Awesome GitHub RepositoriesVector Memory Stores

Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The

Deer-flow is an autonomous agent orchestration platform designed to manage multi-step workflows where AI agents reason, plan, and execute tasks. It functions as a development framework for building agents that utilize various large language models to solve complex problems through structured, sequential, and parallel reasoning. The platform distinguishes itself through a secure, sandboxed execution engine that isolates generated code and system operations from the host environment. This architecture allows agents to safely test and validate solutions within ephemeral containers, ensuring that

Claude-flow is an autonomous agent coordination platform and orchestration framework designed for building complex, multi-step workflows powered by large language models. It functions as a TypeScript-based engine that decomposes high-level objectives into executable action sequences, enabling the creation of collaborative agent teams that operate with minimal manual oversight. The platform distinguishes itself through its ability to federate autonomous agents across network boundaries using secure communication channels and identity verification. It integrates a goal-oriented planning engine

This project is an autonomous agent framework designed to integrate large language models with popular messaging platforms. It functions as a middleware platform that enables automated, multimodal interactions by decomposing complex user goals into sequential plans, executing them through external tools, and maintaining persistent context across sessions. The framework distinguishes itself through a modular skill architecture and a hybrid memory system. Users can extend system capabilities by installing custom logic modules from community hubs or generating them through natural language. The

Zeroclaw is a modular framework for building and deploying autonomous agents that integrate AI models, messaging platforms, and hardware interfaces. It functions as a multi-agent orchestrator and embedded systems controller, providing a unified runtime for managing agent lifecycles, memory, and security policies across diverse environments. The system distinguishes itself through its focus on secure, verifiable hardware and software orchestration. It enforces strict security boundaries, including command allowlisting, resource throttling, and interactive human-in-the-loop approval for sensiti

Headroom is an AI gateway proxy and token optimizer designed to reduce the cost and latency of large language model interactions. It functions as an intermediary that intercepts traffic between clients and providers to apply context compression, request routing, and format translation. The system differentiates itself through a Model Context Protocol server implementation that delivers compression and retrieval tools to compatible AI hosts. It employs a content-aware compression pipeline and tiered importance scoring to trim redundant data from logs and tool outputs while preserving essential

Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity s

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

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

This project is a Python-based framework that functions as a generative AI agent for programmatic data analysis. It enables users to interact with structured data sources through natural language prompts, translating these requests into executable code to perform analysis, data cleaning, and visualization. By maintaining conversational context across multi-turn interactions, the system allows for iterative exploration and the building of complex data narratives. The framework distinguishes itself through a robust semantic layer and secure execution model. It maps raw datasets to descriptive m

Vanna is a Python framework designed to build conversational interfaces that translate natural language into executable database queries. It functions as an enterprise-grade toolkit that connects language models to relational databases, allowing users to retrieve information through conversational prompts rather than manual code. The system maintains context across interactions by utilizing vector databases to store historical query patterns and schema metadata. The framework distinguishes itself through a focus on security and schema-aware generation. It incorporates granular access control,

Vector similarity search extension for PostgreSQL.

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

Eliza is a modular framework designed for building and deploying autonomous agents that operate across diverse digital environments. It functions as an orchestrator for intelligent software, enabling agents to manage tasks, maintain persistent memory, and execute automated processes through a centralized runtime. The framework distinguishes itself through a plugin-based architecture that facilitates cross-platform social automation and blockchain transaction capabilities. By utilizing state-machine logic for decision-making and vector-based memory for context retention, the system allows agen

Weaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query. The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result

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

Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

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

Ironclaw is an LLM orchestration framework and AI agent gateway designed to connect large language models with external tools, messaging interfaces, and persistent memory systems. It functions as a communication layer that routes interactions between users and AI models via HTTP webhooks and various messaging channels. The system focuses on secure tool execution through a WebAssembly sandbox and isolated containers, which allows the framework to run untrusted code and dynamically generate new tools from natural language descriptions. Security middleware provides prompt injection defense and s

TurboVec is a high-performance Rust vector database and quantized search index designed for storing and retrieving high-dimensional embeddings. It functions as a pluggable vector store for large language model orchestration frameworks, providing a memory-efficient alternative to standard in-memory storage. The project distinguishes itself through a high-dimensional vector compressor that utilizes random rotation and data-oblivious scalar quantization to reduce memory footprints. Retrieval is accelerated via SIMD kernels that process distance calculations and search operations for increased th