JUHE API Marketplace

How to Add Semantic Memory Search to OpenClaw: Vector-Powered Hybrid Retrieval with Auto-Sync

7 min read
By Olivia Bennett

Before we dive into the technical details, imagine boosting your OpenClaw setup with smarter memory search that combines keyword and semantic recall — all powered by a unified AI API. This approach not only improves retrieval quality but also automates keeping your indexed memory fresh, so your AI assistant stays sharp and responsive. Let’s get started with adding semantic memory search to OpenClaw, step-by-step.

Setting the Stage: Why Semantic Memory in OpenClaw?

If you’re already familiar with OpenClaw, you know it’s a powerful tool for managing markdown memories and running LLM agents. But traditional keyword-based search can sometimes miss the mark, especially when you need context-aware retrieval. Semantic memory search helps by understanding the intent and meaning behind queries — not just exact words.

Combining this with a vector index adds a layer of AI-enhanced retrieval. Instead of scanning text linearly, the system looks for conceptually similar content, enabling hybrid retrieval: keyword plus semantic embedding search. The result? More relevant, smarter responses from your AI.

This tutorial shows how to extend OpenClaw with semantic memory by integrating vector search APIs via WisGate — an AI API platform that lets you access top-tier models for completions, embeddings, images, and more through one straightforward interface. WisGate’s affordable pricing and stable output times make it a great pick for upgrading your AI workflows.

Configuring OpenClaw’s Models Section for WisGate

The first technical step is to tell OpenClaw to use WisGate as a custom provider for its AI models. This is done by editing the OpenClaw configuration file stored locally in your home directory.

Here’s how you do it:

  1. Open a terminal on your development machine.
  2. Edit the OpenClaw JSON config file with your preferred text editor, for example:
nano ~/.openclaw/openclaw.json
  1. Inside the models section, insert the following block to define a custom provider named "moonshot" that connects to WisGate’s API:
"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
        }
      ]
    }
  }
}

  1. Save the file (Ctrl + O then Enter) and exit nano (Ctrl + X).

  2. Restart OpenClaw:

  • Stop it gracefully with Ctrl + C
  • Relaunch with openclaw tui

This setup points OpenClaw’s AI calls to WisGate’s API endpoint with your API key (replace "WISGATE-API-KEY" with your actual token). Using the Claude Opus 4.6 model gives you a large 256k context window and up to 8k tokens per request for more flexible and richer interactions.

Enabling Vector-Powered Hybrid Retrieval

With the model configuration ready, the next step is to integrate vector embeddings that power semantic memory search. The idea here is to store vector embeddings of your markdown memory contents and perform similarity searches alongside keyword lookups. Hybrid retrieval combines these signals for improved relevance.

Many LLM agents including OpenClaw can be set up to maintain an embedding index in parallel with text memories. The vectors are generated by encoding your markdown snippets using a WisGate embedding model (such as Claude Opus’s embedding variant).

You’ll want to automate:

  • Embedding creation when memory files update.
  • Storing and updating vector indexes.
  • Querying the vector store first for semantic similarity.
  • Falling back to keyword search if vector results are insufficient.

This hybrid retrieval process can be controlled via OpenClaw’s config files or plugin extensions. In your query pipeline, the application first sends an embedding request to WisGate API, followed by a vector similarity query, merging results with the existing keyword filter.

This new method helps your AI to retrieve relevant contextual information efficiently, even if the exact keywords aren’t mentioned.

Setting Up Auto-Sync for Markdown Memory Files

An important feature for maintaining your vector index accuracy is auto-sync. Since your memory markdown files may change frequently, you want the vector index to update accordingly without manual intervention.

You can schedule an auto-sync task that:

  • Detects changes to .md memory files.
  • Re-embeds updated content via WisGate calls.
  • Refreshes your local or cloud vector database.

This can be set up using cron jobs or background queue processors integrated with OpenClaw. For example, a polling script watches the memory directory, and on detecting changes, triggers embedding API calls and index updates.

With this automatic pipeline, your hybrid retrieval stays fresh and reflective of the most recent data you’ve collected or edited.

Such a setup improves overall AI assistant accuracy dramatically, especially in dynamic environments.

Making the WisGate API Call for Semantic Search

When a user query arrives, your OpenClaw agent performs the following steps to leverage WisGate for semantic search:

  1. Generate an embedding vector for the query text using WisGate’s embedding endpoint.
  2. Search the vector index to find the most similar memory vectors.
  3. Combine these semantic hits with a keyword search if needed.
  4. Feed the retrieved content as context to the Claude Opus 4.6 model via WisGate completions API for final response generation.

Calling WisGate’s API involves HTTP requests structured to their spec, using your API key and endpoints like https://api.wisgate.ai/v1.

Here’s a simplified example schema for making a completion call:

POST https://api.wisgate.ai/v1/openai-completions
Authorization: Bearer WISGATE-API-KEY
Content-Type: application/json

{
  "model": "claude-opus-4-6",
  "prompt": "...",
  "max_tokens": 512
}

For embedding requests:

POST https://api.wisgate.ai/v1/openai-embeddings
Authorization: Bearer WISGATE-API-KEY
Content-Type: application/json

{
  "model": "claude-opus-4-6-embedding",
  "input": "Your query or document text"
}

Integrating these calls into your code completes the semantic retrieval enhancement cycle.

Pricing & Specs: What to Expect from WisGate API

A key advantage of using WisGate is cost efficiency and consistent performance. For image generation tasks, WisGate charges $0.058 per image, compared to an official reference rate of $0.068. Although this tutorial focuses on semantic search, it’s worth noting this price difference if you explore multimodal AI integrations.

More broadly, WisGate provides:

  • Stable 20-second processing time for base64 outputs ranging from 0.5k to 4k tokens.
  • Access to the Claude Opus 4.6 model with a generous 256,000 token context window and up to 8,192 max tokens per completion.
  • An API base URL at https://api.wisgate.ai/v1 that you can call with your API key.

For embedding and completion APIs, this pricing and specification balance enables faster development and lower total costs compared to other platforms.

If you want to explore image, video, or coding models alongside text-based AI, WisGate’s unified API supports those as well.

Conclusion and Next Steps

Adding semantic memory search with vector-powered hybrid retrieval and auto-sync to OpenClaw brings your AI assistant a step closer to understanding context deeply and remembering better. By configuring OpenClaw to use WisGate’s Claude Opus models, setting up embedding pipelines, and automating index refreshes, you build a more intelligent retrieval system with manageable effort and cost.

To recap:

  • Update the ~/.openclaw/openclaw.json file with WisGate model definitions.
  • Leverage vector embeddings for semantic search alongside keyword queries.
  • Implement auto-sync processes to keep markdown memory files indexed.
  • Make WisGate API calls to generate embeddings and completions.
  • Benefit from WisGate’s pricing and stable performance.

Ready to upgrade your OpenClaw semantic search? Get started now with WisGate’s API platform at https://wisgate.ai/, and browse available AI models at https://wisgate.ai/models.

Expanding your AI memory capabilities is just a few configuration steps away.

How to Add Semantic Memory Search to OpenClaw: Vector-Powered Hybrid Retrieval with Auto-Sync | JuheAPI