OpenClaw user struggles with AI agent automation after successful Claude Code pipeline
Claude Code success vs. OpenClaw agent frustration
A user on r/openclaw shared their experience trying to automate image recreation using nanobanana for their marketing agency clients. They achieved a working pipeline with Claude Code in just one hour by talking with the model, providing API keys, and having it launch multiple tests, use multiple tools to extract backgrounds, and refine template prompts through visual analysis of images.
The user then attempted to teach this process to an AI agent within their OpenClaw setup, running on Gemini 3.1 Pro. The agent exhibited several problems:
- Bad reasoning capabilities
- Slow response times
- Incorrect outputs
- Failure to achieve the same results as Claude Code after nearly a day of attempts
The user suspects the model choice might be the issue, specifically mentioning that "using gem3.1 pro thru vertex is the problem." They're considering two potential solutions: giving their Claude Code to the agent so it could perform the task as quickly as they did, or switching to another model entirely.
The case highlights a common challenge in AI automation workflows: successful results with one model don't always transfer smoothly to agent-based implementations, particularly when different underlying models are involved.
📖 Read the full source: r/openclaw
👀 See Also
User reports Claude outperforms GPT-4o on deep document analysis: catches logical contradictions, rewrites tone accurately
A developer who was a ChatGPT loyalist shares concrete experience: Claude 3.5 Sonnet caught three logical contradictions in a 15k-word technical doc that GPT-4o missed, and rewrote sections while matching the author's voice exactly.
Using OpenClaw as a Financial Monitoring and Document Management System
A user configured OpenClaw with read-only bank API access to monitor transactions, generate reports, track cash flow, and manage subscription tracking. The setup also includes automated invoice collection via WhatsApp and document organization in Google Drive and Excel.
Using Lava's MCP Gateway with Claude Code for Low-Cost Content Workflow
A user connected Lava's MCP gateway to Claude Code and accessed research tools like Exa, Serper, and Tavily without accounts or API keys, creating a social media content workflow for $0.03.
Claude MCP workflow automates LinkedIn lead re-engagement with adaptive constraints
A developer built a workflow using Claude with MCP to automatically re-engage old LinkedIn connections, identifying leads, generating contextual messages, and handling platform constraints adaptively. Out of 7 targeted leads, 5 messages were sent successfully while 2 were skipped due to LinkedIn restrictions.