// learn

Learn about agent memory

Clear, jargon-free guides on how memory works for AI agents. Start with the basics if you are new, or jump to the practical guides if you already know what you need.

// start here
Agent memory explained for beginners →
Brand new to this? What agent memory is, why your agent forgets, and how to give it memory, in plain language with no assumed background.
Persistent memory for AI agents →
What persistent memory is, why agents need it, the difference between structured and semantic memory, and how it works inside a request.
Why AI agents need long-term memory →
The benefits, one by one: continuity across sessions, personalization, leaner and cheaper prompts, reusing what already worked, and coordinating multiple agents.
Does your agent need memory? A 10-question check →
A 2-minute interactive self-assessment. Work out whether you need a memory layer, which kind (structured or semantic), and what to do next. It says "not yet" when that is the honest answer.
Agent memory glossary →
Plain-English definitions of every term you will run into: context window, embedding, vector database, namespace, TTL, semantic search, MCP, and more.
// going deeper
Add memory to a LangChain agent in 5 minutes →
A working tutorial with copy-paste code: give a LangChain agent persistent memory using a simple HTTP API, no vector database, no framework lock-in.
Agent memory without a vector database →
When you actually need a vector database for agent memory, when you don't, and the simpler alternative that skips the setup.
Facts that change over time →
Why overwriting a value loses something you often need, and how assertions keep the history: what a fact was, who changed it, and what happens when two agents disagree.
Building a memory API without a vector database →
The build story behind AgentRAM: why it was built without a vector database, what was used instead, and lessons from shipping it solo.
// compare tools
AI agent memory providers compared (2026) →
A fair overview of the main memory tools, Mem0, Zep, Letta, Supermemory, and AgentRAM, and how to pick by the shape of your problem.
AgentRAM vs Mem0 →
How AgentRAM's simple HTTP memory compares to Mem0's vector and graph memory with autonomous extraction.
AgentRAM vs Zep →
How AgentRAM compares to Zep's enterprise-grade temporal knowledge graphs and compliance features.
AgentRAM vs Letta →
How a simple memory API compares to Letta, a full stateful agent framework with OS-inspired tiered memory.
AgentRAM vs Supermemory →
How simple key-value HTTP memory compares to Supermemory's multi-source ingestion and hybrid search platform.
AgentRAM vs LangMem →
How a simple hosted key-value store compares to LangMem, LangChain's LLM-driven memory SDK that extracts and consolidates for you.
AgentRAM vs Cognee →
How a simple hosted key-value store compares to Cognee, the open-source knowledge-graph memory platform that turns your data into graph and semantic memory.

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