Sentry
The Sentry agent connector is a Python package that equips AI agents to interact with Sentry 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.
Connector for the Sentry error monitoring and performance tracking API. Provides access to projects, issues, events, and releases within your Sentry organization. Supports listing and retrieving detailed information about error tracking data, project configurations, and software releases.
Example prompts
The Sentry connector is optimized to handle prompts like these.
- List all projects in my Sentry organization
- Show me the issues for a specific project
- List recent events from a project
- Show me all releases for my organization
- Get the details of a specific project
- What are the most common unresolved issues?
- Which projects have the most events?
- Show me issues that were first seen this week
- Find releases created in the last month
Unsupported prompts
The Sentry connector isn't currently able to handle prompts like these.
- Create a new project in Sentry
- Delete an issue
- Update a release
- Resolve all issues in a project
Entities and actions
This connector supports the following entities and actions. For more details, see this connector's full reference documentation.
| Entity | Actions |
|---|---|
| Projects | List, Get, Context Store Search |
| Issues | List, Get, Context Store Search |
| Events | List, Get, Context Store Search |
| Releases | List, Get, Context Store Search |
| Project Detail | Get |
Sentry API docs
See the official Sentry API reference.
SDK installation
uv pip install airbyte-agent-sdk
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 SentryConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
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 {})
LangChain
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
OpenAI Agents
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])
FastMCP
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
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
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 {})
LangChain
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
OpenAI Agents
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])
FastMCP
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
Open source
In open source mode, you provide API credentials directly to the connector.
- Pydantic AI
- LangChain
- OpenAI Agents
- FastMCP
Pydantic AI
from pydantic_ai import Agent
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.>"
)
)
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 {})
LangChain
from langchain_core.tools import tool
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.>"
)
)
@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
OpenAI Agents
from agents import Agent, function_tool
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.>"
)
)
# 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])
FastMCP
from fastmcp import FastMCP
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.>"
)
)
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
Authentication
For all authentication options, see the connector's authentication documentation.
Version information
Connector version: 1.0.4