OllamaFreeAPI: a zero-key Python client for shared Ollama nodes

What OllamaFreeAPI actually removes from your setup

Running Ollama locally means downloading multi-gigabyte weights and keeping a machine awake. Renting a GPU means a credit card. OllamaFreeAPI targets the gap between those two: the README describes a public gateway to managed Ollama servers with zero-configuration access, load balancing across global nodes and a free tier that requires no API keys. The audience is narrow but real. Someone teaching a Python class, writing a demo for a talk, or testing prompt shapes before paying for inference can import one class and get text back. The package itself is small: pyproject.toml declares a single dependency, ollama>=0.1.0, and requires Python 3.7 or newer. Everything else, the routing, the node selection, the model catalogue, lives on the other side of the network call. That is the trade. You install a thin client and inherit someone else's infrastructure decisions.

How the client, the node pool and the model catalogue fit together

The repository layout is the clearest documentation here. There is an ollamafreeapi/ package, a server.py at the top level, a scripts/ directory, an etc/ directory, and an ollamafreeapi/ollama_json/ folder that setup.py ships as package data. That last detail matters: the client carries JSON metadata inside the wheel, which is consistent with the README's claim that it validates active models and picks servers before sending a request. The public surface is a single class. OllamaFreeAPI() takes no required arguments in any README example, and the methods are list_models, get_model_info, chat, stream_chat, get_model_servers, generate_api_request and get_llm_params. Underneath, the dependency on ollama-python means the request shape is Ollama's own, so the difference between this and a local Ollama install is the base URL and the absence of a key, not the payload format. The README states the gateway is load balanced across global nodes and that availability is checked in real time, but it does not say how a node is chosen, what happens when one fails mid-stream, or whether a retry re-sends the prompt. Those are the questions a client library normally answers in its docs, and docs/client.md is referenced but not reproduced here.

Installing OllamaFreeAPI and sending a first prompt

The README gives one install line, and it is the upgrade form. Run it and pip resolves the single ollama>=0.1.0 dependency alongside the package itself.

# Install or upgrade to the latest version
pip install ollamafreeapi --upgrade

After that, the streaming example from the README is the quickest way to confirm the gateway is reachable. The loop yields text fragments, so print with flush=True or the output will look stalled.

from ollamafreeapi import OllamaFreeAPI

client = OllamaFreeAPI()

# Stream responses in real-time
for chunk in client.stream_chat('What is quantum computing?', model='llama3.2:3b'):
    print(chunk, end='', flush=True)

If you would rather not manage the loop, the non-streaming call returns the whole answer. The README's example uses gpt-oss:20b with a temperature of 0.7, which is worth noticing because it is one of the larger models in the featured list and the one most likely to queue behind other users.

from ollamafreeapi import OllamaFreeAPI

client = OllamaFreeAPI()
response = client.chat(
    model="gpt-oss:20b",
    prompt="Explain neural networks like I'm five",
    temperature=0.7
)
print(response)

Before trusting any model name, call list_models() and compare the result with the README's featured list. The README's statistics block says 16 active models across three families, gemma, llama and qwen, while the featured section also names deepseek-r1:latest, mistral:latest, mistral-nemo:custom, bakllava:latest and smollm2:135m. Those two lists do not agree, and the live catalogue is the one that counts.

The model list is smaller than the pitch, and the node pool is invisible

The README's opening bullet list advertises LLaMA, Mistral, DeepSeek and Qwen, and the feature table claims model variety as an advantage. The statistics block further down says 16 active models across three families: gemma, llama, qwen. Mistral and DeepSeek appear in the featured models, so the family count is either stale or counting differently. Either way, a reader who arrives expecting fifty-plus models, as the repository description states, will find a catalogue roughly a third of that size. The second limitation is structural. Six server nodes are listed, all community-run, with no published uptime, no status page and no rate limit documented in the README. Every prompt you send passes through a machine you do not control. For a public weather demo that is fine. For anything containing customer names, internal code or credentials, it is not, and the README never claims otherwise. The third issue is versioning: pyproject.toml pins 0.1.4, there are no retrieved releases, and the last push to the default branch was on 2026-04-15. That is five months before today. The repository is not archived, but no release has been cut and no changelog is present, so a breaking change in a node's Ollama version could reach you before any note does.

OllamaFreeAPI against running Ollama yourself

The obvious alternative is installing Ollama locally and calling it with the same ollama-python library. The difference is where the compute lives and who owns the failure. With local Ollama you download the weights once, keep the model resident in memory, and get latency that does not depend on a stranger's bandwidth. You also get the full model library rather than a curated subset, and nothing you type leaves the machine. What you give up is the reason this package exists: no GPU, no disk space, no setup beyond pip. A middle option is pointing the plain ollama client at a remote host you rent yourself. That costs money and an afternoon of configuration, and in exchange the node pool has a name attached to it. OllamaFreeAPI sits at the free end of that spectrum, and the README does not pretend otherwise. It is a convenience layer over shared capacity, and shared capacity behaves like shared capacity.

Licence, maintenance and what an upgrade costs you

The README badge and the classifiers in both pyproject.toml and setup.py all say MIT, and a LICENSE file is referenced. The repository's licence field is listed as unknown, which is a metadata gap rather than a contradiction, but if you need the licence text for a compliance review, read the LICENSE file in the repository rather than the badge. The practical implication of MIT is that you can vendor the package, but the service behind it is not covered by that licence at all, and nothing in the README grants you a right to the nodes' capacity. On maintenance, the last push was 2026-04-15 and there are no retrieved releases. Upgrading is a single pip command, but the cost is not the upgrade itself: it is that client and server can drift. A new client may expect a model name that a node has not pulled, or a response field a node does not send. Pinning the version in your requirements file is the only lever the material gives you.

Editorial conclusion

Adopt OllamaFreeAPI for prototypes, classroom demos and throwaway scripts where a shared node answering llama3.2:3b is enough and you accept that a third party sees your prompts. Do not adopt it for production traffic, private data or work that needs a published uptime figure, because the README offers none and the client is version 0.1.4 on a repository whose last push was 2026-04-15. Before you commit, run pip show ollamafreeapi to confirm the installed version, call list_models() to see which of the advertised models actually answer, and read ollamafreeapi/ollama_json/ to find out what the client does when a node is unreachable.

Frequently asked questions

Official sources

1. Issues
2. mfoud444/ollamafreeapi on GitHub
3. README