Export ChatGPT history to OpenClaw memory system

✍️ OpenClawRadar📅 Published: March 20, 2026🔗 Source
Export ChatGPT history to OpenClaw memory system
Ad

A Reddit post details a method for exporting ChatGPT conversation history and importing it into OpenClaw's memory system, allowing local AI agents to access years of accumulated context.

Ad

Process steps

The method involves five main steps:

  • Data Request: Request a Data Export from ChatGPT settings. The download link may take hours to a day to arrive.
  • Cleanup: Extract the downloaded zip file and keep only conversation data files (named conversations--xxx.json or starting with conversations). Delete extra files like user.json and model_comparisons.json.
  • Converter Setup: Use the ai-chat-md-export tool to convert JSON files to Markdown. Install globally via npm:
    npm install -g ai-chat-md-export
  • Batch Conversion: Run conversion commands from the terminal in the folder containing JSON files:
    Windows (CMD):
    mkdir output_md
    for /r %f in (*.json) do ai-chat-md-export -i "%f" -p chatgpt -o ./output_md/
    Linux and Mac:
    mkdir -p output_md
    find . -name "*.json" -exec ai-chat-md-export -i {} -p chatgpt -o ./output_md/ \;
  • Data Transfer: Upload the generated Markdown files to the OpenClaw server using SCP:
    scp -r output_md/*.md [email protected]:~/.openclaw/workspace/memory/openai/
    Replace the IP address and username with your specific setup.

Once files are placed in the openai memory folder, OpenClaw can index them, providing the agent with long-term memory of historical conversations. The post notes this process also works for Claude history.

📖 Read the full source: r/openclaw

Ad

👀 See Also

Understanding AI Agent Architecture: Deterministic vs Probabilistic Layers
Guides

Understanding AI Agent Architecture: Deterministic vs Probabilistic Layers

A Reddit user shares a mental model for AI agent systems that separates deterministic layers (scripts, commands, APIs) from probabilistic layers (LLM reasoning and decisions). The key insight: push as much work as possible to the deterministic side.

OpenClawRadar
Setting up OpenClaw on macOS with a unified AI provider endpoint
Guides

Setting up OpenClaw on macOS with a unified AI provider endpoint

A developer shares their experience installing OpenClaw on macOS, including the requirement for Node.js 24, using Homebrew for installation, configuring a custom OpenAI-compatible provider like ZenMux, and setting up a background daemon. Key troubleshooting tips include WhatsApp's default message blocking and using the openclaw doctor command.

OpenClawRadar
Open-source launch playbook for OSS LLM and local AI projects
Guides

Open-source launch playbook for OSS LLM and local AI projects

An open-source playbook addresses discoverability issues for LLM and local AI projects by providing structured guidance on pre-launch preparation, launch-day execution, and post-launch follow-up. It includes templates and strategies for community distribution, creator outreach, and SEO optimization.

OpenClawRadar
A Management Framework for Leading AI Agents Effectively
Guides

A Management Framework for Leading AI Agents Effectively

A former backend lead identifies a plateau in AI agent productivity and proposes a framework based on three disciplines: cybernetics, information theory, and management. The framework details two operational modes: the Captain and the Architect.

OpenClawRadar