Full Code — Python & TypeScript SDK

Build agents entirely in code with the AgentBreeder SDK. Full type safety, custom routing logic, state machines, and programmatic deploy pipelines.

Who this is for: Senior engineers, AI researchers, ML scientists, and teams building production systems that need custom orchestration, complex evals, or CI/CD-driven deploys.

Install

pip install agentbreeder-sdk

npm install @agentbreeder/sdk

Supported Frameworks

The SDK supports all 6 frameworks. Pass the framework name as a string — AgentBreeder resolves the right runtime.

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

All 7 deployment targets available via .with_deploy():

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

Python SDK

Install and full-featured deploy

from agenthub import Agent

agent = (
    Agent("support-agent")
        .with_version("1.0.0")
        .with_description("Handles tier-1 customer support")
        .with_team("customer-success")
        .with_owner("alice@company.com")
        .with_tags(["support", "zendesk", "production"])
        .with_framework("langgraph")
        .with_model(
            primary="claude-sonnet-4",
            fallback="gpt-4o",
            temperature=0.7,
        )
        .with_tools([
            "tools/zendesk-mcp",
            "tools/order-lookup",
        ])
        .with_knowledge_bases([
            "kb/product-docs",
            "kb/return-policy",
        ])
        .with_prompts(system="prompts/support-system-v3")
        .with_guardrails(["pii_detection", "hallucination_check", "content_filter"])
        .with_access(
            visibility="team",
            allowed_callers=["team:engineering", "team:customer-success"],
            require_approval=False,
        )
        .with_deploy(
            cloud="aws",
            runtime="app-runner",
            region="us-east-1",
            scaling={"min": 1, "max": 10, "target_cpu": 70},
            secrets=["ZENDESK_API_KEY", "OPENAI_API_KEY"],
        )
)

result = agent.deploy()
print(result.endpoint)

Framework-specific config

from agenthub import Agent

agent = (
    Agent("research-agent")
        .with_framework("langgraph")
        .with_model(primary="claude-sonnet-4")
        .with_tools(["tools/web-search", "tools/arxiv"])
        .with_deploy(cloud="gcp", runtime="cloud-run")
)

from agenthub import Agent
from agenthub.config import ClaudeSDKConfig

agent = (
    Agent("reasoning-agent")
        .with_framework("claude_sdk")
        .with_model(primary="claude-opus-4")
        .with_framework_config(ClaudeSDKConfig(
            thinking={"type": "adaptive", "effort": "high"},
            prompt_caching=True,
        ))
        .with_deploy(cloud="aws", runtime="app-runner")
)

from agenthub import Agent

agent = (
    Agent("research-crew")
        .with_framework("crewai")
        .with_model(primary="gpt-4o")
        .with_tools(["tools/web-search", "tools/wikipedia"])
        .with_deploy(cloud="local")
)

from agenthub import Agent
from agenthub.config import GoogleADKConfig

agent = (
    Agent("workspace-agent")
        .with_framework("google_adk")
        .with_model(primary="gemini-2.0-flash")
        .with_framework_config(GoogleADKConfig(
            session_backend="database",
            memory_service="vertex_ai_bank",
            artifact_service="gcs",
            gcs_bucket="my-agent-artifacts",
        ))
        .with_deploy(cloud="gcp", runtime="cloud-run")
)

from agenthub import Agent

agent = (
    Agent("triage-agent")
        .with_framework("openai_agents")
        .with_model(primary="gpt-4o-mini", fallback="gpt-4o")
        .with_tools(["tools/zendesk-mcp"])
        .with_deploy(cloud="azure", runtime="container-apps")
)

from agenthub import Agent

agent = (
    Agent("legacy-wrapper")
        .with_framework("custom")
        .with_model(primary="claude-sonnet-4")
        .with_entrypoint("server:app")          # your ASGI/WSGI app
        .with_deploy(cloud="kubernetes")
)

Deploy to Claude Managed Agents

from agenthub import Agent
from agenthub.config import ClaudeManagedConfig

agent = (
    Agent("managed-support")
        .with_framework("claude_sdk")
        .with_model(primary="claude-sonnet-4")
        .with_deploy(
            cloud="claude-managed",
            secrets=["ANTHROPIC_API_KEY"],
        )
        .with_claude_managed(ClaudeManagedConfig(
            environment={"networking": "unrestricted"},
            tools=[{"type": "agent_toolset_20260401"}],
        ))
)

result = agent.deploy()
# result.endpoint = "anthropic://agents/agt_01...?env=env_01..."
print(result.endpoint)

Generate and validate agent.yaml

agent.to_yaml("agent.yaml")       # export for YAML-based workflows
agent.validate()                   # validate without deploying

TypeScript SDK

import { Agent } from "@agentbreeder/sdk";

const agent = new Agent("support-agent", {
  version: "1.0.0",
  description: "Handles tier-1 customer support",
  team: "customer-success",
  owner: "alice@company.com",
  tags: ["support", "zendesk", "production"],
})
  .withFramework("langgraph")
  .withModel({
    primary: "claude-sonnet-4",
    fallback: "gpt-4o",
    temperature: 0.7,
  })
  .withTools(["tools/zendesk-mcp", "tools/order-lookup"])
  .withKnowledgeBases(["kb/product-docs", "kb/return-policy"])
  .withPrompts({ system: "prompts/support-system-v3" })
  .withGuardrails(["pii_detection", "hallucination_check"])
  .withAccess({ visibility: "team", allowedCallers: ["team:engineering"] })
  .withDeploy({
    cloud: "aws",
    runtime: "app-runner",
    region: "us-east-1",
    secrets: ["ZENDESK_API_KEY"],
  });

const result = await agent.deploy();
console.log(result.endpoint);

Export as YAML:

agent.toYaml("agent.yaml");

Multi-Agent Orchestration (Full Code)

The Full Code tier unlocks the Orchestration SDK — define pipelines of agents with custom routing, state, and execution strategies.

from agenthub.orchestration import Pipeline, Agent

pipeline = (
    Pipeline("support-pipeline")
        .add_agent("triage",     Agent("triage-agent").with_model("claude-haiku-4-5"))
        .add_agent("specialist", Agent("specialist-agent").with_model("claude-sonnet-4"))
        .add_agent("escalation", Agent("escalation-agent").with_model("claude-opus-4"))
        .with_strategy("router")          # router | sequential | parallel | supervisor | hierarchical | fan-out
        .with_routing(lambda ctx: (
            "specialist" if ctx.intent in ("billing", "technical")
            else "escalation" if ctx.priority == "urgent"
            else "triage"
        ))
        .with_deploy(cloud="aws", runtime="app-runner")
)

result = pipeline.deploy()

Supported strategies:

Strategy	Description
`router`	Classifies the request and routes to the right agent
`sequential`	Runs agents in order, passing output as input
`parallel`	Runs all agents simultaneously, merges results
`supervisor`	One agent manages sub-agents dynamically
`hierarchical`	Tree of supervisors and workers
`fan-out`	One input split across N agents, results aggregated

Agent-to-Agent (A2A) Calls

Agents deployed with AgentBreeder automatically expose an A2A endpoint. Call other agents directly:

from agenthub.a2a import A2AClient

# Discover agents in your org registry
client = A2AClient()
agents = client.list()   # returns all A2A-enabled agents

# Call another agent
response = client.call(
    agent="specialist-agent",
    message="Customer is asking about order #12345",
    context={"team": "customer-success"},
)
print(response.text)

Restrict which agents can call yours via access.allowed_callers in agent.yaml:

access:
  allowed_callers:
    - team:engineering
    - agent:triage-agent

CI/CD Integration

# deploy.py — run in GitHub Actions / GitLab CI
import os
from agenthub import Agent

agent = (
    Agent("support-agent")
        .with_version(os.environ["AGENT_VERSION"])
        .with_framework("langgraph")
        .with_model(primary="claude-sonnet-4")
        .with_deploy(
            cloud="aws",
            runtime="app-runner",
            region=os.environ["AWS_REGION"],
        )
)

result = agent.deploy()
print(f"Deployed: {result.endpoint}")

Or use the CLI in CI:

agentbreeder deploy agent.yaml \
  --target app-runner \
  --region us-east-1 \
  --version ${{ github.sha }}

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	✓	✓	✓	✓	✓	✓	✓

Claude Managed + any framework

The claude-managed deployment target works with every framework. When framework is set to anything other than claude_sdk, AgentBreeder wraps your agent in a Claude-managed container adapter automatically.

Next Steps

What	Where
Orchestration strategies	Orchestration SDK →
Agent-to-agent protocol	A2A Guide →
All `agent.yaml` fields	agent.yaml Reference →
All CLI commands	CLI Reference →
MCP server setup	MCP Servers →
RAG & knowledge bases	RAG →
Versioned prompts	Prompts →
Start with the dashboard	No Code →
Write plain YAML	Low Code →

Full Code — Python & TypeScript SDK

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