FreeLLMAPI: The Open-Source Proxy With 19,600+ GitHub Stars That Gives You 7.4 Billion Free Tokens Per Month

AI development is expensive. Every major lab—Google, Groq, Mistral, Cohere, NVIDIA—offers generous free tiers, but managing 28 different SDKs, rate limits, and API keys is a nightmare. Enter FreeLLMAPI, the open-source project with 19,600+ GitHub stars that solves this problem elegantly.

Instead of juggling multiple providers, you run one local proxy that stacks every free tier you have access to. Point any OpenAI-compatible client (Cursor, Claude Code, Aider, your own scripts) at http://localhost:3000/v1, and FreeLLMAPI routes your requests intelligently across providers, fails over automatically when one hits a rate limit, and tracks usage so you never exceed a free cap.

This isn't just a toy for hobbyists. With 474 model families and 635 free endpoints, you're looking at roughly 7.4 billion tokens per month of working inference capacity—enough to run serious experiments, prototype production features, or power your personal AI tools without spending a dime.

Why FreeLLMAPI Exists: The Free Tier Fragmentation Problem

Every serious AI lab now offers a free tier. Google gives you millions of Gemini tokens monthly. Groq offers thousands of requests per day for ultra-fast inference. Mistral, Cohere, NVIDIA, HuggingFace, and dozens more all have generous free programs.

The problem? Each provider has:

• Its own SDK and authentication method
• Different rate limits (RPM, RPD, TPM, TPD)
• Unique model names and API quirks
• Separate dashboards for monitoring usage

Stacking these free tiers manually is painful. You end up with 28 environment variables, brittle fallback logic, and no idea which provider just returned a 429 error.

FreeLLMAPI collapses that complexity into one OpenAI-compatible endpoint. Your code doesn't need to know about providers—it just talks to /v1/chat/completions like you're using OpenAI, and the router handles the rest.

How FreeLLMAPI Works: Smart Routing and Automatic Failover

FreeLLMAPI isn't just a dumb proxy. It's a sophisticated router that makes intelligent decisions about which provider to use for each request.

Six Routing Strategies

The router uses live per-model speed, capability, and reliability scores to rank your provider chain. When you send a request, it picks the best available model based on:

1. Speed: Latency and throughput metrics
2. Capability: Model quality scores for your use case
3. Reliability: Historical success rates
4. Rate limit headroom: How close you are to hitting caps
5. Cost: Preferring free over paid when both are available
6. Sticky sessions: Keeping conversations on one model for coherence

Automatic Failover with Cooldowns

When a provider returns a 429 (rate limit) or 5xx error, FreeLLMAPI doesn't just retry the same endpoint. It:

• Immediately fails over to the next model in your chain
• Applies a cooldown period to the failing provider
• Rotates through multiple API keys if you have them
• Tracks per-key usage to stay under every free-tier cap

This means your application never sees a rate limit error. The router absorbs the complexity, and you get consistent responses.

Per-Key Rate Tracking

FreeLLMAPI maintains RPM (requests per minute), RPD (requests per day), TPM (tokens per minute), and TPD (tokens per day) counters for every combination of provider, model, and API key. It even learns providers' reported ceilings dynamically, so routing always stays under every cap.

No more guessing whether you've hit Google's daily limit or Groq's minute limit. The router knows.

Key Features That Make FreeLLMAPI Production-Ready

Full OpenAI API Compatibility

FreeLLMAPI implements the complete OpenAI API surface:

POST /v1/chat/completions      # Chat completions (streaming & non-streaming)
POST /v1/responses              # Codex CLI support
POST /v1/completions            # Editor autocomplete
POST /v1/embeddings             # Text embeddings
POST /v1/images/generations     # Image generation
POST /v1/videos/generations     # Video generation
POST /v1/audio/speech           # Text-to-speech
POST /v1/audio/transcriptions   # Speech-to-text
GET  /v1/models                 # List available models

This means any OpenAI-compatible client works out of the box. No code changes needed.

Anthropic Messages API Support

Claude Code and the official Anthropic SDKs expect the /v1/messages endpoint with Anthropic's wire format. FreeLLMAPI speaks this protocol natively, so you can use Claude Code with your free tier pool:

# One-command setup for Claude Code
npx freellmapi setup-claude

This configures Claude Code to use FreeLLMAPI as its backend, routing Claude requests through your free providers.

Native Gemini and Ollama Surfaces

Gemini CLI can use the /v1beta endpoint (generateContent, streaming, token counting, models list). For local-first workflows, FreeLLMAPI offers opt-in Ollama emulation that serves NDJSON chat/generate, tags, metadata, and embeddings for tools like Zed and JetBrains AI.

Fusion: Multi-Model Synthesis

This is where FreeLLMAPI gets really interesting. Request the virtual fusion model, and the router:

1. Fans your prompt out to a panel of diverse free models in parallel
2. Collects draft responses from each
3. Uses a judge model to synthesize one high-quality answer

You get the wisdom of the crowd without paying for multiple premium API calls.

Encrypted Keys and Zero-Trust Security

Your provider API keys are encrypted at rest using AES-256-GCM. When a request goes out, only the necessary provider sees its key. The router itself never logs or persists sensitive data.

For Claude Code and Codex, FreeLLMAPI offers zero-persistence launchers (freellmapi launch, freellmapi launch-codex) that inject credentials into the child process only, never touching disk.

Self-Updating Model Catalog

The free-tier landscape shifts weekly. Providers launch new models, retire old ones, and change quotas without notice. FreeLLMAPI tracks all of this automatically.

The router pulls a signed model catalog from freellmapi.co twice a day. New models, quota changes, and compatibility fixes land without a git pull. Free installs track the monthly snapshot (models join 30 days after they land in the live feed), while premium routers ($19/year) get updates same-day.

Real-World Example: Powering Your AI Coding Workflow

Let's say you're building a side project and want to use AI coding assistants without paying for premium API access. Here's how you'd set up FreeLLMAPI:

Step 1: Install FreeLLMAPI

# Via npm
npm install -g freellmapi

# Or via Docker
docker pull ghcr.io/tashfeenahmed/freellmapi:latest
docker run -d -p 3000:3000 ghcr.io/tashfeenahmed/freellmapi:latest

Step 2: Add Your Free API Keys

Visit http://localhost:3000 and navigate to the Keys page. Add your free-tier API keys from:

• Google AI Studio (Gemini)
• Groq
• Mistral
• Cohere
• Any other supported provider

Step 3: Configure Your Coding Tools

# Set up Claude Code
npx freellmapi setup-claude

# Set up Codex CLI
npx freellmapi setup-codex

# Set up Aider
npx freellmapi setup-aider

# Or manually configure any OpenAI client
export OPENAI_BASE_URL=http://localhost:3000/v1
export OPENAI_API_KEY=freellmapi-key

Step 4: Start Coding

Now when you use Claude Code, Codex, or Aider, your requests route through FreeLLMAPI. The router picks the best free model for each request, fails over automatically when needed, and tracks usage across all providers.

You get the same experience as using a paid API, but you're drawing from a pool of 7.4 billion free tokens per month.

Key Benefits of FreeLLMAPI

• Massive free capacity: ~7.4 billion tokens per month across 34 providers
• Zero code changes: OpenAI-compatible API works with existing tools
• Intelligent routing: Picks the best model based on speed, quality, and availability
• Automatic failover: Never see a rate limit error again
• Encrypted security: AES-256-GCM encryption for API keys
• Self-updating: New models and quota changes land automatically
• Multi-modal support: Chat, embeddings, images, video, audio
• Works with popular tools: Claude Code, Codex, Aider, Cursor, Cline, and more
• Local-first: Runs on your machine, no cloud dependency
• Open source: MIT licensed, community-driven development

When to Use FreeLLMAPI (and When Not To)

Perfect For:

• Experimentation: Testing different models and approaches without burning credits
• Side projects: Personal tools and prototypes
• Learning: Understanding how different models perform on your tasks
• Development environments: Day-to-day coding assistance
• Hobbyists: AI enthusiasts who want to explore without costs

Not Ideal For:

• Production workloads: Free tiers have unpredictable availability
• Critical applications: Rate limits can hit at any time
• High-throughput services: You'll exhaust free tiers quickly
• Enterprise use: Paid APIs offer SLAs and support

FreeLLMAPI is explicit about this: it's for personal experimentation, not production infrastructure.

FAQ: Common Questions About FreeLLMAPI

Is FreeLLMAPI really free?

Yes, the core router is completely free and open source (MIT license). There's an optional premium tier ($19/year) that provides same-day access to new models and quota changes, but the free version is fully functional with a 30-day delay on catalog updates.

How many tokens can I actually use per month?

It depends on which providers you add, but with all 34 supported providers, you can access roughly 7.4 billion tokens per month. Each provider has its own limits (e.g., Google might give you 5M tokens/month, Groq might give you 14,400 requests/day), and FreeLLMAPI tracks all of them to keep you under every cap.

Do I need to modify my existing code to use FreeLLMAPI?

No. FreeLLMAPI implements the OpenAI API spec, so any OpenAI-compatible client works out of the box. Just change your base_url to http://localhost:3000/v1 and you're done. Claude Code, Codex, Aider, Cursor, and most other AI tools have one-command setup scripts.

What happens when a provider hits its rate limit?

FreeLLMAPI automatically fails over to the next available provider. It applies a cooldown to the rate-limited provider, rotates through multiple API keys if you have them, and tracks per-key usage to prevent future rate limit errors. Your application never sees the error—it just gets a response from a different provider.

Is my API key data secure?

Yes. FreeLLMAPI encrypts your provider API keys at rest using AES-256-GCM encryption. The router never logs or persists sensitive data. For extra security, Claude Code and Codex have zero-persistence launchers that inject credentials into the child process only, never touching disk.

Can I use FreeLLMAPI with local models like Ollama?

Yes. FreeLLMAPI supports custom providers, so you can point it at any OpenAI-compatible endpoint—including local Ollama, LM Studio, llama.cpp, or vLLM instances. This lets you mix local and cloud models in your routing chain.

Does FreeLLMAPI work with image generation and other modalities?

Absolutely. FreeLLMAPI supports /v1/images/generations, /v1/videos/generations, /v1/audio/speech, /v1/audio/transcriptions, and /v1/embeddings. It routes these requests across providers that support each modality.

What's the "fusion" model I keep hearing about?

Fusion is a multi-model synthesis feature. When you request the virtual fusion model, FreeLLMAPI fans your prompt out to multiple diverse models in parallel, collects their draft responses, and uses a judge model to synthesize one high-quality answer. It's like getting the wisdom of the crowd without paying for multiple premium API calls.

Do I need to update FreeLLMAPI manually when new models are released?

No. FreeLLMAPI pulls a signed model catalog from freellmapi.co twice a day. New models, quota changes, and compatibility fixes land automatically without a git pull. Free installs track the monthly snapshot (30-day delay), while premium installs get updates same-day.

Can I use FreeLLMAPI in production?

FreeLLMAPI is designed for personal experimentation, not production workloads. Free tiers have unpredictable availability, rate limits can hit at any time, and providers can change their offerings without notice. For production applications, use paid APIs with proper SLAs and support.

Ready to experiment with AI without burning through credits? Check out FreeLLMAPI on GitHub and start stacking those free tiers today.

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