Latest Blog Posts
Recently, I released a new version of [CleverChatty](https://github.com/Gelembjuk/cleverchatty/) with built-in support for the **A2A (Agent-to-Agent) protocol**. This addition enables AI agents to **call each other as tools**, opening the door to more dynamic, modular, and intelligent agent systems.
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## 🔄 What Is the A2A Protocol?
The **A2A protocol** defines a standard for communication and collaboration between AI agents. It allows one agent to delegate tasks to another, much like how humans might assign work to collaborators with specific expertise.
Many blog posts and art
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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 cl
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Mem0, CleverChatty, AI, memory
In this post, I’ll walk through how to integrate the [Mem0](https://github.com/mem0ai/mem0) memory model with [CleverChatty-CLI](https://github.com/Gelembjuk/cleverchatty-cli), a command-line framework for building AI assistants.
**Spoiler:** It turned out to be a lot easier than I expected.
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## 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 us
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**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.
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## 🌐 What is CleverChatty?
[CleverChatty](https://github.com/Gelembjuk/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
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mcp, ai-orchestrator, ai-assistant, rag, ai-memory, ai-agents
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.
## 1. A Standard MCP Server Interface
Several MC
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RAG, MCP, AI Orchestrator, CleverChatty, LLM, AI Assistant
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
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