diff --git a/solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/README.md b/solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/README.md
index 3b64602..a01fe2c 100644
--- a/solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/README.md
+++ b/solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/README.md
@@ -1,140 +1,86 @@
-# Human‑in‑the‑Loop Agent via Middleware (LangGraph)
+# Human‑in‑the‑Loop Agent with LangGraph
 
-This repository contains a minimal example of a **Human‑in‑the‑Loop** agent built on top of LangGraph.  
-The agent pauses whenever it needs to call an external tool, prints the tool request and waits for a user decision (`approve` or `reject`). After the decision is supplied, execution resumes automatically.
+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.
 
-> ⚠️ The example uses OpenAI’s API (or any compatible LLM). Make sure you have an API key set in the environment variable `OPENAI_API_KEY`.
+> **⚠️ Prerequisites** – The example uses OpenAI‑compatible APIs.  
+> Make sure you have an API key set in the environment variable `OPENAI_API_KEY`.
 
 ---
 
-## Table of Contents
-
-- [Features](#features)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Running the Agent](#running-the-agent)
-  - [Interactive Demo (`solution.py`)](#interactive-demo-solutionpy)
-  - [Unit Tests (`tests/test_solution.py`)](#unit-tests-test_solutionpy)
-- [Example Usage](#example-usage)
-- [License](#license)
-
----
-
-## Features
-
-| Feature | Description |
-|---------|-------------|
-| **Human‑in‑the‑Loop** | Agent stops before calling any tool, prints the request and waits for user input. |
-| **Interrupts via `interrupt_before=["tools"]`** | Configurable interruption point in LangGraph. |
-| **Tool Example** | Simple `get_weather(city, date)` function that returns a mock weather string. |
-| **Checkpointing** | Uses an in‑memory checkpoint (`MemorySaver`) to preserve state across interruptions. |
-
----
-
-## Prerequisites
-
-- Python 3.10+
-- An OpenAI API key (or any compatible LLM endpoint)
+## 📦 Installation
 
 ```bash
-export OPENAI_API_KEY="sk-..."
+# 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 and activate a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # Windows: .\.venv\Scripts\activate
+
+# 3️⃣ Install dependencies
+pip install --upgrade pip
+pip install langchain-openai langgraph langchain-tools
 ```
 
----
-
-## Installation
-
-1. **Clone the repository**
-
-   ```bash
-   git clone https://github.com/yourusername/human-in-loop-langgraph.git
-   cd human-in-loop-langgraph
-   ```
-
-2. **Create a virtual environment (optional but recommended)**
-
-   ```bash
-   python -m venv .venv
-   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
-   ```
-
-3. **Install dependencies**
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-   *If you don’t have a `requirements.txt`, create one with the following content:*
-
-   ```text
-   langchain-openai>=0.2.0
-   langgraph>=0.1.0
-   ```
+> **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.
 
 ---
 
-## Running the Agent
+## 🚀 Running the Agent
 
-### Interactive Demo (`solution.py`)
-
-The main script demonstrates how to start the agent and handle interruptions.
+The main logic is in `solution.py`.  
+Run it directly:
 
 ```bash
 python solution.py
 ```
 
-**What happens:**
+### What Happens?
 
-1. The user enters a prompt (e.g., “What’s the weather in Paris on 2024‑12‑01?”).
-2. The agent processes the request, decides it needs to call `get_weather`, and pauses.
-3. The tool request is printed:
-
-   ```
-   Tool requested: get_weather
-   Arguments: {'city': 'Paris', 'date': '2024-12-01'}
-   ```
-
-4. You are prompted to type `approve` or `reject`.
-5. After your decision, the agent resumes and prints the final answer.
+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.
 
 ---
 
-### Unit Tests (`tests/test_solution.py`)
+## 🔧 Example Interaction
 
-Run the test suite to verify that the interruption logic works as expected:
-
-```bash
-pytest tests/test_solution.py
+```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 tests simulate a user approving the tool call automatically and check that the final output contains the weather string.
+If you type `reject`, the agent will receive a rejection message and can, for example, ask for clarification.
 
 ---
 
-## Example Usage
+## 📁 Project Structure
 
-Below is a quick snippet you can paste into a Python REPL or another script to see the agent in action:
-
-```python
-from solution import agent, memory  # assuming solution.py defines them
-
-# Start a new thread/session
-config = {"configurable": {"thread_id": "demo-session"}}
-
-# Invoke with a user message
-response = agent.invoke(
-    {"messages": [{"role": "user", "content": "What's the weather in Tokyo on 2025-01-15?"}]},
-    config=config,
-)
-
-print("\nFinal response:")
-print(response["messages"][-1]["content"])
-```
-
-When you run this, you’ll see the same interruption prompt as described above.
+| File | Purpose |
+|------|---------|
+| `solution.py` | Full implementation of the HITL agent. |
+| `README.md` | This documentation file. |
 
 ---
 
-## License
+## 🛠️ Customization
 
-MIT © 2026. Feel free to fork and adapt for your own projects.
\ No newline at end of file
+- **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.
+
+---
+
+## 📜 License
+
+MIT © 2026. Feel free to fork and adapt for your own projects!
\ No newline at end of file