agentbreeder 1.10.0

Define Once. Deploy Anywhere. Govern Automatically.

AgentBreeder™

Stop wrangling agents. Start shipping them.

One YAML file. Any framework. Any cloud. Governance built in.

Quick Start · Install · Docs · Contributing

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Your company has 47 AI agents. Nobody knows what they cost, who approved them, or which ones are still running. Three teams built the same summarizer. The security team hasn't audited any of them.

AgentBreeder fixes this.

Write one agent.yaml. Run agentbreeder deploy. Your agent is live — with RBAC, cost tracking, audit trail, and org-wide discoverability. Automatic. Not optional.

╔═══════════════════════════════════════════════════════════════╗
║                   AGENTBREEDER DEPLOY                         ║
╠═══════════════════════════════════════════════════════════════╣
║                                                               ║
║  ✅  YAML parsed & validated                                  ║
║  ✅  RBAC check passed (team: engineering)                    ║
║  ✅  Dependencies resolved (3 tools, 1 prompt)                ║
║  ✅  Container built (langgraph runtime)                      ║
║  ✅  Deployed to GCP Cloud Run                                ║
║  ✅  Health check passed                                      ║
║  ✅  Registered in org registry                               ║
║  ✅  Cost attribution: engineering / $0.12/hr                 ║
║                                                               ║
║  ENDPOINT: https://support-agent-a1b2c3.run.app              ║
║  STATUS:   ✅ LIVE                                            ║
║                                                               ║
╚═══════════════════════════════════════════════════════════════╝

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The Problem

AI coding tools make it easy to build agents. Nobody has made it easy to ship them responsibly.

What happens today	What happens with AgentBreeder
Every framework has its own deploy story	One YAML, any framework, any cloud
No RBAC — anyone deploys anything	RBAC validated before the first container builds
No cost tracking — $40k surprise cloud bills	Cost attributed per team, per agent, per model
No audit trail — "who deployed that?"	Every deploy logged with who, what, when, where
No discoverability — duplicate agents everywhere	Org-wide registry — search before you build
Governance is bolted on after the fact	Governance is a structural side effect of deploying

Governance is not configuration. It is a side effect of the pipeline. There is no way to skip it.

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How It Works

# agent.yaml — this is the entire config
name: customer-support-agent
version: 1.0.0
team: customer-success
owner: [e-mail]

framework: langgraph          # or: openai_agents, claude_sdk, crewai, google_adk, custom

model:
  primary: claude-sonnet-4
  fallback: gpt-4o

tools:
  - ref: tools/zendesk-mcp    # pull from org registry
  - ref: tools/order-lookup

deploy:
  cloud: gcp                  # or: aws, azure, local, kubernetes
  scaling:
    min: 1
    max: 10

pip3 install agentbreeder
agentbreeder deploy ./agent.yaml

Eight atomic steps run in sequence: parse → RBAC → resolve deps → build container → provision infra → deploy → health check → register. If any step fails, the entire deploy rolls back.

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Three Ways to Build

All three tiers compile to the same internal format. Same deploy pipeline. Same governance. No lock-in.

Tier	Who	How	Eject to
No Code	PMs, analysts, citizen builders	Visual drag-and-drop canvas — pick model, tools, prompts from the registry	Low Code
Low Code	ML engineers, DevOps	Write agent.yaml in any IDE	Full Code (agentbreeder eject)
Full Code	Senior engineers, researchers	Python/TS SDK with full programmatic control	—

from agenthub import Agent

agent = (
    Agent("support-agent", version="1.0.0", team="eng")
    .with_model(primary="claude-sonnet-4", fallback="gpt-4o")
    .with_tools(["tools/zendesk-mcp", "tools/order-lookup"])
    .with_deploy(cloud="gcp", min_scale=1, max_scale=10)
)
agent.deploy()

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What's Supported

Frameworks — LangGraph · OpenAI Agents · Claude SDK · CrewAI · Google ADK · Custom

Cloud targets — AWS (ECS Fargate, App Runner) · GCP Cloud Run · Azure Container Apps · Kubernetes · Local Docker · Claude Managed Agents

LLM providers — Anthropic · OpenAI · Google · Ollama (local, free) · LiteLLM · OpenRouter

RAG & memory — ChromaDB (vector search) · Neo4j (knowledge graph / GraphRAG) · MCP memory server

MCP & A2A — MCP server registry · MCP sidecar injection · Agent-to-Agent (A2A) JSON-RPC protocol · multi-level orchestration

Platform — RBAC · cost tracking · audit trail · org registry · MCP hub · multi-agent orchestration · RAG · evaluations · A2A protocol · AgentOps fleet dashboard · community marketplace

Full feature matrix and supported versions → docs/features

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CLI Reference

Command	What it does
agentbreeder quickstart	Full local bootstrap — Docker, stack, seed data, 5 sample agents, dashboard
agentbreeder setup	Configure Ollama + cloud API keys (interactive wizard)
agentbreeder seed	Seed ChromaDB and Neo4j; ingest your own docs with --docs
agentbreeder init	Scaffold a new agent project (interactive)
agentbreeder deploy	Deploy an agent (local, AWS, GCP, Azure, K8s)
agentbreeder chat	Chat with a deployed agent; --local uses Ollama directly
agentbreeder validate	Validate agent.yaml without deploying
agentbreeder list	List registered agents, tools, models, prompts
agentbreeder describe	Show full detail for a registered agent
agentbreeder provider	Manage LLM provider connections and API keys
agentbreeder scan	Auto-discover Ollama models and MCP servers
agentbreeder logs	Stream logs from a deployed agent
agentbreeder status	Show deploy status of all agents
agentbreeder teardown	Remove a deployed agent and its cloud resources
agentbreeder up / down	Start / stop the local platform
agentbreeder eval	Run evaluations against an agent
agentbreeder orchestration	Manage multi-agent orchestrations

Full CLI reference → agentbreeder.io/docs/cli

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Install

Pick the method that matches your environment:

Method	Command
pip (Python 3.11+)	pip3 install agentbreeder
Homebrew (macOS)	brew tap agentbreeder/agentbreeder && brew install agentbreeder
Docker	docker run rajits/agentbreeder-cli --help
npm	npm install @agentbreeder/sdk

After any install, the same commands are available:

agentbreeder quickstart       # full local platform in one command
agentbreeder setup            # configure Ollama + API keys
agentbreeder seed             # seed ChromaDB and Neo4j knowledge bases
agentbreeder deploy           # deploy an agent (local, AWS, GCP, Azure)
agentbreeder chat my-agent    # chat with a deployed agent

agentbreeder: command not found? pip's script directory may not be on your PATH — fix it here. On macOS, Homebrew is the easiest install.

Install from source: agentbreeder.io/docs/how-to#install-from-source →

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Quick Start

Option A — Full local platform (recommended for first-timers)

pip3 install agentbreeder
agentbreeder quickstart

That single command:

• Detects and guides Docker/Podman install if needed
• Starts the full stack: API · Dashboard · PostgreSQL · Redis · ChromaDB (RAG) · Neo4j (GraphRAG) · MCP servers · LiteLLM gateway
• Seeds a ChromaDB knowledge base and a Neo4j knowledge graph with sample data
• Deploys 5 sample agents (RAG, GraphRAG, MCP search, A2A orchestrator, assistant)
• Opens the visual dashboard at http://localhost:3001

Takes ~3 minutes on first run (image pulls). Then:

agentbreeder chat assistant                   # chat with the assistant agent
agentbreeder chat rag-agent                   # ask questions about AgentBreeder docs
agentbreeder chat graph-agent                 # query the knowledge graph
agentbreeder chat a2a-orchestrator            # let the orchestrator route your question
agentbreeder chat my-agent --local            # chat via Ollama — no API server needed

Deploy to cloud from the same setup:

agentbreeder quickstart --cloud aws           # local + deploy to AWS ECS Fargate
agentbreeder quickstart --cloud gcp           # local + deploy to GCP Cloud Run
agentbreeder quickstart --cloud azure         # local + deploy to Azure Container Apps

Option B — Build your own agent

pip3 install agentbreeder
agentbreeder setup                # configure Ollama + API keys (interactive wizard)
agentbreeder init                 # scaffold a new agent project
agentbreeder validate             # validate agent.yaml
agentbreeder deploy --target local       # deploy locally with Docker
agentbreeder deploy --target aws         # deploy to AWS ECS Fargate
agentbreeder deploy --target gcp         # deploy to GCP Cloud Run

Full quickstart guide → agentbreeder.io/docs/quickstart · How AgentBreeder compares →

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Documentation

Quickstart	Full local platform in one command
agent.yaml reference	Every field, every option
CLI reference	All commands
RAG & GraphRAG	ChromaDB vector search + Neo4j knowledge graphs
MCP & A2A	MCP server registry + Agent-to-Agent protocol
How-To guides	Install, deploy, orchestrate, evaluate
SDK reference	Python + TypeScript

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Contributing · Issues · Discussions · Apache 2.0