Mastering OpenClaw's Session Management

OpenClaw's session management is the backbone of its secure and scalable architecture. Whether you're running a personal assistant or a multi-user service, understanding how sessions work ensures privacy, context continuity, and efficient resource use.

The Foundation: Sessions Are Isolated by Default

At its core, OpenClaw treats each conversation as a distinct session. This isolation prevents cross-talk between users and ensures that private context stays private. The gateway maintains a central session store, making it the source of truth for all state, regardless of the client interface you're using (macOS app, WebChat, or CLI).

Sessions are categorized by type and transport:

• Direct messages (DMs): One-on-one conversations.
• Group chats: Multi-user channels on platforms like WhatsApp, Telegram, or Discord.
• Cron jobs: Automated background tasks.
• Webhooks: Incoming events from external systems.

Each session has a unique sessionKey, which determines how messages are routed and context is preserved.

Secure DM Mode: Protecting User Privacy

If your OpenClaw agent receives messages from multiple people—such as a public-facing support bot or a team chat assistant—you must enable secure DM mode to prevent accidental data leaks.

The default setting (dmScope: "main") shares the same session context across all direct messages. This is convenient for single-user setups but dangerous in multi-user environments. For example:

1. User A asks about a private medical appointment.
2. User B then asks, "What were we talking about?"
3. Without isolation, the agent might answer using User A's private context.

How to Enable Secure DM Mode

To fix this, configure dmScope in your ~/.openclaw/openclaw.json:

{
  "session": {
    "dmScope": "per-channel-peer"
  }
}

This setting isolates each user's conversation by channel and sender ID. For even stricter isolation with multi-account setups (e.g., multiple WhatsApp numbers), use per-account-channel-peer.

When the same user contacts you on different channels, you can map them to a single identity using session.identityLinks:

{
  "session": {
    "identityLinks": {
      "alice": ["telegram:123456789", "discord:987654321012345678"]
    }
  }
}

This ensures Alice gets consistent context whether she messages via Telegram or Discord.

Session Lifecycle and Reset Policies

Sessions don’t last forever. OpenClaw provides flexible reset policies to control when a session expires and a fresh one begins. Two primary modes are available:

• Daily reset: Sessions expire at a fixed time each day (default: 4:00 AM local time on the gateway host).
• Idle reset: Sessions expire after a period of inactivity (e.g., 2 hours).

You can use both, and whichever condition is met first will trigger the reset. Per-type and per-channel overrides allow fine-grained control:

{
  "session": {
    "reset": {
      "mode": "daily",
      "atHour": 4,
      "idleMinutes": 120
    },
    "resetByType": {
      "direct": { "mode": "idle", "idleMinutes": 240 },
      "group": { "mode": "idle", "idleMinutes": 120 }
    },
    "resetByChannel": {
      "discord": { "mode": "idle", "idleMinutes": 10080 } // 1 week
    }
  }
}

Manual resets are also possible with /new or /reset commands in chat.

Session Pruning: Optimizing Cost and Performance

For agents using Anthropic models with prompt caching, session pruning is a critical optimization. It reduces the size of old toolResult messages in memory before each model call, minimizing cache write costs when a session becomes stale.

Pruning only affects the in-memory context—it does not alter the persistent JSONL transcript. By default, it runs when the last Anthropic call exceeds a TTL (time-to-live), preserving the last few assistant messages and clearing oversized tool outputs.

Example configuration:

{
  "agent": {
    "contextPruning": {
      "mode": "cache-ttl",
      "ttl": "5m",
      "hardClear": {
        "enabled": true,
        "placeholder": "[Old tool result content cleared]"
      }
    }
  }
}

This setup keeps your prompt cache fresh and cost-efficient without losing long-term memory.

Compaction: Managing Long Conversations

Long-running sessions accumulate messages and can exceed the model’s context window. When this happens, OpenClaw performs compaction—summarizing older parts of the conversation into a concise summary that’s stored in the session history.

Key points:

• Compaction is persistent: once a summary is created, it’s saved in the JSONL file.
• /compact can be used to manually trigger compaction.
• Auto-compaction runs when nearing the token limit.
• Unlike pruning, compaction affects both memory and disk.

Use /status to monitor compaction activity and /context detail to inspect what’s currently in context.

Advanced: Controlling Session Delivery

You can block outgoing messages from specific session types using sendPolicy. For example, to prevent cron jobs from sending messages:

{
  "session": {
    "sendPolicy": {
      "rules": [
        { "action": "deny", "match": { "keyPrefix": "cron:" } }
      ],
      "default": "allow"
    }
  }
}

Runtime overrides allow temporary changes with /send on, /send off, or /send inherit.

Conclusion

Mastering session management in OpenClaw empowers you to build secure, scalable, and efficient AI agents. By configuring isolation, resets, pruning, and compaction appropriately, you ensure privacy, optimize costs, and maintain high performance—whether for personal use or production systems.

For deeper exploration, use openclaw sessions --json to inspect active sessions and read the full Session Management docs.