warpllm

A warp-speed, robust AI gateway written for rust, node, and python applications - built for planet scale by the community.

Quickstart

pip install warpllm              # python
npm install @warpllm/warpllm     # node
cargo add warpllm                # rust

export OPENAI_API_KEY=sk-...

Python

from warpllm import WarpLLM

client = WarpLLM()

completion = client.chat_completions({
    "model": "openai/gpt-5-nano",
    "messages": [{"role": "user", "content": "Hello!"}],
})

print(completion["choices"][0]["message"]["content"])

Node

import { WarpLLM } from '@warpllm/warpllm'

const client = new WarpLLM()

const completion = await client.chatCompletions({
  model: 'openai/gpt-5-nano',
  messages: [{ role: 'user', content: 'Hello!' }],
})

console.log(completion.choices[0].message.content)

Rust — chat_completions is async and warpllm ships no runtime, so bring your own: cargo add tokio --features macros,rt-multi-thread.

use warpllm::{ChatCompletionRequestMessage, Client, ClientConfig, CreateChatCompletionRequest};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new(ClientConfig::default())?;

    let completion = client
        .chat_completions(CreateChatCompletionRequest {
            model: "openai/gpt-5-nano".to_string(),
            messages: vec![ChatCompletionRequestMessage {
                role: "user".to_string(),
                content: "Hello!".to_string(),
                ..Default::default()
            }],
            ..Default::default()
        })
        .await?;

    let content = completion.choices[0].message.content.as_deref();
    println!("{}", content.unwrap_or_default());
    Ok(())
}

Switching providers is a string change

Keys are read from the environment when the client is built, so export the one the provider needs and change the model string. Nothing else moves.

Model string	Key it needs
`openai/gpt-5-nano`	`OPENAI_API_KEY`
`deepseek/deepseek-v4-flash`	`DEEPSEEK_API_KEY`
`kimi/kimi-k3`	`MOONSHOT_API_KEY`
`opencode/glm-5.2`	`OPENCODE_API_KEY`
`openrouter/anthropic/claude-sonnet-4`	`OPENROUTER_API_KEY`

The provider/ prefix is required. warpllm matches the whole string against its roster, so a bare gpt-5-nano — or any name it doesn't know — is an error rather than a guess at an upstream default.

Narrowing a client to the providers it serves

By default a client serves the whole roster and reads every provider's variable. Declare the ones you mean and it reads no others, routes to no others, and takes a key directly for the callers who keep theirs somewhere the environment can't reach:

WarpLLM(providers={"openai": {}, "deepseek": {"api_key": "sk-..."}})

new WarpLLM({ providers: { openai: {}, deepseek: { apiKey: 'sk-...' } } })

ClientConfig {
    providers: Some(BTreeMap::from([
        ("openai".into(), ProviderConfig::default()),
        ("deepseek".into(), ProviderConfig { api_key: Some(key) }),
    ])),
    ..Default::default()
}

An empty entry means "serve this one, key from the environment". A request for a model under a provider you didn't declare is refused before any upstream call, and a provider name the roster doesn't hold fails when the client is built.

Runnable versions of all three, with comments, are in examples/.

Mission

This project is to lay out the most resilient open source productionization layer for AI-deployments. Designed for you if you want:

To work with multiple AI providers or your own models.
To keep your AI services up and running with 0 downtime.
Speed (minimal overhead latency).
A granular view of your metrics (uptime, P95 latency, costs, etc).
Control over:
1. Where your data goes.
2. Your AI budget across providers.

Status

[!IMPORTANT] The published packages are 0.4.0, which adds the OpenCode Zen provider and lets a client declare the providers it serves. It is source-breaking for Rust only: ClientConfig gained a field, so an exhaustive struct literal no longer compiles — add providers: None, or switch to ..Default::default(). Python and TypeScript are purely additive. See the changelog before upgrading from 0.3.x.

The OpenAI-compatible HTTP gateway has landed on main but is not released yet.

	Released (0.4.0)	On `main`
OpenAI chat completions, non-streaming	Yes	Yes
`provider/model` routing strings	Provider registry	Provider registry
DeepSeek, OpenRouter	Yes	Yes
Kimi	Yes	Yes
OpenCode Zen	Yes	Yes
Declaring the providers a client serves	Yes	Yes
OpenAI-compatible HTTP gateway	—	Unreleased
Streaming	Yes	Yes
Failover, load balancing, caching, metrics	—	—

Unlisted models are rejected rather than guessed at, so routing a name warpllm doesn't know is an error, not a surprise upstream bill.

Layers

An SDK - provide a request and we translate it to work with different providers and models out of box.
[Unreleased] A proxy - run a self-hosted proxy that speaks the OpenAI API:
1. [Coming Soon] Failover - define multiple models to handle outages / errors
2. [Coming Soon] Load Balancing - define a % of requests to be handled per model
3. [Coming Soon] Prompt Response Caching - define a TTL and avoid paying twice for the same prompt

Key focus points

Native SDK support - Written once in rust, compiled for maximum performance, available for rust/typescript/python.
Self hostable - Avoid vendor lock-in (e.g. from cloud provider or model provider), or data leaving your infra.
Warp-speed execution - What we named ourselves after. Machine level code, faster than a typescript or python native library.
Compact file size - Pre-compiled into binary format, not verbose text files.

Roadmap

The roadmap lives in GitHub issues — one issue per item, so direction is discussed where the work happens. Add a comment if you see something missing, or if something there matters enough to you that it should move up.

Contributing

We're excited to have you join us. See the contribution guide for how to get started.

A big thank you to the contributors below who have helped build this AI gateway to this point!

License

The warpllm core is open source under the Apache License 2.0.

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Download URL: warpllm-0.4.0-cp310-abi3-macosx_10_12_x86_64.whl
Upload date: Aug 17, 2026
Size: 2.3 MB
Tags: CPython 3.10+, macOS 10.12+ x86-64
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.13

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The following attestation bundles were made for warpllm-0.4.0-cp310-abi3-macosx_10_12_x86_64.whl:

Publisher: release-python.yml on warpllm/warpllm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: warpllm-0.4.0-cp310-abi3-macosx_10_12_x86_64.whl
- Subject digest: 45e9bc8ab8bcf7d703f90f0833294481e327b2c13510dbc03aafff4404ead97c
- Sigstore transparency entry: 2498622163
- Sigstore integration time: Aug 17, 2026
Source repository:
- Permalink: warpllm/warpllm@4cc075ff32526390240a9ae8c654776fd3569ae0
- Branch / Tag: refs/tags/v0.4.0
- Owner: https://github.com/warpllm
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: release-python.yml@4cc075ff32526390240a9ae8c654776fd3569ae0
- Trigger Event: push

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0.5.0

Sep 2, 2026 9 files

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0.4.0 This release

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Aug 13, 2026 9 files

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Jul 16, 2026 9 files

Jul 15, 2026 9 files

Jul 15, 2026 9 files

Jul 15, 2026 9 files

warpllm 0.4.0

warpllm

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