Awesome GitHub RepositoriesComposable Memory Architectures

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel

Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

memU is a long-term memory system for AI agents that provides a persistent knowledge base. It extracts facts and preferences from conversations into structured memories, organizing this information through a hierarchical knowledge base based on a file-system architecture of nested categories and linked resources. The system includes a multimodal data ingestion pipeline that converts audio, video, and images into standardized natural language for storage in large language model contexts. It also features a model provider abstraction layer, offering a unified interface to use interchangeable la

Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation. The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operate

Promptbase 是一个提示工程框架，旨在设计、测试和优化大语言模型的提示。它提供了一个系统，通过评估工具包比较输出与基准数据集来衡量模型准确性和性能。该项目还包括一个编排流水线，用于跨云端端点自动化多组件机器学习任务，以及一个用于准备检索增强生成（RAG）数据集的实用程序。 该框架通过高级响应质量优化脱颖而出，利用思维链（chain-of-thought）生成器产生中间推理步骤，并使用基于嵌入的语义搜索进行动态少样本（few-shot）示例检索。它实现了集成方法以提高预测准确性，采用基于复杂性的查询路由和多个模型变体的多数投票聚合。 该系统涵盖了数据管理和自动化的更广泛功能，包括将外部数据格式化为用于训练的结构化文件，以及通过命令行实用程序编排模型执行流水线。

MemOS is an open-source persistent memory layer for AI agents and large language models, providing a self-hosted server that stores and retrieves structured memory across sessions. It enables AI systems to recall user preferences, history, and context without retraining, using a graph-based API and a web management interface for viewing, editing, and organizing memory items, skills, traces, and knowledge bases. The system distinguishes itself through a portable memory interchange protocol that allows memory to be transferred between different AI models, devices, and applications, along with a

OpenSquilla 是一个 LLM 智能体编排框架，旨在利用有向无环图协调多步 AI 工作流和工具执行。它作为一个集中式系统，用于管理专门的技能包并执行复杂的推理序列。 该项目通过一个路由网关脱颖而出，该网关根据复杂性、成本和性能将任务定向到不同的 AI 提供商。它利用多层 AI 记忆系统，通过本地嵌入和 SQLite 组织工作、情景和语义知识，并配有一个安全执行沙盒，通过基于风险的权限配置文件隔离智能体生成的代码。 该平台涵盖了广泛的功能，包括多渠道部署到 Web 和消息平台、通过 cron 进行自动任务调度，以及用于连接外部工具的 Model Context Protocol 网桥。它还提供全面的监控和可观测性工具，用于跟踪 Token 成本、审计运行时决策以及管理可重用技能目录。 该系统包括用于工作区初始化和技能生命周期管理的命令行工具。