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Pool 18 LLM providers through MCP: ask, panel, tokenmax, route, models, quota, and stats.
Pool 18 LLM providers through MCP: ask, panel, tokenmax, route, models, quota, and stats.
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Set these up before or after installing:
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-0xzr-freellmpool": {
"args": [
"freellmpool"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Pool the free tiers of 19 LLM providers (200+ live-validated, 300+ cataloged models) behind one OpenAI-compatible endpoint — as a CLI, a Python library, or a local proxy. Works with no API keys.
FAQ: where prompts go, ToS posture, failover, bans, and comparisons.
Fresh install to first free-model reply takes about 19 seconds on a clean Linux/Python 3.12 environment, with zero API keys:
python3 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install freellmpool
freellmpool ask --max-tokens 32 "Reply with one short sentence: freellmpool is ready."
CI runs the same path from this checkout with
FREELLMPOOL_QUICKSTART_PACKAGE=. scripts/quickstart-test.sh.
Groq, Cerebras, NVIDIA NIM, Google Gemini, OpenRouter, GitHub Models, Cloudflare, Mistral, Cohere and others each give away a free tier — but each has its own SDK, rate limits, and daily cap. freellmpool puts them in one pool: it sends each request to a provider you have access to, fails over to the next when one is rate limited or down, and tracks per-day usage so you get the most out of every tier.
Several providers (Pollinations, OVHcloud, and Kilo Gateway) need no API key, and LLM7 works without one, so the quickstart above answers immediately.
Add keys for the other providers to unlock more models and higher limits.
freellmpool's proxy speaks both the OpenAI and the Anthropic API, so coding agents run against pooled free tiers with no code changes — just point them at the proxy:
freellmpool proxy # starts http://localhost:8080
freellmpool code claude # prints the one-line setup for Claude Code
# (also: codex, aider, cline, continue, cursor, opencode)
Claude Code gateway mode can also be launched directly:
ANTHROPIC_BASE_URL=http://localhost:8080 \
ANTHROPIC_AUTH_TOKEN=dummy \
ANTHROPIC_MODEL=auto \
ANTHROPIC_SMALL_FAST_MODEL=auto \
CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1 \
claude
Your existing OpenAI/Anthropic apps work the same way — set OPENAI_BASE_URL (or
ANTHROPIC_BASE_URL) to the proxy and keep your code unchanged.
OpenCode gets a deeper integration: a live in-editor dashboard (routing mode,
$ saved, tokens served free, provider race, latency), per-request quality routing
via the model picker (freellmpool/auto|fast|quality|fair), and freellmpool_status
/ freellmpool_models tools — see integrations/opencode-tui
and the guide.
New in 0.11: capacity tools — freellmpool capacity status shows which free
tiers are usable right now, freellmpool providers health live-probes them, and
freellmpool keys add walks you through configuring more (see
Capacity & provider health and
docs/CAPACITY.md).
New in 0.10: an async API (AsyncPool), an MCP server (freellmpool mcp),
latency-aware routing with freellmpool benchmark, observability hooks, and a
plugin system for custom providers. See the changelog.
pip install freellmpool # or: pipx install freellmpool
Only dependency is httpx. Python 3.11+.
freellmpool ask "Write a haiku about sqlite"
git diff | freellmpool ask "Write a commit message for this"
freellmpool tokenmax "Hardest question you've got" # 🌈 blast EVERY model, synthesize the swarm
freellmpool providers # which providers are configured
freellmpool models # every provider/model id
freellmpool stats # lifetime tokens served free + avoided cost
freellmpool badge -o badge.svg # a shareable SVG badge of that total
freellmpool tokenmax is the tongue-in-cheek maximum-effort mode: it fans your
prompt out to every model across every provider at once, prints each answer, and
synthesizes one best verdict — flashing a rainbow TOKENMAXXING animation in your
terminal while it runs. (Also available as the tokenmax MCP tool — see
docs/MCP.md.)
freellmpool stats is a running, persistent lifetime total (it survives restarts
and upgrades). Embed freellmpool badge in a README, or serve it live from the proxy
at /badge.svg (set FREELLMPOOL_PUBLIC_BADGE=1 to make it publicly embeddable).
Pin a provider or model; common OpenAI/Anthropic model names are mapped to a free equivalent so existing scripts keep working:
freellmpool ask -m groq/llama-3.3-70b-versatile "hi"
freellmpool ask -p cerebras,groq "hi"
freellmpool ask -m gpt-4o-mini "hi" # routed to a free model
Run a local server that speaks the OpenAI API, then point any OpenAI-compatible tool at it:
freellmpool proxy
export OPENAI_BASE_URL=http://localhost:8080/v1
export OPENAI_API_KEY=unused
from openai import OpenAI
client = OpenAI()
print(client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "hi"}],
).choices[0].message.content)
# audio → text (Whisper), same client:
print(client.audio.transcriptions.create(
model="auto", file=open("audio.mp3", "rb"),
).text)
Or with curl (multipart upload):
curl -s http://localhost:8080/v1/audio/transcriptions \
-F file=@audio.mp3 -F model=auto
The proxy also implements the OpenAI Responses API (for the Codex CLI) and the
Anthropic Messages API (for Claude Code), so coding agents can run on free models
too. freellmpool code <agent> prints the exact setup:
freellmpool code aider # also: claude, codex, cline, continue, cursor, opencode
Endpoints: /v1/chat/completions (token streaming, tool calling), /v1/embeddings,
/v1/audio/transcriptions (Whisper, multipart upload), /v1/responses, /v1/messages,
/v1/models, and a /dashboard page showing usage.
Setup snippets for specific tools are in docs/INTEGRATIONS.md
and docs/AGENTS.md. The repo also includes an experimental
metaswarm review adapter for using freellmpool as an
external-tools reviewer/second opinion.
from freellmpool import Pool
pool = Pool.from_default_config()
reply = pool.ask("Summarize the plot of Hamlet in 20 words.")
print(reply.text, "—", reply.provider_id)
vectors = pool.embed(["first document", "second document"]).vectors
with open("audio.mp3", "rb") as f:
text = pool.transcribe(f.read(), "audio.mp3").text # Whisper, failover across providers
Async is the same API with await:
from freellmpool import AsyncPool
async with AsyncPool.from_default_config() as pool:
reply = await pool.aask("Summarize the plot of Hamlet in 20 words.")
Pass on_event=... to either pool to receive structured routing/cache events
(attempt/success/error/cooldown/cache_hit/cache_miss/exhausted) for logging or tracing. Add
your own endpoint with register_provider(...), or a new request shape with
register_adapter(name, fn).
freellmpool benchmark times one call per configured provider and prints
latency and success, so you can see which of your free tiers are fastest right
now. The router learns the same latency/success signal from real traffic as it
runs; set FREELLMPOOL_ROUTING=fast to prefer the lowest-latency provider
instead of the default least-used-first.
$ freellmpool benchmark
provider/model status latency note
cerebras/llama-3.3-70b ok 180 ms 6 tok
groq/llama-3.3-70b ok 240 ms 6 tok
ovh/Meta-Llama-3_3-70B FAIL - HTTP 429
Free tiers drift through the day — keys expire, providers go down, daily caps fill. These commands tell you what's usable right now and what to set up next:
freellmpool capacity status --target 5 # who's healthy / near quota / missing a key
freellmpool providers health # send one tiny request to each, time it
freellmpool keys checklist --target 5 # which keys to add to reach N healthy providers
freellmpool keys add groq # configure a key (and record metadata)
capacity status is local-first: it reads your catalog, environment, and
per-day quota counters and labels each provider healthy, low_quota,
exhausted, invalid_key, or missing. It also syncs an advisory external
catalog (mnfst/awesome-free-llm-apis)
to suggest free providers you could add — advisory only; your providers.toml
stays the source of truth for routing. keys add <name> can even import a
suggested provider from that catalog or create an OpenAI-compatible stub and
autodiscover its models. The proxy /dashboard shows the same capacity at a
glance. Full reference: docs/CAPACITY.md.
freellmpool mcp runs a Model Context Protocol server over stdio, so Claude
Desktop, Claude Code, or Cursor can hand subtasks to free models. See
docs/MCP.md. A server.json is included for the
MCP registry.
llm CLIThere's a plugin: llm install llm-freellmpool → llm -m freellmpool "..." with
no API key. Source: 0xzr/llm-freellmpool.
freellmpool reads keys from the environment and uses whatever is set. None are required. Step-by-step signup links for each (all free, no card) are in docs/ACCOUNTS.md.
| Provider | Env var | Notes |
|---|---|---|
| Pollinations | — | no key needed |
| OVHcloud | — | no key needed (anonymous tier) |
| LLM7 | LLM7_API_KEY | optional |
| Groq | GROQ_API_KEY | fast |
| Cerebras | CEREBRAS_API_KEY | fast, large daily cap |
| NVIDIA NIM | NVIDIA_API_KEY | |
| OpenRouter | OPENROUTER_API_KEY | free models |
| Google Gemini | GEMINI_API_KEY | |
| GitHub Models | GITHUB_TOKEN | any PAT |
| Cloudflare | CLOUDFLARE_API_TOKEN + CLOUDFLARE_ACCOUNT_ID | |
| Mistral, Cohere, SambaNova, Z.ai, Ollama Cloud, LongCat | see .env.example |
A config.toml (see config.toml.example) can hold keys,
model aliases, and settings instead of env vars.
Run freellmpool doctor for a no-network local check of package version, config
paths, configured provider count, routing mode, quota/cache locations, external
catalog cache age, and bundled catalog validity.
Response caching is off unless FREELLMPOOL_CACHE_TTL (seconds) or
[settings] cache_ttl is positive. When enabled, cache rows live in SQLite with
WAL mode and TTL pruning; FREELLMPOOL_CACHE_MAX_ENTRIES caps retained rows
(default 10000, set 0 to disable size pruning).
Quota counters are written immediately by default. Long-running proxy/MCP
processes can reduce file churn with FREELLMPOOL_QUOTA_FLUSH_EVERY=N, which
batches up to N successful requests before flushing. Shutdown paths and
quota.snapshot() flush pending counts, so dashboards and process exits still
see current totals.
For each request, freellmpool builds the list of (provider, model) pairs you
have access to, then orders providers least-used-first and picks a least-used
model inside that provider. This keeps providers with large catalogs, like
NVIDIA, from receiving more traffic only because they expose more models. A
provider that returns a 429 is set aside for a cooldown window. Daily counts are
kept in ~/.config/freellmpool/quota.json and reset at UTC midnight.
Every call records latency and success per model target. A provider whose targets
are currently failing sinks to the back automatically; with
FREELLMPOOL_ROUTING=fast the fastest measured provider goes first instead.
freellmpool benchmark warms these metrics on demand. To restore the old
per-model balancing behavior, set FREELLMPOOL_ROUTING=legacy or
FREELLMPOOL_ROUTING=model (or FREELLMPOOL_ROUTING=model-fast for the old
per-model fastest-first ordering).
Quality routing (FREELLMPOOL_ROUTING=quality). Free tiers' strongest models
have the smallest daily caps, so a naive pool gets weaker as the day fills. Quality
routing matches each prompt's difficulty to each model's capability: hard
prompts (long input, code, reasoning cues) go to the strongest available model, and
easy ones go to lightweight models — which rations scarce strong-model quota so the
pool stays sharp for longer. Capability is grounded in real benchmark data, not
guessed from names; models no benchmark lists fall back to a name heuristic.
The bundled, offline scores come from LMArena Elo (an
MIT-licensed snapshot) and the Aider code-editing
leaderboard (Apache-2.0), normalized to a common percentile scale. For much
broader coverage, run freellmpool capability sync with a free
Artificial Analysis API key
(FREELLMPOOL_AA_API_KEY) — its Intelligence Index covers most current and
open-weight models and takes precedence. The fetched AA data is cached locally
under your own key (never bundled, per AA's terms). freellmpool capability status shows current coverage. Scores via LMArena and Aider; intelligence index
via Artificial Analysis when keyed.
Context windows. Free models often have small context windows. freellmpool
never truncates your input; instead, when a model rejects a request as too long,
it learns that model's limit and stops routing oversized requests there, escalating
only to larger-window models. If nothing fits it raises a clear
ContextWindowExceeded (with the estimated input size) instead of a generic
failure — over the proxy that's a 413. You can declare a model's window with
context = N in providers.toml to skip it proactively.
Architecture notes: docs/ARCHITECTURE.md.
providers.toml fixes it for everyone.127.0.0.1 by
default; if you expose it, set a key (--api-key).| Tool | Keyless start | # providers | Failover | MCP server | CLI | Transcription | Local/self-hosted | License |
|---|---|---|---|---|---|---|---|---|
| freellmpool | Yes: Pollinations, OVHcloud, Kilo Gateway; LLM7 is key-optional | 19 built-in chat providers | Yes: tries the next provider on rate limits, timeouts, 5xx, empty replies, and transport errors | Yes: freellmpool mcp | Yes: freellmpool ask, tokenmax, providers, proxy, and more | Yes: OpenAI-compatible /v1/audio/transcriptions with provider failover | Yes: local Python package and local proxy | MIT |
| OpenRouter free models | No: OpenRouter account/API key required | One hosted OpenRouter account routing across many upstreams; the free-model router currently lists free variants | Yes: OpenRouter handles provider routing/fallbacks | Not a native MCP server; OpenRouter docs show MCP-client/tool patterns | No first-party local CLI in the docs checked | Yes: OpenRouter now documents audio transcription APIs | No: hosted service | Proprietary service |
| LiteLLM | No: bring provider keys or hosted LiteLLM credentials | 100+ LLM providers | Yes: router/fallbacks, including transcription fallbacks | Yes: LiteLLM Proxy includes an MCP Gateway | Yes: SDK/proxy command surface, not a one-shot free-model CLI | Yes: /audio/transcriptions support | Yes: self-host the proxy or use hosted LiteLLM | MIT for core repo; commercial license for enterprise-only pieces |
| FreeLLMAPI | No: add your own free-tier provider keys; keyless providers can be configured after setup | 16 free-tier providers plus custom OpenAI-compatible endpoints | Yes: fallback chain on 429, 5xx, and timeouts | No native MCP server in the README checked | Dashboard/server, desktop app, and Docker; no first-class one-shot CLI in the README checked | No: /v1/audio/* is listed as not yet supported | Yes: self-hosted Node/Docker proxy | MIT |
FreeLLMAPI predates this project, and the overlap is independent convergence: both projects noticed that legitimate free tiers are useful when treated carefully. freellmpool's niche is the keyless, pip-installable client for squeezing hosted free tiers from a CLI, library, local proxy, and MCP server; OpenRouter is the polished hosted route; LiteLLM is the mature bring-your-own-key gateway.
Table sources: freellmpool's catalog and proxy code in this repo; OpenRouter's quickstart, free-model, routing, and audio docs; LiteLLM's README, MCP docs, and audio transcription docs; FreeLLMAPI's README.
Is there a free, OpenAI-compatible LLM API gateway? Yes — freellmpool is a free,
MIT-licensed gateway that exposes one OpenAI-compatible endpoint backed by the free
tiers of 19 providers. pip install freellmpool and point any OpenAI client at the
local proxy.
How do I use multiple free LLM APIs at once? freellmpool pools them: each request goes to a provider you have access to, fails over to the next when one is rate-limited or down, and tracks per-day usage so load spreads across tiers.
Can I run Claude Code or Codex on free models? Yes — the proxy speaks both the
OpenAI and Anthropic APIs. Set OPENAI_BASE_URL or ANTHROPIC_BASE_URL to the proxy
and run Codex, Claude Code, aider, Cline, Continue, or Cursor unchanged. For Claude
Code, set CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1 so /v1/models is discovered
through the Anthropic bridge. See freellmpool code <agent>. (Claude Code path is
experimental: text + tools, no vision.)
Do I need an API key? No — Pollinations, OVHcloud, and Kilo Gateway work with no key, and LLM7 is key-optional, so a fresh install answers immediately. Add free keys for the other providers for more models and higher limits.
Is it free and open source? Yes, MIT-licensed. More at the project page.
New providers and fixes to stale limits are the most useful contributions, and
both are usually a small change to providers.toml. See
CONTRIBUTING.md. Maintainer-ready newcomer tasks are drafted in
docs/GOOD_FIRST_ISSUES.md. Tests run with no network access:
python -m pip install -e ".[dev]" && ruff check . && pytest
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