Agents That Remember Everything
HeartBeatAgents gives your AI agents true persistent memory. Three distinct memory types work together to store conversations, accumulate knowledge, and retain learned behaviors across sessions, users, and even across agent boundaries.
Memory Store
Three Memory Types, One Unified System
Inspired by cognitive science, HeartBeatAgents separates memory into three complementary systems that mirror how humans remember.
Episodic Memory
Conversation HistoryEvery conversation your agent has is stored as an episodic memory. Agents recall past interactions, reference previous decisions, and maintain continuity across sessions. Important moments are scored higher and persist longer.
Semantic Memory
Facts & KnowledgeAgents build a structured knowledge graph of facts, preferences, and domain knowledge. When a user mentions they prefer morning meetings or that their team uses React, the agent stores it as semantic memory and recalls it in future conversations.
Procedural Memory
Learned PatternsAs agents complete tasks, they encode successful strategies as procedural memories. If an agent learns that deploying to staging before production reduces errors, it retains that behavioral pattern and applies it automatically in future workflows.
Memory Capabilities
Advanced vector search, intelligent scoring, and natural memory lifecycle management.
Vector Embeddings (1536-dim)
All memories are encoded as 1536-dimensional vector embeddings using state-of-the-art embedding models. This enables semantic search that understands meaning, not just keywords, so agents find relevant memories even when wording differs.
Importance Scoring
Not all memories are created equal. HeartBeatAgents assigns importance scores to every memory based on emotional significance, decision impact, and recurrence frequency. Critical memories persist while trivia fades naturally.
Automatic Decay
Inspired by human memory, less important memories gradually decay over time. This prevents memory bloat and ensures agents always surface the most relevant context. Decay rates are configurable per agent and per memory type.
Cross-Agent Memory Sharing
Agents on the same team can share semantic and procedural memories. When one agent learns a customer preference or a successful workflow pattern, other agents in the organization can access that knowledge immediately.
Technical Specifications
- Embedding model
- text-embedding-3-large (configurable)
- Vector dimensions
- 1,536
- Retrieval method
- Cosine similarity with HNSW index
- Memory types
- 3 (Episodic, Semantic, Procedural)
- Importance scoring
- 0.0 - 1.0 continuous scale
- Decay rate
- Configurable per agent (default: 0.995/day)
- Max memories per agent
- Unlimited (plan-dependent storage)
- Cross-agent sharing
- Team and organization level
Give your agents a memory
Agents that remember outperform agents that do not. Start building context-aware AI today.