LangGraph samples: Update README run commands for flattened structure

Author: mfateev

langgraph_samples/agentic_rag/README.md

@@ -67,12 +67,12 @@ The sample includes a knowledge base with documents about:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/rag/agentic_rag/run_worker.py
+uv run langgraph_samples/agentic_rag/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/rag/agentic_rag/run_workflow.py
+uv run langgraph_samples/agentic_rag/run_workflow.py
 ```
 
 ## Expected Output

langgraph_samples/approval_workflow/README.md

@@ -31,12 +31,12 @@ Request → [Process & Assess Risk] → [Interrupt] → [Notify Approver] → [W
 
 **Terminal 1 - Start the worker:**
 ```bash
-uv run langgraph_samples/human_in_loop/approval_workflow/run_worker.py
+uv run langgraph_samples/approval_workflow/run_worker.py
 ```
 
 **Terminal 2 - Start a workflow:**
 ```bash
-uv run langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
+uv run langgraph_samples/approval_workflow/run_workflow.py
 ```
 
 The worker will print notification instructions like:
@@ -46,20 +46,20 @@ Workflow ID: approval-abc12345
 Request: Please approve purchase for $500.00 (Risk: medium)
 
 To respond, run:
-  Approve: uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason 'Your reason'
-  Reject:  uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason 'Your reason'
+  Approve: uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --reject --reason 'Your reason'
 ```
 
 **Terminal 3 - Respond to the approval request:**
 ```bash
 # Check status
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --status
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --status
 
 # Approve
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason "Within budget"
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --approve --reason "Within budget"
 
 # Or reject
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
 ```
 
 ## Response Script Options

langgraph_samples/deep_research/README.md

@@ -51,12 +51,12 @@ Each search runs as a separate Temporal activity, enabling parallel execution.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/rag/deep_research/run_worker.py
+   uv run langgraph_samples/deep_research/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/rag/deep_research/run_workflow.py
+   uv run langgraph_samples/deep_research/run_workflow.py
    ```
 
 ## Sample Output

langgraph_samples/hello_world/README.md

@@ -21,12 +21,12 @@ Minimal LangGraph agent with Temporal integration. A single-node graph that proc
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/basic/hello_world/run_worker.py
+uv run langgraph_samples/hello_world/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/basic/hello_world/run_workflow.py
+uv run langgraph_samples/hello_world/run_workflow.py
 ```
 
 ## Expected Output

langgraph_samples/plan_and_execute/README.md

@@ -57,12 +57,12 @@ The executor agent has access to:
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/planning/plan_and_execute/run_worker.py
+   uv run langgraph_samples/plan_and_execute/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/planning/plan_and_execute/run_workflow.py
+   uv run langgraph_samples/plan_and_execute/run_workflow.py
    ```
 
 ## Sample Output

langgraph_samples/react_agent/README.md

@@ -41,12 +41,12 @@ Each node execution is:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/basic/react_agent/run_worker.py
+uv run langgraph_samples/react_agent/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/basic/react_agent/run_workflow.py
+uv run langgraph_samples/react_agent/run_workflow.py
 ```
 
 ## Expected Output

langgraph_samples/reflection/README.md

@@ -56,12 +56,12 @@ Each node runs as a Temporal activity with automatic retry.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/advanced/reflection/run_worker.py
+   uv run langgraph_samples/reflection/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/advanced/reflection/run_workflow.py
+   uv run langgraph_samples/reflection/run_workflow.py
    ```
 
 ## Sample Output

langgraph_samples/supervisor/README.md

@@ -68,12 +68,12 @@ With Temporal, the multi-agent system gains:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/multi_agent/supervisor/run_worker.py
+uv run langgraph_samples/supervisor/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/multi_agent/supervisor/run_workflow.py
+uv run langgraph_samples/supervisor/run_workflow.py
 ```
 
 ## Expected Output