It is a tool designed to help AI agents learn and adapt from their interactions over time by extracting important information from conversations, refining their behavior through prompt optimization, and maintaining long-term memory.
It is a tool designed to help AI agents learn and adapt from their interactions over time by extracting important information from conversations, refining their behavior through prompt optimization, and maintaining long-term memory. LangMem provides both functional primitives for use with any storage system and native integration with LangGraph’s storage layer, enabling agents to continuously improve, personalize responses, and maintain consistent behavior across sessions. It allows users to configure their environment with an API key for their preferred LLM provider and create agents that actively manage their own long-term memory with minimal code.
LangMem’s memory tools, such as `create_manage_memory_tool` and `create_search_memory_tool`, enable agents to control what information is stored, extract key details from conversations, and search past interactions when needed. These tools work seamlessly within any LangGraph application, allowing users to integrate them into existing agents or build custom memory systems. For development, memories can be stored in process memory using `InMemoryStore`, but for production, persistent storage options like `AsyncPostgresStore` are recommended to ensure memories are retained across server restarts.
The agent autonomously decides what to store and when to retrieve memories, requiring no special commands during interactions. It maintains context between chats, enabling it to search for relevant memories using `create_search_memory_tool` when prompted about past interactions. This functionality allows the agent to store critical information, search its memory when appropriate, and persist knowledge across conversations. For further customization and detailed examples, users can refer to the provided documentation and quickstart guides. LangMem is part of the LangChain ecosystem and is available on GitHub under the LangChain organization.
It is a framework and suite of applications designed for developing and deploying large language model (LLM) applications based on Qwen (version 2.0 or higher).
It is a platform that allows users to create, customize, and deploy AI agents for various business and personal workflows without requiring any coding knowledge.
It is a composable open-source AI framework designed for building and deploying production-ready applications powered by large language models (LLMs) and multimodal AI.
It is an open-source, modern-design AI chat framework called Lobe Chat that supports multiple AI providers, including OpenAI, Claude 3, Gemini, Ollama, Qwen, and DeepSeek.
It is an autonomous system powered by large language models (LLMs) that, given high-level instructions, can plan, use tools, carry out steps of processing, and take actions to achieve specific goals.
It is an AI super assistant that provides access to state-of-the-art (SOTA) large language models (LLMs) and enables users to build, automate, and optimize AI-driven solutions for a wide range of applications.
It is a developer framework and platform designed to build production-ready AI agents capable of finding information, synthesizing insights, generating reports, and taking actions over complex enterprise data.
It is an open-source platform designed for developing and orchestrating generative AI applications, enabling users to build and manage AI workflows, agents, and complex systems with advanced tools like RAG pipelines and prompt design.
It is a Python framework called Langroid, developed by researchers from Carnegie Mellon University (CMU) and the University of Wisconsin-Madison (UW-Madison), designed to build lightweight, extensible, and intuitive LLM-powered applications.
It is an intelligent assistant designed to serve the entire software development lifecycle, powered by a Multi-Agent Framework and integrated with DevOps Toolkits, Code & Documentation Repository Retrieval Augmented Generation (RAG), and other tools.
It is a personal AI assistant/agent designed to operate directly in your terminal, equipped with tools to perform a wide range of tasks such as using the terminal, running code, editing files, browsing the web, utilizing vision capabilities, and more.
It is a Chrome extension called Qodo Merge that integrates AI-powered chat and code review tools directly into GitHub to analyze pull requests, automate reviews, highlight changes, suggest improvements, and ensure code changes adhere to best practices.
It is a platform designed to integrate generative AI (GenAI) agents into business applications, enabling dynamic digital interactions, enhanced productivity, and improved performance using large language models (LLMs), natural language processing, and proprietary data.
It is an AI-powered platform called Kwal that automates and streamlines the hiring process by conducting human-like interviews, engaging candidates, and analyzing call data to improve recruitment efficiency.
It is a platform that uses advanced speech-to-text and speech understanding models to provide reliable, accurate, and scalable solutions for transcribing and analyzing voice data.
It is a decentralized AI safety and infrastructure protocol designed to provide essential guardrails for AI systems, ensuring they are developed and used responsibly.
It is an AI-powered business intelligence (BI) platform called Zenlytic that provides Intelligent Analytics to teams by combining dashboards, self-serve data exploration, and an AI data analyst named Zoë.
