OpenAI Compatibility

agirouter is compatible with OpenAI's libraries, making it easy to try out our open-source models on existing applications.

agirouter endpoints for chat, language and code, images, and embeddings are fully compatible with OpenAI's API.

If you have an application that uses one of OpenAI's client libraries, you can easily configure it to point to agirouter API servers, and start running your existing applications using our open-source models.

Configuring OpenAI to use agirouter API

To start using agirouter with OpenAI's client libraries, pass in your agirouter key to the

api_key

option, and change the

base_url

https://api.agirouter.xyz/v1

import os
import openai

client = openai.OpenAI(
  api_key=os.environ.get("agirouter_API_KEY"),
  base_url="https://api.agirouter.xyz/v1",
)

You can find your API key in your settings page. If you don't have an account, you can register for free.

Querying an Inference Model

Now that your OpenAI client is configured to point to agirouter, you can start using one of our open-source models for your inference queries.

For example, you can query one of our chat models, like Llama 3.1 8B:

import os
import openai

client = openai.OpenAI(
  api_key=os.environ.get("agirouter_API_KEY"),
  base_url="https://api.agirouter.xyz/v1",
)

response = client.chat.completions.create(
  model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
  messages=[
    {"role": "system", "content": "You are a travel agent. Be descriptive and helpful."},
    {"role": "user", "content": "Tell me the top 3 things to do in San Francisco"},
  ]
)

print(response.choices[0].message.content)

Or you can use a language model to generate a code completion:

import os
import openai

client = openai.OpenAI(
  api_key=os.environ.get("agirouter_API_KEY"),
  base_url="https://api.agirouter.xyz/v1",
)

response = client.completions.create(
  model="meta-llama/Llama-2-7b-hf",
  prompt="def bubbleSort(): ",
  max_tokens=175
)

print(response.choices[0].text)

Streaming with OpenAI

You can also use OpenAI's streaming capabilities to stream back your response:

import os
import openai

system_content = "You are a travel agent. Be descriptive and helpful."
user_content = "Tell me about San Francisco"

client = openai.OpenAI(
  api_key=os.environ.get("agirouter_API_KEY"),
  base_url="https://api.agirouter.xyz/v1",
)

stream = client.chat.completions.create(
  model="Qwen/Qwen2.5-7B-Instruct-Turbo",
  messages=[
    {"role": "system", "content": system_content},
    {"role": "user", "content": user_content},
  ],
  stream=True,
)

for chunk in stream:
  print(chunk.choices[0].delta.content or "", end="", flush=True)

Generating Embeddings

Use our embedding models to generate an embedding for some text input:

import os
import openai

client = openai.OpenAI(
  api_key=os.environ.get("agirouter_API_KEY"),
  base_url="https://api.agirouter.xyz/v1",
)

response = client.embeddings.create(
  model="agiroutercomputer/m2-bert-80M-8K-retrieval",
  input="Our solar system orbits the Milky Way galaxy at about 515,000 mph"
)

print(response.data[0].embedding)

Community Libraries

The agirouter is also supported by most OpenAI libraries built by the community.

Feel free to reach out to support if you come across some unexpected behavior when using our API.

🚀 Getting Started

⚡ Inference