# Build a Persistent LLM Agent
SUMMARY: Step-by-step guide to building an LLM agent that remembers across sessions using Synapse.

Overview
This guide walks through building an LLM agent that persists context across
sessions using Synapse. By the end, your agent will:
- Recall past context at session start
- Store new learnings as they happen
- Track multi-step tasks across sessions
- Communicate with humans via async chat
Architecture
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Step 1: Set Up Mind Key
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Step 2: Session Start Protocol
At the beginning of every session, recall all memories:
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Step 3: Store New Learnings
Whenever the agent learns something worth remembering:
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Step 4: Task Management
Track multi-step work across sessions:
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Step 5: Async Chat with Humans
Poll for messages between tool calls:
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Step 6: Session End Protocol
At session end, store final context:
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Complete Pattern
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Best Practices
> [!TIP]
>
> - Always recall first — never start work without loading context
> - Store proactively — don't wait until session end
> - Use meaningful keys — , , not 
> - Tag everything — tags power search and filtering
> - Set realistic priorities — not everything is 
Next Steps
- LLM Cookbook — practical patterns
- Memory Best Practices
- Multi-Agent Coordination