Agent Memory System

Give your AI agent persistent, long-term memory across conversations and sessions.

Memory Types Implemented

Episodic Memory

Stores episodes/events from conversations:

• Key facts extracted per conversation
• Decisions made and context
• User preferences and patterns
• "Remembering" past interactions

Semantic Memory

Structured knowledge storage:

• Entity definitions and relationships
• Facts about the world
• Domain knowledge base
• Learned procedures

Procedural Memory

Agent's own capabilities:

• Known skills and tools
• How to use different APIs
• Response patterns that worked

Architecture

User Input
    ↓
Short-term (current session context)
    ↓
Memory Retrieval → Top-k relevant memories (vector search)
    ↓
Context Injection → Combined prompt
    ↓
LLM Response
    ↓
Memory Storage → Extract new facts, update entities

Features

• Vector-based storage (ChromaDB or Pinecone)
• Entity extraction (spaCy NER)
• Conversation summarization (every N turns)
• Relevance scoring for retrieval
• Forgetting/summarization of old memories

Use Cases

• Personal AI assistant that remembers you
• Customer support agent with context
• Research agent with persistent knowledge
• Trading agent with market memory
• Personal CRM (remembering people and their context)

Technical Stack

• ChromaDB / Pinecone (vector store)
• spaCy (entity extraction)
• LangChain (memory abstractions)
• PostgreSQL (structured memory)

Pricing

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