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

Here is a fully-featured example showing all major sections:

# Identity
name: support-agent
version: 1.0.0
description: "Handles tier-1 customer support"
team: customer-success
owner: alice@company.com
tags: [support, zendesk, production]

# Framework
framework: langgraph   # langgraph | openai_agents | claude_sdk | crewai | google_adk | custom

# Model
model:
  primary: claude-sonnet-4      # any model in your registry
  fallback: gpt-4o              # used if primary is unavailable
  temperature: 0.7
  max_tokens: 4096

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

# Knowledge Bases (RAG)
knowledge_bases:
  - ref: kb/product-docs
  - ref: kb/return-policy

# Versioned Prompts
prompts:
  system: prompts/support-system-v3   # registry ref (versioned, cached)

# Guardrails
guardrails:
  - pii_detection           # strip PII from outputs
  - hallucination_check     # flag low-confidence responses
  - content_filter          # block harmful content

# Deployment
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
    target_cpu: 70
  resources:
    cpu: "1"
    memory: "2Gi"
  env_vars:
    LOG_LEVEL: info
  secrets:
    - ZENDESK_API_KEY
    - OPENAI_API_KEY

# Access Control
access:
  visibility: team              # public | team | private
  allowed_callers:
    - team:engineering
    - team:customer-success
  require_approval: false

For the full field reference see agent.yaml Reference →.

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:

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:

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 kubernetes

Works with EKS, GKE, AKS, and self-hosted clusters. Ensure your kubectl context is pointed at the target cluster.

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"
  session_db_url: ""                # required if session_backend is "database"
  memory_service: memory            # "memory" | "vertex_ai_bank" | "vertex_ai_rag"
  artifact_service: memory          # "memory" | "gcs"
  gcs_bucket: ""                    # required if artifact_service is "gcs"

Claude Managed

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

Common Workflows

Check status and interact

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

Search and inspect the registry

agentbreeder search "support"          # search across all agents, tools, prompts, models
agentbreeder list                      # list all your deployed agents
agentbreeder describe my-agent         # detailed info about one agent

Roll back

agentbreeder deploy agent.yaml --version 1.0.0

Schedule a recurring run

agentbreeder schedule my-agent --cron "0 9 * * 1-5"    # weekdays at 9am

Run evaluations

agentbreeder eval my-agent --suite evals/support.yaml

Manage secrets

agentbreeder secret set ZENDESK_API_KEY           # stores in your configured backend
agentbreeder secret list
agentbreeder secret delete ZENDESK_API_KEY

Supported secret backends: environment variables, AWS Secrets Manager, GCP Secret Manager, HashiCorp Vault.

Discover available models and MCP servers

agentbreeder scan                         # discovers local MCP servers + models
agentbreeder scan --provider ollama       # Ollama models
agentbreeder scan --provider openrouter   # OpenRouter models
agentbreeder scan --provider litellm      # LiteLLM gateway

Tear down

agentbreeder teardown my-agent

Eject to Full Code

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

agentbreeder eject my-agent --to code

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	Kubernetes	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 →
MCP server setup	MCP Servers →
RAG & knowledge bases	RAG →
Versioned prompts	Prompts →
Multi-agent orchestration	Orchestration SDK →
Full Python/TypeScript SDK	Full Code →
No-code dashboard UI	No Code →

Low Code — agent.yaml + CLI

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