Document the ReAct agent sample showing durable tool execution
with temporal_tool() and the think-act-observe pattern.

Update all run_worker.py and run_workflow.py files to use the
ClientConfig.load_client_connect_config() pattern for environment-based
configuration support. Also simplify react_agent run_workflow.py to
run a single query with clean output.

Add CLAUDE.md documenting the client initialization pattern.

Add remaining sample files:
- react_agent: graph, tools, workflow definitions
- approval_workflow: graph, workflow with human-in-the-loop pattern

…ADME

- Add comprehensive README for human_in_loop/approval_workflow sample
- Update react_agent README to reflect simplified single-query output

…l_workflow

- Add notify_approver activity that prints approval instructions with workflow ID
- Add run_respond.py CLI tool for approvers to approve/reject workflows
- Update workflow to call notification activity when hitting interrupt
- Simplify run_workflow.py to start one workflow and wait for result
- Update README with 3-terminal workflow instructions

…act_agent

The react_agent sample was using ChatOpenAI directly without wrapping
it with temporal_model(), meaning LLM calls were not durable. Now both
LLM calls and tool invocations run as Temporal activities.

Use the new Temporal-provided create_durable_react_agent which:
- Automatically wraps model and tools for durable execution
- Runs agent nodes inline in workflow with model/tool calls as activities

Use the new model_activity_options and tool_activity_options parameters
with activity_options() instead of the old individual timeout parameters.

Update react_agent sample to use LangGraph's native create_react_agent
instead of the removed create_durable_react_agent wrapper.

The Temporal integration now works directly with native LangGraph APIs -
each node runs as a Temporal activity, providing durability without
special wrappers.

… deprecated)

…c loop

- Change query to require multiple tool calls (weather + temperature conversion)
- Remove unused search_knowledge tool, keep only get_weather and calculate
- Update README to document multi-step reasoning behavior

- Add agentic_rag sample with document grading and query rewriting
- Add README files for human_in_loop and RAG sample categories
- Update main README to link to all sample categories
- Add langchain and langchain-openai dependencies for RAG sample

Update installation instructions to reflect that the LangGraph integration
is currently available as a preview feature in branch repositories:
- SDK: mfateev/sdk-python@langgraph-plugin
- Samples: mfateev/samples-python@langgraph_plugin

create_agent always returns a compiled graph, so no need for defensive
compile() call with hasattr check.

…t_agent

- Update imports from langgraph.prebuilt to langchain.agents
- Change prompt parameter to system_prompt
- Add multi_agent/supervisor sample
- Document the correct API in CLAUDE.md

- Deep Research Agent: Multi-step research with parallel search execution
  using Send API, result evaluation, and report synthesis
- Plan-and-Execute Agent: Structured planning with sequential step
  execution, dynamic replanning, and tool integration
- Reflection Agent: Generate-critique-revise loop with quality scoring
  and iterative improvement until criteria met

- Update all README files to use `uv run` format for running scripts
- Remove redundant Usage sections from run_*.py files
- Keep usage documentation centralized in README files only

- Replace mock search results with real DuckDuckGo web search
- Add langchain-community and duckduckgo-search dependencies
- Update README to reflect real search functionality
- Add error handling for failed searches

Move all samples from nested category directories to top level:
- basic/hello_world -> hello_world
- basic/react_agent -> react_agent
- human_in_loop/approval_workflow -> approval_workflow
- multi_agent/supervisor -> supervisor
- rag/agentic_rag -> agentic_rag
- rag/deep_research -> deep_research
- planning/plan_and_execute -> plan_and_execute
- advanced/reflection -> reflection

Update all import paths accordingly and refresh README.md with
a table listing all samples.

- Test basic workflow execution with query processing
- Test empty query handling
- Add fixture to clear global registry between tests

Tests:
- Added tests for all LangGraph samples (hello_world, approval_workflow,
  react_agent, supervisor, agentic_rag, deep_research, plan_and_execute,
  reflection)
- Added conftest.py with shared fixtures (clear_registry, requires_openai)
- Tests requiring OpenAI API are skipped when OPENAI_API_KEY is not set

Bug fixes:
- Fixed serialization bugs in reflection and plan_and_execute samples
- After Temporal serialization, Pydantic models become dicts
- Added helper functions to handle both object and dict access for:
  - Critique objects in reflection sample
  - Plan, PlanStep, StepResult objects in plan_and_execute sample

Shows how to call Temporal activities directly from a LangGraph node
using the run_in_workflow=True metadata option.

…oaches

- approval_workflow_interrupt: Uses LangGraph interrupt() with workflow signal handling
- approval_workflow_signal: Uses run_in_workflow=True to handle signals in graph node

- Renamed approval_workflow_signal to approval_workflow_condition
- Both samples now in langgraph_samples/human_in_the_loop/
- Updated all import paths and documentation

- Fix approval_wait_condition graph to use temporal_node_metadata()
- Import workflow inside function for sandboxing
- Add tests for approval_graph_interrupt sample
- Add tests for approval_wait_condition sample
- Remove old approval_workflow_test.py

Rename the samples directory and test directory to match the plugin name
for better clarity and consistency.

Add get_graph_ascii() and get_graph_mermaid() queries to both
human_in_the_loop workflow samples, demonstrating how to expose
graph execution progress via Temporal queries.

These queries return:
- ASCII art diagram with progress indicators (✓/▶/○)
- Mermaid flowchart with colored nodes by status

- Add GraphStateResponse dataclass with typed ApprovalState values
- Cast snapshot.values to ApprovalState for type safety
- Flatten the response structure for easier consumption:
  - values: typed ApprovalState
  - next: list of next nodes
  - step: current step count
  - interrupted: boolean flag
  - interrupt_node: node that triggered interrupt
  - interrupt_value: value passed to interrupt()