Tools and platforms providing long-term memory, knowledge persistence, and context management for AI agents — including cross-session memory, knowledge graphs, and personalization
AI coding assistant skill that turns any folder of code, docs, papers, images, or videos into a queryable knowledge graph. Works with Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot CLI, and more.
A Claude Code plugin that automatically captures coding session context, compresses it with AI, and injects relevant context back into future sessions for persistent memory.
Multi-agent swarm intelligence engine that extracts seed information from the real world, constructs a high-fidelity parallel digital world with thousands of agents, and predicts future trajectories through social evolution simulations.
Embedchain is a universal memory layer for AI agents, enabling quick integration of diverse data sources into LLMs for context-aware AI applications.
Mem0 is a long-term memory layer for AI agents, supporting cross-session memory management and personalized context retrieval.
Context compression layer that compresses tool outputs, logs, files, and RAG chunks before they reach the LLM, saving 60-95% of tokens while preserving answer quality. Available as library, proxy, or MCP server.
Context7 is Upstash's context-engineering toolkit for agents, helping applications manage long context windows, retrieval injection, and history compression.
MemPalace is an open-source AI memory system providing a persistent long-term memory layer for AI agents, with ChromaDB vector storage and MCP protocol integration.
A generative speech model for daily dialogue, providing AI agents with natural and fluent voice synthesis with fine-grained prosody control.
Khoj is a self-hostable AI second brain that answers questions from the web or your docs, builds custom agents, schedules automations, and performs deep research.
High-performance code intelligence MCP server that indexes codebases into a persistent knowledge graph, supporting 66 languages with sub-millisecond queries and 99% fewer tokens.
Graphiti is a temporal knowledge-graph engine for agent memory, helping systems continuously accumulate long-term context.
A knowledge engine for AI agent memory that builds knowledge graphs and memory layers in 6 lines of code, supporting graph databases, vector stores, and more for knowledge extraction and retrieval.
An extremely fast and scalable memory engine for the AI era. Provides a unified Memory API for AI applications with large-scale knowledge storage and efficient retrieval.
OpenViking is an open-source context database from Volcengine that unifies management of agent memory, resources, and skills through a filesystem paradigm, enabling hierarchical context delivery and self-evolution.
Kotaemon is an open-source RAG-based tool for chatting with your documents, featuring a clean chat interface and support for multiple LLM and embedding model backends.
A memory upgrade for coding agents. Provides persistent contextual memory for Claude Code, Codex, and other coding agents to improve long-task consistency.
A persistent memory system for AI coding agents, designed around real-world benchmarks to preserve context across sessions.
Open-source vector similarity search extension for PostgreSQL, enabling native vector storage and ANN retrieval in relational databases, a foundational component for building agent memory and RAG systems.
MaxKB is an open-source knowledge base Q&A and agent building platform powered by LLMs, with vector retrieval, workflow orchestration, and multi-model support out of the box.
Hindsight is an agent memory system that learns autonomously, supporting memory retention, recall, and reflection to give AI agents persistent experiential memory.
An open-source AI coworker with persistent memory, supporting multi-turn conversations and context retention for knowledge management and collaborative task completion.
MemVid is a long-term memory layer for AI agents that uses video encoding for lightweight single-file storage, replacing complex RAG pipelines with instant retrieval.
Agent-native memory infrastructure that turns agent execution and conversation into structured, persistent state with an LLM-agnostic memory layer, MCP integration, and Python/TypeScript dual SDK support.
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A systematic comparison of the three categories of agent memory -- working, long-term, and shared -- covering storage media, lifecycle, retrieval methods, typical frameworks, and design patterns, fully addressing agent personalization and multi-agent collaboration engineering.
A deep dive into the four-layer agent memory architecture, with practical code for vector retrieval and memory compression to help you build scalable long-term memory systems.
A 2026 review of the 25 Model Context Protocol (MCP) servers most worth integrating, covering GitHub, databases, browsers, filesystem, code search, Google Workspace, Slack, Notion, and more. Includes official SDK vs community implementations and configuration in Claude Code, Cursor, and Cline.
Learn how to build stateful AI agents with long-term memory using Letta (formerly MemGPT), solving the LLM context window limitation.
Long-conversation agents fail at context management, not model capability. A systematic comparison of sliding window, retrieval injection, and layered compression strategies with practical decay diagnosis and recovery patterns.
Systematic comparison of Microsoft GraphRAG (community detection + global summarization) and HKUDS LightRAG (dual-layer graph + incremental update) across index construction, retrieval paradigm, cost structure, query quality, and use cases. Includes a decision tree by data scale, query type, and ops budget.