Why Agent Memory Matters
3 entriesMemory preserves context across conversations, personalizes responses and improves relevance, enables learning and adaptation, and cuts repeated work and wasted tokens. It is the difference between an agent you brief once and an agent you brief every single time.
The Memory Types
4 entriesShort-term memory is temporary, lasts within a session and is lost when it ends. Long-term memory is persistent: facts, preferences and history. Semantic memory holds meanings, knowledge and relationships. Episodic memory holds past experiences, events and interactions. Procedural memory holds skills, workflows and rules. Each is stored and retrieved differently, so choosing which types you need is the first architecture decision.
The Memory Loop
4 entriesCapture observes interactions and what matters. Store organizes it in the right memory store. Retrieve fetches relevant memories when needed. Reason uses them to decide better. Update learns from new interactions and refreshes what is held. Databases store; memory systems store, retrieve contextually, reason and update, and the loop is the difference.
The Architecture
6 entriesA typical design has working memory for the short term, a vector store for semantic recall, a key-value or document store for long-term facts, and a retrieval step that reranks and filters before the LLM reasons. The goal is not more memory but the right memory at the right time.
How Retrieval Works
5 entriesKeyword search is simple and fast. Vector search finds semantic similarity through embeddings. Hybrid search combines both and is usually the strongest option. Graph-based memory adds relationships between facts.
Best Practices
4 entriesStore what matters, not everything. Keep memory fresh and current. Use clear structure and metadata. Respect privacy and give users control. Evaluate retrieval quality regularly.
Challenges
2 entriesRelevance: finding the right memory among thousands. Scalability: growing memory costs compute and storage. Privacy: protecting user data in persistent stores. Forgetting: knowing what to discard matters as much as what to keep. Latency: fast retrieval without blocking the agent.
Memory Platforms & Projects
12 entriesOpen-source and hosted projects for giving agents memory. Check each project's activity and licence.
Related Pages
2 entriesNeighbouring pages on this site.
Frequently Asked Questions
What is agent memory?
The ability of an AI agent to keep and use information beyond a single prompt: facts, preferences, past events and learned procedures, stored outside the model and retrieved when relevant.
What are the types of agent memory?
Short-term memory within a session, long-term persistent memory, semantic memory for meanings and relationships, episodic memory for past events, and procedural memory for skills and rules.
How is a memory system different from a database?
A database stores. A memory system also retrieves contextually, reasons over what it finds and updates itself from new interactions, which is the capture, store, retrieve, reason, update loop.
Should an agent remember everything?
No. Store what matters, keep it fresh, and decide what to forget. More memory raises cost, latency and privacy risk, and can bury the useful facts.
Which retrieval method should I use?
Hybrid retrieval, combining keyword and vector search, is usually the strongest option. Add graph relationships when facts depend on each other.
How current is this page?
Links and project activity were checked in October 2026. The structure of this page follows an Agent Memory summary provided by the site owner, expanded with documentation and projects. Memory tools change quickly, so check each project before adopting it; descriptions summarize each project's own documentation and are not rankings.