grasp_agents 0.1.13

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