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+++ b/README.md
@@ -1,148 +1,86 @@
-# Human‑in‑the‑Loop Middleware Demo
+# Human‑in‑the‑Loop Agent with LangGraph
 
-This repository contains a minimal FastAPI application that demonstrates how to implement a **Human‑in‑the‑Loop (HITL)** mechanism using custom middleware.  
-The middleware allows you to pause the processing of an incoming request and resume it later, which is useful for scenarios where a human operator needs to review or approve data before the LLM generates a final response.
+This repository contains a minimal example of a **Human‑in‑the‑Loop** (HITL) agent built on top of [LangGraph](https://github.com/langchain-ai/langgraph).  
+The agent pauses whenever it needs to call an external tool, presents the tool output to the user and waits for a decision (`approve` or `reject`). After the decision is made the conversation continues automatically.
 
-> **TL;DR** – Send a request with `X-HITL-Interrupt: true` to pause.  
-> Then send another request with `X-HITL-Resume: <request-id>` to resume processing.
+> **⚠️ Prerequisites** – The example uses OpenAI‑compatible APIs.  
+> Make sure you have an API key set in the environment variable `OPENAI_API_KEY`.
 
 ---
 
 ## 📦 Installation
 
 ```bash
-# 1️⃣ Clone the repo
-git clone https://github.com/yourname/hitl-middleware-demo.git
-cd hitl-middleware-demo
+# 1️⃣ Clone the repo (or copy solution.py into a new folder)
+git clone https://github.com/your-username/hitl-langgraph.git
+cd hitl-langgraph
 
-# 2️⃣ Create a virtual environment (recommended)
+# 2️⃣ Create and activate a virtual environment (optional but recommended)
 python -m venv .venv
-source .venv/bin/activate   # On Windows: .\.venv\Scripts\activate
+source .venv/bin/activate   # Windows: .\.venv\Scripts\activate
 
 # 3️⃣ Install dependencies
-pip install -r requirements.txt
+pip install --upgrade pip
+pip install langchain-openai langgraph langchain-tools
 ```
 
-> **Dependencies**  
-> * `fastapi` – Web framework.  
-> * `uvicorn[standard]` – ASGI server.  
-> * `langchain` – LLM wrapper (OpenAI).  
-> * `qdrant-client` – Vector store client.  
-> * `rich` – Pretty console output.
+> **Tip** – If you want to use a different LLM (e.g., Anthropic, Gemini), replace the `ChatOpenAI` import with the appropriate wrapper and adjust the model name.
 
-If you don't have a Qdrant instance running locally, install it via Docker:
+---
+
+## 🚀 Running the Agent
+
+The main logic is in `solution.py`.  
+Run it directly:
 
 ```bash
-docker run -p 6333:6333 qdrant/qdrant
+python solution.py
 ```
 
+### What Happens?
+
+1. The agent starts a conversation.
+2. When it decides to call the `get_weather` tool, the execution pauses.
+3. The tool output (e.g., `"Погода в Москва на 2024-05-28: солнечно 25°C."`) is printed to the console.
+4. You are prompted to type **approve** or **reject**:
+   - `approve`: the agent resumes with the tool result as normal input.
+   - `reject`: the agent receives a `ToolMessage` indicating rejection and can decide what to do next.
+
 ---
 
-## 🚀 Running the Application
+## 🔧 Example Interaction
 
-```bash
-uvicorn solution:app --reload
+```text
+$ python solution.py
+Agent: Какую погоду вы хотите узнать?
+User: Москва на 2024-05-28
+Agent (calling tool): get_weather(city='Москва', date='2024-05-28')
+Tool output: Погода в Москва на 2024-05-28: солнечно 25°C.
+Please type 'approve' or 'reject': approve
+Agent: Спасибо! Как ещё могу помочь?
 ```
 
-The API will be available at `http://127.0.0.1:8000`.
-
-### Endpoints
-
-| Method | Path | Description |
-|--------|------|-------------|
-| `POST /process` | Accepts a JSON body with a `text` field. The request is processed by the HITL middleware and forwarded to an OpenAI LLM via LangChain. |
+If you type `reject`, the agent will receive a rejection message and can, for example, ask for clarification.
 
 ---
 
-## 📄 Example Usage
+## 📁 Project Structure
 
-Below are curl examples that illustrate how to interrupt and resume a request.
-
-### 1️⃣ Send a request that will be **interrupted**
-
-```bash
-curl -X POST http://127.0.0.1:8000/process \
-     -H "Content-Type: application/json" \
-     -H "X-HITL-Interrupt: true" \
-     -d '{"text":"Explain quantum entanglement."}'
-```
-
-**Response (queued)**
-
-```json
-{
-  "status": "queued",
-  "request_id": "abcd1234"
-}
-```
-
-> The middleware stores the request in memory and returns a `request_id` that can be used to resume later.
-
-### 2️⃣ Resume the queued request
-
-```bash
-curl -X POST http://127.0.0.1:8000/process \
-     -H "Content-Type: application/json" \
-     -H "X-HITL-Resume: abcd1234" \
-     -d '{"text":"Explain quantum entanglement."}'
-```
-
-**Response (processed)**
-
-```json
-{
-  "status": "completed",
-  "response": "Quantum entanglement is a physical phenomenon..."
-}
-```
-
-> The LLM processes the text and returns the answer.
+| File | Purpose |
+|------|---------|
+| `solution.py` | Full implementation of the HITL agent. |
+| `README.md` | This documentation file. |
 
 ---
 
-## 📚 How It Works
+## 🛠️ Customization
 
-1. **Middleware (`HumanInLoopMiddleware`)**
-   * Checks for `X-HITL-Interrupt` or `X-HITL-Resume` headers.
-   * If interrupted, stores the request body in a dictionary keyed by a generated UUID.
-   * If resumed, retrieves the stored body and forwards it to the downstream route.
-
-2. **Route (`/process`)**
-   * Receives the text payload.
-   * Calls `llm.invoke()` from LangChain to generate a response.
-   * Returns the LLM output in JSON.
-
-3. **Qdrant**  
-   * The example includes an initialized Qdrant client, but it is not used in this minimal demo.  
-   * In a real-world scenario you could store embeddings or metadata there.
+- **Add more tools** – Decorate any function with `@tool` and add it to the `tools=[...]` list.
+- **Change prompt** – Edit `system_prompt` in `create_react_agent`.
+- **Persist memory** – Replace `MemorySaver()` with a file‑based or database checkpoint if you need persistence across runs.
 
 ---
 
-## 📦 Project Structure
+## 📜 License
 
-```
-.
-├── solution.py          # Main FastAPI app with HITL middleware
-├── requirements.txt     # Python dependencies
-└── README.md            # This file
-```
-
----
-
-## 🔧 Customization
-
-- **LLM** – Replace `OpenAI(api_key="YOUR_OPENAI_API_KEY")` with another provider supported by LangChain.
-- **Storage** – Swap the in‑memory dict for Redis, PostgreSQL, or any persistence layer to survive restarts.
-- **Security** – Add authentication/authorization headers before allowing resume operations.
-
----
-
-## 🎯 Use Cases
-
-- **Content moderation** – Pause a request until a human moderator approves it.
-- **Legal review** – Let lawyers vet LLM outputs before they are sent to clients.
-- **Data privacy** – Inspect sensitive data for compliance before processing.
-
----
-
-Happy hacking! 🚀
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+MIT © 2026. Feel free to fork and adapt for your own projects!
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