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Architectures that manage and store historical interaction data to provide context for ongoing artificial intelligence conversations.
Explore 7 awesome GitHub repositories matching artificial intelligence & ml · Conversational Memory Systems. Refine with filters or upvote what's useful.
This repository serves as a comprehensive collection of resources, templates, and starter code for building artificial intelligence applications. It provides a centralized hub for developers to access practical implementations of common workflows, including retrieval-augmented generation pipelines and autonomous agent loops, alongside educational materials designed to support rapid prototyping and experimentation. The project distinguishes itself by offering a dual focus on technical implementation and critical analysis. It provides a library of lightweight, single-file agents and tutorials f
Implementations of chatbots maintain state and interaction history across multiple user sessions.
Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations
Limits the amount of historical data retrieved by selecting only the most recent messages for AI context.
This project is a collection of tutorials and guides for building large language model applications using the LangChain framework, written in Chinese. It serves as a learning resource for developing software that integrates language models with memory and chain-based logic. The resource provides specific walkthroughs for implementing retrieval augmented generation systems using vector stores and document loaders. It includes guides on creating autonomous agents that dynamically select and execute external tools, as well as tutorials for translating plain text queries into executable database
Manages and stores historical interaction data to provide context for ongoing AI conversations.
OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents. The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries. The project covers conversational AI trai
Implements architectures to manage and store historical interaction data for maintaining context in ongoing AI conversations.
This is an open-source platform for creating, hosting, and interacting with persistent AI characters that maintain personality and memory across conversations. The system orchestrates the full lifecycle of an AI companion by combining character definitions, conversation history, memory retrieval, model abstraction, and external communication channels into a unified runtime pipeline. The platform enables users to define detailed character personalities through structured configuration files that shape conversational behavior, and supports multi-turn dialogue through a memory system that stores
Stores chat history and retrieves contextual memories from a vector database for coherent ongoing dialogue.
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
Tracks chat history across requests and replays interactions to maintain consistent model context.
Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project
Provides capabilities to reduce token consumption by filtering and trimming conversation history to fit model windows.