Configure agents in YAML and deploy with one command. No infrastructure expertise needed.

Low Code — agent.yaml + CLI

Write one agent.yaml file, run agentbreeder deploy. That's it.

Who this is for: ML engineers, DevOps engineers, platform engineers, and architects who are comfortable editing YAML and running terminal commands — but don't want to write boilerplate infrastructure code.

Supported Frameworks

Set framework: to any of the 6 supported values. AgentBreeder picks the right runtime container, installs the correct dependencies, and injects the framework's server adapter automatically.

Value	Framework
`langgraph`	LangGraph (LangChain)
`openai_agents`	OpenAI Agents SDK
`claude_sdk`	Anthropic Claude SDK
`crewai`	CrewAI
`google_adk`	Google Agent Development Kit
`custom`	Bring your own entrypoint

Supported Deployment Targets

Set deploy.cloud and optionally deploy.runtime. Every combination works.

`cloud`	`runtime` (default)	Also supports
`local`	`docker-compose`	—
`aws`	`ecs-fargate`	`app-runner`
`gcp`	`cloud-run`	—
`azure`	`container-apps`	—
`kubernetes`	`deployment`	—
`claude-managed`	(no container)	—

Getting Started

1. Install

pip install agentbreeder

2. Initialize a project

agentbreeder init

The wizard asks 5 questions and generates a ready-to-run project:

my-agent/
├── agent.yaml        # ← the only file AgentBreeder needs
├── agent.py          # working agent code for your chosen framework
├── requirements.txt
├── .env.example
└── README.md

3. Edit agent.yaml

name: support-agent
version: 1.0.0
team: customer-success
owner: alice@company.com
framework: langgraph             # langgraph | openai_agents | claude_sdk | crewai | google_adk | custom

model:
  primary: claude-sonnet-4       # any model in your registry
  fallback: gpt-4o               # used if primary is unavailable
  temperature: 0.7

tools:
  - ref: tools/zendesk-mcp       # from your org's tool registry
  - ref: tools/order-lookup

deploy:
  cloud: aws                     # aws | gcp | azure | kubernetes | local | claude-managed
  runtime: app-runner            # default for aws: ecs-fargate; also: app-runner
  region: us-east-1
  scaling:
    min: 1
    max: 10
  secrets:
    - ZENDESK_API_KEY
    - OPENAI_API_KEY

4. Validate

agentbreeder validate agent.yaml

Runs three checks: YAML syntax, JSON Schema, and semantic validation (framework supported, team exists).

5. Deploy

agentbreeder deploy agent.yaml --target local

agentbreeder deploy agent.yaml --target app-runner --region us-east-1

Required in deploy.env_vars:

deploy:
  cloud: aws
  runtime: app-runner
  env_vars:
    AWS_ACCOUNT_ID: "123456789012"
    AWS_REGION: us-east-1

agentbreeder deploy agent.yaml --target ecs-fargate --region us-east-1

Required in deploy.env_vars:

deploy:
  cloud: aws
  runtime: ecs-fargate
  env_vars:
    AWS_ACCOUNT_ID: "123456789012"
    AWS_REGION: us-east-1

agentbreeder deploy agent.yaml --target cloud-run --region us-central1

Set GOOGLE_APPLICATION_CREDENTIALS and GOOGLE_CLOUD_PROJECT in your environment.

agentbreeder deploy agent.yaml --target container-apps

agentbreeder deploy agent.yaml --target claude-managed

No container is built. Add ANTHROPIC_API_KEY to secrets:

deploy:
  cloud: claude-managed
  secrets:
    - ANTHROPIC_API_KEY

claude_managed:
  environment:
    networking: unrestricted
  tools:
    - type: agent_toolset_20260401

Framework-Specific Configuration

Some frameworks support extra config blocks in agent.yaml. These are optional — AgentBreeder picks sensible defaults.

Claude SDK

claude_sdk:
  thinking:
    type: adaptive          # "adaptive" (default) | "enabled"
    effort: high            # "low" | "medium" | "high"
  prompt_caching: true      # cache system prompt for latency + cost savings

Google ADK

google_adk:
  session_backend: memory           # "memory" | "database" | "vertex_ai"
  memory_service: memory            # "memory" | "vertex_ai_bank" | "vertex_ai_rag"
  artifact_service: memory          # "memory" | "gcs"

Claude Managed

claude_managed:
  environment:
    networking: unrestricted        # "unrestricted" | "restricted"
  tools:
    - type: agent_toolset_20260401  # full built-in toolset

Common Workflows

Check status after deploy

agentbreeder status
agentbreeder logs my-agent --follow
agentbreeder chat my-agent

Roll back

agentbreeder deploy agent.yaml --version 1.0.0

Tear down

agentbreeder teardown my-agent

Scan for available models and MCP servers

agentbreeder scan                    # discovers all local MCP servers + models
agentbreeder scan --provider ollama  # Ollama models
agentbreeder scan --provider openrouter

Eject to Full Code

When you need custom routing, state machines, or programmatic control:

agentbreeder eject --to sdk

This generates a Python or TypeScript SDK project from your agent.yaml. Your YAML is preserved as the source of truth — the SDK wraps it with full programmatic access. See Full Code →.

All Frameworks × All Deployment Targets

	Local	App Runner	ECS Fargate	Cloud Run	Container Apps	Claude Managed
LangGraph	✓	✓	✓	✓	✓	✓
OpenAI Agents	✓	✓	✓	✓	✓	✓
Claude SDK	✓	✓	✓	✓	✓	✓
CrewAI	✓	✓	✓	✓	✓	✓
Google ADK	✓	✓	✓	✓	✓	✓
Custom	✓	✓	✓	✓	✓	✓

Next Steps

What	Where
All `agent.yaml` fields	agent.yaml Reference →
All CLI commands	CLI Reference →
Multi-agent orchestration	Orchestration SDK →
Full Python/TypeScript SDK	Full Code →
No-code dashboard UI	No Code →

Low Code — agent.yaml + CLI

On this page