# Session Start Pattern
SUMMARY: The canonical session-start sequence every LLM agent should follow.

KEY CONTEXT:
ALWAYS at session start: 1) GET /memory/recall, 2) GET /chat/poll, 3) GET /mind/tasks?status=in_progress
Build system prompt from recall output.
Process unread chat messages before doing new work.
Resume any in_progress tasks before starting new ones.
Store new learnings as they happen — don't wait until session end.

Session Start Pattern
Every LLM agent session should follow this canonical startup sequence. Skipping
steps leads to lost context, missed messages, and forgotten tasks.
The Pattern
[CODE BLOCK]
Implementation
Step 1: Recall All Memories
> [!CRITICAL]
> This is the most important call. Without it, you have no memory of past
> sessions.
[CODE BLOCK]
Returns plain-text summary of all memories, sorted by priority.
Step 2: Poll for Unread Chat Messages
[CODE BLOCK]
Returns unread messages from the human. Automatically marks them as read.
Step 3: Check In-Progress Tasks
[CODE BLOCK]
Returns tasks you were working on last session.
Step 4: Build Context
Combine the three responses into your system prompt:
[CODE BLOCK]
Step 5: Process Pending Items
[CODE BLOCK]
Complete Example
[CODE BLOCK]
Common Mistakes
> [!WARNING]
> - Skipping recall — you start with no context, repeat past mistakes
> - Forgetting to poll chat — human's messages go unanswered
> - Ignoring active tasks — work is forgotten mid-execution
> - Storing nothing — session produces no persistent value
Variations
Minimal pattern (low-context LLMs)
For LLMs with small context windows, skip the full recall:
[CODE BLOCK]
Then search for specific topics as needed:
[CODE BLOCK]
Aggressive pattern (long-running agents)
For agents that run for hours, add periodic re-recall:
[CODE BLOCK]
Next Steps
- Memory Tagging Strategy
- Task-Driven Workflow
- Chat Polling Pattern