Expanding OpenClaw Search Capabilities with Exa.ai and Perplexity

Ampliar las capacidades de búsqueda de OpenClaw

OpenClaw provides built-in support for Brave Search and Perplexity to power its web exploration. Developers can further extend its reach by implementing custom skills for neural engines like Exa.ai to handle complex research queries.

Why This Matters

While keyword-based search engines like Brave are efficient for facts, they often fail at the conceptual depth required for technical research. Integrating Exa.ai introduces semantic search costs—approximately $0.01 per query with summaries—but significantly reduces the total query volume by returning contextually relevant documents rather than simple keyword matches.

Key Insights

• Exa.ai provides neural/semantic search that identifies content based on conceptual meaning rather than keyword matches.
• OpenClaw skills act as plugins, allowing the addition of new API integrations via a specific directory structure without core code modifications.
• Perplexity integration requires an explicit model setting of ‘sonar-pro’ to avoid 400 ‘Invalid model’ errors when using direct API keys.
• Exa.ai operational costs range from $0.005 for basic queries to $0.015 for deep searches with AI summaries.
• The OpenClaw gateway must be restarted using ‘openclaw gateway restart’ to apply configuration changes to built-in providers.

Working Examples

Skill definition file (SKILL.md) for Exa.ai integration.

---
name: exa-search
description: Neural/semantic web search using Exa.ai API. Use for research queries requiring high-quality, semantically relevant results. Better than keyword search for conceptual queries, finding similar content, or research-heavy tasks. Requires EXA_API_KEY environment variable.
---
# Exa Search
Neural search via Exa.ai. Returns semantically relevant results, not just keyword matches.

Core Python logic for executing a neural search via the Exa.ai API.

import urllib.request
import json
import os

API_URL = "https://api.exa.ai/search"

def search(query, num_results=5):
    api_key = os.environ.get("EXA_API_KEY")
    payload = {"query": query, "numResults": num_results, "type": "auto"}
    headers = {"x-api-key": api_key, "Content-Type": "application/json"}
    req = urllib.request.Request(API_URL, data=json.dumps(payload).encode("utf-8"), headers=headers, method="POST")
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read().decode("utf-8"))

OpenClaw configuration for native Perplexity integration.

{
  "tools": {
    "web": {
      "search": {
        "enabled": true,
        "provider": "perplexity",
        "perplexity": {
          "apiKey": "pplx-your-api-key-here",
          "model": "sonar-pro"
        }
      }
    }
  }
}

Practical Applications

• Research Use Case: Deploying Exa.ai skills for deep conceptual research where Brave results lack relevance. Pitfall: Missing .env configuration results in ‘EXA_API_KEY not set’ errors.
• Synthesis Use Case: Using Perplexity for direct answers with citations instead of link lists. Pitfall: Using ‘perplexity/sonar-pro’ model name instead of ‘sonar-pro’ leads to 400 status errors.
• Hybrid Setup: Combining Perplexity as a built-in provider for speed and Exa.ai as a skill for depth.

References:

• https://dev.to/victoraguilarc/apex-d-openclaw-mejorar-la-busqueda-4m5l
• https://exa.ai
• https://www.perplexity.ai/settings/api