Delete directory 'solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware'

solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/README.md

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-# Human‑in‑the‑Loop Middleware Demo  
-A minimal LangChain project that demonstrates how to pause an agent before every tool call and let a user approve or reject the action via the terminal.
-
----
-
-## Table of Contents
-- [Project Overview](#project-overview)
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-- [Running the Demo](#running-the-demo)
-  - [1️⃣ `main.py` – Interactive Agent](#1-mainpy---interactive-agent)
-  - [2️⃣ `tool_example.py` – Custom Tool](#2-tool_exempley---custom-tool)
-- [Example Interaction](#example-interaction)
-- [Project Structure](#project-structure)
-
----
-
-## Project Overview
-The agent is created with **HumanInTheLoopMiddleware**.  
-When the agent wants to use a tool (e.g., `get_weather`), it pauses, prints a prompt in the terminal, and waits for user input:
-
-| Decision | Effect |
-|----------|--------|
-| `approve` | Tool call proceeds |
-| `reject`  | Tool call is skipped; the agent continues reasoning |
-| `edit`    | (Optional) User can modify the tool arguments before resuming |
-
-The middleware automatically handles the pause/resume logic via `Command(resume={"decisions": [...]})`.
-
----
-
-## Prerequisites
-- Python 3.10+  
-- A working OpenAI API key (or any LLM provider supported by LangChain)
-
-Set your key in an environment variable:
-
-```bash
-export OPENAI_API_KEY="sk-..."
-```
-
----
-
-## Installation
-
-1. **Clone the repo**
-
-   ```bash
-   git clone https://github.com/yourusername/human-in-loop-demo.git
-   cd human-in-loop-demo
-   ```
-
-2. **Create a virtual environment (optional but recommended)**
-
-   ```bash
-   python -m venv .venv
-   source .venv/bin/activate  # Windows: .venv\Scripts\activate
-   ```
-
-3. **Install dependencies**
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-> `requirements.txt` contains:
-> ```
-> langchain==0.2.*
-> langgraph==0.1.*
-> python-dotenv  # optional, for .env support
-> ```
-
----
-
-## Running the Demo
-
-### 1️⃣ `main.py` – Interactive Agent
-
-```bash
-python main.py
-```
-
-You will see a prompt like:
-
-```
-Agent: What would you like to know?
-User: Tell me the weather in London.
-Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'London'}? [approve/reject/edit]
-> 
-```
-
-Type `approve`, `reject`, or `edit` and press **Enter**.  
-If you choose `edit`, you’ll be prompted to modify the JSON arguments.
-
-### 2️⃣ `tool_example.py` – Custom Tool
-
-The demo includes a simple weather tool (`get_weather`).  
-You can add more tools by editing `tools/__init__.py` or creating new modules.
-
----
-
-## Example Interaction
-
-```
-$ python main.py
-Agent: Hi! How can I help you today?
-User: What's the weather in Paris?
-
-Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'Paris'}? [approve/reject/edit]
-> approve
-Tool get_weather called. Result: "Sunny, 22°C"
-
-Agent: The current weather in Paris is Sunny, 22°C.
-User: Thanks!
-```
-
-If you type `reject`, the agent will continue without calling the tool:
-
-```
-Agent: (pausing) Would you like to call tool 'get_weather' with arguments {'location': 'Paris'}? [approve/reject/edit]
-> reject
-Agent: I couldn't retrieve the weather. Could you provide more details?
-```
-
----
-
-## Project Structure
-
-```
-human-in-loop-demo/
-├── main.py                # Entry point – runs the interactive agent
-├── tool_example.py        # Example custom tool (get_weather)
-├── requirements.txt       # Dependencies
-└── README.md              # This file
-```
-
-Feel free to extend the project by adding new tools, customizing prompts, or integrating a different LLM provider. Happy hacking!

solutions/69a86305c46fd26feae6bcaa_Human-in-the-Loop_через_middleware/requirements.txt

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-<|channel|>commentary to=repo_browser.print_tree code<|message|>{"path": "", "depth": 2}