In this post, I’m excited to announce a new version of CleverChatty that introduces server mode — unlocking powerful new capabilities for building AI assistants and agents that can interact over the network.

Previously, CleverChatty functioned only as a command-line interface (CLI) for interacting with LLM-based assistants. A typical use case involved a single user chatting with an AI model via the terminal. With this latest update, CleverChatty can now run as a server, enabling:

• Concurrent communication with multiple clients
• Background operation on local or cloud environments
• Integration into distributed agent systems

But that’s not all. The biggest leap forward? Full support for A2A (Agent-to-Agent) protocol.

CleverChatty

In this post, I’ll walk through how to integrate the Mem0 memory model with CleverChatty-CLI, a command-line framework for building AI assistants.

Spoiler: It turned out to be a lot easier than I expected.

Quick Overview of the Projects

Before we dive into the integration, here’s a quick recap of the two key components involved:

• Mem0 “Mem0” (pronounced mem-zero) adds an intelligent memory layer to AI assistants and agents. It enables personalized experiences by remembering user preferences, adapting to their needs, and continuously learning over time. It’s particularly useful for customer support bots, personal assistants, and autonomous agents.

• CleverChatty-CLI A command-line interface for interacting with LLM-based chat systems. It supports MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), and I plan to add support for A2A (Agent-to-Agent) communication soon. The CLI is built for experimentation, testing, and prototyping AI interactions.

CleverChatty, a lightweight AI Chat tool supporting multiple LLM providers, now includes support for Streamable HTTP with MCP servers. This update enables more flexible and efficient communication with AI models, making integration with custom tools and services even smoother.

🌐 What is CleverChatty?

CleverChatty is a minimalist AI chat interface that works with various large language model (LLM) providers — including OpenAI, Anthropic, Google, and local models like Ollama. It’s designed for users and developers who want a simple, extensible tool that supports MCP-based tool usage.

Until now, CleverChatty only supported STDIO and SSE (Server-Sent Events) as transport protocols for connecting with MCP servers. With the latest update, it now supports Streamable HTTP, expanding compatibility and flexibility.

Over the past couple of months, I’ve been experimenting with the Model Context Protocol (MCP) — building AI agents and tools around it. While the experience has been promising, I’ve noticed a few areas where MCP could be expanded or improved.

These aren’t critical issues, but adding them would make MCP more complete and developer-friendly.

Here’s my current wishlist:

1. A Standard MCP Server Interface
2. Bidirectional Notifications
3. Built-in or Native Transport Layer

Let’s walk through each of these in more detail.

Good news! I've extended my lightweight AI orchestrator, CleverChatty, to support Retrieval-Augmented Generation (RAG) by integrating it using the Model Context Protocol (MCP).

Quick Recap

• RAG (Retrieval-Augmented Generation) is an AI technique that enhances language models by retrieving relevant external documents (e.g., from databases or vector stores) based on a user’s query. These documents are then used as additional context during response generation, enabling more accurate, up-to-date, and grounded outputs.

• MCP (Model Context Protocol) is a standard for how external systems—such as tools, memory, or document retrievers—communicate with language models. It enables structured, portable, and extensible context exchange, making it ideal for building complex AI systems like assistants, copilots, or agents.

• CleverChatty is a simple AI orchestrator that connects LLMs with tools over MCP and supports external memory. My goal is to expand it to work with modern AI infrastructure—RAG, memory, tools, agent-to-agent (A2A) interaction, and beyond. It’s provided as a library, and you can explore it via the CLI interface: CleverChatty CLI.