Best AI Agent Memory (2026)

Letta is a developer platform for creating AI agents that retain context across sessions, learn from interactions, and improve their behavior over time. Unlike stateless chatbots, Letta agents maintain persistent memory, allowing them to recall past conversations, user preferences, and accumulated knowledge. The framework provides infrastructure for managing agent memory, reasoning, and tool use, with support for multiple LLM providers. Developers can build, deploy, and observe agents through SDKs and a visual interface, making it suited for applications like personal assistants, customer support, and autonomous workflows that benefit from continuity.

• Stateful agents with persistent memory
• Self-editing memory blocks
• Multi-LLM provider support
• Tool and function calling
• Agent Development Environment (ADE)
• REST API and Python/TypeScript SDKs

2

AI Drive

AI

4.7 (6)

· free

전체 리뷰

AI Drive AI , PDF, , . , GPT-5, Claude, Gemini AI . Automatic OCR, Smart Metadata Extraction, Multi-Session Chat, Custom AI Agents . AI Drive , TLS , AES-256 , Google US Data Centers, , ISO 27001, SOC-2 Type 2.

• AI
• AI OCR
• Smart
• Multi-Session Chat
• Custom AI
• GPT-5, Claude, Gemini AI

3

Zep

Agent memory platform for enterprise-scale AI, built on context graphs.

4.5 (6)

· free

전체 리뷰

Zep is an enterprise-scale memory platform designed for AI agents, addressing the challenge of maintaining and managing agent memory across numerous users, business data, and past interactions. It aims to provide agents with a continuously learning and evolving understanding of their operational environment, thereby enhancing personalization and accuracy in agent interactions. The core of Zep's architecture is its proprietary Context Graph Engine, which constructs and manages a "Context Lake" of millions of individual context graphs. These graphs are built from diverse sources, including chat history, business data, and user interactions. Zep processes this information to create token-efficient, relevant context for agents through automated context assembly. A key capability is its temporal context graph, which automatically invalidates old facts when new information emerges, ensuring agents always reason with the most current data. Previous states are preserved as history, allowing agents to query what was true at any past date. This system also incorporates "Observations," where Zep analyzes graph structures to surface patterns, recurrences, and co-occurrences in memory, providing agents with a global perspective beyond isolated facts. Zep emphasizes enterprise-grade governance, offering features like attribute-based access control, policy-driven data retention, and full provenance tracking. Every fact within the graph traces back to its original source episode, enabling auditability. The platform is engineered for performance, demonstrating sub-200ms retrieval latency even with graph sizes up to 100 million entities. Designed for seamless integration, Zep can be added to existing agent frameworks or used independently, with SDKs available for Python, TypeScript, and Go. It aims to serve as a foundational layer in the enterprise agent stack, providing a scalable and governed solution for managing complex, evolving agent memory.

• Context Graph Engine
• Context Lake for millions of graphs
• Automated Context Assembly
• Temporal context reasoning
• Provenance tracing for facts
• Observations from memory patterns

4

Mem0

A persistent memory layer designed to provide long-term, personalized context for large language models and AI agents.

4.3 (6)

· free

전체 리뷰

Mem0 is an AI memory layer that integrates with AI assistants and agents to provide personalized and continuous context across interactions. It aims to solve the challenge of maintaining user preferences, adapting to individual needs, and enabling continuous learning for AI systems. The tool utilizes a distinct memory algorithm that focuses on a single-pass, add-only extraction approach, meaning new information is added without overwriting existing memories. Key to its operation are agent-generated facts, which are treated as first-class information. Mem0 also incorporates entity linking, where entities are extracted, embedded, and interconnected across memories to enhance retrieval accuracy. Furthermore, it employs multi-signal retrieval, combining semantic, BM25 keyword, and entity matching to fuse diverse scoring signals, alongside temporal reasoning for time-aware retrieval. Mem0 offers core capabilities such as multi-level memory management, handling User, Session, and Agent states with adaptive personalization. It provides a developer-friendly experience with an intuitive API and cross-platform SDKs for Python and Node.js. Applications include AI assistants requiring consistent, context-rich conversations, customer support chatbots recalling past interactions, healthcare systems tracking patient preferences, and adaptive experiences in productivity tools and gaming. Deployment options are flexible, including a Python/npm library for testing and prototyping, a self-hosted server for teams managing their own infrastructure, and a fully managed cloud platform for zero-operations production use. The platform also reports high benchmark scores on memory evaluation frameworks like LoCoMo, LongMemEval, and BEAM, highlighting its efficiency and recall capabilities.

• Multi-Level Memory (User, Session, Agent state)
• Single-pass, add-only memory extraction
• Entity linking for retrieval boosting
• Multi-signal retrieval (semantic, BM25, entity matching)
• Temporal reasoning for time-aware retrieval
• Developer API, Python SDK, Node.js CLI