memory
Memory determines how your agent retains, recalls, and reasons over past interactions and knowledge.
Types at a Glance
None
No memory
One-shot lookups, stateless utilities
Conversation
Within session
Interactive chatbots, support agents
Persistent
Across sessions
Personal assistants, user preference tracking
Vector
Semantic retrieval
Large knowledge bases, document Q&A
None
Completely stateless. Every request is independent. The agent has no awareness of previous interactions. Ideal for tools that perform a single action and return a result.
Conversation
Retains a rolling window of recent messages within a single session. When the session ends (user disconnects or TTL expires), memory is cleared.
Settings: Max Messages (default: 50), TTL in minutes (default: 60)
Persistent
Stores facts, preferences, and context permanently across all sessions. The agent remembers everything until entries expire based on TTL.
Settings: Max Messages (default: 50), TTL in minutes (default: 60)
Use cases: An agent that remembers a user's portfolio, risk tolerance, preferred tokens, or notification preferences.
Vector
Stores embeddings in a vector database and retrieves relevant context via semantic similarity search at query time. This is the most powerful memory type — it scales to millions of stored entries.
Settings: Max retrieval results (default: 50), TTL in minutes (default: 60), Vector DB provider and collection name
Use cases: An agent that reasons over hundreds of research papers, a full codebase, or months of market data.
Combining Memory with Data Sources
Vector memory works best when paired with Data Sources and the RAG Pipeline tool. Data sources provide the knowledge; vector memory provides the retrieval mechanism; RAG Pipeline orchestrates the flow.
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