OpenClaw Memory: Local Persistence and Context Management

What Is the Memory System?

The Memory system gives your OpenClaw agent the ability to remember. Without it, every message would be treated as a brand-new conversation with no prior context. Memory handles storing conversation history, managing context windows, and providing the Brain with the relevant information it needs to generate coherent, contextual responses.

Local-First Persistence

OpenClaw takes a local-first approach to data storage. Conversation history, session metadata, and agent state are stored on your own machine by default. This design choice reflects a core principle: your agent’s data belongs to you, not to a cloud service.

Data is typically stored within the OpenClaw workspace directory, organized by channel and conversation. The storage format is lightweight and human-readable, making it easy to inspect, back up, or migrate.

What Gets Stored

• Conversation turns — Each user message and agent response, with timestamps.
• Session metadata — Channel source, user identifiers, and conversation IDs.
• Tool call records — Which skills were invoked during a conversation and their results.
• Context summaries — Compressed representations of older conversation segments.

Context Window Management

Every LLM has a finite context window. A model that supports 200K tokens still cannot hold an infinite conversation in a single prompt. The Memory system handles this constraint transparently.

Strategies

• Sliding window — The most recent N messages are always included in full. Older messages are truncated or omitted.
• Summarization — When conversations grow long, the Memory system can compress older segments into summaries, preserving key facts while reducing token count.
• Priority tagging — Certain messages (like the user’s stated preferences or task instructions) can be tagged as high-priority and will be retained in context longer.

These strategies work together to ensure the agent always has enough context to respond meaningfully without exceeding model limits.

Conversation History

The Memory system maintains per-conversation and per-user history. This means your Telegram bot can remember the context of a discussion that started hours ago, and a cron-triggered task can reference the outcome of a previous run.

Cross-Channel Memory

If a user interacts with your agent through both Telegram and a web interface, the Memory system can optionally link these conversations, giving the agent a unified view of the user across channels.

Integration with the Brain

The Brain queries the Memory system at the start of every request. It retrieves the relevant conversation history, any stored summaries, and metadata about the current session. This information is assembled into the prompt context before being sent to the LLM.

The flow is straightforward:

1. Gateway dispatches a message to the Brain.
2. Brain asks Memory for conversation context.
3. Memory returns the relevant history, trimmed to fit the model’s context window.
4. Brain includes this context in the LLM prompt.

Backup and Portability

Since all data is stored locally, backing up your agent’s memory is as simple as copying the workspace directory. You can also migrate an agent’s memory to a new machine by transferring these files. There is no vendor lock-in and no dependency on external services for your agent’s knowledge.