Exa

This connector is optimized for AI agents. For the data replication connector, see Exa.

The Exa agent connector is a Python package that equips AI agents to interact with Exa through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

Exa is an AI-powered search engine that finds the exact content you're looking for on the web using embeddings-based search. This connector provides access to Exa's search, contents retrieval, and find-similar endpoints. All endpoints use POST requests with JSON bodies. Requires an Exa API key from dashboard.exa.ai.

Example prompts

The Exa connector is optimized to handle prompts like these.

Search for latest news on Airbyte
Find web pages similar to https://airbyte.com
Get the full text content of https://airbyte.com
Search for AI research papers published this year
Find company pages related to data integration startups

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Search Results	List
Contents	List
Similar Results	List

Exa API docs

See the official Exa API reference.

Interfaces

Use the Exa connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "exa"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "exa"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "exa",
  "entity": "search_results",
  "action": "list"
}'

Python SDK

Installation

uv pip install airbyte-agent-sdk

Usage

Connectors can run in hosted or open source mode.

Hosted

In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

The connect() factory returns a fully typed ExaConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Exa Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

mcp = FastMCP("Exa Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Legacy alternatives

These examples are kept for existing integrations. For new agents, use build_connector_tools above. The legacy ExaConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

@tool
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ExaConnector.tool_utils(framework="openai_agents")
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Exa Assistant", tools=[exa_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.exa import ExaConnector

connector = connect("exa", workspace_name="<your_workspace_name>")

mcp = FastMCP("Exa Agent")

@mcp.tool
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ExaConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ExaConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ExaConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Exa Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ExaConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

mcp = FastMCP("Exa Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Open source

In open source mode, you provide API credentials directly to the connector.

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())

LangChain
from airbyte_agent_sdk import build_connector_tools
from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

tools = build_connector_tools(connector, framework="langchain")
langchain_tools = [
    StructuredTool.from_function(
        coroutine=tool,
        name=tool.__name__,
        description=tool.__doc__,
    )
    for tool in tools.as_list()
]

OpenAI Agents
from airbyte_agent_sdk import build_connector_tools
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

tools = build_connector_tools(connector, framework="openai_agents")
openai_tools = [function_tool(tool, strict_mode=False) for tool in tools.as_list()]

agent = Agent(name="Exa Assistant", tools=openai_tools)

FastMCP
from airbyte_agent_sdk import build_connector_tools
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

mcp = FastMCP("Exa Agent")

for tool in build_connector_tools(connector, framework="mcp").as_list():
    mcp.tool(tool)

Legacy alternatives

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

@tool
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ExaConnector.tool_utils(framework="openai_agents")
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Exa Assistant", tools=[exa_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.exa import ExaConnector
from airbyte_agent_sdk.connectors.exa.models import ExaAuthConfig

connector = ExaConnector(
    auth_config=ExaAuthConfig(
        api_key="<Your Exa API key from dashboard.exa.ai/api-keys>"
    )
)

mcp = FastMCP("Exa Agent")

@mcp.tool
@ExaConnector.tool_utils
async def exa_execute(entity: str, action: str, params: dict | None = None):
    """Execute Exa connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Authentication

For all authentication options, see the connector's authentication documentation.

IP allow list

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information

Connector version: 1.0.0

Example prompts​

Entities and actions​

Exa API docs​

Interfaces​

CLI​

Python SDK​

Installation​

Usage​

Hosted​

Legacy alternatives​

Open source​

Legacy alternatives​

Authentication​

IP allow list​

Version information​

Example prompts

Entities and actions

Exa API docs

Interfaces

CLI

Python SDK

Installation

Usage

Hosted

Legacy alternatives

Open source

Legacy alternatives

Authentication

IP allow list

Version information