FreeLLMAPI: The Open-Source Tool With 22,400+ GitHub Stars That Gives You 7.4 Billion Free AI Tokens Every Month

Every major AI lab now offers a free tier — a few million tokens here, a few thousand requests there. Individually, each tier is limited. But stacked together across 34 providers, they represent roughly 7.4 billion tokens per month of real inference capacity.

The problem? Managing 34 different SDKs, 34 different rate limits, and 34 places a request can fail is a nightmare. That's exactly what FreeLLMAPI solves — and why it has exploded to over 22,400 GitHub stars in just four months.

If you're a developer learning to build AI-powered applications, or if you simply want to experiment with large language models without burning through your credit card, FreeLLMAPI might be the most important tool you set up this year.

What Is FreeLLMAPI and Why Does It Matter?

FreeLLMAPI is an open-source, self-hosted API gateway that collects free-tier API keys from dozens of AI providers and exposes them through a single OpenAI-compatible /v1 endpoint. You point any OpenAI SDK client at your local server, and FreeLLMAPI's intelligent router handles the rest — choosing the best available model, falling over to the next provider when one hits a rate limit, and tracking per-key usage so you stay under every free-tier cap.

Think of it as a unified AI gateway that turns dozens of limited free plans into one robust, high-availability inference API — running entirely on your machine.

Here's what makes it special:

• 34 free providers including Google, Groq, Cerebras, Mistral, OpenRouter, Cloudflare, Cohere, NVIDIA, HuggingFace, and 22 more
• 635 free model endpoints spanning 474 model families
• Full OpenAI API compatibility — chat completions, embeddings, image generation, audio, video, and more
• Smart routing with 6 strategies — speed, capability, and reliability scoring
• AES-256-GCM encrypted key storage — your provider keys never leave your machine unencrypted
• Self-updating model catalog — new free models appear automatically

How FreeLLMAPI Works Under the Hood

FreeLLMAPI is built in TypeScript and runs as a lightweight Node.js server (~40 MB RSS at idle). Here's the architecture in simple terms:

1. Key Management

You add your free-tier API keys through a clean React dashboard. Each key is encrypted with AES-256-GCM and stored in a local SQLite database. Your applications only ever interact with a single unified freellmapi-... bearer token — they never see your provider keys directly.

2. Smart Routing Engine

When a request arrives, the router evaluates your provider chain based on live per-model scores for speed, capability, and reliability. It selects the best available model, checks per-key rate counters (RPM, RPD, TPM, TPD), and dispatches the request. If a provider returns a 429 (rate limit) or 5xx error, the router automatically retries the next model in your fallback chain.

// Example: Using FreeLLMAPI with the OpenAI SDK
import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:3001/v1',
  apiKey: 'freellmapi-your-unified-key',
});

const response = await client.chat.completions.create({
  model: 'gpt-4o-mini', // Router picks the best free provider
  messages: [
    { role: 'user', content: 'Explain async/await in JavaScript' }
  ],
});

console.log(response.choices[0].message.content);

3. Model Catalog & Self-Updates

The free-tier landscape shifts weekly — providers launch new models, retire old ones, and change quotas without notice. FreeLLMAPI syncs a signed model catalog from freellmapi.co twice daily, so your install keeps up without a git pull. New models, quota changes, and compatibility fixes land automatically.

4. Fusion Mode (Multi-Model Synthesis)

One of FreeLLMAPI's most innovative features is Fusion. When you request the virtual fusion model, the router fans your prompt out to multiple diverse free models in parallel, then a judge model synthesizes one high-quality answer from all the drafts. It's like having a panel of AI experts reviewing each other's work — for free.

Setting Up FreeLLMAPI in Under 5 Minutes

FreeLLMAPI runs anywhere Node.js 20+ runs — macOS, Windows, Linux, or even a Raspberry Pi. The fastest path uses Docker:

# One-liner Docker install (generates encryption key, pulls image, starts container)
curl -fsSL https://freellmapi.co/install.sh | bash

After installation:

1. Open http://localhost:3001 in your browser
2. Add your provider API keys on the Keys page (free sign-ups from Google, Groq, Mistral, etc.)
3. Reorder the Fallback Chain to your preference
4. Copy your unified API key from the dashboard header

That's it. Now any OpenAI-compatible application can use your unified endpoint.

Desktop App Option

For those who prefer a native experience, FreeLLMAPI offers a menu-bar desktop app (macOS .dmg and Windows .exe) that runs the entire router + dashboard from your system tray, with a glass popover showing live request stats.

Compatible With Every Major Coding Agent

FreeLLMAPI shines brightest when paired with AI coding assistants. It includes one-command setup generators for all major coding agents:

# Set up Claude Code to use your free API pool
npx freellmapi setup-claude --url http://localhost:3001 --api-key YOUR_KEY

# Set up Codex CLI
npx freellmapi setup-codex --url http://localhost:3001 --api-key YOUR_KEY

# Set up Aider
npx freellmapi setup-aider --url http://localhost:3001 --api-key YOUR_KEY

Supported agents include Claude Code, Codex CLI, Gemini CLI, Aider, Cline, Roo Code, Continue, OpenCode, Goose, Qwen Code, Cursor, Zed, and JetBrains AI. Each generator supports --dry-run, creates timestamped backups, and merges into existing configurations without clobbering them.

Beyond coding agents, FreeLLMAPI also supports:

• Anthropic Messages API (/v1/messages) — Claude Code and Anthropic SDKs work directly
• Native Gemini surface (/v1beta) — Gemini CLI speaks its native wire format
• Ollama emulation — Zed, JetBrains, and other local-model clients connect seamlessly
• MCP server — agents can introspect available models and provider health via the Model Context Protocol

Real-World Example: Building a Chatbot Without Spending a Dime

Let's say you're building a customer support chatbot for a side project. Normally, you'd need to choose a single provider and pay for tokens. With FreeLLMAPI, your architecture looks like this:

import OpenAI from 'openai';

// All your AI needs — one endpoint
const ai = new OpenAI({
  baseURL: 'http://localhost:3001/v1',
  apiKey: process.env.FREELM_KEY,
});

// Chat completions — routed to the best free model
async function handleUserMessage(message) {
  const response = await ai.chat.completions.create({
    model: 'gpt-4o-mini', // Router handles provider selection
    messages: [
      { role: 'system', content: 'You are a helpful support agent.' },
      { role: 'user', content: message }
    ],
    stream: true,
  });

  for await (const chunk of response) {
    process.stdout.write(chunk.choices[0]?.delta?.content || '');
  }
}

// Embeddings for your knowledge base — also free
async function embedDocuments(docs) {
  const embeddings = await ai.embeddings.create({
    model: 'text-embedding-3-small',
    input: docs,
  });
  return embeddings.data.map(d => d.embedding);
}

// Image generation for marketing — also routed
async function generateBannerImage(prompt) {
  const image = await ai.images.generate({
    model: 'dall-e-3',
    prompt: prompt,
    size: '1024x1024',
  });
  return image.data[0].url;
}

The router automatically handles failover — if Google's Gemini hits its rate limit, the request seamlessly moves to Groq, then Mistral, then the next provider in your chain. Sticky sessions keep conversations coherent by staying on one model for 30 minutes, with an optional context handoff note when switching does occur.

Key Benefits of FreeLLMAPI

• Zero cost for substantial AI capacity — 7.4 billion tokens/month from free tiers, enough for serious development and experimentation
• Single API endpoint — one OpenAI-compatible URL replaces dozens of provider-specific integrations
• Automatic failover — when one provider rate-limits you, requests flow to the next seamlessly
• Privacy-first architecture — your keys are encrypted locally; requests go from your machine to providers directly
• Self-updating catalog — new free models appear without manual updates
• Fusion mode — combine multiple models for higher-quality outputs
• Prompt compression — reduce token usage by deduplicating prompts and trimming stale context
• Works everywhere — Docker, desktop app, or bare Node.js on any platform including ARM devices
• 60 language support — dashboard auto-detects your language including full RTL support
• MIT licensed — fully open source with an active community

FreeLLMAPI vs. Alternatives

Frequently Asked Questions

Is FreeLLMAPI really free?

Yes, the core software is completely free and open-source (MIT license). It aggregates the free tiers that AI providers already offer. You'll need to sign up for free accounts at providers like Google AI Studio, Groq, Mistral, etc., but none of these require payment. There is an optional premium plan ($19/year) that provides same-day model catalog updates instead of the 30-day delay for free installs.

Is it legal to stack free tiers like this?

FreeLLMAPI is designed for personal experimentation. Each provider's free tier terms of service apply individually. FreeLLMAPI doesn't bypass any rate limits — it respects each provider's quotas and tracks them meticulously. However, you should review each provider's terms to ensure your usage pattern is compliant, especially for commercial use.

What happens when a provider's free tier changes or disappears?

FreeLLMAPI's self-updating catalog handles this automatically. When a provider changes quotas, retires a model, or adds a new one, the router syncs these changes twice daily. If a provider goes down entirely, the smart router skips it and routes to the next available model in your chain.

Can I use FreeLLMAPI in production?

FreeLLMAPI is designed for personal experimentation and development. For production workloads, you should have paid API agreements with your providers. That said, the router itself is production-quality — it handles failover, encryption, and rate limiting robustly. Many developers use it as a development and testing gateway before switching to paid plans for production.

Does FreeLLMAPI work with coding assistants like Cursor and Copilot?

Yes! FreeLLMAPI includes one-command setup generators for Claude Code, Codex CLI, Aider, Cline, Roo Code, Continue, Cursor, Zed, JetBrains AI, and many more. For Cursor specifically, you can point it at your FreeLLMAPI instance's public URL to use your free provider pool instead of a paid subscription.

How much infrastructure do I need to run FreeLLMAPI?

Almost none. FreeLLMAPI uses about 40 MB of RAM at idle and runs on any machine with Docker or Node.js 20+. It works on a Raspberry Pi, a cheap VPS, or your development laptop. Since it's a proxy (requests go from your machine to providers), there's no heavy computation — just routing logic.

What about data privacy?

FreeLLMAPI is local-first and single-user by design. Your provider keys are AES-256-GCM encrypted in a local SQLite database and only decrypted in-memory per request. Your requests go directly from your machine to the upstream providers — FreeLLMAPI's server never sits between you and the AI providers as a middleman.

Ready to unlock billions of free AI tokens? Check out FreeLLMAPI on GitHub and explore our coding courses to level up your development skills.