orchestrations

Orchestration Getting Started Samples

Installation

The orchestrations package is included when you install agent-framework (which pulls in all optional packages):

pip install agent-framework

Or install the orchestrations package directly:

pip install agent-framework-orchestrations

Orchestration builders are available via the agent_framework.orchestrations submodule:

from agent_framework.orchestrations import (
    SequentialBuilder,
    ConcurrentBuilder,
    HandoffBuilder,
    GroupChatBuilder,
    MagenticBuilder,
)

Samples Overview (by directory)

concurrent

Sample	File	Concepts
Concurrent Orchestration (Default Aggregator)	concurrent_agents.py	Fan-out to multiple agents; fan-in with default aggregator returning combined Messages
Concurrent Orchestration (Custom Aggregator)	concurrent_custom_aggregator.py	Override aggregator via callback; summarize results with an LLM
Concurrent Orchestration (Custom Agent Executors)	concurrent_custom_agent_executors.py	Child executors own Agents; concurrent fan-out/fan-in via ConcurrentBuilder
Concurrent Orchestration as Agent	concurrent_workflow_as_agent.py	Build a ConcurrentBuilder workflow and expose it as an agent via workflow.as_agent(...)
Tool Approval with ConcurrentBuilder	concurrent_builder_tool_approval.py	Require human approval for sensitive tools across concurrent participants
ConcurrentBuilder Request Info	concurrent_request_info.py	Review concurrent agent outputs before aggregation using .with_request_info()

sequential

Sample	File	Concepts
Sequential Orchestration (Agents)	sequential_agents.py	Chain agents sequentially with shared conversation context
Sequential Orchestration (Custom Executor)	sequential_custom_executors.py	Mix agents with a summarizer that appends a compact summary
Sequential Orchestration as Agent	sequential_workflow_as_agent.py	Build a SequentialBuilder workflow and expose it as an agent via workflow.as_agent(...)
Tool Approval with SequentialBuilder	sequential_builder_tool_approval.py	Require human approval for sensitive tools in SequentialBuilder workflows
SequentialBuilder Request Info	sequential_request_info.py	Request info for agent responses mid-orchestration using .with_request_info()

group-chat

Sample	File	Concepts
Group Chat with Agent Manager	group_chat_agent_manager.py	Agent-based manager using with_orchestrator(agent=) to select next speaker
Group Chat Philosophical Debate	group_chat_philosophical_debate.py	Agent manager moderates long-form, multi-round debate across diverse participants
Group Chat with Simple Selector	group_chat_simple_selector.py	Group chat with a simple function selector for next speaker
Group Chat Orchestration as Agent	group_chat_workflow_as_agent.py	Build a GroupChatBuilder workflow and wrap it as an agent for composition
Tool Approval with GroupChatBuilder	group_chat_builder_tool_approval.py	Require human approval for sensitive tools in group chat orchestration
GroupChatBuilder Request Info	group_chat_request_info.py	Steer group discussions with periodic guidance using .with_request_info()

handoff

Sample	File	Concepts
Handoff (Simple)	handoff_simple.py	Single-tier routing: triage agent routes to specialists, control returns to user after each specialist response
Handoff (Autonomous)	handoff_autonomous.py	Autonomous mode: specialists iterate independently until invoking a handoff tool using .with_autonomous_mode()
Handoff with Code Interpreter	handoff_with_code_interpreter_file.py	Retrieve file IDs from code interpreter output in handoff workflow
Handoff with Tool Approval + Checkpoint	handoff_with_tool_approval_checkpoint_resume.py	Capture tool-approval decisions in checkpoints and resume from persisted state
Handoff Orchestration as Agent	handoff_workflow_as_agent.py	Build a HandoffBuilder workflow and expose it as an agent, including HITL request/response flow

magentic

Sample	File	Concepts
Magentic Workflow	magentic.py	Orchestrate multiple agents with a Magentic manager and streaming
Magentic + Human Plan Review	magentic_human_plan_review.py	Human reviews or updates the plan before execution
Magentic + Checkpoint Resume	magentic_checkpoint.py	Resume Magentic orchestration from saved checkpoints
Magentic Orchestration as Agent	magentic_workflow_as_agent.py	Build a MagenticBuilder workflow and reuse it as an agent

Tips

Magentic checkpointing tip: Treat MagenticBuilder.participants keys as stable identifiers. When resuming from a checkpoint, the rebuilt workflow must reuse the same participant names; otherwise the checkpoint cannot be applied and the run will fail fast.

Handoff workflow tip: Handoff workflows maintain the full conversation history including any Message.additional_properties emitted by your agents. This ensures routing metadata remains intact across all agent transitions. For specialist-to-specialist handoffs, use .add_handoff(source, targets) to configure which agents can route to which others with a fluent, type-safe API.

Sequential orchestration note: Sequential orchestration uses a few small adapter nodes for plumbing:

• input-conversation normalizes input to list[Message]
• to-conversation:<participant> converts agent responses into the shared conversation
• complete publishes the final output event (type='output')

These may appear in event streams (executor_invoked/executor_completed). They're analogous to concurrent's dispatcher and aggregator and can be ignored if you only care about agent activity.

Why AzureOpenAIResponsesClient?

Orchestration samples use AzureOpenAIResponsesClient rather than the CRUD-style AzureAIAgent client. Orchestrations create agents locally and do not require server-side lifecycle management (create/update/delete). AzureOpenAIResponsesClient is a lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents, which is ideal for orchestration patterns like Sequential, Concurrent, Handoff, GroupChat, and Magentic.

Environment Variables

Orchestration samples that use AzureOpenAIResponsesClient expect:

• AZURE_AI_PROJECT_ENDPOINT (Azure AI Foundry Agent Service (V2) project endpoint)
• AZURE_AI_MODEL_DEPLOYMENT_NAME (model deployment name)

These values are passed directly into the client constructor via os.getenv() in sample code.