Seoul Labs

npm install -g openclaw@latest

Error: Cannot find module puppeteer-core/lib/cjs/puppeteer/common/Browser.js

This exact path failure breaks custom workspace skills the moment you attempt to map deep DOM structures during complex web harvesting. The core gateway architecture relies heavily on dynamic node-based module evaluation, but the internal dependency tree in recent releases often leaves headless execution contexts completely stranded.

When your data scraping pipeline halts because an AI agent cannot resolve a local chrome path, you are forced to re-evaluate how runtime orchestration behaves under stress. The promise of open-source autonomous agents like OpenClaw lies in total data sovereignty and local skill execution, yet the realities of extracting structured intelligence from guarded enterprise platforms introduce persistent friction points. Moving from basic chat interaction to a resilient, custom research engine requires bypassing the standard automated workflows and configuring low-level execution policies directly within your workspace files. This analysis explores how to harden the platform for reliable market analysis, manage complex session routing, and address the architectural tradeoffs that documentation frequently overlooks.

Dynamic Session Partitioning And Workspace Insulation

Isolating your research environments is the first defense against context contamination during simultaneous multi-tenant market runs. The main configuration block inside ~/.openclaw/openclaw.json exposes a session management object that controls how sessions split based on sender identities or custom workspaces. When configuring a targeted competitor intelligence sweep, relying on a single global agent state guarantees that temporary scraping failures or cookie modifications will bleed into unrelated market assessment tasks.

Creating separate workspace directories with dedicated config layers forces the underlying node process to spawn completely clean operational sandboxes. Every workspace utilizes an independent HEARTBEAT.md file, which acts as a scheduling file that OpenClaw evaluates every 30 minutes to execute reserved tasks. Each agent workspace can maintain this file independently to trigger specific cron-like collection behaviors, though overpopulating this log scales up token costs rapidly during initialization. If an agent loops through a target pricing grid and encounters a session timeout, the resulting failure remains strictly bound to that specific workspace sub-process without affecting global system stability.

{
  "session": {
    "dmScope": "per-channel-peer",
    "reset": {
      "mode": "daily",
      "idleMinutes": 15
    }
  }
}

The design tradeoff here centers on resource allocation and memory overhead. Running five concurrent research sweeps means five distinct headless browser instances initializing simultaneously on your machine, a scenario that quickly bogs down standard developer hardware. While cloud-hosted alternative setups handle this via proprietary infrastructure layers, a local execution engine demands explicit timeout limits and strict process management to prevent runaway worker daemons.

Custom Skill Orchestration For Target Dom Extraction

Building custom extraction capabilities requires working directly with the internal skill directory structure instead of relying on generic prompt instructions. Standard conversational tools often fail when encountering dynamic front-end structures, pagination blocks, or complex network challenges. To build a resilient scraping tool, you must author a precise SKILL.md file inside your active workspace directory, defining exact binary dependencies and tool permissions within the permitted specification layout.

name: market_data_harvest
description: Extract structural pricing data from dynamic tables
metadata:
  openclaw:
    requires:
      bins:
        - node
allowed-tools:
  - exec
  - file_system_writer

const puppeteer = require('puppeteer-core');
// Custom navigation logic to bypass standard tracking scripts

The underlying agent parses these declarations to determine which automation tools to initialize when the market analysis workflow begins. Standard implementations often depend on basic CSS selectors that break the moment a target platform updates its presentation layer layout. By forcing the tool execution path to evaluate robust fallback strategies, such as searching for specific data attributes or parsing raw network responses directly from the network tab, the pipeline maintains continuity even during silent structural changes.

Bypassing Behavioral Detection Guardrails Safely

Modern market research inevitably clashes with advanced enterprise security firewalls designed to block automated collection infrastructure. Standard headless browser profiles leave obvious server-side footprints, including inconsistent TLS signatures, incomplete HTTP headers, and missing device telemetry indicators. When your agent attempts to harvest competitive data from travel portals or e-commerce platforms, the requests are often immediately flagged as malicious automated traffic unless your low-level transport layer mimics authentic user properties.

Altering the user-agent string is insufficient because advanced detection engines cross-reference header order against actual browser capabilities. You must modify the underlying execution configuration to inject realistic runtime parameters, randomizing viewport dimensions and slowing down click patterns to break predictable behavioral sequences. The native gateway implementation permits embedding these customization steps directly within the tool execution logic, allowing the agent to dynamically calculate variable delays between interactions.

{
  "browser": {
    "headless": true,
    "args": ["--disable-blink-features=AutomationControlled"],
    "ignoreHTTPSErrors": true
  }
}

This structural adjustment introduces an unavoidable processing penalty. Intentional delays and browser spoofing operations lengthen the extraction timeline, transforming what could be a brief scraping task into an extended background operation. This represents a fundamental architectural tradeoff where raw speed is sacrificed to achieve long-term reliable access to crucial data fields.

State Persistence And Plaintext Memory Mechanics

Maintaining a reliable record of past extraction cycles prevents redundant network requests and optimizes API consumption. Unlike traditional stateless extraction scripts that evaluate every target page from scratch, this framework records operation logs, long-term context, and successful run details within human-readable markdown documents. The primary data repository relies on MEMORY.md, a plaintext file that the agent dynamically parses and modifies at the conclusion of every successful harvest loop.

When this file expands past the configured bootstrap limit, OpenClaw automatically truncates the copy injected into the active LLM context window while leaving the master file on disk completely intact. Running /context list or openclaw doctor exposes the variance between your physical raw file sizes and the actual filtered strings passed to the model wrapper. To prevent context degradation from excessive history truncation, the clean pattern involves offloading detailed chronological execution telemetry to daily tables inside the memory directory while preserving only core semantic entities within the primary persistence file.

# Agent Long-Term Memory
- Last parsed timestamp: 2026-06-11T10:00:00Z
- Successful targets: competitive_pricing_matrix
- Pending verification: seasonal_discount_tables

Relying entirely on text files introduces distinct scaling challenges as your historical log data expands. As the file size grows over several months of continuous data collection, the context window consumption scales up linearly until the automatic truncation thresholds trigger. Managing this friction point requires implementing an automated rotation system that archives older memory entries into cold storage once the file size exceeds operational thresholds.