How to Build a Second Brain with OpenClaw: Text Anything to Remember, Search Everything Later

Start building your personal second brain today by integrating OpenClaw and WisGate API—capture anything you want to remember and find it instantly later. With this guide, you'll learn how to input text memories, store embeddings, and retrieve information efficiently through a custom-built interface.

Introduction to the Concept of a Second Brain Using OpenClaw

A "second brain" is a personal knowledge base that helps you store and search information effortlessly. Instead of relying solely on your memory, you create a system where you can text notes, ideas, or any data you want to remember. Later, you can search through all stored data to quickly find what you need.

OpenClaw is an open-source AI memory agent that enables this by converting your text inputs into embeddings — numerical representations that machines can store and analyze. It acts as the interface between you and your second brain, ingesting text and allowing fast semantic retrieval.

By combining OpenClaw with WisGate’s API, which provides access to advanced AI models like Claude Opus 4.6, you can create a scalable, cost-effective second brain. WisGate’s API supports large context windows and efficient token handling, ideal for building comprehensive memory storage and search applications.

Setting Up OpenClaw with WisGate API

To get your second brain running, you first need to configure OpenClaw to use WisGate as its AI provider. This involves editing the OpenClaw configuration file to add WisGate’s API base URL, your API key, and the model you want to use.

Editing the `openclaw.json` Configuration File

OpenClaw stores its settings in a JSON configuration file located at ~/.openclaw/openclaw.json. You’ll edit this file to define WisGate as a custom provider under the models section.

Open your terminal and run:

nano ~/.openclaw/openclaw.json

Then, add the following configuration snippet inside the models.providers block, defining a provider named "moonshot" that connects to WisGate’s API. Replace WISGATE-API-KEY with your actual WisGate API key.

json

"models": {
  "mode": "merge",
  "providers": {
    "moonshot": {
      "baseUrl": "https://api.wisgate.ai/v1",
      "apiKey": "WISGATE-API-KEY",
      "api": "openai-completions",
      "models": [
        {
          "id": "claude-opus-4-6",
          "name": "Claude Opus 4.6",
          "reasoning": false,
          "input": ["text"],
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          },
          "contextWindow": 256000,
          "maxTokens": 8192
        }
      ]
    }
  }
}

This configuration tells OpenClaw to route its completion and memory synthesis calls through the WisGate API endpoint https://api.wisgate.ai/v1, using the Claude Opus 4.6 model customized for large context windows (256k tokens) and a maximum output of 8,192 tokens.

Restarting OpenClaw to Apply Changes

After saving your edits, you need to restart OpenClaw so the changes take effect. Use these terminal commands inside nano:

Press Ctrl + O to save the file.
Press Enter to confirm the filename.
Press Ctrl + X to exit nano.

Then, stop the currently running OpenClaw process if any by pressing:

Ctrl + C

Finally, start the OpenClaw text user interface again:

openclaw tui

Your OpenClaw installation is now set up to communicate with WisGate’s API for memory completion and retrieval.

Understanding the Core Components: Memory Ingestion, Embeddings, and Storage

At the heart of this second brain system are three core components: how text input is ingested, transformed into embeddings, and stored for future retrieval.

When you type or send any textual memory to OpenClaw, it ingests the text and sends it to the WisGate API’s Claude model to generate an embedding. An embedding is a high-dimensional vector that numerically encodes the semantic meaning of the text.

These embeddings are stored in a database or vector store within OpenClaw’s framework. This vectorized data allows OpenClaw to perform semantic search — you can query your memory with natural language and retrieve contextually relevant data rather than exact keyword matches.

This pattern follows retrieval-augmented generation (RAG), where external memory stores enhance language model responses. Your second brain effectively combines raw text memories, embedding vectors, and fast search interfaces to provide quick, relevant results.

Building a Semantic Search Interface with Next.js

Having your memories stored and embedded is just one part — you need an interface to search and view those memories efficiently. Next.js, a popular React framework, is a great choice for building a custom dashboard that queries your OpenClaw backend.

The Next.js app connects to your OpenClaw API and performs semantic search by sending natural language queries. It then displays ranked results based on similarity scores of the embedding vectors.

You can build UI components such as search bars, memory lists, and detailed views for each memory entry. This gives you a visual way to explore your second brain and instantly find any piece of information you previously stored.

By integrating API calls to the WisGate endpoint through OpenClaw, your Next.js dashboard supports live query completions and retrievals powered by the "claude-opus-4-6" model.

This approach turns your personal knowledge base into an interactive, user-friendly tool for memory management, leveraging advanced AI without building the models yourself.

Making WisGate API Calls for Memory Synthesis and Retrieval

Behind the scenes, OpenClaw makes HTTP requests to WisGate’s API at:

https://api.wisgate.ai/v1

It uses the Claude Opus 4.6 model, which supports a massive 256,000 token context window and returns up to 8,192 tokens in one completion. The model configuration specifies zero input or output costs within OpenClaw’s costing system, making resource usage transparent.

Example API payloads include your textual input converted into prompt data and requests for embedding vectors. WisGate handles the complex language modeling and returns text completions or vectors.

This combination allows OpenClaw to synthesize memories from raw text and retrieve relevant information efficiently, enabling your second brain workflow.

Pricing and Performance Considerations

When choosing an AI service for your second brain, cost and performance are key factors.

WisGate’s API offers image generation at approximately $0.058 per image, about 15% cheaper than the official rate of $0.068 per image. Even though this article focuses on textual memory synthesis, it highlights WisGate’s cost advantage.

Benchmarks show WisGate consistently delivers around 20-second response times for base64 output payloads ranging from 500 to 4,000 characters.

Using the "claude-opus-4-6" model on WisGate, you get a stable and large context window (256k tokens) with a max output of 8,192 tokens. This performance combined with lower cost makes WisGate a practical choice for memory augmentation setups.

For more on pricing and available models, visit WisGate’s homepage: https://wisgate.ai/models and explore creative assets with the AI Studio image tool: https://wisgate.ai/studio/image.

Conclusion and Next Steps

Building your own second brain using OpenClaw and WisGate API blends advanced AI memory management with affordable, scalable infrastructure. By following the step-by-step configuration and understanding the core concepts of ingestion, embedding, and semantic search, you can capture and recall anything important efficiently.

The custom Next.js dashboard adds a practical interface layer to interact with your memories when needed.

Get started now by signing up for WisGate at https://wisgate.ai/ and try out the "claude-opus-4-6" model for your next-generation personal memory system.

Explore the API documentation and create a second brain that grows and evolves with you.