Documentation Index
Fetch the complete documentation index at: https://exa.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Exa provides OpenAI-compatible endpoints that work seamlessly with the OpenAI SDK:
| Endpoint | OpenAI Interface | Models Available | Use Case |
|---|
/chat/completions | Chat Completions API | exa, exa-research, exa-research-pro | Traditional chat interface |
/responses | Responses API | exa-research, exa-research-pro | Modern, simplified interface |
Exa will parse through your messages and send only the last message to /answer
or /research.
Answer
To use Exa’s /answer endpoint via the chat completions interface:
- Replace base URL with
https://api.exa.ai
- Replace API key with your Exa API key
- Replace model name with
exa.
See the full /answer endpoint reference here. Need custom behavior when routing through /answer? Contact us at hello@exa.ai and we can help tailor the integration. from openai import OpenAI
client = OpenAI(
base_url="https://api.exa.ai", # use exa as the base url
api_key="YOUR_EXA_API_KEY", # update your api key
)
completion = client.chat.completions.create(
model="exa",
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What are the latest developments in quantum computing?"}
],
# use extra_body to pass extra parameters to the /answer endpoint
extra_body={
"text": True # include full text from sources
}
)
print(completion.choices[0].message.content) # print the response content
print(completion.choices[0].message.citations) # print the citations
Research
To use Exa’s research models via the chat completions interface:
- Replace base URL with
https://api.exa.ai
- Replace API key with your Exa API key
- Replace model name with
exa-research or exa-research-pro
See the full /research endpoint reference here. import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.exa.ai",
api_key=os.environ["EXA_API_KEY"],
)
completion = client.chat.completions.create(
model="exa-research", # or exa-research-pro
messages=[
{"role": "user", "content": "What makes some LLMs so much better than others?"}
],
stream=True,
)
for chunk in completion:
if chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
Research via Responses API
You can also access Exa’s research models using OpenAI’s newer Responses API format:
from openai import OpenAI
client = OpenAI(
base_url="https://api.exa.ai",
api_key="YOUR_EXA_API_KEY"
)
response = client.responses.create(
model="exa-research", # or "exa-research-pro"
input="Summarize the impact of CRISPR on gene therapy with recent developments"
)
print(response.output)
The Responses API provides a simpler interface for single-turn research tasks.
For more details on using Exa with OpenAI’s Responses API, including web
search tool integration, see the OpenAI Responses API
guide. Chat Wrapper
Exa provides a Python wrapper that automatically enhances any OpenAI chat completion with RAG capabilities. With one line of code, you can turn any OpenAI chat completion into an Exa-powered RAG system that handles search, chunking, and prompting automatically.
from openai import OpenAI
from exa_py import Exa
# Initialize clients
openai = OpenAI(api_key='OPENAI_API_KEY')
exa = Exa('EXA_API_KEY')
# Wrap the OpenAI client
exa_openai = exa.wrap(openai)
# Use exactly like the normal OpenAI client
completion = exa_openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is the latest climate tech news?"}]
)
print(completion.choices[0].message.content)
exa.search() function.
completion = exa_openai.chat.completions.create(
model="gpt-4o",
messages=messages,
use_exa="auto", # "auto", "required", or "none"
num_results=5, # defaults to 3
result_max_len=1024, # defaults to 2048 characters
include_domains=["arxiv.org"],
category="research paper",
start_published_date="2019-01-01"
)
