Sentry authentication
This page documents the authentication and configuration options for the Sentry agent connector.
Hosted mode (most cases)
In hosted mode, create the connector through the Airbyte Agent CLI or API, then execute operations using the CLI, Python SDK, or API. If you need a step-by-step guide, see the developer quickstart.
OAuth
This authentication method isn't available for this connector.
Token
Create a connector with Token credentials.
credentials fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
auth_token | str | Yes | Sentry authentication token. Log into Sentry and create one at Settings > Account > API > Auth Tokens. |
replication_config fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
organization | str | Yes | The slug of the organization to replicate data from. |
project | str | Yes | The slug of the project to replicate data from. |
Example request:
curl -X POST "https://api.airbyte.ai/api/v1/integrations/connectors" \
-H "Authorization: Bearer <YOUR_BEARER_TOKEN>" \
-H "Content-Type: application/json" \
-d '{
"workspace_name": "<WORKSPACE_NAME>",
"connector_type": "Sentry",
"name": "My Sentry Connector",
"credentials": {
"auth_token": "<Sentry authentication token. Log into Sentry and create one at Settings > Account > API > Auth Tokens.>"
},
"replication_config": {
"organization": "<The slug of the organization to replicate data from.>",
"project": "<The slug of the project to replicate data from.>"
}
}'
Execution
After creating the connector, execute operations using the CLI, Python SDK, or API.
If your Airbyte client can access multiple organizations, set the default organization with airbyte-agent organizations use, include organization_id in AirbyteAuthConfig, or include X-Organization-Id in raw API calls.
CLI
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": "sentry"
}'
Describe the connector to see its supported entities and actions:
airbyte-agent connectors describe --json '{
"workspace": "<your_workspace_name>",
"name": "sentry"
}'
Execute an action:
airbyte-agent connectors execute --json '{
"workspace": "<your_workspace_name>",
"name": "sentry",
"entity": "<entity>",
"action": "<action>",
"params": {}
}'
Python SDK
The connect() factory returns a fully typed SentryConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.sentry import SentryConnector
connector = connect("sentry", workspace_name="<your_workspace_name>")
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.sentry import SentryConnector
connector = connect("sentry", workspace_name="<your_workspace_name>")
@tool
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry 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
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.sentry import SentryConnector
connector = connect("sentry", 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)
@SentryConnector.tool_utils(framework="openai_agents")
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry 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="Sentry Assistant", tools=[sentry_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.sentry import SentryConnector
connector = connect("sentry", workspace_name="<your_workspace_name>")
mcp = FastMCP("Sentry Agent")
@mcp.tool
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry 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
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.sentry import SentryConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = SentryConnector(
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>"
)
)
agent = Agent("openai:gpt-4o")
@agent.tool_plain
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
return await connector.execute(entity, action, params or {})
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.sentry import SentryConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = SentryConnector(
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>"
)
)
@tool
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry 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
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.sentry import SentryConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = SentryConnector(
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>"
)
)
# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@SentryConnector.tool_utils(framework="openai_agents")
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry 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="Sentry Assistant", tools=[sentry_execute])
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.sentry import SentryConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig
connector = SentryConnector(
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("Sentry Agent")
@mcp.tool
@SentryConnector.tool_utils
async def sentry_execute(entity: str, action: str, params: dict | None = None):
"""Execute Sentry connector operations."""
result = await connector.execute(entity, action, params or {})
return result.model_dump(mode="json") if hasattr(result, "model_dump") else result
API
curl -X POST 'https://api.airbyte.ai/api/v1/integrations/connectors/<connector_id>/execute' \
-H 'Authorization: Bearer <YOUR_BEARER_TOKEN>' \
-H 'X-Organization-Id: <YOUR_ORGANIZATION_ID>' \
-H 'Content-Type: application/json' \
-d '{"entity": "<entity>", "action": "<action>", "params": {}}'
Open source mode
In open source mode, provide API credentials directly to the connector.
OAuth
This authentication method isn't available for this connector.
Token
credentials fields you need:
| Field Name | Type | Required | Description |
|---|---|---|---|
auth_token | str | Yes | Sentry authentication token. Log into Sentry and create one at Settings > Account > API > Auth Tokens. |
Example request:
from airbyte_agent_sdk.connectors.sentry import SentryConnector
from airbyte_agent_sdk.connectors.sentry.models import SentryAuthConfig
connector = SentryConnector(
auth_config=SentryAuthConfig(
auth_token="<Sentry authentication token. Log into Sentry and create one at Settings > Account > API > Auth Tokens.>"
)
)
Configuration
The Sentry connector also needs these configuration values to construct the base API URL.
- Hosted CLI:
airbyte-agent connectors createdoesn't currently accept these configuration fields directly. For hosted connectors that need these values, create the connector with the hosted APIreplication_config, then use the CLI for describe and execute operations after creation. - Hosted API: pass these values in the connector creation
replication_config. - Open source mode: provide these values with your local connector setup so the connector can build the correct API base URL.
| Variable | Type | Required | Default | Description |
|---|---|---|---|---|
hostname | string | Yes | sentry.io | Host name of Sentry API server. For self-hosted instances, specify your host name here. Otherwise, leave as sentry.io. |