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-# human-in-the-loop-cherez-middleware
+# Human‑in‑the‑Loop Middleware Demo

-Решение: Human-in-the-Loop через middleware
+This repository contains a minimal **LangChain** agent that demonstrates how to pause execution at every tool call and ask the user for approval or rejection before continuing.  
+The core of this behaviour is provided by `HumanInTheLoopMiddleware`, which automatically injects a confirmation step into the agent’s workflow.
+
+> ⚠️  The example uses the open‑source **Ollama** LLM backend.  
+> If you don’t have Ollama installed, follow the instructions in the *Prerequisites* section.
+
+---
+
+## Table of Contents
+
+- [Description](#description)
+- [Prerequisites](#prerequisites)
+- [Installation](#installation)
+- [Running the Demo](#running-the-demo)
+- [Example Interaction](#example-interaction)
+
+---
+
+## Description
+
+`solution.py` implements a LangChain agent that:
+
+1. **Creates** an LLM chain using `ChatOllama`.
+2. **Wraps** it with a `MemorySaver` checkpoint for state persistence.
+3. **Adds** the `HumanInTheLoopMiddleware`, which:
+   - Intercepts every tool call,
+   - Prints the intended action to the terminal (via `rich`),
+   - Prompts the user for `"approve"` or `"reject"`,
+   - Resumes execution based on the response.
+
+This pattern is useful when you want a human operator to supervise or validate each step of an automated workflow, without having to manually interrupt the agent.
+
+---
+
+## Prerequisites
+
+| Item | Version | Notes |
+|------|---------|-------|
+| Python | ≥ 3.10 | Tested with 3.11 |
+| pip | – | Standard package installer |
+| **Ollama** | Latest | Install from https://ollama.ai/ and run `ollama serve` before starting the demo. |
+| **OpenAI‑compatible model** | e.g., `llama2:7b` | Pull via `ollama pull llama2:7b`. |
+
+> **Tip:** If you prefer a different LLM, replace `ChatOllama` with any LangChain-compatible LLM (e.g., OpenAI, Anthropic).
+
+---
+
+## Installation
+
+```bash
+# 1. Clone the repo
+git clone https://github.com/your-username/human-in-the-loop-demo.git
+cd human-in-the-loop-demo
+
+# 2. Create a virtual environment (optional but recommended)
+python -m venv .venv
+source .venv/bin/activate   # On Windows: .\.venv\Scripts\activate
+
+# 3. Install dependencies
+pip install --upgrade pip
+pip install langchain langgraph langchain-ollama rich
+```
+
+> **Note:**  
+> `langgraph` is required for the checkpointing and middleware support.
+
+---
+
+## Running the Demo
+
+```bash
+# Make sure Ollama server is running:
+#   ollama serve
+
+# Run the agent script
+python solution.py
+```
+
+You will be prompted to enter a question. The agent will then:
+
+1. Generate a plan.
+2. For each tool call, pause and display the intended action.
+3. Await your `"approve"` or `"reject"` input before proceeding.
+
+---
+
+## Example Interaction
+
+```text
+$ python solution.py
+Enter your question: What is the capital of France?
+
+[Agent] Thinking...
+[HumanInTheLoopMiddleware] Tool call planned: "search" with query "capital of France"
+Approve? (yes/no): yes
+
+[Agent] Executing tool...
+Result: Paris
+
+[Agent] Continuing...
+
+Final answer: The capital of France is Paris.
+```
+
+If you type `no` instead of `yes`, the agent will skip that tool call and attempt to continue with its plan, potentially leading to a different outcome.
+
+---
+
+## Customization
+
+- **Different tools** – Add more tools via `@tool` decorators or by passing a list to `create_agent`.
+- **Custom prompt** – Modify the middleware’s confirmation message by subclassing `HumanInTheLoopMiddleware`.
+- **Persist state** – The `MemorySaver` checkpoint can be swapped for a database-backed store if you need long‑term persistence.
+
+---
+
+Happy experimenting! 🚀