LangMem
LangMem is a lightweight, flexible Python SDK from LangChain that equips AI agents with long-term memory capabilities, enabling them to extract, store, update, and retrieve meaningful information from past interactions to become smarter and more personalized over time. It supports three memory types and offers both hot-path tools for real-time memory management and background consolidation for efficient updates beyond active sessions. Through a storage-agnostic core API, LangMem integrates seamlessly with any backend and offers native compatibility with LangGraph’s long-term memory store, while also allowing type-safe memory consolidation using schemas defined in Pydantic. Developers can incorporate memory tools into agents using simple primitives to enable seamless memory creation, retrieval, and prompt optimization within conversational flows.
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Membase
Membase is a long-term memory service for people and developers building AI agents. It brings information from conversations, files, web pages and connected apps into a persistent memory that users can review, correct, delete and export. A web app and Chrome extension let users manage memories, save browser content and share selected information with supported AI tools. Developers connect custom agents through MCP, the REST API, command-line interface, or Python and TypeScript SDKs. Separate memories and access controls support personal, project and client contexts, with controls to grant or revoke access. The hosted service stores and processes source content to build and retrieve memory.
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Pinecone
The AI Knowledge Platform.
The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.
Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval.
Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results.
Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.
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EverOS
EverOS is a persistent memory infrastructure platform for AI agents that provides long-term, multimodal, cross-platform context across agent workflows. The system retrieves relevant memories before model calls, allowing agents to reuse prior context without repeatedly loading large histories into the prompt window. EverOS also includes self-evolving skills, which capture successful execution trajectories as cases and promote recurring patterns into reusable procedural knowledge. The platform can ingest PDFs, images, documents, spreadsheets, slides, Markdown, and URLs and convert them into searchable memory for inference-time retrieval. It supports cloud and self-hosted deployment, exports memories in Markdown, and integrates with tools such as Claude Code, Codex, OpenClaw, Hermes, MCP, and OpenAI- or Anthropic-compatible SDKs.
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