Awesome GitHub RepositoriesInteraction History Caching

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

SWE-agent is an autonomous software engineering platform designed to automate repository maintenance and issue resolution. By orchestrating language models to navigate codebases, diagnose software bugs, and apply fixes, the framework functions as an autonomous agent capable of executing shell commands, editing source code, and managing pull requests within isolated, containerized environments. The platform distinguishes itself through its focus on end-to-end task autonomy and observability. It features a robust trajectory logging system that records every thought, action, and environment obse

Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ

This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover

Dieses Projekt ist ein Multi-Channel-KI-Agent- und Chatbot-Framework, das es ermöglicht, eine einzige KI-Intelligenz über verschiedene Messaging-Plattformen, Web-Interfaces und E-Mail-Konten hinweg bereitzustellen. Es fungiert als Cross-Model-KI-Gateway und bietet ein einheitliches Interface, um Requests zwischen verschiedenen Large-Language-Model-Anbietern weiterzuleiten. Das System zeichnet sich durch seine autonomen Aufgabenplanungs- und Wissensmanagementfunktionen aus. Es kann komplexe Ziele in sequentielle Ausführungsschritte unter Verwendung externer Tools und eines Headless-Browsers zerlegen, während es gleichzeitig Informationen aus Konversationen extrahiert, um eine strukturierte Wissensdatenbank mit visuellen Beziehungsdiagrammen aufzubauen. Die Plattform enthält ein mehrstufiges Gedächtnissystem für den Langzeit-Kontextabruf, eine dynamische Skill-Plugin-Engine zur Erweiterung der Funktionalität und die Fähigkeit, Remote-Systembefehle auszuführen. Sie unterstützt zudem multimodale Workflows, die den Austausch von Bildern, Sprachnachrichten und Dateien über verschiedene Kommunikationskanäle hinweg ermöglichen. Die Instanz kann über eine Web-Konsole, ein Terminal-basiertes Befehlsinterface oder via In-Chat-Slash-Befehle verwaltet werden.

Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

SimpleMem is a persistent memory system for AI assistants designed to maintain context across different user chat sessions. It functions as a memory server and multimodal vector database that stores and retrieves information from text, images, audio, and video. The project features a context compression engine that distills interaction histories into compact units to reduce token consumption. It utilizes a distributed memory orchestrator and worker-thread parallel processing to reduce latency when querying large-scale dialogue datasets. The system implements a hybrid indexing approach combin