Step 01: Analogy
Building blocks and deployment infrastructure for your AI Agent.
Agent Kernel is like the operating system for the AI assistants, think Linux for your agents.
Your Agent Logic
InstructionsToolsFramework SDK
Agent Kernel Runtime
AgentsAgent RunnerSession ManagementHooks
Storage & Memory
In-MemoryRedisDynamoDBCosmosDBFirestore
Knowledge Bases
ChromaDBNeo4jStarburstSQLDB
Observability
LangFuseOpenLLMetry
Cloud Infrastructure
AWS LambdaECSAzure FunctionsContainer AppsGCP Cloud RunGCP Cloud Run Functions
Interfacing
CLIMCPA2AREST API
Channels
SlackTeamsWhatsAppTelegramMessengerInstagramGmail
Step 02: Features
All Enterprise Features Available
Free And Open-Source
Build & Interface
CLI for Prototyping
Easy interfacing your agents on your laptop via Agent Kernel's command line interface.
REST API Server
FastAPI-based server out of the box. No boilerplate. Just run your agent.
Native MCP and A2A support
Expose agents as tools (MCP) and enable agent-to-agent collaboration (A2A). Makes integration with external AI systems straightforward.
Multi-Framework Support
Run OpenAI Agents, LangGraph, CrewAI, Google ADK, Smolagents, LiveKit side-by-side. Keep one runtime across teams while using the best framework per use case.
Runtime & Extensibility
Pluggable Session & Memory
Start local in-memory, scale to Redis, DynamoDB, or Cosmos DB in production. Switch via config, not code rewrites.
Execution Hooks
Pre/post hooks for RAG injection, input validation, response moderation, analytics.
Multimodal Support
In-built framework-neutral multimodal support across all integration channels. Handle files/images cleanly and keep sessions lightweight. Additional voice and video support via LiveKit.
Ship & Secure
Guardrails and Content Safety
Input and output protection in the same runtime pipeline. Supports policy checks for safety, PII handling, and jailbreak defense.
Cloud Deployment
Pre-built Terraform modules for AWS Lambda, ECS, Azure Functions, Container Apps, GCP Cloud Run, GCP Cloud Run Functions.
Reliability
Built for resilient cloud deployments with health checks and failover patterns.
Integrate & Observe
Messaging Integrations
Slack, WhatsApp, Instagram, Telegram, Gmail, Teams, Messenger plug and play.
Testing Framework
pytest-integrated test runner. Write deterministic automated test scenarios for your AI agents like any other code.
Observability
Langfuse and OpenLLMetry tracing with one config line. No manual instrumentation. Trace requests, latency, tool calls, and token behavior.
Step 03: Framework
Use The Framework You Prefer
Choose a supported framework that fits your team, while Agent Kernel gives you a consistent production-ready layer for deployment, APIs, sessions, and integrations.
| 1 | # 1. Install the CLI |
| 2 | pip install agentkernel[openai] |
| 1 | from agents import Agent as OpenAIAgent |
| 2 | from agentkernel.cli import CLI |
| 3 | from agentkernel.openai import OpenAIModule |
| 4 | |
| 5 | agent = OpenAIAgent( |
| 6 | name="assistant", |
| 7 | instructions="You are a helpful assistant.", |
| 8 | ) |
| 9 | |
| 10 | OpenAIModule([agent]) |
| 11 | |
| 12 | if __name__ == "__main__": |
| 13 | CLI.main() |
Step 04: How It Works
How Agent Kernel Fits In
You write your AI agent's logic. Agent Kernel handles everything else: the infrastructure, the cloud deployment, memory, knowledge bases, hooks, observability & traceability, LLM cost tracking, and the integrations so your agent is live and talking to real users in days.
Go deeper with Agent Kernel
Explore the platform capabilities and real-world workflows behind secure, production-ready AI agents.
Ready to Ship Your
First Agent?
Free, open-source, Apache 2.0. No licensing costs, no vendor lock-in. Join hundreds of developers building production AI agents with Agent Kernel.
