Overview

Agent Kernel provides various built in integrations to connect your AI agents with external platforms and services. These integrations allow you to deploy your agents in real-world environments and interact with users through different channels.

Execution Hooks

Agent Kernel provides powerful execution hooks that let you customize agent behavior at runtime.

• Execution Hooks - Pre-execution and post-execution hooks for guardrails, RAG context injection, response moderation, and more. See the detailed hooks documentation for complete guide with examples.

Observability & Monitoring

• Langfuse - Open-source LLM engineering platform for tracing, evaluating, and monitoring AI applications. See Traceability and Observability for detailed setup and usage.
• OpenLLMetry (Traceloop) - OpenTelemetry-based observability for LLM applications with support for multiple backends including Traceloop, Datadog, New Relic, and Honeycomb. See Traceability and Observability for detailed setup and usage.
• AWS CloudWatch - OpenTelemetry traces exported to AWS X-Ray and searchable in CloudWatch Transaction Search. See Traceability and Observability for detailed setup and usage.

Realtime voice

• LiveKit - Connect OpenAI Agents SDK and Google ADK agents to LiveKit WebRTC rooms with execution.mode: realtime. A stateful LiveKitEdgeGateway, hosted by GatewayRunner, receives user audio and text and delivers the model's streamed voice and transcript back to the room.

Social media

These are built on REST APIs and you can install custom integrations as well.

Built-in

The following built-in integrations are available.

• Slack - Deploy agents as Slack bots that can respond to mentions and direct messages in Slack workspaces
• WhatsApp - Deploy agents as WhatsApp bots
• Messenger - Deploy agents as FB Messenger bots
• Instagram - Deploy agents as Instagram DM bots
• Telegram - Deploy agents as Telegram bots
• Gmail - Deploy agents as Gmail bots that automatically read and reply to emails
• Microsoft Teams - Deploy agents as Microsoft Teams bots via Azure Bot Framework, supporting 1:1 chats, group chats, and channels
• AG-UI - Serve agents over the AG-UI protocol to a custom frontend: streamed events for the answer, reasoning and tool calls, plus a shared state object the UI and the agent both read and write. Unlike the entries above this is not a hosted chat platform — it is a protocol your own UI speaks

How a messaging integration works

Every messaging integration is an adapter pair with the execution queue between them, so the agent never runs inside the webhook turn: a slow model call can no longer exceed the platform's delivery timeout and make it redeliver the message.

• The inbound adapter verifies the delivery, normalizes it (resolving the session and the platform's own message id at the edge), stores any attachments, and enqueues.
• The agent runner executes it, knowing nothing about the platform.
• The outbound adapter delivers the reply through the platform's API.

Mount an integration with IOHandler.run(...), which starts the queue topology alongside the webhook routes. RESTAPI.run([...]) builds a bare API with no runner behind it, so it rejects an integration handler rather than silently accepting messages nothing will answer.

On AWS Lambda, wrap the same handler in a LambdaWebhookHost and register its handle with Lambda.register in the request-handler Lambda; the serverless agent runner and response handler carry the reply back to the platform. See Messaging integrations on AWS Lambda for the Terraform routes, the authorizer bypass and the REST-modes-only limit.

Bring your own delivery for any platform by pointing its outbound_adapter setting at a dotted path to your own OutboundAdapter subclass; bring your own parsing by passing your own InboundAdapter subclass to the handler.

Custom

For REST API based 'custom integrations' you can implement RESTRequestHandler and pass it to the IOHandler.run(handlers=[...]) method alongside the messaging handlers.

from fastapi import APIRouter
from agentkernel.api import RESTRequestHandler
from agentkernel.integration.adapter import WebhookRESTRequestHandler
from agentkernel.pipeline import IOHandler
from agentkernel.slack import SlackInboundAdapter

class CustomHandler(RESTRequestHandler):
  def get_router(self) -> APIRouter:
      """
        - GET /health: Health check
        - GET /api/v1/agents: List available agents
      """
      router = APIRouter()

      @router.get("/health")
      def health():
          return {"status": "ok"}

      @router.get("/api/v1/agents")
      def list_agents():
          return {"agents": list(Runtime.instance().agents().keys())}

      @router.get("/rag_agent")
      def handle_rag(req: Request):
          return self._handler(req)

  def _handler(req):
      # Do a vector search and return something

if __name__ == "__main__":
    # Can pass multiple handlers
    IOHandler.run(handlers=[WebhookRESTRequestHandler(SlackInboundAdapter()), CustomHandler()])