Membase

Engram is a fully managed memory and context service purpose-built to help AI agents remember, learn, and improve over time. Instead of treating memory as an ever-growing pile of raw conversations and events, it turns noisy interaction data into structured, durable, and evolving memories. Applications can send raw text, complete conversations, or pre-extracted facts through a REST API or Python SDK without preprocessing. Engram then runs asynchronous pipelines that extract relevant information, transform it by deduplicating and reconciling it with existing knowledge, and commit a clean memory state without blocking the application’s main workflow. It resolves inconsistencies, adapts to changing preferences and time-evolving facts, and keeps context relevant and efficient. Agents can retrieve ranked memories in real time through vector similarity, BM25 keyword search, or hybrid retrieval, reducing the need to resend entire conversation histories.

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.