freellmapi

What freellmapi does

Aggregate free tiers from dozens of providers, plus custom OpenAI-compatible chat, embedding, image, and audio endpoints, behind a single /v1 API. Keys are stored encrypted. A router picks the best available model for each request, falls over to the next provider when one is rate-limited, and tracks per-key usage so you stay under every free-tier cap.

Your router updates its own model catalog from a signed feed: new free models, quota changes, and compatibility fixes land without a git pull. Free installs get the monthly snapshot, so a model reaches them 30 days after it joins the live feed; premium routers get it the same day. Go live at freellmapi.co ($19/yr, cancel anytime).

Why this exists

Every serious AI lab now offers a free tier, a few million tokens a month, a few thousand requests a day. On its own each tier is a toy. Stacked together, they add up to roughly 7.4 billion tokens per month of working inference capacity, across 474 model families / 635 provider endpoints from small-and-fast to reasonably capable.

The problem is that stacking them by hand is painful: thirty-four different SDKs, thirty-four different rate limits, thirty-four places a request can fail. FreeLLMAPI collapses that into one OpenAI-compatible endpoint. Point any OpenAI client library at your local server, and it routes transparently across whichever providers you've added keys for.

And the free-tier landscape shifts weekly: providers launch models, retire them, and change quotas without notice.

Compatible CLIs & coding agents

… plus Continue, Goose, Qwen Code, Kilo Code, Crush, Zed, JetBrains AI, DeepSeek Harness, MiMo Code, AtomCode, OpenClaw, Hermes Agent, Pi, Reasonix and any OpenAI-compatible, Anthropic, Gemini or Ollama client

Most agents configure themselves with one command that reads your live catalog, backs up the existing config and merges into it:

All supported agents and their setup commands → · Per-tool recipes, MCP server and URL tokens →

Features

• Every OpenAI-style surface — /v1/chat/completions, /v1/responses (what Codex CLI needs), /v1/completions (editor ghost-text autocomplete), /v1/images/generations, /v1/videos/generations, /v1/audio/speech, /v1/audio/transcriptions, /v1/embeddings, and /v1/models — streaming and non-streaming, from the official SDKs or any OpenAI-compatible client. API reference →

• Anthropic Messages API — /v1/messages speaks Anthropic's wire format over the same router, so Claude Code and the official Anthropic SDKs run against your free pool. Details →

• Native Gemini + Ollama surfaces — Gemini CLI can use /v1beta (generateContent, streaming, token counting, models), while opt-in Ollama emulation serves NDJSON chat/generate, tags, metadata, and embeddings for Zed, JetBrains, and other local-model clients.

How it works

One request in, the best free model out: the router picks the highest-priority model with a healthy key that's under all its rate limits, decrypts the key in memory, and calls the provider — on a 429/5xx it cools that key down and retries the next model in your chain. The component walkthrough, routing internals, and operational details live in docs/en/architecture/00-high-level-index.md.

Explore the project

Read the project documentation for installation, requirements and usage instructions. The overview above draws on the project's README; follow the original documentation when setting it up.