- Explore MCP Servers
- genpilot
Genpilot
What is Genpilot
GenPilot is a tool that simplifies the creation, development, and management of single and multi-agent systems powered by Generative AI. It adheres to the Model Context Protocol (MCP), ensuring seamless integration with various MCP servers through an intuitive terminal or web interface.
Use cases
Use cases for GenPilot include creating information retrieval agents, summarizing content from the internet, automating customer inquiries, and developing collaborative systems where multiple agents interact to solve complex problems.
How to use
To use GenPilot, install it via pip with Python 3.10 or later. Initialize the client using ‘litellm’ and create agents with defined roles and tools. The user interface supports both terminal and Streamlit UI for interaction.
Key features
Key features of GenPilot include seamless integration with MCP servers, user-friendly terminal and web interfaces, support for multiple agents, and the ability to define custom tools for specific tasks.
Where to use
GenPilot can be used in various fields such as software development, AI research, customer support automation, and any domain requiring multi-agent systems powered by Generative AI.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Genpilot
GenPilot is a tool that simplifies the creation, development, and management of single and multi-agent systems powered by Generative AI. It adheres to the Model Context Protocol (MCP), ensuring seamless integration with various MCP servers through an intuitive terminal or web interface.
Use cases
Use cases for GenPilot include creating information retrieval agents, summarizing content from the internet, automating customer inquiries, and developing collaborative systems where multiple agents interact to solve complex problems.
How to use
To use GenPilot, install it via pip with Python 3.10 or later. Initialize the client using ‘litellm’ and create agents with defined roles and tools. The user interface supports both terminal and Streamlit UI for interaction.
Key features
Key features of GenPilot include seamless integration with MCP servers, user-friendly terminal and web interfaces, support for multiple agents, and the ability to define custom tools for specific tasks.
Where to use
GenPilot can be used in various fields such as software development, AI research, customer support automation, and any domain requiring multi-agent systems powered by Generative AI.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
GenPilot
GenPilot streamlines the creation, development, and management of single and multi-agent systems powered by Generative AI. Adhering to the Model Context Protocol (MCP), it ensures seamless integration with a variety of MCP servers, enabling both developers and end-users to efficiently transform concepts and prototypes into fully realized solutions. All of this is facilitated through an intuitive, user-friendly terminal and web interface.
Installation
Require Python 3.10 or later.
pip install genpilot
Usage
The client is initialized using litellm. Please refer to the guide for details on different providers.
import genpilot as gp
import asyncio
# 1. User Interface: Also supports Streamlit UI, allowing all agents to share the same chat interface.
terminal = gp.TerminalChat()
# 2. Define a Tool to search and summarize information
def search_and_summarize(query):
"""Search for information on the internet and return a summary."""
return f"Here's the summary for '{query}': [Summarized info]."
# 3. Define an Agent for summarizing search results
info_explorer = gp.Agent(
name="Information Explorer",
model_config={
"name": "groq/llama-3.3-70b-versatile",
},
chat=terminal,
tools=[search_and_summarize],
system=(
"Your role is to search the internet and summarize relevant information for a given query. "
"Use the search tool to find and condense information for the user, ensuring clarity and relevance."
),
)
# 4. Run the Agent with a query
response = asyncio.run(info_explorer("What's the latest news about AI advancements?"))
print(response)
Why GenPilot?
-
MCP Agent: Leverage the MCP servers provided by the ecosystem to empower agents, allowing them to connect and interact within a richer, more expansive environment.
-
Multi-Agent System: Seamlessly scale from single-agent tasks to complex multi-agent workflows, inspired by Routines and Handoffs.
-
User-Friendly Interface: Offers an intuitive interface for prototyping and quick implementation, whether through a web UI (Streamlit, Chainlit) or terminal. Get started quickly and effortlessly with minimal effort.
-
Enhanced Autonomy: GenPilot can internally register and invoke tools, reducing reliance on external agents and minimizing unnecessary interactions.
-
Governed Actions

GenPilot’s actions are governed by three permission levels:
auto: Permission requested only for system/environment-modifying actionsalways: Permission requested for all actionsnone: No permission requests
-
Memory [PROCESSING]: GenPilot enhances accuracy with customizable memory:
-
ChatBufferMemoryA short-term memory solution designed to retrieve the most recent message along with the current session context. -
ChatVectorMemoryA long-term memory implementation based on LlamaIndex vector memory.
MemGPT: Towards LLMs as Operating Systems
CLIN: A CONTINUALLY LEARNING LANGUAGE AGENT FOR RAPID TASK ADAPTATION AND GENERALIZATIONChatPgMemory…
-
-
RAG Support: GenPilot integrates a retrieval agent that allows local resource or knowledge integration into the multi-agent system. The default implementation leverages LlamaIndex’s ChatEngine.
-
Typed Prompt and Auto Optimizer
Samples
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










