Projects sharing features with MemOS

Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche

Honcho is an agent memory platform: a server and set of SDKs that store conversation history as messages and reason over it in the background to build persistent, evolving context about users and agents. It models humans, agents, groups, and projects as equal peers who join shared sessions inside isolated workspaces, and it derives conclusions about each participant so applications can adapt their responses to the person they are talking to. What sets it apart is the reasoning layer. Queued messages are processed in order per session, and scheduled consolidation passes draw deductive and indu

This project provides a modular framework for building and orchestrating autonomous AI agents. It functions as an agentic workflow engine that manages the full lifecycle of task execution, including model reasoning, tool invocation, and the integration of results. By utilizing a centralized orchestration platform, the system enables the creation of multi-agent teams that collaborate on complex objectives through structured communication and shared task graphs. The framework distinguishes itself through its focus on persistent, stateful operations and multi-agent coordination. It employs file-

This repository is a static analysis of the Claude Code terminal coding assistant, based on a reading of its TypeScript source. It documents how the agent is built, from its startup path and a single shared execution core to the streaming tool-call loop, sub-agents, and terminal interface layers. It is a reference and study resource about the agent rather than the agent itself. The analysis centers on a few areas. One is the shared core that routes queries, sub-agents, tools, and permission checks through the same control flow. Another is memory: layered, file-based records that users can ins

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

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Embedchain is an LLM memory management framework and RAG orchestration engine designed to provide AI agents with a persistent storage layer. It functions as a long-term memory pipeline that extracts facts from unstructured interactions and stores them as permanent knowledge base entries to retain user preferences and interaction history across sessions. The system employs a hybrid vector database interface that combines semantic embeddings with traditional keyword search. It utilizes an entity-linking knowledge graph to connect related information points and applies temporal ranking to distin

This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-i

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

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ

OmniRoute is a unified LLM API gateway that connects multiple AI providers to a single endpoint. Its primary purpose is to simplify the integration of various AI models into tools and agents by translating different provider formats into a standardized API. The project distinguishes itself through a multi-strategy request routing system that optimizes for cost, speed, and availability, including automatic model fallbacks and a circuit-breaker resilience model to isolate provider failures. It employs a local-first security posture, using AES-256-GCM encryption to store API keys and conversatio

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

WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework. It transforms raw documents into queryable knowledge bases and integrates large language models with vector databases to provide grounded AI responses. The system also functions as a Model Context Protocol (MCP) tool server, exposing knowledge search and agentic capabilities to external AI clients. The platform distinguishes itself through an autonomous agent framework that utilizes iterative reasoning, tool calling, and web search to solve multi-step tasks. It implements a sta

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

Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a

MIRIX is an AI agent state orchestrator and long-term memory system designed to provide persistent context for large language models. It functions as a multi-modal AI memory pipeline that processes text, voice, and screen captures into structured knowledge stores, including a dedicated screen activity knowledge base. The project distinguishes itself by integrating a multi-modal observation pipeline that monitors desktop activity in real-time to build a searchable history of user actions. It utilizes a multi-tiered memory hierarchy—separating episodic, semantic, procedural, and core stores—and

Claude-mem is an agentic memory persistence system designed to provide AI assistants with long-term context across multiple development sessions. It functions as a background orchestrator that captures, summarizes, and indexes interaction history, allowing models to maintain continuity and recall technical decisions from past tasks. By utilizing a vector-augmented context engine, the system injects relevant historical observations into active sessions, ensuring that AI agents remain informed without exceeding finite token budgets. The project distinguishes itself through an endless memory arc

Mempalace is a long-term memory management system for large language models that orchestrates the storage and retrieval of conversation history and entity relationships. It functions as a memory orchestrator and Model Context Protocol server, providing AI clients with read and write access to structured knowledge. The system utilizes a temporal knowledge graph to track evolving entity relationships and timelines with validity windows. It employs a hierarchical memory partitioning strategy, organizing data into wings and rooms to isolate specialist agent contexts and restrict semantic searches

OpenReplay is a session replay platform and frontend debugging suite designed to record and play back user browser sessions. It functions as a user behavior monitoring system that captures interaction patterns and technical metadata to identify conversion issues and revenue loss. The platform is distinguished by its self-hosted infrastructure model, allowing the recording and analytics pipeline to be deployed on private servers for full control over data residency. It also includes a browser co-browsing tool for real-time screen sharing and direct communication to provide immediate technical

Nanoclaw is an LLM agent orchestrator and multi-platform chat gateway designed to deploy and manage isolated AI agents. It provides a containerized runtime that executes agents within sandboxed Linux containers, ensuring filesystem and state isolation through dedicated workspaces and host bind-mounts. The project distinguishes itself through a unified routing pipeline that connects agents to diverse messaging platforms, including WhatsApp, Discord, Slack, Telegram, Signal, and iMessage. It integrates the Model Context Protocol to extend agent capabilities via managed external data and functio

Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

This repository is a long-form technical book about how a production AI coding agent runtime, often called an agent harness, is built. It dissects a real-world coding agent's architecture chapter by chapter and turns it into transferable lessons, and it also works as a step-by-step guide to designing and building your own harness around a language model. Python scripts in the repository support the book itself rather than the agent. The book's core subject is the machinery around the model: a streaming conversation loop that repeats tool calls until the model returns plain text, a tool protoc

This is a written reference for designing, building and operating production AI systems, aimed at engineers who work with large language models. It is organized as chapters, worked case studies and interview-preparation material rather than as installable software. The central topics are tool-using LLM agents, retrieval-augmented generation, cost and model selection, evaluation and observability, and security and compliance, with explicit trade-offs among latency, cost, accuracy and safety. The agent material covers control loops such as ReAct and plan-and-execute, graph-based orchestration,