Free to use · Export with Pro
Memory Architecture Visualizer —
design your agent's memory visually.
Map the four memory types — working, episodic, semantic, and procedural — as a directed acyclic graph with token budgets and data flow annotations. Built for LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK.
The four memory layers
Working Memory
The active context window — what the agent is currently reasoning over. Usually 2k–16k tokens of conversation history, tool results, and in-flight data.
Episodic Memory
Past interactions and events retrieved by similarity search. Powers personalization and continuity across sessions in systems like LangGraph and AutoGen.
Semantic Memory
General knowledge stored in a vector database. The core of any RAG pipeline — chunks of documents, embeddings, and retrieved context injected at inference time.
Procedural Memory
Tool definitions, function schemas, and agent instructions that describe what the agent can do. Usually static tokens in the system prompt.
Designed for these agent frameworks
Key capabilities
Auto-layout DAG rendering
Add memory layers and connections — the diagram auto-arranges into a clean directed acyclic graph. No manual positioning, no Figma required.
Token budget simulation
Set token allocations per layer and simulate turn-by-turn context window consumption. See exactly when you'll overflow a GPT-4o 128k context or Gemini 1.5 Pro 1M window.
Export to four formats
One-click export to SVG (vector, infinitely scalable), PNG (2× for presentations), JSON spec (machine-readable architecture definition), and Markdown (for your docs or README). Pro feature.
Why memory architecture matters
Most AI agent failures in production trace back to context decisions made early in the design phase — not the model, not the prompts. An agent that forgets too much has poor continuity. An agent that remembers too much burns tokens and exceeds context limits.
The Memory Architecture Visualizer makes these tradeoffs concrete and communicable. A diagram you can share with your team is worth more than a comment in a LangGraph state definition.