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Documentation Index

Fetch the complete documentation index at: https://docs.memanto.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Daily Workflows & Automation

Automate daily memory management tasks with Memanto.

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What Are Daily Workflows?

Daily workflows are automated tasks that run on a schedule:
  • Daily Summaries - Compile all day’s memories into a summary
  • Conflict Detection - Find contradictory memories
  • Memory Validation - Verify important memories
  • Export & Backup - Regular memory exports

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Daily Summaries

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What They Include

A daily summary shows:
  • Total memories created today
  • Breakdown by memory type
  • Key facts discovered
  • Important decisions made
  • Potential issues detected

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Generate Summary - CLI

memanto daily-summary
See the Memory Commands CLI Reference for output details.

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Conflict Detection

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Find Contradictions - CLI

memanto conflicts
See the Memory Commands CLI Reference for details on resolving conflicts interactively.

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Scheduled Tasks

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Enable Daily Summary Schedule

Enable automated daily summaries: 
memanto schedule enable

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Check Schedule Status

memanto schedule status

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Disable Schedule

memanto schedule disable
See the Schedule Commands CLI Reference for full configuration details. Daily workflows often call memory operations, but command-level details are maintained in the memory guides and CLI reference.

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Automated Backups

Set up a cron job for daily exports: 
#!/bin/bash
# backup_memories.sh

AGENT_ID="customer-support"
BACKUP_DIR="./backups"
DATE=$(date +%Y-%m-%d)

mkdir -p "$BACKUP_DIR"
memanto memory export --output "$BACKUP_DIR/${AGENT_ID}_${DATE}.md"

# Keep only last 30 days
find "$BACKUP_DIR" -type f -name "${AGENT_ID}_*.md" -mtime +30 -delete
Add to crontab: 
crontab -e

# Add line:
0 0 * * * /path/to/backup_memories.sh

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Workflow Automation Examples

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Daily Review Workflow

def daily_workflow(agent_id: str):
    """Run complete daily workflow."""

    # 1. Generate summary (CLI command)
    print("📊 Generating daily summary...")
    # subprocess.run(["memanto", "daily-summary", "--agent", agent_id], check=True)

    # 2. Check for conflicts (CLI command)
    print("🔍 Checking for conflicts...")
    # subprocess.run(["memanto", "conflicts", "--agent", agent_id, "--list"], check=True)

    # 3. Export backup
    print("💾 Exporting backup...")
    # memanto memory export command

    # 4. Report
    print("✓ Daily workflow complete")

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Weekly Analysis

def weekly_workflow(agent_id: str):
    """Run weekly memory analysis."""

    # Recall all new memories from past week
    week_ago = (datetime.now() - timedelta(days=7)).isoformat()

    response = httpx.post(
        f"http://localhost:8000/api/v2/agents/{agent_id}/recall/changed-since",
        json={"since": week_ago},
        headers={"X-Session-Token": session_token, "Content-Type": "application/json"},
    )
    new_memories = response.json()["memories"]

    # Analyze patterns
    type_counts = {}
    for mem in new_memories:
        mem_type = mem['type']
        type_counts[mem_type] = type_counts.get(mem_type, 0) + 1

    print(f"Weekly Summary ({week_ago}):")
    for mem_type, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True):
        print(f"  {mem_type}: {count}")

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Best Practices

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DO

  • Run daily summaries to track memory growth
  • Check conflicts weekly
  • Schedule recurring tasks for consistency

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DON’T

  • Ignore detected conflicts
  • Let old memories accumulate unchecked
  • Leave failed workflows unreviewed

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Next Steps

Automate your memory workflows for consistent, reliable agent operations!