Documentation
Concepts

Memory

Flapjack agents can store and recall information across conversations. Memory is scoped, embedded, and retrieved via semantic search.

Memory gives agents the ability to remember information across conversations. Unlike knowledge (RAG), which is based on uploaded documents, memory is created dynamically during conversations and persists for future use.

How Memory Works

Conversation happens → Agent stores a memory → Future conversation → Agent recalls memory

Memory entries are embedded with vector embeddings and retrieved via semantic similarity search, similar to RAG but for agent-generated knowledge.

Memory Operations

OperationDescription
StoreSave a fact, preference, or observation for later
RecallSearch stored memories by semantic similarity

Memory Scopes

ScopeDescriptionUse Case
agentShared across all threads for this agentUser preferences, learned facts
threadSpecific to one conversation threadContext within a session
resourceTied to a specific external resourcePer-project or per-document notes

Configuration

Memory is configured per-agent in the Flapjack dashboard under Agent → Memory:

SettingDescription
enabledTurn memory on/off
auto_collectAutomatically extract and store memories from conversations
auto_injectAutomatically recall relevant memories at the start of each turn
inject_countNumber of memories to inject per turn
max_memoriesMaximum stored memories for this agent
agent_scopeEnable agent-wide memory
thread_scopeEnable per-thread memory
resource_scopeEnable per-resource memory

Example Flow

  1. User tells the agent: "I prefer TypeScript over Python"
  2. Agent stores this as a memory (automatically or explicitly)
  3. In a future conversation, the agent recalls this preference
  4. Agent tailors its response to use TypeScript examples

Memory vs Knowledge

MemoryKnowledge (RAG)
SourceCreated during conversationsUploaded documents
ScopeAgent, thread, or resourceOrg, agent, or thread
ContentShort facts and preferencesDocument chunks
Created byAgent (auto or explicit)User (upload)
Best forPersonalization, learned contextReference material, FAQs

Next Steps

  • Knowledge — document-based RAG
  • Agents — agent configuration
  • Tools — other agent capabilities
Docs last updated May 11, 2026