A Write-Ahead Log protocol that prevents agents from losing critical context and corrections during conversation compaction by persisting state to disk before responding.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-wal
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-wal using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent WAL?
Agent WAL (Write-Ahead Log) is a specialized persistence protocol designed to solve the most common failure mode in AI agents: the loss of critical context during memory compaction. By implementing a strict write-before-response rule, this addition to your Openclaw Skills ensures that every user correction, technical decision, and analytical conclusion is safely recorded to non-volatile storage.
This system acts as a durable audit trail and a recovery mechanism. Instead of relying solely on the volatile context window of a Large Language Model, Agent WAL creates a structured JSONL log that can be replayed at the start of any session, ensuring the agent maintains a consistent mental model even after context flushes or system restarts.
Agent WAL Use Cases
- Preserving user corrections to prevent the agent from repeating previously identified errors.
- Documenting key architectural decisions and tool selections to maintain project consistency.
- Storing complex analysis and technical conclusions that must survive across multiple sessions.
- Capturing state changes, such as environment configurations or authentication setups, within Openclaw Skills.
- Replaying unapplied logs during session initialization to restore lost context automatically.
How Agent WAL Works
- The agent identifies a trigger event, such as a direct user correction or a significant internal decision.
- Before generating a response to the user, the agent invokes the WAL script to append the entry to the log.
- For high-frequency interactions, entries are stored in a temporary working buffer to optimize performance.
- Prior to conversation compaction, the agent flushes the working buffer to the primary WAL file to ensure durability.
- Upon starting a new session, the agent executes a replay command to retrieve all entries that haven't been incorporated into its long-term memory.
- After processing replayed entries, the agent marks them as applied to maintain a clean synchronization state.
Agent WAL Setup
To deploy Agent WAL within your environment, ensure the Python management script is accessible. The protocol relies on local file system paths for low-latency persistence.
# Append a critical correction before responding to a user
python3 scripts/wal.py append agent1 correction "Use Podman instead of Docker for this project"
# Replay logs at the start of a new session to restore context
python3 scripts/wal.py replay agent1
# Check the current log and buffer status
python3 scripts/wal.py status agent1
Agent WAL Data Schema & Taxonomy
The system utilizes append-only JSONL files located at ~/clawd/memory/wal/ for both the primary log and the temporary buffer. Each entry follows a standardized schema to ensure compatibility across different Openclaw Skills.
| Property | Type | Description |
|---|---|---|
| id | String | Unique UUID for the log entry |
| timestamp | String | ISO8601 timestamp of the event |
| action_type | String | Category: correction, decision, analysis, or state_change |
| payload | String | The actual content to be persisted |
| applied | Boolean | Status indicating if the entry was replayed into context |
Agent WAL Advanced Features
- Batch processing capabilities via the buffer-add and flush-buffer commands to reduce disk I/O overhead.
- Session-aware replay logic that restores unapplied context during agent initialization.
- Automated maintenance routines for pruning logs while retaining a configurable number of historical entries.
- Support for multiple action types, allowing agents to prioritize corrections over general analysis.
- Append-only architecture ensures data integrity and prevents accidental overwrites of historical context.
SKILL.md
Loading
Related Openclaw Skills
Agent Self-Governance
A comprehensive governance framework providing five core protocols to ensure AI agent reliability, verification, and cost-efficiency.
Agent Access Control
A sophisticated security and permission layer providing tiered access control, owner approval workflows, and automated stranger deflection for AI agents.
OpenClaw Media Gen
A powerful multimedia generation skill that unifies Gemini image and Qwen Wan video creation through a single API key.
OpenClaw LLM Router
A unified gateway providing access to over 70 leading LLMs through a single OpenAI-compatible API.
ai-media - AI Media Generation
A high-performance media generation suite for creating photorealistic images, synthetic videos, talking heads, and neural audio via GPU-accelerated workflows.
browser-use
An advanced AI-driven browser automation tool that leverages LLMs to navigate, interact with, and extract structured data from any website using a real Chromium instance.
METADATA
Featured*
