Session Management

Every conversation with your OpenClaw agent happens inside a session. That session holds the conversation history, tool access, and accumulated context. Understanding how sessions work and how to manage them is essential for building reliable, production-ready AI systems.

This guide covers the session lifecycle, automatic cleanup mechanisms, and strategies for keeping your agent performant even across hours or days of continuous operation.

How Sessions Work

When you start a conversation with your OpenClaw agent, a new session is created. That session accumulates messages, tool results, and contextual information as the conversation progresses. Everything stays in memory until the session ends or cleanup triggers.

Session Lifecycle

• Creation: A fresh session starts with the agent's base context loaded from SOUL.md, AGENTS.md, USER.md, TOOLS.md, IDENTITY.md, and any other auto-loaded files
• Active state: The session accumulates messages and tool results. Context grows with each exchange
• Cleanup triggers: When context fills to ~80%, pruning or compaction activates automatically
• Archive or end: Sessions end when the user explicitly ends them, when a timeout is reached, or when archived manually

What Gets Stored

Each session stores:

• All user messages and agent responses
• Tool call requests and their results
• Generated code, file paths, and other artifacts
• Temporary context specific to that conversation

Automatic Cleanup: Pruning and Compaction

OpenClaw automatically manages context size so you do not hit hard limits mid-conversation. Two mechanisms handle this: pruning and compaction.

Pruning

When context space runs low, OpenClaw removes older messages entirely. The system keeps recent exchanges intact while discarding earlier parts of the conversation that are less likely to be relevant.

Pruning is aggressive but safe. It preserves the most recent context where current topics live while removing older, potentially stale information.

Compaction

Compaction takes a gentler approach. Instead of deleting messages, it summarizes them. Multiple message exchanges get condensed into concise summaries that preserve key facts, decisions, and instructions while freeing up space.

Compaction maintains more historical context than pruning but at the cost of some detail. It is the better choice when earlier conversation contains important information you want to retain.

When Each Mechanism Triggers

• Pruning: Triggers at ~80% context capacity for quick space recovery
• Compaction: Triggers at ~90% capacity for deeper context management
• Hard limit: At 100%, oldest messages are dropped regardless of importance

Session Pruning Strategies

You cannot disable automatic cleanup, but you can influence it through how you structure conversations and use session boundaries.

Strategy 1: Explicit Session Boundaries

Use the /new command to start a fresh session when switching topics. This gives you a clean context for the new topic without relying on automatic cleanup to clear old context.

// Starting a new session via command
/new

// This clears context and starts fresh
// Good for: new topics, major task switches, after breaks

Strategy 2: Task Decomposition

Break large tasks into smaller subtasks that fit comfortably in context. Each subtask gets a fresh session or uses subagents for parallel execution. This prevents any single session from accumulating too much context.

Strategy 3: Store Critical Info in Memory Files

Information stored in MEMORY.md survives session boundaries and does not count against context limits. Important instructions, facts, or preferences that must persist belong in memory files, not chat.

// In MEMORY.md - persists across sessions
# Important Context
- Current project: Website redesign
- Client: Acme Corp
- Deadline: End of month
- Key contacts: Sarah (PM), Mike (dev)

# Preferred Approach
- Mobile-first design
- Minimal JavaScript
- Weekly demos on Fridays

Long-Running Session Best Practices

For agents that run continuously for hours or days, apply these practices to maintain performance.

Regular Context Checks

Periodically remind the agent of its current task and status. Even with good memory management, long conversations can drift. A quick summary of current status at the start of each new task helps keep focus.

Use Subagents for Heavy Lifting

Offload intensive tasks to subagents. They run in isolated sessions with their own context management. This keeps your main agent lean while allowing complex work to proceed in parallel.

Monitor Session Size

Keep an eye on context usage. If you notice the agent starting to repeat itself or forget recent instructions, the session likely needs a reset with /new.

Configure Appropriate Timeouts

Set session timeouts that match your use case. Shorter timeouts force more frequent resets (good for volatile tasks). Longer timeouts allow continuity (good for complex, multi-step workflows).

Session Persistence and Resume

OpenClaw can persist session state to disk, allowing agents to resume after restarts.

What Gets Persisted

• Conversation history up to the last save point
• Generated artifacts and file references
• Session metadata and configuration

What Does Not Persist

• Runtime tool state (API tokens, temporary credentials)
• In-memory agent thinking that was not written to files
• External service session state (depends on the service)

Resuming Sessions

To resume a session, the agent loads the persisted state at startup. This works automatically if session persistence is enabled in your configuration. The agent picks up where it left off, though some runtime state may need reinitialization.

Debugging Session Issues

When sessions behave unexpectedly, these diagnostic steps help.

Check Context Usage

Ask the agent to summarize its current context or task. If it struggles to summarize or gives outdated information, context likely needs a reset.

Review Memory Files

OpenClaw loads SOUL.md, AGENTS.md, USER.md, and MEMORY.md at session start. Check these files for conflicting or outdated instructions that might be guiding the agent unexpectedly.

Test with Fresh Sessions

Start a new session with /new and reproduce the issue. If the problem disappears, the original session likely accumulated conflicting context. If it persists, the issue is in your configuration or prompts.

Summary

Session management is automatic in OpenClaw, but understanding how it works helps you build more robust agents. Use explicit session boundaries for topic changes, store critical context in memory files, and monitor long-running sessions for drift.

Combine these session strategies with subagents for parallel work, memory files for persistence, and model routing for cost optimization to build production AI systems that run reliably around the clock.