From 88efd3438f78cdbd78c2f4eb8e27ac1a5615de54 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=9A=D1=83=D1=82?=
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 <kirillkutlahmetov10@gmail.com>
Date: Tue, 26 May 2026 14:45:13 +0000
Subject: [PATCH] add README.md

---
 README.md | 62 +++++++++++++++++++++++++++++--------------------------
 1 file changed, 33 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index a4f5eba..eef8bcd 100644
--- a/README.md
+++ b/README.md
@@ -1,39 +1,43 @@
-# Simple Hierarchical AI Shopping‑List Agent
+# Shopping‑List AI Assistant
 
-## What it does
-This repository contains a minimal example of a **hierarchical LangChain agent** that helps you plan a shopping list. The main agent:
+This repository contains a minimal example of a hierarchical LangChain agent that can estimate prices for products in a city.  The top‑level agent uses the ``get_price`` tool, which internally creates a short‑lived sub‑agent to generate a markdown table row with the price.
 
-1. Accepts a natural‑language request with a list of products and a city.
-2. Calls an internal tool `get_price(product, city)` which itself creates a tiny sub‑agent to generate realistic price tables for the requested product in the specified city.
-3. Aggregates the prices and prints a final summary.
+## Project structure
 
-The example demonstrates:
-- Connecting to a local LLM via LM Studio (OpenAI‑compatible API).
-- Using `@tool` to expose a function that internally runs another agent.
-- Running the main agent with `create_agent` and printing all intermediate tool calls.
-
-## File structure
-| File | Purpose |
-|------|---------|
-| `requirements.txt` | Pinning LangChain, langchain‑openai and dotenv dependencies |
-| `main.py` | Full implementation of the hierarchical agent and demo usage |
-| `README.md` | This documentation |
+- **main.py** – entry point; demonstrates three example calls and prints the full message chain.
+- **agent.py** – defines the ``get_price`` tool used by the main agent.
+- **requirements.txt** – Python dependencies (no pinned versions, only ``>=``).
+- **README.md** – this documentation.
 
 ## Installation
-```bash
-python -m venv .venv
-source .venv/bin/activate   # Windows: .venv\Scripts\activate
-pip install -r requirements.txt
-```
-Make sure LM Studio is running on `http://localhost:1234/v1` (or set `LM_BASE_URL` and `LM_MODEL`).
 
-## Running the demo
+```bash
+pip install -r requirements.txt
+# Ensure LM Studio is running at http://localhost:1234/v1 or set the env vars below:
+export LOCAL_LLM_MODEL=gpt-3.5-turbo
+export LOCAL_LLM_BASE_URL=http://localhost:1234/v1
+export LOCAL_LLM_API_KEY=fake
+```
+
+## Running the examples
+
 ```bash
 python main.py
 ```
-The script will:
-- Run the main agent with a hard‑coded prompt.
-- Print each tool call (`get_price`) and its output.
-- Show aggregated price tables and total cost.
 
-Feel free to modify `user_prompt` in `main.py` or pass input from stdin for interactive use.
+You should see three blocks of output, each showing the assistant’s messages and the tool calls that were made.
+
+## How it works
+
+1. **Sub‑agent** – The ``get_price`` tool creates a lightweight sub‑agent with a focused system prompt to generate a single markdown table row containing product name, price (rub.) and store.
+2. **Main agent** – Uses the ``get_price`` tool to estimate prices for each product in the user’s list.
+3. **Output** – The assistant prints all intermediate messages (tool calls) followed by the final answer summarizing the shopping list and total cost.
+
+## Dependencies
+
+- `langchain-openai>=0.3.0`
+- `langgraph>=0.2.0`
+- `langchain-core>=0.3.0`
+- `python-dotenv>=1.0.0`
+
+All versions are specified with ``>=`` to avoid pinning to potentially non‑existent releases.