agentkernel 0.7.0

Agent Kernel - Unified AI Agents Runtime

Agent Kernel

Agent Kernel is a lightweight AI agent runtime and adapter layer for building and running AI agents across multiple frameworks. Migrate your existing agents to Agent Kernel and instantly utilize pre-built execution and testing capabilities. Deploy the same agent code without modification — see the "Multi-Cloud Deployment" section below for supported platforms.

Features

• Unified API: Common abstractions (Agent, Runner, Session, Module, Runtime) across frameworks
• Multi-Framework Support: OpenAI Agents SDK, CrewAI, LangGraph, Google ADK, Smolagents, and Pydantic AI
• Session Management: Built-in session abstraction with pluggable storage backends
• Knowledge Bases: Unified KnowledgeBase interface with ChromaDB, Neo4j, and Starburst/Trino backends via KnowledgeBuilder
• Sandbox: Execute agent-generated code and shell commands in an isolated, permission-bounded environment with pluggable providers (local_subprocess, docker), workload profiles, policy enforcement, and per-user identity
• Flexible Deployment: Interactive CLI, REST API, serverless, or containerized deployment — see the "Multi-Cloud Deployment" section below
• Pluggable Architecture: Easy to extend with custom framework adapters
• MCP Server: Built-in Model Context Protocol server for exposing agents as MCP tools and exposing any custom tool
• A2A Server: Built-in Agent-to-Agent communication server for exposing agents with a simple configuration change
• REST API: Built-in REST API server for agent interaction
• Test Automation: Built-in test suite for testing agents

Installation

pip install agentkernel

Install optional knowledge base extras as needed:

pip install "agentkernel[chromadb]"
pip install "agentkernel[neo4j]"
pip install "agentkernel[trino]"

For LLM-based thread naming with Conversation Thread Support:

pip install "agentkernel[thread]"

For the Docker sandbox provider (the local_subprocess provider needs no extra):

pip install "agentkernel[sandbox-docker]"

Requirements:

• Python 3.12+

Quick Start

Basic Concepts

• Agent: Framework-specific agent wrapped by an Agent Kernel adapter
• Runner: Framework-specific execution strategy
• Session: Shared state across conversation turns
• Module: Container that registers agents with the Runtime
• Runtime: Global registry and orchestrator for agents

CrewAI Example

from crewai import Agent as CrewAgent
from agentkernel.cli import CLI
from agentkernel.crewai import CrewAIModule

general_agent = CrewAgent(
    role="general",
    goal="Agent for general questions",
    backstory="You provide assistance with general queries. Give direct and short answers",
    verbose=False,
)

math_agent = CrewAgent(
    role="math",
    goal="Specialist agent for math questions",
    backstory="You provide help with math problems. Explain your reasoning at each step and include examples. \
        If prompted for anything else you refuse to answer.",
    verbose=False,
)

# Register agents with Agent Kernel
CrewAIModule([general_agent, math_agent])

if __name__ == "__main__":
    CLI.main()

LangGraph Example

from langgraph.graph import StateGraph
from agentkernel.cli import CLI
from agentkernel.langgraph import LangGraphModule

# Build and compile your graph
sg = StateGraph(...)
compiled = sg.compile()
compiled.name = "assistant"

LangGraphModule([compiled])

if __name__ == "__main__":
    CLI.main()

OpenAI Agents SDK Example

from agents import Agent as OpenAIAgent
from agentkernel.cli import CLI
from agentkernel.openai import OpenAIModule

general_agent = OpenAIAgent(
    name="general",
    handoff_description="Agent for general questions",
    instructions="You provide assistance with general queries. Give short and direct answers.",
)

OpenAIModule([general_agent])

if __name__ == "__main__":
    CLI.main()

Google ADK Example

from google.adk.agents import Agent
from agentkernel.cli import CLI
from agentkernel.adk import GoogleADKModule
from google.adk.models.lite_llm import LiteLlm

# Create Google ADK agents
math_agent = Agent(
    name="math",
    model=LiteLlm(model="openai/gpt-4o-mini"),
    description="Specialist agent for math questions",
    instruction="""
    You provide help with math problems.
    Explain your reasoning at each step and include examples.
    If prompted for anything else you refuse to answer.
    """,
)

GoogleADKModule([math_agent])

if __name__ == "__main__":
    CLI.main()

Interactive CLI

Agent Kernel includes an interactive CLI for local development and testing.

Available Commands:

• !h, !help — Show help
• !ld, !load <module_name> — Load a Python module containing agents
• !ls, !list — List registered agents
• !s, !select <agent_name> — Select an agent
• !n, !new — Start a new session
• !q, !quit — Exit

Usage:

python demo.py

Then interact with your agents:

(assistant) >> !load my_agents
(assistant) >> !select researcher
(researcher) >> What is the latest news on AI?

Multi-Cloud Deployment

Supported Cloud Platforms: AWS, Azure, GCP

Deploy your agents to AWS, Azure, or GCP using the built-in cloud deployment handlers.

AWS Lambda Deployment

Deploy your agents as serverless functions using the built-in Lambda handler.

from openai import OpenAI
from agents import Agent as OpenAIAgent
from agentkernel.aws import Lambda
from agentkernel.openai import OpenAIModule

client = OpenAI()
assistant = OpenAIAgent(name="assistant")

OpenAIModule([assistant])
handler = Lambda.handler

Note that this is just the simple serverless version. A more advanced serverless deployment mode, which uses queues for scalability, is also available. For queue-backed execution modes and response-store configuration, see the AWS Serverless Deployment guide.

The AWS serverless handler accepts both a direct BaseRunRequest payload and the normalized BaseRequest envelope. If a flat run payload is provided, Agent Kernel generates a request_id and normalizes it before processing.

Accepted payloads:

{
  "prompt": "Hello agent",
  "agent": "assistant",
  "session_id": "user-123"
}

{
  "request_id": "req-123",
  "user_id": "user-123",
  "body": {
    "prompt": "Hello agent",
    "agent": "assistant",
    "session_id": "user-123"
  }
}

Azure Functions Deployment

Deploy your agents as Azure Functions using the built-in Azure handler.

from openai import OpenAI
from agents import Agent as OpenAIAgent
from agentkernel.azure import AzureFunctions
from agentkernel.openai import OpenAIModule

client = OpenAI()
assistant = OpenAIAgent(name="assistant")

OpenAIModule([assistant])
handler = AzureFunctions.handler

Request Format:

{
  "request_id": "req-123",
  "user_id": "user-123",
  "body": {
    "prompt": "Hello agent",
    "agent": "assistant",
    "session_id": "user-123"
  }
}

Azure Functions also accepts the normalized envelope, and flat run payloads are normalized in the same way before the request reaches the agent runtime.

Response Format:

{
  "result": "Agent response here"
}

Status Codes:

• 200 — Success
• 400 — No agent available
• 500 — Unexpected error

GCP Cloud Run Deployment

Deploy your agents to GCP Cloud Run using the built-in CloudRun handler.

from agentkernel.gcp import CloudRun
from agentkernel.openai import OpenAIModule

OpenAIModule([...])

@CloudRun.register("/app", method="GET")
def app_handler() -> dict:
    return {"status": "ok"}

def main() -> None:
    CloudRun.run()

if __name__ == "__main__":
    main()

CloudRun is the GCP equivalent of Lambda (AWS) and AzureFunctions (Azure). It wraps RESTAPI and starts a FastAPI/uvicorn server. Custom routes are registered with @CloudRun.register(path, method). Use CloudRun.run() instead of RESTAPI.run() when deploying to GCP.

For full Terraform deployment configuration, see ak-deployment/ak-gcp/ or the GCP deployment docs.

Configuration

Agent Kernel can be configured via environment variables, .env files, or YAML/JSON configuration files.

Configuration Precedence

Values are loaded in the following order (highest precedence first):

1. Environment variables (including variables from .env file)
2. Configuration file (YAML or JSON)
3. Built-in defaults

Configuration File

By default, Agent Kernel looks for ./config.yaml in the current working directory.

Override the config file path:

export AK_CONFIG_PATH_OVERRIDE=config.json
# or
export AK_CONFIG_PATH_OVERRIDE=conf/agent-kernel.yaml

Supported formats: .yaml, .yml, .json

Configuration Options

Logging Configuration

• Field: logging.ak.level

• Type: string

• Default: WARNING

• Description: Agent Kernel logger level (INFO, DEBUG, ERROR, WARNING, CRITICAL)

• Environment Variable: AK_LOGGING__AK__LEVEL

• Field: logging.system.level

• Type: string

• Default: WARNING

• Description: System/root logger level (INFO, DEBUG, ERROR, WARNING, CRITICAL)

• Environment Variable: AK_LOGGING__SYSTEM__LEVEL

Session Store

Configure where agent sessions are stored (supports multi-cloud storage backends).

• Field: session.type
• Type: string
• Options: in_memory, redis, valkey (AWS), dynamodb (AWS), cosmosdb (Azure), firestore (GCP)
• Default: in_memory
• Environment Variable: AK_SESSION__TYPE

Redis Configuration

Required when session.type=redis:

• URL

  • Field: session.redis.url
  • Default: redis://localhost:6379
  • Description: Redis connection URL. Use rediss:// for SSL
  • Environment Variable: AK_SESSION__REDIS__URL

• TTL (Time to Live)

  • Field: session.redis.ttl
  • Default: 604800 (7 days)
  • Description: Session TTL in seconds
  • Environment Variable: AK_SESSION__REDIS__TTL

• Key Prefix

  • Field: session.redis.prefix
  • Default: ak:sessions:
  • Description: Key prefix for session storage
  • Environment Variable: AK_SESSION__REDIS__PREFIX

Valkey Configuration

Required when session.type=valkey (requires the agentkernel[valkey] extra). Valkey is the open-source, Linux Foundation-governed fork of Redis — wire-compatible with Redis and available on AWS ElastiCache at a lower price point than the Redis OSS engine:

• URL

  • Field: session.valkey.url
  • Default: valkey://localhost:6379
  • Description: Valkey connection URL. Use valkeys:// for SSL
  • Environment Variable: AK_SESSION__VALKEY__URL

• TTL (Time to Live)

  • Field: session.valkey.ttl
  • Default: 604800 (7 days)
  • Description: Session TTL in seconds
  • Environment Variable: AK_SESSION__VALKEY__TTL

• Key Prefix

  • Field: session.valkey.prefix
  • Default: ak:sessions:
  • Description: Key prefix for session storage
  • Environment Variable: AK_SESSION__VALKEY__PREFIX

Conversation Thread Support

Adding a thread block to the configuration turns on persistent, named conversation threads keyed by session_id. Once enabled, user_id becomes required on every chat request, a thread is auto-created on a session's first request, and history becomes readable over REST (GET /api/v1/threads and GET /api/v1/threads/{session_id} — optionally protected by a pluggable Authoriser). Sending thread_name on any chat request sets or renames the thread's display name and locks it against automatic naming. Threads created without an explicit thread_name are named by a pluggable naming strategy — by default an LLM call derives a concise title from the first prompt (falling back to a prefix of the prompt when litellm or an API key is unavailable). Attachments in thread mode additionally require multimodal.enabled: true with a shared attachment store (in_memory, redis, or dynamodb — session_cache is rejected). See examples/api/thread-openai and examples/api/multimodal/thread-openai.

• Field: thread.type

• Type: string

• Options: memory, redis, dynamodb (AWS), firestore (GCP), cosmosdb (Azure)

• Default: memory

• Environment Variable: AK_THREAD__TYPE

• Naming Model

  • Field: thread.naming.model
  • Type: string
  • Default: gpt-4o-mini
  • Description: LiteLLM model used to generate thread names (requires the thread extra — pip install "agentkernel[thread]" — and an API key in the environment; falls back to a truncated prompt prefix otherwise)
  • Environment Variable: AK_THREAD__NAMING__MODEL

• Auto-name Max Length

  • Field: thread.naming.max_length
  • Type: integer
  • Default: 80
  • Description: Maximum length of an auto-generated thread name
  • Environment Variable: AK_THREAD__NAMING__MAX_LENGTH

Redis Thread Store

Required when thread.type=redis:

• URL

  • Field: thread.redis.url
  • Default: redis://localhost:6379
  • Description: Redis connection URL. Use rediss:// for SSL
  • Environment Variable: AK_THREAD__REDIS__URL

• TTL (Time to Live)

  • Field: thread.redis.ttl
  • Default: 2592000 (30 days)
  • Description: Thread TTL in seconds (0 disables)
  • Environment Variable: AK_THREAD__REDIS__TTL

• Key Prefix

  • Field: thread.redis.prefix
  • Default: ak:thread:
  • Description: Key prefix for Redis thread storage
  • Environment Variable: AK_THREAD__REDIS__PREFIX

DynamoDB Thread Store

Used when thread.type=dynamodb:

• Table Name

  • Field: thread.dynamodb.table_name
  • Default: ak-agent-threads
  • Description: DynamoDB table name. The table must have a partition key named session_id (S) and a sort key named sk (S)
  • Environment Variable: AK_THREAD__DYNAMODB__TABLE_NAME

• TTL (Time to Live)

  • Field: thread.dynamodb.ttl
  • Default: 0 (disabled)
  • Description: DynamoDB item TTL in seconds
  • Environment Variable: AK_THREAD__DYNAMODB__TTL

Firestore Thread Store

Used when thread.type=firestore:

• Collection Name

  • Field: thread.firestore.collection_name
  • Default: ak-agent-threads
  • Description: Firestore collection name; each document ID is a session_id
  • Environment Variable: AK_THREAD__FIRESTORE__COLLECTION_NAME

• Project ID

  • Field: thread.firestore.project_id
  • Default: null (inferred from Application Default Credentials)
  • Environment Variable: AK_THREAD__FIRESTORE__PROJECT_ID

• Database ID

  • Field: thread.firestore.database_id
  • Default: null (the (default) database)
  • Environment Variable: AK_THREAD__FIRESTORE__DATABASE_ID

• TTL (Time to Live)

  • Field: thread.firestore.ttl
  • Default: 0 (disabled)
  • Description: Thread TTL in seconds
  • Environment Variable: AK_THREAD__FIRESTORE__TTL

Cosmos DB Thread Store

Required when thread.type=cosmosdb:

• Connection String

  • Field: thread.cosmosdb.connection_string
  • Description: Cosmos DB connection string (Azure Portal → Keys). Uses the Table API; entities are partitioned by session_id. No TTL support
  • Environment Variable: AK_THREAD__COSMOSDB__CONNECTION_STRING

• Table Name

  • Field: thread.cosmosdb.table_name
  • Default: akagentthreads
  • Description: Cosmos DB table name for thread storage
  • Environment Variable: AK_THREAD__COSMOSDB__TABLE_NAME

Execution Configuration

Configure queue-backed and serverless execution behavior.

• Execution Mode

  • Field: execution.mode
  • Options: rest_sync, rest_async, stream, async
  • Default: None
  • Description: Selects the execution mode used for queue-backed and serverless request handling
  • Environment Variable: AK_EXECUTION__MODE

• Queues

  • Field: execution.queues

  • Description: Queue settings used by serverless execution modes

  • Input Queue URL

    • Field: execution.queues.input.url
    • Default: None
    • Environment Variable: AK_EXECUTION__QUEUES__INPUT__URL

  • Output Queue URL

    • Field: execution.queues.output.url
    • Default: None
    • Environment Variable: AK_EXECUTION__QUEUES__OUTPUT__URL

  • Input Queue Max Receive Count

    • Field: execution.queues.input.max_receive_count
    • Default: 3
    • Environment Variable: AK_EXECUTION__QUEUES__INPUT__MAX_RECEIVE_COUNT

  • Output Queue Max Receive Count

    • Field: execution.queues.output.max_receive_count
    • Default: 3
    • Environment Variable: AK_EXECUTION__QUEUES__OUTPUT__MAX_RECEIVE_COUNT

  • Input Queue Consumer Count

    • Field: execution.queues.input.no_of_consumers
    • Default: 5
    • Description: Number of independent consumer threads that each poll the input queue in a continuous loop. Only used by containerized deployments — never set for serverless deployments, which have no consumer threads.
    • Environment Variable: AK_EXECUTION__QUEUES__INPUT__NO_OF_CONSUMERS

  • Output Queue Consumer Count

    • Field: execution.queues.output.no_of_consumers
    • Default: 5
    • Description: Number of independent consumer threads that each poll the output queue in a continuous loop. Only used by containerized deployments — never set for serverless deployments, which have no consumer threads.
    • Environment Variable: AK_EXECUTION__QUEUES__OUTPUT__NO_OF_CONSUMERS

  • Queue Batch Size

    • Field: execution.queues.batch_size
    • Default: None
    • Description: Max number of messages fetched per receive call, shared by the input and output queues. Only used by containerized deployments — never set for serverless deployments, which control batch size differently. Controlled by the deployment tooling via env var AK_EXECUTION__QUEUES__BATCH_SIZE — do not set in config.yaml.
    • Environment Variable: AK_EXECUTION__QUEUES__BATCH_SIZE

• Response Store

  • Field: execution.response_store

  • Description: Response persistence settings used by the serverless response handler

  • Type

    • Field: execution.response_store.type
    • Description: Response store backend selector configured in config.yaml; this value is not exported as an environment variable

  • Retry Count

    • Field: execution.response_store.retry_count
    • Default: 5
    • Description: Number of lookup attempts when polling for a response
    • Environment Variable: AK_EXECUTION__RESPONSE_STORE__RETRY_COUNT

  • Delay

    • Field: execution.response_store.delay
    • Default: 5
    • Description: Delay in seconds between response lookup attempts
    • Environment Variable: AK_EXECUTION__RESPONSE_STORE__DELAY

  • Redis Backend

    • Field: execution.response_store.redis
    • Environment Variables: AK_EXECUTION__RESPONSE_STORE__REDIS__URL, AK_EXECUTION__RESPONSE_STORE__REDIS__PREFIX, AK_EXECUTION__RESPONSE_STORE__REDIS__TTL

  • Valkey Backend

    • Field: execution.response_store.valkey
    • Environment Variables: AK_EXECUTION__RESPONSE_STORE__VALKEY__URL, AK_EXECUTION__RESPONSE_STORE__VALKEY__PREFIX, AK_EXECUTION__RESPONSE_STORE__VALKEY__TTL
    • Description: Valkey-backed response storage (requires the agentkernel[valkey] extra)

  • DynamoDB Backend

    • Field: execution.response_store.dynamodb
    • Environment Variables: AK_EXECUTION__RESPONSE_STORE__DYNAMODB__TABLE_NAME, AK_EXECUTION__RESPONSE_STORE__DYNAMODB__TTL
    • Description: DynamoDB-backed response storage with table name and TTL

Use Redis, Valkey, or DynamoDB for the response store backend. The runtime accepts BaseRunRequest payloads directly, normalizes them internally when queueing is required, and uses request_id plus optional user_id as queue message attributes.

API Configuration

Configure the REST API server (if using the API module).

• Host

  • Field: api.host
  • Default: 0.0.0.0
  • Environment Variable: AK_API__HOST

• Port

  • Field: api.port
  • Default: 8000
  • Environment Variable: AK_API__PORT

• Custom Router Prefix

  • Field: api.custom_router_prefix
  • Default: /custom
  • Environment Variable: AK_API__CUSTOM_ROUTER_PREFIX

• Enabled Routes

  • Field: api.enabled_routes.agents
  • Default: true
  • Description: Enable agent interaction routes
  • Environment Variable: AK_API__ENABLED_ROUTES__AGENTS

A2A (Agent-to-Agent) Configuration

• Enabled

  • Field: a2a.enabled
  • Default: false
  • Environment Variable: AK_A2A__ENABLED

• Agents

  • Field: a2a.agents
  • Default: ["*"]
  • Description: List of agent names to enable A2A (use ["*"] for all)
  • Environment Variable: AK_A2A__AGENTS (comma-separated)

• URL

  • Field: a2a.url
  • Default: http://localhost:8000/a2a
  • Environment Variable: AK_A2A__URL

• Task Store Type

  • Field: a2a.task_store_type
  • Options: in_memory, redis
  • Default: in_memory
  • Environment Variable: AK_A2A__TASK_STORE_TYPE

MCP (Model Context Protocol) Configuration

• Enabled

  • Field: mcp.enabled
  • Default: false
  • Environment Variable: AK_MCP__ENABLED

• Expose Agents

  • Field: mcp.expose_agents
  • Default: false
  • Description: Expose agents as MCP tools
  • Environment Variable: AK_MCP__EXPOSE_AGENTS

• Agents

  • Field: mcp.agents
  • Default: ["*"]
  • Description: List of agent names to expose as MCP tools
  • Environment Variable: AK_MCP__AGENTS (comma-separated)

• Stateless HTTP

  • Field: mcp.stateless_http
  • Default: false
  • Description: Run MCP in stateless HTTP mode (no Mcp-Session-Id)
  • Environment Variable: AK_MCP__STATELESS_HTTP

• Endpoint (not configurable)

  • The MCP server is always mounted at /mcp on the main API server.
  • Full URL: http://{api.host}:{api.port}/mcp — use api.port / AK_API__PORT to change the port.

Trace (Observability) Configuration

Configure tracing and observability for monitoring agent execution.

• Enabled

  • Field: trace.enabled
  • Default: false
  • Description: Enable tracing/observability
  • Environment Variable: AK_TRACE__ENABLED

• Type

  • Field: trace.type
  • Options: langfuse, openllmetry, logfire
  • Default: langfuse
  • Description: Type of tracing provider to use
  • Environment Variable: AK_TRACE__TYPE

Langfuse Setup:

To use Langfuse for tracing, install the langfuse extra:

pip install agentkernel[langfuse]

Configure Langfuse credentials via environment variables:

export LANGFUSE_PUBLIC_KEY=pk-lf-...
export LANGFUSE_SECRET_KEY=sk-lf-...
export LANGFUSE_HOST=https://cloud.langfuse.com  # or your self-hosted instance

Enable tracing in your configuration:

trace:
  enabled: true
  type: langfuse

OpenLLMetry (Traceloop) Setup:

To use OpenLLMetry for tracing, install the openllmetry extra:

pip install agentkernel[openllmetry]

Configure Traceloop credentials via environment variables:

export TRACELOOP_API_KEY=your-api-key
export TRACELOOP_BASE_URL=https://api.traceloop.com  # Optional: for self-hosted

Enable tracing in your configuration:

trace:
  enabled: true
  type: openllmetry

Pydantic Logfire Setup:

To use Logfire for tracing, install the logfire extra:

pip install agentkernel[logfire]

Configure the Logfire write token via an environment variable (optional — without a token, Logfire runs locally and does not ship traces):

export LOGFIRE_TOKEN=your-write-token

Enable tracing in your configuration:

trace:
  enabled: true
  type: logfire

Test Configuration

Configure test comparison modes for automated testing. Test configuration is separate from the application configuration: it is not part of config.yaml. It lives in its own test-config.yaml file and is only loaded when the testing utilities (agentkernel.test) are used — see the Test Configuration (test-config.yaml) section for file resolution, environment variables, and migration notes.

• Mode

  • Field: mode
  • Options: fuzzy, judge, fallback
  • Default: fallback
  • Description: Test comparison mode
  • Environment Variable: AK_TEST__MODE

• Judge Model

  • Field: judge.model
  • Default: gpt-4o-mini
  • Description: LLM model for judge evaluation
  • Environment Variable: AK_TEST__JUDGE__MODEL

• Judge Provider

  • Field: judge.provider
  • Default: openai
  • Description: LLM provider for judge evaluation
  • Environment Variable: AK_TEST__JUDGE__PROVIDER

• Judge Embedding Model

  • Field: judge.embedding_model
  • Default: text-embedding-3-small
  • Description: Embedding model for similarity evaluation
  • Environment Variable: AK_TEST__JUDGE__EMBEDDING_MODEL

Test Modes:

• fuzzy: Uses fuzzy string matching (RapidFuzz)
• judge: Uses LLM-based evaluation (Ragas) for semantic similarity
• fallback: Tries fuzzy first, falls back to judge if fuzzy fails

# test-config.yaml (separate file — not config.yaml)
mode: fallback
judge:
  model: gpt-4o-mini
  provider: openai
  embedding_model: text-embedding-3-small

Guardrails Configuration

Configure input and output guardrails to validate agent requests and responses for safety and compliance.

• Input Guardrails

  • Enabled

    • Field: guardrail.input.enabled
    • Default: false
    • Description: Enable input validation guardrails
    • Environment Variable: AK_GUARDRAIL__INPUT__ENABLED

  • Type

    • Field: guardrail.input.type
    • Default: openai
    • Options: openai, bedrock, walledai
    • Description: Guardrail provider type
    • Environment Variable: AK_GUARDRAIL__INPUT__TYPE

  • Config Path

    • Field: guardrail.input.config_path
    • Default: None
    • Description: Path to guardrail configuration JSON file (OpenAI only)
    • Environment Variable: AK_GUARDRAIL__INPUT__CONFIG_PATH

  • Model

    • Field: guardrail.input.model
    • Default: gpt-4o-mini
    • Description: LLM model to use for guardrail validation (OpenAI only)
    • Environment Variable: AK_GUARDRAIL__INPUT__MODEL

  • ID

    • Field: guardrail.input.id
    • Default: None
    • Description: AWS Bedrock guardrail ID (Bedrock only)
    • Environment Variable: AK_GUARDRAIL__INPUT__ID

  • Version

    • Field: guardrail.input.version
    • Default: DRAFT
    • Description: AWS Bedrock guardrail version (Bedrock only)
    • Environment Variable: AK_GUARDRAIL__INPUT__VERSION

• Output Guardrails

  • Enabled

    • Field: guardrail.output.enabled
    • Default: false
    • Description: Enable output validation guardrails
    • Environment Variable: AK_GUARDRAIL__OUTPUT__ENABLED

  • Type

    • Field: guardrail.output.type
    • Default: openai
    • Options: openai, bedrock, walledai
    • Description: Guardrail provider type
    • Environment Variable: AK_GUARDRAIL__OUTPUT__TYPE

  • Config Path

    • Field: guardrail.output.config_path
    • Default: None
    • Description: Path to guardrail configuration JSON file (OpenAI only)
    • Environment Variable: AK_GUARDRAIL__OUTPUT__CONFIG_PATH

  • Model

    • Field: guardrail.output.model
    • Default: gpt-4o-mini
    • Description: LLM model to use for guardrail validation (OpenAI only)
    • Environment Variable: AK_GUARDRAIL__OUTPUT__MODEL

  • ID

    • Field: guardrail.output.id
    • Default: None
    • Description: AWS Bedrock guardrail ID (Bedrock only)
    • Environment Variable: AK_GUARDRAIL__OUTPUT__ID

  • Version

    • Field: guardrail.output.version
    • Default: DRAFT
    • Description: AWS Bedrock guardrail version (Bedrock only)
    • Environment Variable: AK_GUARDRAIL__OUTPUT__VERSION

Guardrail Setup:

To use OpenAI guardrails, install the openai-guardrails package:

pip install agentkernel[openai]

To use AWS Bedrock guardrails, install the AWS package:

pip install agentkernel[aws]

To use Walled AI guardrails, install the Walled AI package:

pip install agentkernel[walledai]

Create guardrail configuration:

For OpenAI: Create configuration files following the OpenAI Guardrails format.

For Bedrock: Create a guardrail in AWS Bedrock and note the guardrail ID and version.

For Walled AI: Set WALLED_API_KEY, use guardrail type walledai, and control PII masking with pii.

Configure guardrails in your configuration:

OpenAI Example:

guardrail:
  input:
    enabled: true
    type: openai
    model: gpt-4o-mini
    config_path: /path/to/guardrails_input.json
  output:
    enabled: true
    type: openai
    model: gpt-4o-mini
    config_path: /path/to/guardrails_output.json

Bedrock Example:

guardrail:
  input:
    enabled: true
    type: bedrock
    id: your-guardrail-id
    version: "1"  # or "DRAFT"
  output:
    enabled: true
    type: bedrock
    id: your-guardrail-id
    version: "1"

Walled AI Example:

guardrail:
  input:
    enabled: true
    type: walledai
    pii: true
  output:
    enabled: true
    type: walledai
    pii: true

Sandbox Configuration

Enable the sandbox capability to let agents execute code and shell commands in an isolated, permission-bounded environment. When enabled, agents automatically gain sandbox tools (run_code, run_command, write_sandbox_file, read_sandbox_file, check_sandbox_task, list_sandbox_sessions, new_sandbox_session, destroy_sandbox_session) and the usage guidance is injected into their system prompt.

Minimal single-backend form (a default profile is synthesized from type + its config block):

sandbox:
  enabled: true
  type: local_subprocess        # provider short name, or a dotted path to a SandboxProvider subclass
  local_subprocess: {}
  broker:
    flavor: thread              # thread (local default) | embedded

Full form with explicit workload profiles (provider + lifetime + policy + identity):

sandbox:
  enabled: true
  agents: [coder]               # optional: attach tools/prompt only to these agents; omit = all
  default_profile: workspace
  principal_resolver: null      # optional dotted path to a PrincipalResolver; null = agent identity
  tool_output_max_chars: 8000
  broker:
    flavor: thread
    wait_timeout: 60.0          # seconds before a sync wait promotes to a background task (0 = always)
  profiles:
    workspace:
      type: docker              # container-isolated; needs the sandbox-docker extra + a Docker daemon
      scope: per_session        # per_call | per_session | per_runtime
      idle_timeout: 1800        # seconds of inactivity before the sandbox is reset on next touch
      identity:
        mode: agent             # agent | user
      policy:
        network_egress: deny    # allow | deny | allowlist
        cpu: 1.0
        memory_mb: 512
        timeout: 30.0           # per-execution wall-clock seconds (always enforced)
        strict: true            # fail closed when the provider can't enforce a policy dimension
      docker:
        image: python:3.12-slim

Key fields:

• enabled (AK_SANDBOX__ENABLED, default false) — master switch; inert when off.
• agents — agent names the tools/prompt attach to; omit for all agents.
• type (per profile) — local_subprocess (no isolation; dev/test), docker (container isolation; sandbox-docker extra), or a dotted path to your own SandboxProvider.
• scope — per_call (fresh per execution), per_session (persists across turns), per_runtime (one shared sandbox per profile).
• policy — network egress, filesystem paths, cpu/memory, timeout; enforced per provider, fail-closed under strict.
• identity.mode + principal_resolver — run code under the agent's or the invoking user's identity.
• broker.flavor — thread (default, for CLI/REST) or embedded (inline/synchronous).

See the Sandbox guide for the full reference and the examples/sandbox and examples/sandbox/identity examples.

Messaging Platform Integrations

Configure integrations with messaging platforms.

Slack

• Agent

  • Field: slack.agent
  • Default: ""
  • Description: Default agent for Slack interactions
  • Environment Variable: AK_SLACK__AGENT

• Agent Acknowledgement

  • Field: slack.agent_acknowledgement
  • Default: ""
  • Description: Acknowledgement message when Slack message is received
  • Environment Variable: AK_SLACK__AGENT_ACKNOWLEDGEMENT

WhatsApp

• Agent

  • Field: whatsapp.agent
  • Default: ""
  • Description: Default agent for WhatsApp interactions
  • Environment Variable: AK_WHATSAPP__AGENT

• Verify Token, Access Token, App Secret, Phone Number ID, API Version

  • Environment Variables: AK_WHATSAPP__VERIFY_TOKEN, AK_WHATSAPP__ACCESS_TOKEN, AK_WHATSAPP__APP_SECRET, AK_WHATSAPP__PHONE_NUMBER_ID, AK_WHATSAPP__API_VERSION

Facebook Messenger

• Agent

  • Field: messenger.agent
  • Default: ""
  • Description: Default agent for Facebook Messenger interactions
  • Environment Variable: AK_MESSENGER__AGENT

• Verify Token, Access Token, App Secret, API Version

  • Environment Variables: AK_MESSENGER__VERIFY_TOKEN, AK_MESSENGER__ACCESS_TOKEN, AK_MESSENGER__APP_SECRET, AK_MESSENGER__API_VERSION

Instagram

• Agent

  • Field: instagram.agent
  • Default: ""
  • Description: Default agent for Instagram interactions
  • Environment Variable: AK_INSTAGRAM__AGENT

• Instagram Account ID, Verify Token, Access Token, App Secret, API Version

  • Environment Variables: AK_INSTAGRAM__INSTAGRAM_ACCOUNT_ID, AK_INSTAGRAM__VERIFY_TOKEN, AK_INSTAGRAM__ACCESS_TOKEN, AK_INSTAGRAM__APP_SECRET, AK_INSTAGRAM__API_VERSION

Telegram

• Agent

  • Field: telegram.agent
  • Default: ""
  • Description: Default agent for Telegram interactions
  • Environment Variable: AK_TELEGRAM__AGENT

• Bot Token, Webhook Secret, API Version

  • Environment Variables: AK_TELEGRAM__BOT_TOKEN, AK_TELEGRAM__WEBHOOK_SECRET, AK_TELEGRAM__API_VERSION

Gmail

• Agent

  • Field: gmail.agent
  • Default: "general"
  • Description: Default agent for Gmail interactions
  • Environment Variable: AK_GMAIL__AGENT

• Client ID, Client Secret, Token File, Poll Interval, Label Filter

  • Environment Variables: AK_GMAIL__CLIENT_ID, AK_GMAIL__CLIENT_SECRET, AK_GMAIL__TOKEN_FILE, AK_GMAIL__POLL_INTERVAL, AK_GMAIL__LABEL_FILTER

Configuration Examples

Environment Variables

Use the AK_ prefix and underscores for nested fields:

export AK_DEBUG=true
export AK_SESSION__TYPE=redis
export AK_SESSION__REDIS__URL=redis://localhost:6379
export AK_SESSION__REDIS__TTL=604800
export AK_SESSION__REDIS__PREFIX=ak:sessions:
export AK_API__HOST=0.0.0.0
export AK_API__PORT=8000
export AK_A2A__ENABLED=true
export AK_MCP__ENABLED=false
export AK_TRACE__ENABLED=true
export AK_TRACE__TYPE=langfuse  # or openllmetry, logfire
# For Langfuse:
# export LANGFUSE_PUBLIC_KEY=pk-lf-...
# export LANGFUSE_SECRET_KEY=sk-lf-...
# export LANGFUSE_HOST=https://cloud.langfuse.com
# For OpenLLMetry:
# export TRACELOOP_API_KEY=your-api-key
# For Logfire:
# export LOGFIRE_TOKEN=your-write-token
# Test harness (loaded from the separate test-config.yaml — see Test Configuration)
export AK_TEST__MODE=fallback  # Options: fuzzy, judge, fallback
export AK_TEST__JUDGE__MODEL=gpt-4o-mini
export AK_TEST__JUDGE__PROVIDER=openai
export AK_TEST__JUDGE__EMBEDDING_MODEL=text-embedding-3-small
# Guardrails configuration
export AK_GUARDRAIL__INPUT__ENABLED=false
export AK_GUARDRAIL__INPUT__TYPE=openai
export AK_GUARDRAIL__INPUT__MODEL=gpt-4o-mini
export AK_GUARDRAIL__INPUT__CONFIG_PATH=/path/to/guardrails_input.json
export AK_GUARDRAIL__OUTPUT__ENABLED=false
export AK_GUARDRAIL__OUTPUT__TYPE=openai
export AK_GUARDRAIL__OUTPUT__MODEL=gpt-4o-mini
export AK_GUARDRAIL__OUTPUT__CONFIG_PATH=/path/to/guardrails_output.json
# Walled AI guardrails
export WALLED_API_KEY=your-walledai-api-key
export AK_GUARDRAIL__INPUT__PII=true
export AK_GUARDRAIL__OUTPUT__PII=true
export AK_DEBUG=true
# Messaging platforms (optional)
export AK_SLACK__AGENT=my-agent
export AK_WHATSAPP__AGENT=my-agent
export AK_MESSENGER__AGENT=my-agent
export AK_INSTAGRAM__AGENT=my-agent
export AK_TELEGRAM__AGENT=my-agent
export AK_GMAIL__AGENT=my-agent
export AK_GMAIL__CLIENT_ID=your-google-client-id
export AK_GMAIL__CLIENT_SECRET=your-google-client-secret

.env File

Create a .env file in your working directory:

AK_DEBUG=false
AK_SESSION__TYPE=redis
AK_SESSION__REDIS__URL=rediss://my-redis:6379
AK_SESSION__REDIS__TTL=1209600
AK_SESSION__REDIS__PREFIX=ak:prod:sessions:
AK_API__HOST=0.0.0.0
AK_API__PORT=8080
AK_A2A__ENABLED=true
AK_A2A__URL=http://localhost:8080/a2a
AK_TRACE__ENABLED=true
AK_TRACE__TYPE=langfuse  # or openllmetry, logfire
# Langfuse credentials (if using langfuse):
# LANGFUSE_PUBLIC_KEY=pk-lf-...
# LANGFUSE_SECRET_KEY=sk-lf-...
# LANGFUSE_HOST=https://cloud.langfuse.com
# OpenLLMetry credentials (if using openllmetry):
# TRACELOOP_API_KEY=your-api-key
# Logfire credentials (if using logfire):
# LOGFIRE_TOKEN=your-write-token

config.yaml

session:
  type: redis
  redis:
    url: redis://localhost:6379
    ttl: 604800
    prefix: "ak:sessions:"
thread: # optional — enables Conversation Thread Support (user_id becomes required on chat requests)
  type: redis
  redis:
    url: redis://localhost:6379
    ttl: 2592000
    prefix: "ak:thread:"
execution:
  mode: rest_sync
  queues:
    input:
      url: https://queue.example.com/<accountno>/<queuename>
      max_receive_count: 3
      no_of_consumers: 5 # Containerized deployments only, ignored by serverless deployments
    output:
      url: https://queue.example.com/<accountno>/<queuename>
      max_receive_count: 3
      no_of_consumers: 5 # Containerized deployments only, ignored by serverless deployments
    # batch_size is set by the deployment tooling — set via AK_EXECUTION__QUEUES__BATCH_SIZE, never here
  response_store:
    type: redis
    retry_count: 5
    delay: 5
    redis: # if this is given, then valkey/dynamodb response store parts cannot be given
      url: redis://localhost:6379
      prefix: "ak:responses:"
      ttl: 3600
    valkey: # if this is given, then redis/dynamodb response store parts cannot be given (requires the `valkey` extra)
      url: valkey://localhost:6379
      prefix: "ak:responses:"
      ttl: 3600
    dynamodb: # if this is given, then redis/valkey response store parts cannot be given
      table_name: table-name
      table_arn: table-arn
      ttl: 3600
api:
  host: 0.0.0.0
  port: 8000
  enabled_routes:
    agents: true
a2a:
  enabled: true
  agents: ["*"]
  url: http://localhost:8000/a2a
  task_store_type: in_memory
mcp:
  enabled: false
  expose_agents: false
  agents: ["*"]
trace:
  enabled: true
  type: langfuse
# Note: test configuration is no longer set here — it lives in a separate
# test-config.yaml file (see the Test Configuration section)
guardrail:
  input:
    enabled: false
    type: openai
    pii: true
    model: gpt-4o-mini
    config_path: /path/to/guardrails_input.json
  output:
    enabled: false
    type: openai
    pii: true
    model: gpt-4o-mini
    config_path: /path/to/guardrails_output.json
  # For Walled AI, set type: walledai, WALLED_API_KEY,
  # and optionally use input/output pii (default: true) to enable/disable PII masking.
slack:
  agent: my-agent
  agent_acknowledgement: "Processing your request..."
whatsapp:
  agent: my-agent
  agent_acknowledgement: "Processing..."
messenger:
  agent: my-agent
instagram:
  agent: my-agent
telegram:
  agent: my-agent
gmail:
  agent: my-agent
  poll_interval: 30
  label_filter: "INBOX"

config.json

{
  "debug": false,
  "session": {
    "type": "redis",
    "redis": {
      "url": "redis://localhost:6379",
      "ttl": 604800,
      "prefix": "ak:sessions:"
    }
  },
  "api": {
    "host": "0.0.0.0",
    "port": 8000,
    "enabled_routes": {
      "agents": true
    }
  },
  "a2a": {
    "enabled": true,
    "agents": ["*"],
    "url": "http://localhost:8000/a2a",
    "task_store_type": "in_memory"
  },
  "mcp": {
    "enabled": false,
    "expose_agents": false,
    "agents": ["*"]
  },
  "trace": {
    "enabled": true,
    "type": "langfuse"
  },
  "guardrail": {
    "input": {
      "enabled": false,
      "type": "openai",
      "model": "gpt-4o-mini",
      "config_path": "/path/to/guardrails_input.json"
    },
    "output": {
      "enabled": false,
      "type": "openai",
      "model": "gpt-4o-mini",
      "config_path": "/path/to/guardrails_output.json"
    }
  },
  "slack": {
    "agent": "my-agent",
    "agent_acknowledgement": "Processing your request..."
  },
  "whatsapp": {
    "agent": "my-agent",
    "agent_acknowledgement": "Processing..."
  },
  "messenger": {
    "agent": "my-agent"
  },
  "instagram": {
    "agent": "my-agent"
  },
  "telegram": {
    "agent": "my-agent"
  },
  "gmail": {
    "agent": "my-agent",
    "poll_interval": 30,
    "label_filter": "INBOX"
  }
}

Configuration Notes

• Empty environment variables are ignored
• Unknown fields in files or environment variables are ignored
• Environment variables override configuration file values
• Configuration file values override built-in defaults
• Nested fields use underscore (_) delimiter in environment variables

Test Configuration (test-config.yaml)

Test harness configuration (comparison mode and judge models) is separate from the application configuration. It is not part of config.yaml — it lives in its own test-config.yaml file, resolved from the current working directory, and is only loaded when the testing utilities (agentkernel.test) are used. A legacy test: section in config.yaml is ignored. See Test Configuration under Configuration Options for the full list of fields and defaults.

test-config.yaml:

mode: fallback
judge:
  model: gpt-4o-mini
  provider: openai
  embedding_model: text-embedding-3-small

Note that the file is un-nested — there is no top-level test: key. If the file is missing, defaults apply silently (fuzzy and fallback tests need no configuration file at all).

Override the test config file path:

export AK_TEST_CONFIG_PATH_OVERRIDE=/path/to/test-config.yaml

Environment variables use the AK_TEST__ prefix and override test-config.yaml values:

export AK_TEST__MODE=fallback  # Options: fuzzy, judge, fallback
export AK_TEST__JUDGE__MODEL=gpt-4o-mini
export AK_TEST__JUDGE__PROVIDER=openai
export AK_TEST__JUDGE__EMBEDDING_MODEL=text-embedding-3-small

Migration note: Earlier versions read test configuration from a test: section in config.yaml. That section is now ignored — move its contents (un-nested, without the test: key) to a sibling test-config.yaml. The AK_TEST__* environment variables are unchanged, so CI pipelines that use them need no updates.

Extensibility

Custom Framework Adapters

To add support for a new framework:

1. Implement a Runner class for your framework
2. Create an Agent wrapper class
3. Create a Module class that registers agents with the Runtime

Example structure:

from agentkernel.core import Agent, Runner, Module

class MyFrameworkRunner(Runner):
    def run(self, agent, prompt, session):
        # Implement framework-specific execution
        pass

class MyFrameworkAgent(Agent):
    def __init__(self, native_agent):
        self.native_agent = native_agent
        self.runner = MyFrameworkRunner()

class MyFrameworkModule(Module):
    def __init__(self, agents):
        super().__init__()
        for agent in agents:
            wrapped = MyFrameworkAgent(agent)
            self.register(wrapped)

Session Management

Sessions maintain state across agent interactions. Framework adapters manage their own session storage within the Session object using namespaced keys:

• "crewai" — CrewAI session data
• "langgraph" — LangGraph session data
• "openai" — OpenAI Agents SDK session data
• "adk" — Google ADK session data
• "pydanticai" — Pydantic AI session data (message history)

Access the session in your runner:

def run(self, agent, prompt, session):
    # Get framework-specific data
    my_data = session.get("my_framework", {})
    
    # Process and update data
    my_data["last_prompt"] = prompt
    
    # Update session
    session.set("my_framework", my_data)

Development

Requirements:

• Python 3.12+
• uv 0.8.0+ (recommended) or pip

Setup:

git clone https://github.com/yaalalabs/agent-kernel.git
cd agent-kernel/ak-py
uv sync  # or: pip install -e ".[dev]"

Run Tests:

uv run pytest
# or: pytest

Code Quality:

The project uses:

• black — Code formatting
• isort — Import sorting
• mypy — Type checking

License

Unless otherwise specified, all content, including all source code files and documentation files in this repository are:

Copyright (c) 2025-2026 Yaala Labs.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

SPDX-License-Identifier: Apache-2.0

Support

• Issues: GitHub Issues
• Documentation: Full Documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.