Introduction
Memory is what allows an AI agent to evolve from a stateless tool into an assistant that learns and adapts. Without it, an agent cannot draw on past interactions, hold context across sessions, or build knowledge over time. Implementing memory well is non-trivial, since it involves storage, retrieval, summarization, and reference management. The six frameworks below take different approaches to that problem, covering long-term memory, retrieval, and context management. For broader context on building agents, see this guide to creating a first code agent.
1. Mem0
Mem0 is a dedicated memory layer for AI applications that provides personalized, long-term memory persisting across sessions and evolving over time. It extracts and stores relevant facts from conversations, supports multiple scopes — user-level, session-level, and agent-level memory — and combines vector search with metadata filtering for retrieval. It is a strong default when the goal is an assistant that remembers a user’s preferences and history.
2. Zep
Zep is a long-term memory store designed specifically for conversational AI. It extracts entities, intents, and facts from conversations and stores them in a structured form, produces progressive summaries that condense long histories while preserving key details, and offers both semantic and keyword retrieval. This makes it well suited to agents such as customer-service assistants that must recall a user’s history across many interactions.
3. LangChain Memory
LangChain includes a comprehensive memory module that provides several memory types and strategies for different use cases. Its main strengths are flexibility and tight integration with the wider LangChain ecosystem, which makes it convenient for teams already building on those tools and wanting to mix and match memory approaches.
4. LlamaIndex Memory
LlamaIndex offers memory capabilities integrated with its data framework. That integration makes it particularly strong for agents that need to remember and reason over structured data and documents, since memory and retrieval over a knowledge base are handled within the same system.
5. Letta
Letta takes inspiration from operating systems to manage context, implementing a virtual context-management system that moves information between the immediate context window and long-term storage. Its tiered memory architecture mirrors a memory hierarchy, treating the model’s context window like RAM and paging information in and out as needed. This is one of the more distinctive approaches to handling conversations that exceed a standard context window.
6. Cognee
Cognee is an open-source memory and knowledge-graph layer for AI applications that structures, connects, and retrieves information. It is built to give agents a dynamic, queryable understanding of data rather than just stored text, combining vector embeddings with graph relationships through an extract, structure, and load pipeline. This suits agents that need interconnected, persistent knowledge across many sources.
Choosing a framework
These tools solve overlapping problems in different ways, so the right choice depends on the use case. Conversation-heavy assistants benefit from Mem0 or Zep; teams already invested in a data framework may prefer LangChain or LlamaIndex memory; long-running, long-context agents are a natural fit for Letta; and applications that need richly connected knowledge favor a graph-based option like Cognee. A practical way to compare them is to build something small with each: a personal assistant with Mem0 that remembers preferences across sessions, a customer-service agent with Zep that recalls a customer’s history, a research agent with LangChain or LlamaIndex memory that retains both conversations and analyzed documents, a long-context agent with Letta, or a persistent knowledge agent with Cognee.
Wrapping up
Persistent memory is increasingly what separates a genuinely useful agent from a one-off chatbot. Each framework here makes different trade-offs between simplicity, retrieval power, and how knowledge is structured, and the most reliable way to decide is to prototype against a realistic task rather than to choose on feature lists alone.
