https://github.com/barun-saha/kodeagent
A minimalistic approach to building AI agents
https://github.com/barun-saha/kodeagent
agent agentic-ai agentic-framework ai ai-agents azure-openai codeagent e2b gemini-api lightweight litellm llm planning python react-agent
Last synced: 16 days ago
JSON representation
A minimalistic approach to building AI agents
- Host: GitHub
- URL: https://github.com/barun-saha/kodeagent
- Owner: barun-saha
- License: apache-2.0
- Created: 2025-04-27T10:36:40.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2026-01-10T13:47:52.000Z (3 months ago)
- Last Synced: 2026-01-11T04:14:31.272Z (3 months ago)
- Topics: agent, agentic-ai, agentic-framework, ai, ai-agents, azure-openai, codeagent, e2b, gemini-api, lightweight, litellm, llm, planning, python, react-agent
- Language: Python
- Homepage: https://kodeagent.readthedocs.io/en/latest/
- Size: 2.27 MB
- Stars: 15
- Watchers: 1
- Forks: 1
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-agents - KodeAgent - saha/kodeagent?style=social) (Frameworks)
- awesome-ai-agents - KodeAgent - saha/kodeagent) | The Minimal Agent Engine to build ReAct & CodeAct agents, with support for code sandbox and observability | (🌟 Core Frameworks)
- Awesome-AI-Agents - KodeAgent - The Minimal Agent Engine, enabling seamless integration with your platform. KodeAgent offers tool-calling (ReAct) and sanboxed code-executing (CodeAct) agents, supported by planning and observation.  (Applications / Autonomous Agent Task Solver Projects)
README
# KodeAgent: The Minimal Agent Engine
[](https://pypi.org/project/kodeagent/)
[](https://codecov.io/gh/barun-saha/kodeagent)
[](https://kodeagent.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/Apache-2.0)
[](https://snyk.io/test/github/barun-saha/kodeagent)
[](https://docs.astral.sh/ruff/)
KodeAgent is a frameworkless, minimalistic approach to building AI agents. Written in ~3 KLOC (~2.2K statements) of pure Python, KodeAgent is designed to be the robust reasoning core inside your larger system, not the entire platform.
## ✅ Why KodeAgent?
KodeAgent adheres to the **Unix Philosophy**: do one thing well and integrate seamlessly.
Use KodeAgent because it offers:
- **ReAct, CodeAct, and Function Calling:** KodeAgent supports ReAct, CodeAct, and native Function Calling paradigms out-of-the-box. This allows agents to reason, generate/execute code, or use a model's native tool-calling capabilities.
- **Guidance and Auto-Correction:** Includes a "Planner" to plan the steps and an internal "Observer" to monitor progress, detect loops or stalled plans, and provide corrective feedback to stay on track.
- **Optimized for SLMs:** The `FunctionCallingAgent` is specifically designed for Small Language Models (SLMs) and models with efficient function-calling support.
- **Scalable:** With only a few dependencies, KodeAgent perfectly integrates into serverless environments, standalone applications, or existing platforms.
- **LLM Agnostic:** Built on LiteLLM, KodeAgent easily swaps between models (e.g., Gemini, GPT, and Claude) and providers (e.g., Ollama) without changing your core logic.
## ✋ Why Not?
Also, here are a few reasons why you shouldn't use KodeAgent:
- KodeAgent is actively evolving, meaning some aspects may change.
- You want to use some of the well-known frameworks.
- You need a full-fledged platform with built-in long-term, persistent memory management.
## 🚀 Quick Start
Install [KodeAgent](https://pypi.org/project/kodeagent/) via pip:
```bash
pip install -U kodeagent # Upgrade existing installation
```
Or if you want to clone the KodeAgent GitHub repository locally and run from there, use:
```bash
git clone https://github.com/barun-saha/kodeagent.git
python -m venv venv
source venv/bin/activate
# venv\Scripts\activate.bat # Windows
pip install -r requirements.txt
```
Now, in your application code, create a ReAct agent and run a task like this (see `examples/_quickstart/kodeagent_quickstart.py`):
```python
from kodeagent import ReActAgent, print_response
from kodeagent.tools import read_webpage, search_web
agent = ReActAgent(
name='Web agent',
model_name='gemini/gemini-2.5-flash-lite',
tools=[search_web, read_webpage],
max_iterations=5,
)
for task in [
'What are the festivals in Paris? How they differ from Kolkata?',
]:
print(f'User: {task}')
async for response in agent.run(task):
print_response(response, only_final=True)
```
That's it! Your agent should start solving the task and keep streaming the updates. After the loop is done, you can access the final result of the task using `agent.task.result`.
You can also create a CodeActAgent, which leverages the core CodeAct pattern to generate and execute Python code on the fly for complex tasks. For example:
```python
from kodeagent import CodeActAgent
from kodeagent.tools import read_webpage, search_web, extract_as_markdown
agent = CodeActAgent(
name='Web agent',
model_name='gemini/gemini-2.0-flash-lite',
tools=[search_web, read_webpage, extract_as_markdown],
run_env='host',
max_iterations=7,
allowed_imports=[
're', 'requests', 'ddgs', 'urllib', 'requests', 'bs4',
'pathlib', 'urllib.parse', 'markitdown'
],
pip_packages='ddgs~=9.5.2;beautifulsoup4~=4.14.2;"markitdown[all]";',
)
```
### Native Function Calling (Optimized for SLMs)
For models that natively support function calling (like Gemini, OpenAI, or specialized SLMs), you can use the `FunctionCallingAgent`. It includes built-in retry logic for transient SLM failures and robust error detection:
```python
from kodeagent import FunctionCallingAgent, print_response
from kodeagent.tools import calculator
agent = FunctionCallingAgent(
# Try with your SLMs here, e.g., 'ollama/granite4:7b-a1b-h' or 'ollama/qwen3:4b-instruct-2507-fp16'
model_name='gemini/gemini-2.0-flash-lite',
tools=[calculator],
litellm_params={'temperature': 0, 'timeout': 90},
)
async for response in agent.run('What is 123 * 456?'):
print_response(response, only_final=True)
```
Use this [Colab notebook](https://colab.research.google.com/drive/1c7RMTCcSYrO7wZgB25bLX9QenDgVDmAP?usp=sharing) to run function-calling agent with several SLMs (uses T4 GPU).
### Memory(less)
By default, any agent in KodeAgent is **memoryless** across tasks—each task begins with no prior context, a clean slate. To enable context from the previous task (only), use **Recurrent Mode**:
```python
# Enable recurrent mode to leverage context from the previous run
async for response in agent.run('Double the previous result', recurrent_mode=True):
print_response(response)
```
This will copy the previous task's description & result into the current task's context.
For more examples, including how to provide files as inputs, see the [examples.py](src/kodeagent/examples.py) module and [API documentation](https://kodeagent.readthedocs.io/en/latest/usage.html).
### API Configuration
KodeAgent uses [LiteLLM](https://github.com/BerriAI/litellm) for model access and [Langfuse](https://langfuse.com/) or [LangSmith](https://www.langchain.com/langsmith) for observability. Set your API keys as environment variables or in a `.env` file:
| Service | Environment Variable |
| :--- | :--- |
| **Gemini** | `GOOGLE_API_KEY` |
| **OpenAI** | `OPENAI_API_KEY` |
| **Anthropic** | `ANTHROPIC_API_KEY` |
| **E2B Sandbox** | `E2B_API_KEY` |
| **Langfuse** | `LANGFUSE_PUBLIC_KEY`, `LANGFUSE_SECRET_KEY` |
| **LangSmith** | `LANGCHAIN_API_KEY`, `LANGCHAIN_TRACING_V2` |
Detailed configuration for various providers can be found in the [LiteLLM documentation](https://docs.litellm.ai/docs/set_keys).
### Code Execution
`CodeActAgent` 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](https://e2b.dev/). You will need to set the `E2B_API_KEY` environment variable.
With `host` as the code running environment, no special steps are required, since it uses the current Python installation. However, with `e2b`, code (and tools) are copied to a different environment and are executed there. Therefore, some additional setup may be required.
You can also specify a `work_dir` to serve as a local workspace. For the `e2b` environment, any files generated by the agent in the sandbox will be automatically downloaded to this local `work_dir`. If specified, `work_dir` could be relative or absolute path, but it **must exist**; otherwise, a temporary directory will be created and used for each run.
```python
from kodeagent import CodeActAgent
agent = CodeActAgent(
name='Data Agent',
model_name='gemini/gemini-2.0-flash-lite',
run_env='e2b',
work_dir='/home/user/agent_workspace', # Local workspace directory to copy files to/from E2B
# ... other parameters
)
```
For example, the Python modules that are allowed to be used in code should be explicitly specified using `allowed_imports`. In addition, any additional Python package that may need to be installed should be specified as a comma-separated list via `pip_packages`.
KodeAgent is under active development. Capabilities are limited. Use with caution.
## 🛠️ Tools
A tool in KodeAgent is just a regular (synchronous) Python function. KodeAgent comes with the following built-in [tools](src/kodeagent/tools.py):
- **`calculator`**: A simple calculator tool to perform basic arithmetic operations. It imports the `ast`, `operator`, and `re` Python libraries.
- **`download_file`**: A tool to download a file from a given URL. It imports the `requests`, `re`, `tempfile`, `pathlib`, and `urllib.parse` Python libraries.
- **`extract_as_markdown`**: A tool to read file contents and return as Markdown using MarkItDown. It imports the `re`, `pathlib`, `urllib.parse`, and `markitdown` Python libraries.
- **`generate_image`**: A tool to generate an image based on a text prompt using the specified model. The (LiteLLM) model name to be used must be mentioned in the task, system prompt, or somehow. It imports the `os`, `base64`, and `litellm` Python libraries.
- **`read_webpage`**: A tool to read a webpage using BeautifulSoup. It imports the `re`, `requests`, `urllib.parse`, and `bs4` Python libraries.
- **`search_arxiv`**: A tool to search arXiv for research papers and return summaries and links. It imports the `arxiv` library.
- **`search_web`**: A web search tool using DuckDuckGo to fetch top search results. It imports the `datetime`, `random`, and `time` Python libraries.
- **`search_wikipedia`**: A tool to search Wikipedia and return summaries and links. It imports the `wikipedia` library.
- **`transcribe_audio`**: A tool to transcribe audio files using OpenAI's Whisper via [Fireworks API](https://fireworks.ai/). Need to set the `FIREWORKS_API_KEY` environment variable. It imports the `os` and `requests` Python libraries.
- **`transcribe_youtube`**: A tool to fetch YouTube video transcripts. It imports the `youtube_transcript_api` library.
Check out the docstrings of these tools in the [tools.py](src/kodeagent/tools.py) module for more details.
To add your own custom tools, simply define a Python function and pass it to the agent via the `tools` parameter. For example:
```python
def my_custom_tool(text: str) -> str:
"""
A custom tool that does something with the input text and returns a result.
Args:
text (str): The input text to process.
Returns:
str: The processed result.
"""
return text
agent = ReActAgent(
name='Custom Tool Agent',
model_name='gemini/gemini-2.5-flash-lite',
tools=[my_custom_tool],
max_iterations=5,
)
```
Module imports and all variables should be inside the tool function. If you're using `CodeActAgent`, KodeAgent will execute the tool function in isolation.
For further details, refer to the [API documentation](https://kodeagent.readthedocs.io/en/latest/). Note: `async` tools are not supported.
## 🔭 Observability
In addition to the logs, KodeAgent enables agent observability via third-party solutions, such as [Langfuse](https://langfuse.com/) and [LangSmith](https://www.langchain.com/langsmith).
To enable tracing, set the relevant environment variables (e.g., `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` for Langfuse, or `LANGCHAIN_API_KEY` and `LANGCHAIN_TRACING_V2='true'` for LangSmith). Note that `langsmith` is not installed by default with KodeAgent and must be installed separately with `pip install langsmith`. Then, in the code, specify `tracing_type` as `langfuse` or `langsmith` when creating the agent:
```python
from kodeagent import ReActAgent
agent = ReActAgent(
name='Web agent',
model_name='gemini/gemini-2.5-flash-lite',
tools=[search_web, read_webpage],
tracing_type='langfuse', # or 'langsmith'
)
```
Tracing is **disabled** by default (rather, a no-op tracer is used). You will need to explicitly enable it, as shown in the code snippet above. The screenshot below shows a sample trace of KodeAgent running a task on the Langfuse dashboard:
## ⊷ Sequence Diagram for CodeAct Agent (via CodeRabbit)
```mermaid
sequenceDiagram
autonumber
actor User
participant Agent
participant Planner
participant LLM as LLM/Prompts
participant Tools
User->>Agent: run(task)
Agent->>Planner: create_plan(task)
Planner->>LLM: request AgentPlan JSON (agent_plan.txt)
LLM-->>Planner: AgentPlan JSON
Planner-->>Agent: planner.plan set
loop For each step
Agent->>Planner: get_formatted_plan()
Agent->>LLM: codeact prompt + {plan, history}
LLM-->>Agent: Thought + Code
Agent->>Tools: execute tool call(s)
Tools-->>Agent: Observation
Agent->>Planner: update_plan(thought, observation, task_id)
end
Agent-->>User: Final Answer / Failure (per codeact spec)
```
## 🧪 Run Tests
To run unit tests, use:
```bash
python -m pytest .\tests\unit -v --cov --cov-report=html
```
For integration tests involving calls to APIs, use:
```bash
python -m pytest .\tests\integration -v --cov --cov-report=html
```
Gemini and E2B API keys should be set in the `.env` file for integration tests to work.
A [Kaggle notebook](https://www.kaggle.com/code/barunsaha/kodeagent-benchmark/) for benchmarking KodeAgent is also available.
### Scalene Profiling
The following results were measured using [Scalene](https://github.com/plasma-umass/scalene) and `psutil` on development machine (Windows 10, Python 3.10). "Peak Memory" refers to the maximum Resident Set Size (RSS), i.e., the actual RAM used by the process.
```shell
python -m scalene run -c scalene.yaml -m src.kodeagent.examples
scalene view
```
| Agent Type | Avg. Runtime | Peak Memory (Scalene) | Peak Memory (psutil) | Notes |
|--------------|--------------|-----------------------|---------------------|----------------------------------------------|
| ReActAgent | ~58s | 30MB | 294MB | Faster, because tools are directly executed |
| CodeActAgent | ~155s | 21MB | 253MB | Slower, because of code review and execution |
**Notes:**
- _Scalene_ reports the maximum _sampled_ RSS during profiling, which is useful for comparing code sections but may miss short-lived or end-of-program memory spikes.
- _psutil_ reports the actual RSS at program _end_, which is typically higher and reflects the real-world memory footprint.
- Actual memory usage may vary depending on your system, Python version, and workload.
## 🗺️ Roadmap & Contributions
To be updated.
## 🙏 Acknowledgement
KodeAgent heavily borrows code and ideas from different places, such as:
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/agent/react_agent/)
- [Smolagents](https://github.com/huggingface/smolagents/tree/main)
- [LangGraph](https://github.com/langchain-ai/langgraph)
- [Building ReAct Agents from Scratch: A Hands-On Guide using Gemini](https://medium.com/google-cloud/building-react-agents-from-scratch-a-hands-on-guide-using-gemini-ffe4621d90ae)
- [LangGraph Tutorial: Build Your Own AI Coding Agent](https://medium.com/@mariumaslam499/build-your-own-ai-coding-agent-with-langgraph-040644343e73)
- Aider, Antigravity, CodeRabbit, GitHub Copilot, Jules, ...
## ⚠️ DISCLAIMER & LIABILITY
AI agents can occasionally cause unintended or unpredictable side effects. We urge users to **use KodeAgent with caution**. Always review generated code and test agents rigorously in a constrained, non-production environment before deployment.
**LIMITATION OF LIABILITY:**
By using this software, you agree that KodeAgent, its developers, contributors, supporters, and any other associated entities shall not be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software.