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Eduard van Valkenburg
977c3adfb2
* python: replace pre-commit with prek, add PEP 723 script deps, clean up dev dependencies - Replace pre-commit with prek (Rust-native, faster pre-commit alternative) - Move supported hooks to repo: builtin for zero-clone speed - Add new builtin hooks: trailing-whitespace, check-merge-conflict, detect-private-key, check-added-large-files - Update all hook versions to latest (pre-commit-hooks v6, pyupgrade v3.21.2, bandit 1.9.3, uv-pre-commit 0.10.0) - Add PEP 723 inline script metadata to 34 samples with external deps - Remove autogen-agentchat/autogen-ext from dev deps (now declared per-sample) - Remove unused dev deps: pytest-env, tomli-w - Add agent-framework-core>=1.0.0b260130 lower bound to all 21 packages - Update CI workflow to use j178/prek-action - Update docs: DEV_SETUP.md, AGENTS.md, CODING_STANDARD.md, SAMPLE_GUIDELINES.md * updated lock * python: fix prek config paths for local execution and CI workflow Remove global 'files: ^python/' filter and strip python/ prefix from all path patterns in .pre-commit-config.yaml so prek finds files when run from the python/ directory. Update CI workflow to use --cd python instead of --config path. Include trailing whitespace fixes and dev dependency cleanup. * python: move helper scripts to scripts/ folder and exclude from checks * python: exclude AGENTS.md from prek markdown code lint * python: exclude AGENTS.md and azure_ai_search sample from markdown lint * fix m365 sample * python: ignore CPY rule for samples with PEP 723 headers * fix in dev_setup * python: replace aiofiles with regular open in samples * python: suppress reportUnusedImport in markdown code block checker * python: use samples pyright config for markdown code block checker Write a temp pyrightconfig.json matching pyrightconfig.samples.json rules (typeCheckingMode=off, only reportMissingImports and reportAttributeAccessIssue). Filter output to only fail on these rules since syntax-level errors (top-level await, undefined vars) are expected in README documentation snippets. * python: use markdown-code-lint with fixed globs instead of prek file list The prek-markdown-code-lint task received all changed files including non-README markdown and files with pre-existing broken imports. Replace with the standard markdown-code-lint task which uses the correct glob patterns (README.md, packages/**/README.md, samples/**/*.md). * python: exclude READMEs with pre-existing broken imports from markdown lint * python: fix broken README code snippets instead of excluding them - ag-ui: replace TextContent (removed) with content.type == 'text' - durabletask: fix import path to durabletask.worker.TaskHubGrpcWorker - orchestrations: use constructor params instead of .participants() method - observability: mark deprecated code blocks as plain text, filter reportMissingImports to agent_framework modules only - remove README excludes from markdown-code-lint task * add revision to gaia download * feat(python): parallelize checks across packages Run (package × task) cross-product in parallel using ThreadPoolExecutor and subprocesses. Key changes: - Add scripts/task_runner.py with shared parallel execution engine - Update run_tasks_in_packages_if_exists.py to accept multiple tasks - Update run_tasks_in_changed_packages.py with --files flag and parallel support - Add check-packages poe task (fmt+lint+pyright+mypy in parallel) - Add prek-markdown-code-lint and prek-samples-check with change detection - Split CI code quality workflow into parallel prek and mypy jobs - Update DEV_SETUP.md to document new parallel behavior Core package changes still trigger checks on all packages. * feat(ci): split code quality into 4 parallel jobs Split the single prek job into parallel jobs: - pre-commit-hooks: lightweight hooks (SKIP=poe-check) - package-checks: fmt/lint/pyright/mypy via check-packages - samples-markdown: samples-lint, samples-syntax, markdown-code-lint - mypy: change-detected mypy checks All 4 jobs run concurrently (×2 Python versions = 8 runners). * feat(ci): use only Python 3.10 for code quality checks * refactor(python): add future annotations and remove quoted types Add `from __future__ import annotations` to 93 package files that used quoted string annotations, then run pyupgrade --py310-plus to remove the now-unnecessary quotes. Fixes https://github.com/microsoft/agent-framework/issues/3578
977c3adfb2
·
2026-02-09 17:51:01 +00:00
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Workflows Getting Started Samples
Installation
Microsoft Agent Framework Workflows support ships with the core agent-framework or agent-framework-core package, so no extra installation step is required.
To install with visualization support:
pip install agent-framework[viz] --pre
To export visualization images you also need to install GraphViz.
Samples Overview
Foundational Concepts - Start Here
Begin with the _start-here folder in order. These three samples introduce the core ideas of executors, edges, agents in workflows, and streaming.
| Sample | File | Concepts |
|---|---|---|
| Executors and Edges | _start-here/step1_executors_and_edges.py | Minimal workflow with basic executors and edges |
| Agents in a Workflow | _start-here/step2_agents_in_a_workflow.py | Introduces adding Agents as nodes; calling agents inside a workflow |
| Streaming (Basics) | _start-here/step3_streaming.py | Extends workflows with event streaming |
Once comfortable with these, explore the rest of the samples below.
Samples Overview (by directory)
agents
| Sample | File | Concepts |
|---|---|---|
| Azure Chat Agents (Streaming) | agents/azure_chat_agents_streaming.py | Add Azure Chat agents as edges and handle streaming events |
| Azure AI Agents (Streaming) | agents/azure_ai_agents_streaming.py | Add Azure AI agents as edges and handle streaming events |
| Azure AI Agents (Shared Thread) | agents/azure_ai_agents_with_shared_thread.py | Share a common message thread between multiple Azure AI agents in a workflow |
| Custom Agent Executors | agents/custom_agent_executors.py | Create executors to handle agent run methods |
| Sequential Workflow as Agent | agents/sequential_workflow_as_agent.py | Build a sequential workflow orchestrating agents, then expose it as a reusable agent |
| Concurrent Workflow as Agent | agents/concurrent_workflow_as_agent.py | Build a concurrent fan-out/fan-in workflow, then expose it as a reusable agent |
| Magentic Workflow as Agent | agents/magentic_workflow_as_agent.py | Configure Magentic orchestration with callbacks, then expose the workflow as an agent |
| Workflow as Agent (Reflection Pattern) | agents/workflow_as_agent_reflection_pattern.py | Wrap a workflow so it can behave like an agent (reflection pattern) |
| Workflow as Agent + HITL | agents/workflow_as_agent_human_in_the_loop.py | Extend workflow-as-agent with human-in-the-loop capability |
| Workflow as Agent with Thread | agents/workflow_as_agent_with_thread.py | Use AgentThread to maintain conversation history across workflow-as-agent invocations |
| Workflow as Agent kwargs | agents/workflow_as_agent_kwargs.py | Pass custom context (data, user tokens) via kwargs through workflow.as_agent() to @ai_function tools |
| Handoff Workflow as Agent | agents/handoff_workflow_as_agent.py | Use a HandoffBuilder workflow as an agent with HITL via FunctionCallContent/FunctionResultContent |
checkpoint
| Sample | File | Concepts |
|---|---|---|
| Checkpoint & Resume | checkpoint/checkpoint_with_resume.py | Create checkpoints, inspect them, and resume execution |
| Checkpoint & HITL Resume | checkpoint/checkpoint_with_human_in_the_loop.py | Combine checkpointing with human approvals and resume pending HITL requests |
| Checkpointed Sub-Workflow | checkpoint/sub_workflow_checkpoint.py | Save and resume a sub-workflow that pauses for human approval |
| Handoff + Tool Approval Resume | checkpoint/handoff_with_tool_approval_checkpoint_resume.py | Handoff workflow that captures tool-call approvals in checkpoints and resumes with human decisions |
| Workflow as Agent Checkpoint | checkpoint/workflow_as_agent_checkpoint.py | Enable checkpointing when using workflow.as_agent() with checkpoint_storage parameter |
composition
| Sample | File | Concepts |
|---|---|---|
| Sub-Workflow (Basics) | composition/sub_workflow_basics.py | Wrap a workflow as an executor and orchestrate sub-workflows |
| Sub-Workflow: Request Interception | composition/sub_workflow_request_interception.py | Intercept and forward sub-workflow requests using @handler for SubWorkflowRequestMessage |
| Sub-Workflow: Parallel Requests | composition/sub_workflow_parallel_requests.py | Multiple specialized interceptors handling different request types from same sub-workflow |
| Sub-Workflow: kwargs Propagation | composition/sub_workflow_kwargs.py | Pass custom context (user tokens, config) from parent workflow through to sub-workflow agents |
control-flow
| Sample | File | Concepts |
|---|---|---|
| Sequential Executors | control-flow/sequential_executors.py | Sequential workflow with explicit executor setup |
| Sequential (Streaming) | control-flow/sequential_streaming.py | Stream events from a simple sequential run |
| Edge Condition | control-flow/edge_condition.py | Conditional routing based on agent classification |
| Switch-Case Edge Group | control-flow/switch_case_edge_group.py | Switch-case branching using classifier outputs |
| Multi-Selection Edge Group | control-flow/multi_selection_edge_group.py | Select one or many targets dynamically (subset fan-out) |
| Simple Loop | control-flow/simple_loop.py | Feedback loop where an agent judges ABOVE/BELOW/MATCHED |
| Workflow Cancellation | control-flow/workflow_cancellation.py | Cancel a running workflow using asyncio tasks |
human-in-the-loop
| Sample | File | Concepts |
|---|---|---|
| Human-In-The-Loop (Guessing Game) | human-in-the-loop/guessing_game_with_human_input.py | Interactive request/response prompts with a human via ctx.request_info() |
| Agents with Approval Requests in Workflows | human-in-the-loop/agents_with_approval_requests.py | Agents that create approval requests during workflow execution and wait for human approval to proceed |
| SequentialBuilder Request Info | human-in-the-loop/sequential_request_info.py | Request info for agent responses mid-workflow using .with_request_info() on SequentialBuilder |
| ConcurrentBuilder Request Info | human-in-the-loop/concurrent_request_info.py | Review concurrent agent outputs before aggregation using .with_request_info() on ConcurrentBuilder |
| GroupChatBuilder Request Info | human-in-the-loop/group_chat_request_info.py | Steer group discussions with periodic guidance using .with_request_info() on GroupChatBuilder |
tool-approval
Tool approval samples demonstrate using @tool(approval_mode="always_require") to gate sensitive tool executions with human approval. These work with the high-level builder APIs.
| Sample | File | Concepts |
|---|---|---|
| SequentialBuilder Tool Approval | tool-approval/sequential_builder_tool_approval.py | Sequential workflow with tool approval gates for sensitive operations |
| ConcurrentBuilder Tool Approval | tool-approval/concurrent_builder_tool_approval.py | Concurrent workflow with tool approvals across parallel agents |
| GroupChatBuilder Tool Approval | tool-approval/group_chat_builder_tool_approval.py | Group chat workflow with tool approval for multi-agent collaboration |
observability
| Sample | File | Concepts |
|---|---|---|
| Executor I/O Observation | observability/executor_io_observation.py | Observe executor input/output data via executor_invoked events (type='executor_invoked') and executor_completed events (type='executor_completed') without modifying executor code |
For additional observability samples in Agent Framework, see the observability getting started samples. The sample demonstrates integrating observability into workflows.
orchestration
Orchestration samples (Sequential, Concurrent, Handoff, GroupChat, Magentic) have moved to the dedicated orchestrations samples directory.
parallelism
| Sample | File | Concepts |
|---|---|---|
| Concurrent (Fan-out/Fan-in) | parallelism/fan_out_fan_in_edges.py | Dispatch to multiple executors and aggregate results |
| Aggregate Results of Different Types | parallelism/aggregate_results_of_different_types.py | Handle results of different types from multiple concurrent executors |
| Map-Reduce with Visualization | parallelism/map_reduce_and_visualization.py | Fan-out/fan-in pattern with diagram export |
state-management
| Sample | File | Concepts |
|---|---|---|
| State with Agents | state-management/state_with_agents.py | Store in state once and later reuse across agents |
| Workflow Kwargs (Custom Context) | state-management/workflow_kwargs.py | Pass custom context (data, user tokens) via kwargs to @tool tools |
visualization
| Sample | File | Concepts |
|---|---|---|
| Concurrent with Visualization | visualization/concurrent_with_visualization.py | Fan-out/fan-in workflow with diagram export |
declarative
YAML-based declarative workflows allow you to define multi-agent orchestration patterns without writing Python code. See the declarative workflows README for more details on YAML workflow syntax and available actions.
| Sample | File | Concepts |
|---|---|---|
| Conditional Workflow | declarative/conditional_workflow/ | Nested conditional branching based on user input |
| Customer Support | declarative/customer_support/ | Multi-agent customer support with routing |
| Deep Research | declarative/deep_research/ | Research workflow with planning, searching, and synthesis |
| Function Tools | declarative/function_tools/ | Invoking Python functions from declarative workflows |
| Human-in-Loop | declarative/human_in_loop/ | Interactive workflows that request user input |
| Marketing | declarative/marketing/ | Marketing content generation workflow |
| Simple Workflow | declarative/simple_workflow/ | Basic workflow with variable setting, conditionals, and loops |
| Student Teacher | declarative/student_teacher/ | Student-teacher interaction pattern |
resources
- Sample text inputs used by certain workflows:
Notes
- Agent-based samples use provider SDKs (Azure/OpenAI, etc.). Ensure credentials are configured, or adapt agents accordingly.
Sequential orchestration uses a few small adapter nodes for plumbing:
- "input-conversation" normalizes input to
list[ChatMessage] - "to-conversation:" 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.
Environment Variables
-
AzureOpenAIChatClient: Set Azure OpenAI environment variables as documented here. These variables are required for samples that construct
AzureOpenAIChatClient -
OpenAI (used in orchestration samples):