KodeAgent

KodeAgent is a minimalistic, framework-less agentic solution built from scratch.

Written in about 1200 lines (including prompts and documentation), KodeAgent comes with built-in ReAct and CodeAgent. Or you can create your own agent by subclassing Agent.

A key motivation beyond KodeAgent is also to teach building agentic frameworks from scratch. KodeAgent introduces a few primitives and code flows that should help you to get an idea about how such frameworks typically work.

Usage

KodeAgent has just about five direct dependencies. Create a virtual environment and install them as follows:

pip install -r requirements.txt

Copy the kodeagent.py file to your source code location. Yes, it's just a single file!

In your application code, create a ReAct agent like this:

from kodeagent import ReActAgent


agent = ReActAgent(
    name='Agent ReAct',
    model_name='gemini/gemini-2.0-flash-lite',
    tools=[get_weather, calculator],
    max_iterations=3,
)

Or if you want to use CodeAgent:

from kodeagent import CodeAgent


agent = CodeAgent(
    name='Agent Code',
    model_name='gemini/gemini-2.0-flash-lite',
    tools=[get_weather, calculator],
    run_env='e2b',
    max_iterations=3,
    allowed_imports=['re'],
)

Now let your agent solve the tasks like this:

from kodeagent import ChatMessage


for task in [
    'What is 10 + 15, raised to 2, expressed in words?',
]:
    print(f'User: {task}')

    async for response in agent.run(task):
        if response['type'] == 'final':
            msg = (
                response['value'].content
                if isinstance(response['value'], ChatMessage) else response['value']
            )
            print(f'Agent: {msg}')
        else:
            print(response)

That's it! Your agent should start solving the task and keep streaming the updates.

The get_weather, calculator tools are inbuilt. The former returns dummy weather status. The latter performs real arithmetic calculations.

KodeAgent uses LiteLLM, enabling it to work with any capable LLM. Currently, KodeAgent has been tested with Gemini 2.0 Flash Lite.

LLM model names, parameters, and keys should be set as per LiteLLM documentation. For example, add GEMINI_API_KEY to the .env to use Gemini API.

Code Execution

CodeAgent executes LLM-generated code to leverage the tools. KodeAgent currently supports two different code run environments:

• host: The Python code will be run on the system where you created this agent. In other words, where the application is running.
• e2b: The Python code will be run on an E2B sandbox. You will need an E2B API key and add to your .env file.

KodeAgent is very much experimental. Capabilities are limited. Use with caution.

Acknowledgement

KodeAgent heavily borrows code and ideas from different places, such as:

• LlamaIndex
• Smolagents
• LangGraph
• Building ReAct Agents from Scratch: A Hands-On Guide using Gemini
• LangGraph Tutorial: Build Your Own AI Coding Agent