Freellmpool

freellmpool

Pool the free tiers of 19 LLM providers cataloged in freellmpool (237 enabled chat routes, 358 cataloged chat models) behind one OpenAI-compatible endpoint — as a CLI, a Python library, or a local proxy. Can start without API keys when a keyless provider is up.

[](https://pypi.org/project/freellmpool/) [](https://github.com/0xzr/freellmpool/actions/workflows/ci.yml) [](LICENSE) [](https://0xzr.github.io/freellmpool/)

[FAQ](FAQ.md): where prompts go, ToS posture, failover, bans, and comparisons.

30-second quickstart

Fresh install to first free-model reply is measured at about 19 seconds under the 30-second target on a clean Linux/Python 3.12 environment, with no API keys when a keyless provider is up:

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 can answer without signup when a keyless provider is available.

To inspect your local provider keys, agent CLIs, proxy config, and Tailscale state before wiring tools, run the print-only init wizard:

freellmpool init --yes
freellmpool init --yes --agent opencode
freellmpool init --yes --agent metaswarm --tailnet

Add keys for the other providers to unlock more models and higher limits.

First-run setup with freellmpool init

freellmpool init inspects provider keys, installed agent CLIs, Tailscale state, and proxy config, then prints one copy-pastable next step without editing files. Run it detect-only first:

freellmpool init --yes

--json emits the same detection as versioned JSON for scripts and agents.

Tailnet / remote agent gateway

Serve the proxy on your Tailscale 100.x address with a generated API key:

freellmpool tailnet serve --port 8080

From a remote machine:

freellmpool tailnet connect  --port 8080

Both sides support --api-key if you want to pin a key instead of using a generated token. Tailnet serving requires auth by default; do not run unauthenticated over non-loopback interfaces.

Metaswarm agent lanes

This project uses one Umans/Kimi K2.7 worker lane, one MiniMax M3 lane, Codex as escalation, and Claude Opus only for final pre-ship review. The installable Metaswarm profile mirrors that posture: one free/cheap worker lane through the local proxy, one larger freellmpool reviewer lane, and Codex/Opus as explicit user-owned paid escalation/final-review lanes only (never silent).

freellmpool init --yes --agent metaswarm --tailnet
freellmpool profile install metaswarm
freellmpool tailnet serve --port 8080
freellmpool profile doctor metaswarm --dry-run

Run a coding agent on free models

freellmpool's proxy speaks the OpenAI API and includes an experimental Anthropic-compatible path, so coding agents can run against pooled free tiers — just point them at the proxy:

freellmpool proxy                       # starts http://localhost:8080
freellmpool code claude                 # prints the one-line setup for Claude Code
freellmpool profile list                # richer installable profiles
freellmpool profile show metaswarm      # Tailnet-aware Metaswarm profile
# (also: codex, aider, cline, continue, cursor, opencode, metaswarm)

Claude Code gateway mode can also be launched directly:

ANTHROPIC_BASE_URL=http://localhost:8080 \
ANTHROPIC_AUTH_TOKEN=dummy \
ANTHROPIC_API_KEY=dummy \
ANTHROPIC_MODEL=auto \
ANTHROPIC_SMALL_FAST_MODEL=auto \
CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1 \
claude

Existing OpenAI-compatible apps work the same way: set OPENAI_BASE_URL=http://localhost:8080/v1 and keep your code unchanged. Anthropic-compatible tools can use the experimental bridge with ANTHROPIC_BASE_URL=http://localhost:8080.

OpenCode gets a deeper integration: a live in-editor dashboard (routing mode, estimated savings, 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](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](#capacity--provider-health) and [docs/CAPACITY.md](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](CHANGELOG.md).

Install

pip install freellmpool      # or: pipx install freellmpool

Only dependency is httpx. Python 3.11+.

Command line

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 models, print answers, optional synthesis
freellmpool providers        # which providers are configured
freellmpool models           # every provider/model id
freellmpool stats            # lifetime tokens served free + estimated cost avoided
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 many available models at once and prints each answer. The CLI adds a synthesized verdict by default unless you pass --no-synthesize; the MCP tool returns the model answers for the calling agent to synthesize. (See [docs/MCP.md](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

Roles

freellmpool roles lists ask-role presets (coder, critic, summarizer, long-context, cheap, fast, second-opinion, ...). Each role sets routing, token budget, temperature, and system-prompt hints without inventing a second routing engine. Explicit flags (--model, --providers, --routing, --max-tokens) win over role defaults, and the verbose output shows when an override happened.

freellmpool ask --role coder "write a pytest for this function"
FREELLMPOOL_MODE=wise freellmpool ask --role cheap "summarize this patch"

As a proxy

Run a local server that speaks the OpenAI API, then point any OpenAI-compatible tool at it. On loopback, any placeholder API key works unless you configured FREELLMPOOL_PROXY_KEY or passed --api-key; Tailnet/LAN serving requires a real proxy bearer token by default.

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 an experimental Anthropic Messages API path (for Claude Code), so coding agents can run on free models too. freellmpool code prints the exact setup, while freellmpool profile install prints the fuller copy-pastable profile without mutating third-party config:

freellmpool code aider       # also: claude, codex, cline, continue, cursor, opencode
freellmpool profile show opencode
freellmpool profile doctor opencode --dry-run

Main proxy surfaces:

• /v1/chat/completions — OpenAI-compatible chat, token streaming, tool calling.
• /v1/responses — minimal Responses API shim for Codex-style agents.
• /v1/messages — experimental Anthropic-compatible Messages path.
• /v1/embeddings and /v1/audio/transcriptions — OpenAI-compatible embedding

and Whisper-style multipart transcription.

• /v1/models — routing aliases plus concrete provider/model ids.
• /freellmpool/battle and /playground — bounded browser/JSON model comparisons.
• /dashboard, /status, /healthz, /badge.svg — local operations surfaces.

/playground and the API routes are auth-protected when the proxy key is set. Setup snippets for specific tools are in [docs/INTEGRATIONS.md](docs/INTEGRATIONS.md) and [docs/AGENTS.md](docs/AGENTS.md). The repo also includes an experimental [metaswarm review adapter](integrations/metaswarm) for using freellmpool as an external-tools reviewer/second opinion. freellmpool profile show metaswarm documents a free/cheap worker lane, a larger reviewer lane, Tailnet client setup, and paid Codex/Opus lanes as explicit user-owned escalation paths only.

As a library

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).

Benchmark your providers

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

Capacity & provider health

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 quota-wise status            # local headroom + recommended mode
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 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](docs/CAPACITY.md).

FREELLMPOOL_MODE=wise is the conservative quota mode: ask defaults to a smaller output budget and spread routing, tokenmax narrows its default fan-out, and broad multi-model calls require confirmation unless you pass --yes. Per-command --mode normal|wise overrides the environment, and [settings] mode = "wise" works from config.toml. The conserve role is a quota-conscious shorthand for small, spread-routed answers.

For a bounded second opinion instead of a full tokenmax blast:

freellmpool ask --second-opinion --opinions 3 "is this implementation plan sound?"
freellmpool ask --role second-opinion --synthesize "which release note is clearer?"

The shared panel asks a few diverse providers, keeps individual failures visible, and can append a non-fatal synthesis when you pass --synthesize.

For a side-by-side comparison you can inspect in the terminal or local browser:

freellmpool battle "which changelog entry is clearer?" --synthesize
freellmpool proxy --port 8080
freellmpool playground --port 8080

Bundled recipes wrap common workflows in JSON files you can inspect and run:

freellmpool recipe list
freellmpool recipe run second-opinion "is this launch plan clear?" --synthesize
freellmpool recipe run pr-review --input patch.diff
freellmpool recipe run repo-summary --path 'src/freellmpool/*.py'
freellmpool recipe run metaswarm-worker-review --input worker.md --validation-output-file validation.txt

Recipes use the same role presets and shared panel helper as ask and battle; there is no separate routing engine.

Local foreground job queue

For slow, quota-aware work that should not block a live session, queue jobs to an append-only JSONL log under your config dir (override with FREELLMPOOL_JOBS_PATH). The queue is foreground-only: jobs run processes one job at a time and records started/completed/failed/cancelled events. Completed ask jobs keep their output in the job log; completed recipe jobs also write run records and Markdown reports via the same report helpers used by freellmpool report.

# queue a recipe job
freellmpool jobs add --recipe pr-review --input patch.diff

# queue an ask job with a role preset
freellmpool jobs add --role summarizer "summarize the latest changelog"

freellmpool jobs list            # replayed state (idempotent across restarts)
freellmpool jobs watch           # one-shot refresh render, no daemon

freellmpool jobs run --dry-run   # print execution order, mutate nothing
freellmpool jobs run --max-failures 2   # halt after N consecutive failures
freellmpool jobs cancel  # append a cancel tombstone, not a mutation

freellmpool report list
freellmpool report last --markdown
freellmpool report last --html --path
freellmpool cost show 

Cancellation is a new tombstone event, not a re-write of the earlier queued record — a crash before jobs run finishes still leaves the queue replayable, and cancelled jobs stay cancelled after restart. Duplicate submissions create distinct jobs; pass `--dedu

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: 0xzr
• Source: 0xzr/freellmpool
• License: MIT
• Homepage: https://0xzr.github.io/freellmpool/

Install and usage instructions live in the source repository linked above.