OpenAI-Compatible Endpoints Reference

Compatibility layer for OpenAI SDK clients.

Canonical Docs Links

Base docs URL: https://exa.ai/docs
OpenAI SDK compatibility: /reference/openai-sdk
OpenAI Responses API with Exa: /reference/openai-responses-api-with-exa

Overview

Exa exposes OpenAI-compatible endpoints so existing OpenAI SDK clients can route requests to Exa with minimal call-site changes. This is useful when:

you already depend on the OpenAI SDK
you want a compatibility-first migration path
you need drop-in chat or responses interfaces

For new Exa-first integrations, prefer native Exa endpoints because the request shapes are clearer and expose Exa-specific semantics directly.

Not the Same as Tool Calling

These compatibility endpoints replace your LLM provider with Exa: you point the OpenAI SDK at https://api.exa.ai and Exa runs the answer or research itself. If you instead want to keep your current agent and have it call Exa as one of its tools, see the tool calling pattern in prompting-and-patterns.md.

Endpoint Mapping

Exa Endpoint	OpenAI Interface	Models	Primary Route
`/chat/completions`	Chat Completions API	`exa`, `exa-research`, `exa-research-pro`	Routes to `/answer` or legacy research behavior
`/responses`	Responses API	`exa-research`, `exa-research-pro`	Research-style compatibility path

Exa’s compatibility layer parses the conversation and forwards the last message into its underlying Exa route.

Basic Setup

Python:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.exa.ai",
    api_key="YOUR_EXA_API_KEY"
)

TypeScript:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.exa.ai",
  apiKey: process.env.EXA_API_KEY
});

`/chat/completions` for Answer-Like Flows

Use model exa when you want compatibility access to Exa’s grounded answer behavior.

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?"}
    ],
    extra_body={"text": True}
)

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

Use extra_body to pass Exa-specific fields such as text.

`/responses` for Research-Style Compatibility

Use exa-research or exa-research-pro when the caller expects the OpenAI Responses API shape.

response = client.responses.create(
    model="exa-research",
    input="Summarize recent battery recycling policy developments."
)

print(response.output)

Model Routing Guidance

exa: chat-completions compatibility for answer-like flows
exa-research: research-style compatibility
exa-research-pro: higher-effort research-style compatibility

Treat these as compatibility-layer model names, not as replacements for learning Exa’s native endpoint semantics.

Critical Pitfalls

OpenAI-compatible endpoints are secondary to native Exa endpoints for new integrations.
Remember to use extra_body for Exa-specific fields on OpenAI SDK clients.
The compatibility layer forwards the last message; do not assume it preserves every native Exa feature in the same shape as the direct endpoints.

API

Data

MCP

SDKs

Integrations

Agentic payments

Admin

Additional Resources

Openai compat

OpenAI-Compatible Endpoints Reference

Canonical Docs Links

Overview

Not the Same as Tool Calling

Endpoint Mapping

Basic Setup

`/chat/completions` for Answer-Like Flows

`/responses` for Research-Style Compatibility

Model Routing Guidance

Critical Pitfalls

​OpenAI-Compatible Endpoints Reference

​Canonical Docs Links

​Overview

​Not the Same as Tool Calling

​Endpoint Mapping

​Basic Setup

​/chat/completions for Answer-Like Flows

​/responses for Research-Style Compatibility

​Model Routing Guidance

​Critical Pitfalls

OpenAI-Compatible Endpoints Reference

Canonical Docs Links

Overview

Not the Same as Tool Calling

Endpoint Mapping

Basic Setup

`/chat/completions` for Answer-Like Flows

`/responses` for Research-Style Compatibility

Model Routing Guidance

Critical Pitfalls