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.
FastCode is a token-efficient framework for comprehensive code understanding and analysis, delivering superior speed, exceptional accuracy, and cost-effectiveness for large-scale codebases and software architectures. It features a three-phase framework for semantic-structural code representation, lightning-fast codebase navigation, and cost-efficient context management.
A polyglot document intelligence framework with a Rust core that extracts text, metadata, and structured information from PDFs, Office documents, images, and 50+ formats. Available for Rust, Python, Ruby, Java, Go, PHP, Elixir, C#, TypeScript (Node/Bun/Wasm/Deno) or use via CLI, REST API, or MCP server.
Powering the future of open-source AI agent development. Discover, run, and compose AI agents from any framework. Build production-grade AI agents in both Python and Typescript. Join our community on Discord, BlueSky, and YouTube.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
