* 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
DevUI Samples
This folder contains sample agents and workflows designed to work with the Agent Framework DevUI - a lightweight web interface for running and testing agents interactively.
What is DevUI?
DevUI is a sample application that provides:
- A web interface for testing agents and workflows
- OpenAI-compatible API endpoints
- Directory-based entity discovery
- In-memory entity registration
- Sample entity gallery
Note
: DevUI is a sample app for development and testing. For production use, build your own custom interface using the Agent Framework SDK.
Quick Start
Option 1: In-Memory Mode (Simplest)
Run a single sample directly. This demonstrates how to wrap agents and workflows programmatically without needing a directory structure:
cd python/samples/02-agents/devui
python in_memory_mode.py
This opens your browser at http://localhost:8090 with pre-configured agents and a basic workflow.
Option 2: Directory Discovery
Launch DevUI to discover all samples in this folder:
cd python/samples/02-agents/devui
devui
This starts the server at http://localhost:8080 with all agents and workflows available.
Sample Structure
Each agent/workflow follows a strict structure required by DevUI's discovery system:
agent_name/
├── __init__.py # Must export: agent = Agent(...)
├── agent.py # Agent implementation
└── .env.example # Example environment variables
Available Samples
Agents
| Sample | Description | Features | Required Environment Variables |
|---|---|---|---|
| weather_agent_azure/ | Weather agent using Azure OpenAI with API key authentication | Azure OpenAI integration, function calling, mock weather tools | AZURE_OPENAI_API_KEY, AZURE_OPENAI_CHAT_DEPLOYMENT_NAME, AZURE_OPENAI_ENDPOINT |
| foundry_agent/ | Weather agent using Azure AI Agent (Foundry) with Azure CLI authentication (run az login first) |
Azure AI Agent integration, Azure CLI authentication, mock weather tools | AZURE_AI_PROJECT_ENDPOINT, FOUNDRY_MODEL_DEPLOYMENT_NAME |
Workflows
| Sample | Description | Features | Required Environment Variables |
|---|---|---|---|
| declarative/ | Declarative YAML workflow with conditional branching | YAML-based workflow definition, conditional logic, no Python code required | None - uses mock data |
| workflow_agents/ | Content review workflow with agents as executors | Agents as workflow nodes, conditional routing based on structured outputs, quality-based paths (Writer -> Reviewer -> Editor/Publisher) | AZURE_OPENAI_API_KEY, AZURE_OPENAI_CHAT_DEPLOYMENT_NAME, AZURE_OPENAI_ENDPOINT |
| spam_workflow/ | 5-step email spam detection workflow with branching logic | Sequential execution, conditional branching (spam vs. legitimate), multiple executors, mock spam detection | None - uses mock data |
| fanout_workflow/ | Advanced data processing workflow with parallel execution | Fan-out/fan-in patterns, complex state management, multi-stage processing (validation -> transformation -> quality assurance) | None - uses mock data |
Standalone Examples
| Sample | Description | Features |
|---|---|---|
| in_memory_mode.py | Demonstrates programmatic entity registration without directory structure | In-memory agent and workflow registration, multiple entities served from a single file, includes basic workflow, simplest way to get started |
Environment Variables
Each sample that requires API keys includes a .env.example file. To use:
- Copy
.env.exampleto.envin the same directory - Fill in your actual API keys
- DevUI automatically loads
.envfiles from entity directories
Alternatively, set environment variables globally:
export OPENAI_API_KEY="your-key-here"
export OPENAI_CHAT_MODEL_ID="gpt-4o"
Using DevUI with Your Own Agents
To make your agent discoverable by DevUI:
- Create a folder for your agent
- Add an
__init__.pythat exportsagentorworkflow - (Optional) Add a
.envfile for environment variables
Example:
# my_agent/__init__.py
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient
agent = Agent(
name="MyAgent",
description="My custom agent",
client=OpenAIChatClient(),
# ... your configuration
)
Then run:
devui /path/to/my/agents/folder
API Usage
DevUI exposes OpenAI-compatible endpoints:
curl -X POST http://localhost:8080/v1/responses \
-H "Content-Type: application/json" \
-d '{
"model": "agent-framework",
"input": "What is the weather in Seattle?",
"extra_body": {"entity_id": "agent_directory_weather-agent_<uuid>"}
}'
List available entities:
curl http://localhost:8080/v1/entities
Learn More
Troubleshooting
Missing API keys: Check your .env files or environment variables.
Import errors: Make sure you've installed the devui package:
pip install agent-framework-devui --pre
Port conflicts: DevUI uses ports 8080 (directory mode) and 8090 (in-memory mode) by default. Close other services or specify a different port:
devui --port 8888