grasp_agents 0.1.12

Grasp Agents Library

Grasp Agents

Overview

Grasp Agents is a modular Python framework for building agentic AI pipelines and applications. It provides reusable agent classes, message handling, LLM integration, memory, and orchestration utilities. The framework is designed for flexibility, composability, and clarity, enabling rapid prototyping and robust development of multi-agent systems.

Features

• Modular agent base classes
• Message and memory management
• LLM and tool orchestration
• Logging and usage tracking
• Extensible architecture

Project Structure

• src/grasp_agents/ — Core framework modules
  • base_agent.py, llm_agent.py, comm_agent.py: Agent classes
  • agent_message.py, agent_message_pool.py: Messaging
  • memory.py: Memory management
  • cloud_llm.py, llm.py: LLM integration
  • tool_orchestrator.py: Tool orchestration
  • usage_tracker.py, grasp_logging.py: Usage and logging
  • data_retrieval/, openai/, typing/, workflow/: Extensions and utilities
• configs/ — Configuration files
• data/ — Logs and datasets

Quickstart & Installation Variants (UV Package manager)

Option 1: UV Package Manager Project

Note: You can check this sample project code in the src/grasp_agents/examples/demo/uv folder. Feel free to copy and paste the code from there to a separate project. There are also examples for other package managers.

1. Prerequisites

Install the UV Package Manager:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Create Project & Install Dependencies

mkdir my-test-uv-app
cd my-test-uv-app
uv init .

Create and activate a virtual environment:

uv venv
source .venv/bin/activate

Add and sync dependencies:

uv add grasp_agents
uv sync

3. Example Usage

Create a file, e.g., hello.py:

Ensure you have a .env file with your OpenAI and Google AI Studio API keys set

OPENAI_API_KEY=your_openai_api_key
GOOGLE_AI_STUDIO_API_KEY=your_google_ai_studio_api_key

import asyncio
from typing import Any

from grasp_agents.llm_agent import LLMAgent
from grasp_agents.openai.openai_llm import (
    OpenAILLM,
    OpenAILLMSettings,
)
from grasp_agents.typing.io import (
    AgentPayload,
)
from grasp_agents.run_context import RunContextWrapper

from dotenv import load_dotenv

load_dotenv()

class Response(AgentPayload):
    response: str


chatbot = LLMAgent[Any, Response, None](
    agent_id="chatbot",
    llm=OpenAILLM(
        model_name="gpt-4o",
        llm_settings=OpenAILLMSettings(),
    ),
    sys_prompt=None,
    out_schema=Response,
)


@chatbot.parse_output_handler
def output_handler(conversation, ctx, **kwargs) -> Response:
    return Response(response=conversation[-1].content)


async def main():
    ctx = RunContextWrapper(print_messages=True)
    out = await chatbot.run("Hello, agent!", ctx=ctx)
    print(out.payloads[0].response)


asyncio.run(main())

Run your script:

uv run hello.py

Development

To develop and test the library locally, follow these steps:

1. Install UV Package Manager

Make sure UV is installed on your system:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install Dependencies

Create a new virtual environment and install dependencies:

uv venv
source .venv/bin/activate
uv sync

3. Test Example for VS Code

• Install the Jupyter Notebook extension.

• Ensure you have a .env file with your OpenAI and Google AI Studio API keys set (see .env.example).

OPENAI_API_KEY=your_openai_api_key
GOOGLE_AI_STUDIO_API_KEY=your_google_ai_studio_api_key

• Open src/grasp_agents/examples/notebooks/agents_demo.ipynb.

You're now ready to run and experiment with the example notebook.

4. Recommended VS Code Extensions

• Ruff -- for formatting and code analysis
• Pylint -- for linting
• Pylance -- for type checking

Releasing: Maintainers Only

To release a new version of the package, follow these steps:

1. Create a new branch for the release: git checkout -b release-X.Y.Z.

2. Update the version in pyproject.toml.

3. Commit the changes with a message like "Bump version to X.Y.Z". git commit -m "Bump version to X.Y.Z".

4. Make a pull request to the master branch.

5. Once the pull request is approved and merged, checkout the master branch: git checkout master.

6. Pull the latest changes: git pull origin master.

7. Tag the release: git tag vX.Y.Z. Note that a tag name should be in the format vX.Y.Z.

8. Push the tag to the remote repository: git push --tags.

9. This will trigger the release workflow, which will build and publish the package to PyPI.