AI Topic: Agent Memory

Memory layers, frameworks, and services that enable AI agents to store, recall, and manage information across sessions. These tools provide persistent, semantic, and contextual memory for agents, supporting personalization, long-term context retention, graph-based relationships, and hybrid RAG + memory workflows.

AI Tools in Agent Memory (7)

Agent Skills

24d

Agent Frameworks

An open specification for packaging reusable skills that give autonomous agents domain expertise, new capabilities, and repeatable workflows.

Pieces

2mo

Knowledge Management

AI-powered desktop app for developers that captures workflow context, builds on-device long-term memory, and integrates with IDEs, browsers, CLIs, and local LLMs for context-aware coding.

LangMem

5mo

Agent Memory

Open-source SDK from LangChain for long-term memory in LLM agents, with hot-path tools, a background memory manager, and native LangGraph storage integration.

Letta

5mo

Agent Memory

Open-source platform for stateful AI agents with persistent memory. Build, observe, and deploy agents via ADE, REST API, SDKs, and a desktop app. Connect tools via MCP; use any model; run self-hosted or on Letta Cloud.

Zep

5mo

Agent Memory

Context engineering platform that gives AI agents long-term memory via a temporal knowledge graph, Graph RAG, and context assembly. SDKs for Python/TS/Go, MCP server support, and usage-based pricing.

Mem0

5mo

Agent Memory

Memory layer for AI apps and agents. Open-source SDK + managed API that adds long-term, semantic and graph memory with fast retrieval, policy controls, and analytics.

Supermemory

6mo

Agent Memory

A developer-friendly memory API with semantic search and content ingestion for building AI apps that remember.

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