktop

llm-router install --host desktop

Prints the snippet for claude_desktop_config.json. No hooks in Desktop — use llm_auto for savings tracking.

GitHub Copilot (VS Code)

llm-router install --host copilot

Prints the snippet for .vscode/mcp.json and a copilot-instructions.md template.

All at once

llm-router install --host all   # installs/prints all hosts

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

---

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

# Per-provider monthly budget caps (overridden by llm-router budget set)
LLM_ROUTER_BUDGET_OPENAI=20.0
LLM_ROUTER_BUDGET_GEMINI=5.0
LLM_ROUTER_BUDGET_GROQ=3.0
LLM_ROUTER_BUDGET_DEEPSEEK=3.0

# Enterprise integrations
HELICONE_API_KEY=sk-helicone-...         # enables Helicone routing properties
LLM_ROUTER_HELICONE_PULL=false           # pull spend from Helicone API
LLM_ROUTER_LITELLM_BUDGET_DB=/path/to/litellm.db  # LiteLLM Proxy DB
LLM_ROUTER_LITELLM_USER=my-team          # optional LiteLLM user/team filter
LLM_ROUTER_SPEND_AGGREGATION=max         # max | sum when sources are independent

Enforcement Modes

Choose how strict routing should be. The easiest way is llm-router onboard, which lets you pick a mode interactively.

Mode	Behaviour	Best for
shadow	Observe routing decisions, never blocks	Safest first install
suggest	Show route hints, allow direct answers	Low-friction adoption
enforce	Block routed violations until the route is followed	Maximum savings

LLM_ROUTER_ENFORCE=hard is the strict compatibility alias for enforce. Legacy soft and off values are still supported for direct CLI or env-based control.

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

---

Budget Control

Per-Provider Caps (CLI)

Set monthly spend caps per provider — persisted to ~/.llm-router/budgets.json:

llm-router budget list                  # show all providers with cap, spend, pressure
llm-router budget set openai 20         # $20/month cap for OpenAI
llm-router budget set gemini 5          # $5/month cap for Gemini
llm-router budget remove openai         # remove cap (reverts to env-var or unlimited)

Caps set via CLI take priority over LLM_ROUTER_BUDGET_* env vars. The router automatically routes away from providers approaching their caps.

Via Env Vars

LLM_ROUTER_BUDGET_OPENAI=20.0          # $20/month cap for OpenAI
LLM_ROUTER_BUDGET_GEMINI=5.0           # $5/month cap for Gemini
LLM_ROUTER_BUDGET_GROQ=3.0
LLM_ROUTER_BUDGET_DEEPSEEK=3.0
LLM_ROUTER_BUDGET_TOGETHER=5.0
LLM_ROUTER_BUDGET_PERPLEXITY=10.0
LLM_ROUTER_BUDGET_MISTRAL=5.0
LLM_ROUTER_MONTHLY_BUDGET=50           # global cap across all providers

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 routes away from providers approaching their monthly caps.

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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.

Tabs: Overview · Performance · Config · Logs · 💰 Budget

The dashboard now starts with a tokenized local URL and protects API calls with that token, so local metrics stay private by default.

The Budget tab shows per-provider spend vs. cap with editable cap inputs. Changes are persisted to ~/.llm-router/budgets.json and take effect immediately.

Prometheus metrics

GET http://localhost:7337/metrics

Exposes standard text exposition format, no extra dependencies:

# HELP llm_router_spend_usd Monthly spend per provider in USD
llm_router_spend_usd{provider="openai"} 3.42
llm_router_budget_pressure{provider="openai"} 0.17
llm_router_budget_cap_usd{provider="openai"} 20.0
llm_router_savings_usd_total 12.87

OpenTelemetry tracing

Install the optional tracing extra when you want route-level spans in Tempo, Jaeger, Honeycomb, or any OTLP-compatible backend:

pip install "claude-code-llm-router[tracing]"
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
OTEL_SERVICE_NAME=llm-router

Spans cover the routing hot path:

• classify_complexity
• score_all_models
• build_chain
• route_and_call
• provider_call

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Enterprise Integrations

Helicone

Add observability headers to every routed call and optionally pull spend data back from the Helicone API:

HELICONE_API_KEY=sk-helicone-...      # enables routing properties in every call
LLM_ROUTER_HELICONE_PULL=true         # pull spend per provider from Helicone API

When LLM_ROUTER_HELICONE_PULL=true, Helicone spend is merged with local SQLite spend and LiteLLM spend — budget pressure uses the maximum across all sources.

Headers injected on every call:

Helicone-Auth: Bearer sk-helicone-...
Helicone-Property-Router-Task-Type: code
Helicone-Property-Router-Model: openai/gpt-4o
Helicone-Property-Router-Complexity: moderate
Helicone-Property-Router-Profile: balanced

LiteLLM BudgetManager

Teams running a LiteLLM Proxy with budget management can point llm-router at the proxy's SQLite DB:

LLM_ROUTER_LITELLM_BUDGET_DB=/path/to/litellm.db
LLM_ROUTER_LITELLM_USER=my-team        # optional — filter by user/team key

The router reads spend_logs aggregated by provider (e.g. openai/gpt-4o → openai) and incorporates LiteLLM spend into pressure calculations. This ensures routing pressure reflects traffic flowing through both direct llm-router calls and the LiteLLM proxy simultaneously.

Budget cap lookup from budget_limits table (LiteLLM Proxy ≥ 1.30) is also supported.

If Helicone and LiteLLM are tracking genuinely separate traffic streams, set:

LLM_ROUTER_SPEND_AGGREGATION=sum

The default max mode is safer when multiple systems may be observing the same requests.

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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: Route every AI coding task to the cheapest capable model. Works across Claude Code, Cursor, VS Code, Codex, Gemini CLI, and more.

Phase 1 — Foundation ✅ Complete

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

Phase 2 — Smarter Routing ✅ Complete

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

Phase 3 — Team Infrastructure ✅ Complete

Version	Headline
v3.0	Team Dashboard — shared savings across the whole team
v3.1	Multi-Host + Cross-Session Savings — llm_auto, Codex/Desktop/Copilot adapters, persistent savings across sessions
v3.2	Policy Engine — org/project/user routing policy, spend caps, audit log
v3.3	Slack Digests + Codex Plugin — weekly savings digest, spend-spike alerts, Codex marketplace plugin

Phase 4 — Multi-Host Expansion ✅ Complete

Version	Headline
v3.4	Agent-Context Routing — subscription-first chain reordering when Codex or Claude Code is active
v3.5	Multi-Agent CLI Compatibility — OpenCode, Gemini CLI, Copilot CLI, OpenClaw, Factory Droid, Trae
v3.6	VS Code + Cursor IDE Support — native MCP config, routing rules, idempotent install
v4.0	Token Efficiency + Real-Time Spend — tool slim mode, session spend meter, reroute learning, quickstart wizard
v4.1	Playwright DOM Compression + Enforcement Fixes — DOM compression hook, PostToolUse MCP matcher fix, smart enforcement default
v4.2	Quota-Aware Routing + Context-Aware Classification — Ollama-first CC-mode for simple tasks, qwen3.5:32b in BALANCED chains, short code follow-up context inheritance

Phase 5 — Reliability + Observability ✅ Complete

Version	Headline
v5.0	Adaptive Universal Router — budget oracle, model discovery, live benchmarks, scorer, dynamic chain builder
v5.1	Budget Management UX + Enterprise Integrations — persistent caps, dashboard budget tab, Prometheus metrics, Helicone + LiteLLM spend
v5.2	Audit Remediation Release — atomic hooks, stricter CI, structlog, OpenTelemetry spans, classifier evals, release automation

What's Next

Version	Headline	Status
v5.3	Learned Routing — self-training classifier from llm_rate feedback, prompt-set benchmarks, personal routing patterns	📅 Planned
v5.4	Team Controls — policy approvals, richer dashboard analytics, provider-level SLO alerts	📅 Planned

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Development

uv sync --extra dev
uv run pytest tests/ -q --ignore=tests/test_agno_integration.py
uv run ruff check src/ tests/
uv run python scripts/eval_classifier.py --limit 20
uv run python scripts/release.py 5.2.0 --dry-run

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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_{Built with LiteLLM and MCP}