Stream‑Mode AI Agent

A lightweight Python project that demonstrates how to run a LangChain agent in streaming mode using the langchain-ollama and langgraph libraries.
The agent is defined in agent.py. Instead of waiting for the entire response, it streams tokens back to the console as they are generated.

What It Does
Prerequisites
Installation
Running the Agent
Example Usage

What It Does

Implements a simple LangChain agent that can call external tools (e.g., web search, calculator).
Uses ChatOllama as the LLM backend.
Streams the generated answer token‑by‑token to the console with agent.stream().
Works out of the box on any machine that has Ollama installed locally.

Prerequisites

Component	Minimum Version	Notes
Python	3.10+	Tested on 3.11
Ollama	Latest stable	Must have a model (default: `llama3`) downloaded.
Nomic Embed Text	Latest	For embeddings used by the agent.
Qdrant	Optional	If you want to persist embeddings locally.

Installing Ollama

curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3   # or any other model you prefer

Installation

# Clone the repo (or copy agent.py into your project)
git clone https://github.com/yourusername/stream-ai-agent.git
cd stream-ai-agent

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

requirements.txt contains:

langchain>=0.2.0
langchain-ollama>=0.1.0
langgraph>=0.1.0
nomic-embed-text>=0.1.0
python-dotenv>=1.0.0

Running the Agent

The main entry point is agent.py.
Run it with:

# Basic usage – prompts the agent to answer a question
python agent.py "What is the capital of France?"

If you want to see the streaming output in real time, simply execute the script as shown above. The console will display each token as soon as it is produced by the LLM.

Example Usage

$ python agent.py "Explain quantum computing in simple terms."
🤖  Quantum computing is a type of computation that uses quantum bits (qubits) instead of classical bits...
🤖  ...to perform certain calculations much faster than traditional computers.

The 🤖 emoji indicates the streaming output from the LLM.
You can also pass multiple arguments or use environment variables to change the model:

OLLAMA_MODEL=llama2 python agent.py "How many moons does Mars have?"

Customizing the Agent

Change the LLM – edit LLM_MODEL in agent.py.
Add tools – extend the tools list with any BaseTool subclass.
Persist embeddings – configure Qdrant by setting QDRANT_URL and QDRANT_API_KEY.

Happy streaming! 🚀

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