# Stream‑mode LangChain Agent

## What this project does

This repository contains a minimal example that demonstrates how to replace the
`invoke()` call of a LangChain agent with the streaming interface `stream()`.  The
output is printed token by token so that the user can see the agent “think” in
real time.

The script uses the BroJS LLM provider and a tiny mock tool called
`get_price` to illustrate how tools are called while streaming.

## File structure

```
├── main.py          # Entry point – shows three example invocations
├── requirements.txt # Dependencies (no exact versions)
└── README.md        # This file
```

## Installation

```bash
pip install -r requirements.txt
export JOURNAL_MCP_PAT=YOUR_BROJS_TOKEN  # required for the LLM
python main.py
```

## Example output

Running `python main.py` produces something like:

```
=== Example 1 ===
--- Step 0 ---
Paris is the capital of France.
--- End ---

=== Example 2 ===
--- Step 0 ---
The price of milk in Kazan is 89 rubles.
--- End ---

=== Example 3 ===
--- Step 0 ---
The price of bread in Kazan is 45 rubles.
The price of coffee in Moscow is 120 rubles.
--- End ---
```

## How it works

* The agent is created with `create_agent` and a single tool.
* `agent.stream()` returns an iterator that yields tuples `(chunk_type,
  chunk_data)` where `chunk_type` can be `'messages'` or `'updates'`.
* Tokens are printed as they arrive, and step changes are highlighted with
  a separator line.

Feel free to extend the tool set or modify the prompt – the streaming logic
remains unchanged.