# OpenClaw web search best practices: getting maximum accuracy from Parallel

## **Why search accuracy matters for AI agents**

The best web search for OpenClaw depends on what you're building. For quick prototypes, the built-in tools work fine. For production agents that need reliable, evidence-based responses, Parallel's Search API delivers the highest accuracy available. It's purpose-built for AI reasoning rather than adapted from traditional search infrastructure.

OpenClaw[OpenClaw](https://openclaw.ai/) (formerly ClawdBot and MoltBot) is an open-source personal AI assistant created by Peter Steinberger[Peter Steinberger](https://x.com/steipete). It runs locally on your hardware and integrates with large language models (LLMs) like Claude, GPT, and DeepSeek to execute tasks autonomously. You interact with it through messaging platforms like Telegram, WhatsApp, Signal, or Discord, and it handles everything from email management to home automation to web research.

The quality of search results directly shapes how well your agent reasons through problems. When your agent receives poor information, the downstream effects compound quickly. Incomplete results force the agent to fill gaps with fabricated information (hallucinations). Outdated results lead to conclusions based on stale data. And without reliable web data, agents can't complete objectives that depend on current information.

The difference between a useful AI agent and a frustrating one often comes down to web search quality. You might have perfect prompts and flawless orchestration, yet still get unreliable outputs because the underlying research results weren't accurate enough.

## **How Parallel compares to OpenClaw search providers**

When evaluating search providers for OpenClaw, you're choosing between general-purpose tools and purpose-built AI infrastructure. Each option makes different tradeoffs between accuracy, cost, and ease of integration.

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### **Parallel Search API**

Parallel's Search API returns ranked results with dense, compressed excerpts optimized specifically for LLM consumption. Rather than constructing keyword queries, your agent declares a _semantic objective_ in natural language and we return URLs ranked by token relevancy. Every response includes source URLs, page titles, publish dates, and query-relevant excerpts by default, so your agent can verify where information originated without extra configuration.

On benchmarks including HLE, BrowseComp, FRAMES, and SimpleQA, Parallel consistently achieves the highest accuracy at the lowest cost per query[Parallel consistently achieves the highest accuracy at the lowest cost per query](/blog/search-api-benchmark) compared to Exa, Tavily, Perplexity, and OpenAI.

### **Brave Search API**

Brave is OpenClaw's officially recommended and default search provider. It maintains its own independent web-scale index (one of only three in the Western world outside Big Tech) and delivers reliable results through a straightforward API. Brave has recently expanded its AI-focused offerings, including an "LLM Context" endpoint and new Skills support built in response to the OpenClaw ecosystem. For many use cases, it's a solid choice that works out of the box.

### **Tavily**

Tavily targets AI agent workflows specifically, providing search results optimized for LLM consumption with optional AI-generated answer summaries. It supports domain filtering, content extraction, and structured results. Tavily is available to OpenClaw users as an MCP server or community-built skill, and a pull request for native web_search provider support[pull request for native web_search provider support](https://github.com/openclaw/openclaw/pull/11978) is under review. It's a strong middle-ground option, though it can hit limitations when your agent requires deep synthesis across many sources.

### **Built-in WebSearch tool**

When you're running OpenClaw with Anthropic's Claude as the underlying LLM, you get access to built-in WebSearch and WebFetch tools with zero configuration. These handle initial testing and simple prototypes well. However, they rely on the Anthropic API for search, which means they're only available with Claude-based setups and don't offer the same level of control or accuracy tuning you get from a dedicated search API.

## **Integrating Parallel Search API with OpenClaw**

Setting up Parallel with OpenClaw takes about ten minutes. You have two integration paths: our production MCP server (recommended) or a custom skill that calls the API directly.

### **Option A: MCP integration (recommended)**

Parallel ships a production MCP server that works with any MCP-aware client, including OpenClaw. This is the fastest path to integration.

**1. Get your API key.** Head to Platform[Platform](https://platform.parallel.ai/) and create an account. Navigate to API keys and generate a new key. Parallel holds SOC 2 Type 2 certification, so enterprise teams can typically proceed without additional security review.

**2. Add the Parallel Search MCP to OpenClaw.** You can add MCP servers through OpenClaw's configuration. The Parallel Search MCP server URL is:

**https://search-mcp.parallel.ai/mcp**

This provides two tools to your agent: web_search for searching the web with semantic objectives, and web_fetch for extracting content from specific URLs.

**3. Store your API key securely.** Add your Parallel API key to your environment or OpenClaw's config. Never commit API keys to version control. For production deployments, use a secrets manager.

**export PARALLEL_API_KEY="your_api_key_here"**

**4. Validate the integration.** Ask your agent something with a verifiable, current answer. If the response includes source URLs from Parallel, you're live.

### **Option B: Custom skill with direct API calls**

If you prefer direct API access, you can build a custom OpenClaw skill that calls Parallel's Search API. The endpoint accepts a natural language objective and returns ranked, excerpt-rich results:

The API returns structured JSON with ranked URLs, page titles, publish dates, and compressed excerpts ready for your agent's context window.

For a full custom skill implementation, see the Parallel Search API documentation[Parallel Search API documentation](https://docs.parallel.ai/search/search-quickstart) and OpenClaw's skill creation guide[skill creation guide](https://docs.openclaw.ai/).

## **Parallel API settings for maximum accuracy**

Beyond basic integration, the Search API offers parameters that let you tune search behavior for your specific use case.

### **Choosing the right mode**

The Search API supports two modes. The default, one-shot, returns comprehensive results optimized for single-query use cases. The agentic mode returns more concise, token-efficient results designed for multi-step agent workflows where your agent makes several search calls in sequence. Choose agentic when your agent orchestrates multiple search-then-reason loops, and one-shot when a single comprehensive answer matters most.

### **Structuring queries for better results**

How your agent formats queries significantly impacts result quality. Parallel's Search API accepts both a natural language objective and optional search_queries (keyword-style queries). Use both for best results:

The objective tells Parallel what your agent is trying to accomplish ("Find Columbus-based corporate law firms specializing in disability care"). The search_queries provide keyword variations that improve recall ("Columbus corporate law disability," "Ohio disability law firm"). This combination of semantic intent and keyword breadth consistently outperforms either approach alone.

### **Setting result limits**

The number of results you request affects both token consumption and accuracy. For fact-finding tasks, 3 to 5 results usually suffice. For synthesis tasks where your agent compares information across sources, 8 to 12 results provide better coverage without overwhelming the context window. The max_chars_per_result parameter gives you additional control over how much content each result contributes.

### **Controlling content freshness**

The fetch_policy parameter controls whether Parallel returns cached content or forces a live crawl. For time-sensitive queries (stock prices, breaking news), set a policy that prioritizes freshness. For stable information (company descriptions, historical facts), cached results reduce latency and cost. We maintain a proprietary index of billions of pages updated with millions of new pages daily, so cached results are typically fresh enough for most use cases.

## **Advanced configuration for production deployments**

Production deployments introduce concerns that don't matter during prototyping: reliability, cost management, and performance at scale.

### **Implementing search result caching**

Caching at the application layer reduces costs and improves latency for repeated queries. A reasonable approach: cache results for stable queries (company overviews, historical facts) while bypassing cache for time-sensitive queries (market data, news). Set cache TTLs based on how quickly the underlying information changes.

### **Configuring multi-provider fallbacks**

Even the most reliable APIs experience occasional downtime. Configure Parallel as your primary provider with Brave or Tavily as fallbacks. OpenClaw supports multi-provider configurations through its tools.web.search config and MCP server setup, so your agent continues functioning even if the primary provider is temporarily unavailable.

### **Handling rate limits**

Parallel's Search API supports 600 requests per minute. Implement exponential backoff in your retry logic to handle rate limit responses gracefully. Most OpenClaw deployments benefit from a simple retry wrapper that waits progressively longer between attempts. For higher-volume needs, contact our team about dedicated capacity.

## **Common OpenClaw search integration mistakes**

Five issues consistently cause problems in production deployments.

**Skipping source validation.** Treating all search results as equally trustworthy leads to unreliable agent outputs. Parallel returns source URLs, dates, and excerpts with every response. Use them to verify claims before your agent presents conclusions.

**Over-fetching results.** Requesting 20 results when 5 would suffice wastes tokens and increases latency. Start with fewer results and increase only if your agent's accuracy improves with more context.

**Ignoring query structure.** Vague, underspecified queries return irrelevant results. Guide your agent's prompts toward specific objectives with keyword variations. "Current market cap of NVIDIA as of 2025" outperforms "what's NVIDIA worth?"

**Hardcoding credentials.** API keys in source code create security vulnerabilities and complicate key rotation. Store credentials in environment variables or a secrets manager.

**No fallback configured.** A single search provider creates a single point of failure. Configure at least one backup provider so your agent degrades gracefully.

## **Measuring search quality in OpenClaw agents**

How do you know if your search configuration actually improves agent performance? You measure it.

Track three metrics over time. First, result relevance: what percentage of returned results directly address the query? Second, source reliability: are the sources authoritative and current? Third, response grounding: can the agent cite specific sources for its conclusions?

When an agent produces an incorrect output, trace backward to see what search results it received. Log every search query alongside its results. Degradation in any of these metrics signals a configuration problem or a change in upstream data quality.

## **Build accurate AI agents with Parallel**

We build web search and research APIs purpose-built for AI agents. Our platform delivers the highest benchmark accuracy with source attribution on every response, so you can verify where your agent's information comes from.

Whether you're building research agents, customer-facing chat experiences, or automated data pipelines, accurate web search forms the foundation. Start building with Parallel's APIs[Start building with Parallel's APIs](https://platform.parallel.ai/).

## **FAQs about OpenClaw web search with Parallel**

### **Does Parallel work with OpenClaw's MCP protocol?**

Yes. We ship a production MCP server[MCP server](https://docs.parallel.ai/integrations/mcp/search-mcp) at https://search-mcp.parallel.ai/mcp that provides web_search and web_fetch tools to any MCP-aware client, including OpenClaw. This is the recommended integration path.

### **Does Parallel have Agent Skills available on Skills.sh?**

Yes, Parallel currently has four skills:

1. Web Search: Searches for the best context

2. Web Extract: Turns URLs into context

3. Data Enrichment: Enriches text lists/CSVs

4. Deep Research: Performs comprehensive research

You can install them all via npx:

npx skills add parallel-web/parallel-agent-skills --all --global

### **What is Parallel's rate limit for OpenClaw integrations?**

The Search API supports 600 requests per minute. For higher-volume production deployments, contact support@parallel.ai to arrange dedicated capacity.

### **Can I use Parallel alongside another search provider in OpenClaw?**

Yes. OpenClaw supports multiple search providers through its configuration system and MCP server setup. You can configure Parallel as your primary provider with Brave or Tavily as fallbacks, which maximizes both accuracy and reliability.

### **How does Parallel handle fresh versus indexed search results?**

We maintain a proprietary web-scale index of billions of pages, with millions of new pages added daily. By default, the Search API serves results from this index for low-latency responses. For time-sensitive queries, the fetch_policy parameter lets you force live crawls to guarantee the freshest possible content. You can also set page-age triggers and timeout thresholds to balance freshness against latency.

### **Is Parallel SOC 2 compliant for enterprise OpenClaw deployments?**

Yes. We hold SOC 2 Type 2 certification and enforce zero data retention. We don't train on customer data. Enterprise teams can typically proceed without additional security review.