mirror of
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Eduard van Valkenburg
1e350ea22f
* PR2: Wire context provider pipeline and update all internal consumers - Replace AgentThread with AgentSession across all packages - Replace ContextProvider with BaseContextProvider across all packages - Replace context_provider param with context_providers (Sequence) - Replace thread= with session= in run() signatures - Replace get_new_thread() with create_session() - Add get_session(service_session_id) to agent interface - DurableAgentThread -> DurableAgentSession - Remove _notify_thread_of_new_messages from WorkflowAgent - Wire before_run/after_run context provider pipeline in RawAgent - Auto-inject InMemoryHistoryProvider when no providers configured * fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files * refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider) * fix: update remaining ag-ui references (client docstring, getting_started sample) * fix: make get_session service_session_id keyword-only to avoid confusion with session_id * refactor: rename _RunContext.thread_messages to session_messages * refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists * rename: remove _new_ prefix from test files * refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider) * fix: read full history from session state directly instead of reaching into provider internals * fix: update stale .pyi stubs, sample imports, and README references for new provider types * fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples * refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider * refactor: UserInfoMemory stores state in session.state instead of instance attributes * feat: add Pydantic BaseModel support to session state serialization Pydantic models stored in session.state are now automatically serialized via model_dump() and restored via model_validate() during to_dict()/from_dict() round-trips. Models are auto-registered on first serialization; use register_state_type() for cold-start deserialization. Also export register_state_type as a public API. * fix mem0 * Update sample README links and descriptions for session terminology - Replace 'thread' with 'session' in sample descriptions across all READMEs - Update file links for renamed samples (mem0_sessions, redis_sessions, etc.) - Fix Threads section → Sessions section in main samples/README.md - Update tools, middleware, workflows, durabletask, azure_functions READMEs - Update architecture diagrams in concepts/tools/README.md - Update migration guides (autogen, semantic-kernel) * Fix broken Redis README link to renamed sample * Fix Mem0 OSS client search: pass scoping params as direct kwargs AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs, while AsyncMemoryClient (Platform) expects them in a filters dict. Adds tests for both client types. Port of fix from #3844 to new Mem0ContextProvider. * Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic - Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase - Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages - Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests - Add tests/workflow/__init__.py for relative import support - Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep) * Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection Chat clients that store history server-side by default (OpenAI Responses API, Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this during auto-injection and skips InMemoryHistoryProvider unless the user explicitly sets store=False. * Fix broken markdown links in azure_ai and redis READMEs * Fix getting-started samples to use session API instead of removed thread/ContextProvider API * updates to workflow as agent * fix group chat import * Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread - Fix: Propagate conversation_id from ChatResponse back to session.service_session_id in both streaming and non-streaming paths in _agents.py - Rename AgentThreadException → AgentSessionException - Remove stale AGUIThread from ag_ui lazy-loader - Rename use_service_thread → use_service_session in ag-ui package - Rename test functions from *_thread_* to *_session_* - Rename sample files from *_thread* to *_session* - Update docstrings and comments: thread → session - Update _mcp.py kwargs filter: add 'session' alongside 'thread' - Fix ContinuationToken docstring example: thread=thread → session=session - Fix _clients.py docstring: 'Agent threads' → 'Agent sessions' * Fix broken markdown links after thread→session file renames * fix azure ai test
169 lines
17 KiB
Markdown
169 lines
17 KiB
Markdown
# Workflows Getting Started Samples
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## Installation
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Microsoft Agent Framework Workflows support ships with the core `agent-framework` or `agent-framework-core` package, so no extra installation step is required.
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To install with visualization support:
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```bash
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pip install agent-framework[viz] --pre
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```
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To export visualization images you also need to [install GraphViz](https://graphviz.org/download/).
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## Samples Overview
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## Foundational Concepts - Start Here
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Begin with the `_start-here` folder in order. These three samples introduce the core ideas of executors, edges, agents in workflows, and streaming.
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| Sample | File | Concepts |
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| -------------------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
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| Executors and Edges | [\_start-here/step1_executors_and_edges.py](./_start-here/step1_executors_and_edges.py) | Minimal workflow with basic executors and edges |
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| Agents in a Workflow | [\_start-here/step2_agents_in_a_workflow.py](./_start-here/step2_agents_in_a_workflow.py) | Introduces adding Agents as nodes; calling agents inside a workflow |
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| Streaming (Basics) | [\_start-here/step3_streaming.py](./_start-here/step3_streaming.py) | Extends workflows with event streaming |
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Once comfortable with these, explore the rest of the samples below.
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---
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## Samples Overview (by directory)
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### agents
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| Sample | File | Concepts |
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| -------------------------------------- | -------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
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| Azure Chat Agents (Streaming) | [agents/azure_chat_agents_streaming.py](./agents/azure_chat_agents_streaming.py) | Add Azure Chat agents as edges and handle streaming events |
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| Azure AI Agents (Streaming) | [agents/azure_ai_agents_streaming.py](./agents/azure_ai_agents_streaming.py) | Add Azure AI agents as edges and handle streaming events |
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| Azure AI Agents (Shared Thread) | [agents/azure_ai_agents_with_shared_session.py](./agents/azure_ai_agents_with_shared_session.py) | Share a common message session between multiple Azure AI agents in a workflow |
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| Custom Agent Executors | [agents/custom_agent_executors.py](./agents/custom_agent_executors.py) | Create executors to handle agent run methods |
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| Workflow as Agent (Reflection Pattern) | [agents/workflow_as_agent_reflection_pattern.py](./agents/workflow_as_agent_reflection_pattern.py) | Wrap a workflow so it can behave like an agent (reflection pattern) |
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| Workflow as Agent + HITL | [agents/workflow_as_agent_human_in_the_loop.py](./agents/workflow_as_agent_human_in_the_loop.py) | Extend workflow-as-agent with human-in-the-loop capability |
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| Workflow as Agent with Session | [agents/workflow_as_agent_with_session.py](./agents/workflow_as_agent_with_session.py) | Use AgentSession to maintain conversation history across workflow-as-agent invocations |
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| Workflow as Agent kwargs | [agents/workflow_as_agent_kwargs.py](./agents/workflow_as_agent_kwargs.py) | Pass custom context (data, user tokens) via kwargs through workflow.as_agent() to @ai_function tools |
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### checkpoint
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| Sample | File | Concepts |
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| ------------------------------ | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| Checkpoint & Resume | [checkpoint/checkpoint_with_resume.py](./checkpoint/checkpoint_with_resume.py) | Create checkpoints, inspect them, and resume execution |
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| Checkpoint & HITL Resume | [checkpoint/checkpoint_with_human_in_the_loop.py](./checkpoint/checkpoint_with_human_in_the_loop.py) | Combine checkpointing with human approvals and resume pending HITL requests |
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| Checkpointed Sub-Workflow | [checkpoint/sub_workflow_checkpoint.py](./checkpoint/sub_workflow_checkpoint.py) | Save and resume a sub-workflow that pauses for human approval |
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| Handoff + Tool Approval Resume | [orchestrations/handoff_with_tool_approval_checkpoint_resume.py](./orchestrations/handoff_with_tool_approval_checkpoint_resume.py) | Handoff workflow that captures tool-call approvals in checkpoints and resumes with human decisions |
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| Workflow as Agent Checkpoint | [checkpoint/workflow_as_agent_checkpoint.py](./checkpoint/workflow_as_agent_checkpoint.py) | Enable checkpointing when using workflow.as_agent() with checkpoint_storage parameter |
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### composition
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| Sample | File | Concepts |
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| ---------------------------------- | ------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------- |
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| Sub-Workflow (Basics) | [composition/sub_workflow_basics.py](./composition/sub_workflow_basics.py) | Wrap a workflow as an executor and orchestrate sub-workflows |
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| Sub-Workflow: Request Interception | [composition/sub_workflow_request_interception.py](./composition/sub_workflow_request_interception.py) | Intercept and forward sub-workflow requests using @handler for SubWorkflowRequestMessage |
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| Sub-Workflow: Parallel Requests | [composition/sub_workflow_parallel_requests.py](./composition/sub_workflow_parallel_requests.py) | Multiple specialized interceptors handling different request types from same sub-workflow |
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| Sub-Workflow: kwargs Propagation | [composition/sub_workflow_kwargs.py](./composition/sub_workflow_kwargs.py) | Pass custom context (user tokens, config) from parent workflow through to sub-workflow agents |
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### control-flow
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| Sample | File | Concepts |
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| -------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------- |
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| Sequential Executors | [control-flow/sequential_executors.py](./control-flow/sequential_executors.py) | Sequential workflow with explicit executor setup |
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| Sequential (Streaming) | [control-flow/sequential_streaming.py](./control-flow/sequential_streaming.py) | Stream events from a simple sequential run |
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| Edge Condition | [control-flow/edge_condition.py](./control-flow/edge_condition.py) | Conditional routing based on agent classification |
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| Switch-Case Edge Group | [control-flow/switch_case_edge_group.py](./control-flow/switch_case_edge_group.py) | Switch-case branching using classifier outputs |
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| Multi-Selection Edge Group | [control-flow/multi_selection_edge_group.py](./control-flow/multi_selection_edge_group.py) | Select one or many targets dynamically (subset fan-out) |
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| Simple Loop | [control-flow/simple_loop.py](./control-flow/simple_loop.py) | Feedback loop where an agent judges ABOVE/BELOW/MATCHED |
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| Workflow Cancellation | [control-flow/workflow_cancellation.py](./control-flow/workflow_cancellation.py) | Cancel a running workflow using asyncio tasks |
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### human-in-the-loop
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| Sample | File | Concepts |
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| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------- |
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| Human-In-The-Loop (Guessing Game) | [human-in-the-loop/guessing_game_with_human_input.py](./human-in-the-loop/guessing_game_with_human_input.py) | Interactive request/response prompts with a human via `ctx.request_info()` |
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| Agents with Approval Requests in Workflows | [human-in-the-loop/agents_with_approval_requests.py](./human-in-the-loop/agents_with_approval_requests.py) | Agents that create approval requests during workflow execution and wait for human approval to proceed |
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| Agents with Declaration-Only Tools | [human-in-the-loop/agents_with_declaration_only_tools.py](./human-in-the-loop/agents_with_declaration_only_tools.py) | Workflow pauses when agent calls a client-side tool (`func=None`), caller supplies the result |
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Builder-oriented request-info samples are maintained in the orchestration sample set
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(sequential, concurrent, and group-chat builder variants).
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### tool-approval
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Builder-based tool approval samples are maintained in the orchestration sample set.
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### observability
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| Sample | File | Concepts |
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| ------------------------ | -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
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| Executor I/O Observation | [observability/executor_io_observation.py](./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 |
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For additional observability samples in Agent Framework, see the [observability concept samples](../02-agents/observability/README.md). The [workflow observability sample](../02-agents/observability/workflow_observability.py) demonstrates integrating observability into workflows.
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### orchestration
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Orchestration-focused samples (Sequential, Concurrent, Handoff, GroupChat, Magentic), including builder-based
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`workflow.as_agent(...)` variants, are documented in the [orchestrations](./orchestrations/README.md) directory.
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### parallelism
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| Sample | File | Concepts |
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| ------------------------------------ | ------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------- |
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| Concurrent (Fan-out/Fan-in) | [parallelism/fan_out_fan_in_edges.py](./parallelism/fan_out_fan_in_edges.py) | Dispatch to multiple executors and aggregate results |
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| Aggregate Results of Different Types | [parallelism/aggregate_results_of_different_types.py](./parallelism/aggregate_results_of_different_types.py) | Handle results of different types from multiple concurrent executors |
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| Map-Reduce with Visualization | [parallelism/map_reduce_and_visualization.py](./parallelism/map_reduce_and_visualization.py) | Fan-out/fan-in pattern with diagram export |
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### state-management
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| Sample | File | Concepts |
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| -------------------------------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------- |
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| State with Agents | [state-management/state_with_agents.py](./state-management/state_with_agents.py) | Store in state once and later reuse across agents |
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| Workflow Kwargs (Custom Context) | [state-management/workflow_kwargs.py](./state-management/workflow_kwargs.py) | Pass custom context (data, user tokens) via kwargs to `@tool` tools |
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### visualization
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| Sample | File | Concepts |
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| ----------------------------- | -------------------------------------------------------------------------------------------------- | ------------------------------------------- |
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| Concurrent with Visualization | [visualization/concurrent_with_visualization.py](./visualization/concurrent_with_visualization.py) | Fan-out/fan-in workflow with diagram export |
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### declarative
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YAML-based declarative workflows allow you to define multi-agent orchestration patterns without writing Python code. See the [declarative workflows README](./declarative/README.md) for more details on YAML workflow syntax and available actions.
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| Sample | File | Concepts |
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| Conditional Workflow | [declarative/conditional_workflow/](./declarative/conditional_workflow/) | Nested conditional branching based on user input |
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| Customer Support | [declarative/customer_support/](./declarative/customer_support/) | Multi-agent customer support with routing |
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| Deep Research | [declarative/deep_research/](./declarative/deep_research/) | Research workflow with planning, searching, and synthesis |
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| Function Tools | [declarative/function_tools/](./declarative/function_tools/) | Invoking Python functions from declarative workflows |
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| Human-in-Loop | [declarative/human_in_loop/](./declarative/human_in_loop/) | Interactive workflows that request user input |
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| Marketing | [declarative/marketing/](./declarative/marketing/) | Marketing content generation workflow |
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| Simple Workflow | [declarative/simple_workflow/](./declarative/simple_workflow/) | Basic workflow with variable setting, conditionals, and loops |
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| Student Teacher | [declarative/student_teacher/](./declarative/student_teacher/) | Student-teacher interaction pattern |
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### resources
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- Sample text inputs used by certain workflows:
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- [resources/long_text.txt](./resources/long_text.txt)
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- [resources/email.txt](./resources/email.txt)
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- [resources/spam.txt](./resources/spam.txt)
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- [resources/ambiguous_email.txt](./resources/ambiguous_email.txt)
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Notes
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- Agent-based samples use provider SDKs (Azure/OpenAI, etc.). Ensure credentials are configured, or adapt agents accordingly.
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Sequential orchestration uses a few small adapter nodes for plumbing:
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- "input-conversation" normalizes input to `list[Message]`
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- "to-conversation:<participant>" converts agent responses into the shared conversation
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- "complete" publishes the final output event (type='output')
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These may appear in event streams (executor_invoked/executor_completed). They're analogous to
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concurrent’s dispatcher and aggregator and can be ignored if you only care about agent activity.
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### Environment Variables
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Workflow samples that use `AzureOpenAIResponsesClient` expect:
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- `AZURE_AI_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
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- `AZURE_AI_MODEL_DEPLOYMENT_NAME` (model deployment name)
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These values are passed directly into the client constructor via `os.getenv()` in sample code.
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