Memori is an agent-native memory infrastructure that acts as an LLM-agnostic layer to transform AI agent execution and conversations into structured, persistent state for production systems. It integrates seamlessly into existing architectures, allowing agents to automatically capture and recall information from past interactions without requiring changes to core code or prompts.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
A Model Context Protocol (MCP) service that provides access to Ansible Automation Platform (AAP) APIs through OpenAPI specifications.
This article details the creation of a simple, 50-line agent using Model Context Protocol (MCP) and Hugging Face's tools, demonstrating how easily agents can be built with modern LLMs that support function/tool calling.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
