Metaphor
ModelLanguage model powered search.
Capabilities12 decomposed
latency-optimized web search with configurable speed-quality tradeoff
Medium confidenceExecutes web searches across a 70M+ company-indexed proprietary web crawl with four configurable latency profiles (instant <180ms, fast ~450ms, auto ~1s, deep 5-60s). Uses a custom ranking system optimized for AI query patterns rather than traditional SEO signals, returning results as JSON with URLs, titles, and snippets. The ranking model appears trained on relevance to LLM-based downstream tasks rather than human click-through data.
Implements four distinct latency profiles (instant/fast/auto/deep) with explicit speed-quality tradeoffs, optimized for AI agent integration rather than human search UX. Ranking algorithm trained on LLM relevance patterns rather than traditional SEO signals, enabling faster convergence on AI-useful results.
Faster than Perplexity/Brave for agent-integrated search (180ms instant mode vs. typical 1-3s round-trip) and claims 54.4% accuracy on FRAMES benchmark vs. Perplexity's 54.2%, with superior performance on Tip-of-Tongue (44.5% vs 36.7%) and Seal0 (21.6% vs 19.3%) retrieval tasks.
deep search with multi-step reasoning and structured output extraction
Medium confidenceExecutes iterative, multi-step web research workflows that decompose complex queries into sub-queries, retrieve results for each step, and synthesize findings into structured JSON outputs. Uses an internal reasoning loop (likely LLM-based chain-of-thought) to determine follow-up searches and extract entities/relationships from results. Outputs are schema-flexible JSON suitable for downstream processing without additional parsing.
Implements internal multi-step reasoning loop that iteratively refines searches based on intermediate results, then extracts and structures findings into JSON without requiring pre-defined schemas. Reasoning process is opaque to user but optimized for complex research tasks that would require 3-5 manual search iterations.
Automates multi-step research workflows that competitors (Perplexity, Brave) require manual query refinement for, and outputs structured JSON directly suitable for agent consumption vs. unstructured prose answers.
domain and content-type filtering with whitelist/blacklist
Medium confidenceAllows search queries to be constrained by domain whitelist (search only specified domains) or blacklist (exclude specified domains), and by content type (e.g., exclude news, focus on documentation). Filtering is applied server-side during ranking, reducing irrelevant results before returning to client. Enables focused searches (e.g., 'search only GitHub and Stack Overflow' or 'exclude news and social media').
Applies domain and content-type filtering server-side during ranking, reducing irrelevant results before returning to client. Enables focused searches without post-processing filtering.
More efficient than client-side filtering (reduces data transfer and processing); server-side filtering ensures ranking is aware of constraints, improving result quality vs. post-hoc filtering.
real-time web indexing with configurable crawl freshness
Medium confidenceMaintains a continuously-updated web index with configurable crawl frequency for different content types. News and frequently-updated content are crawled more frequently; static documentation less frequently. Enables searches to return recently-published content (e.g., news articles, blog posts) without waiting for manual re-indexing. Crawl freshness is not user-configurable but varies by content type and source authority.
Maintains continuously-updated web index with content-type-specific crawl frequencies, enabling searches to return recently-published content without manual re-indexing. Crawl policies are optimized for AI agent use cases (frequent updates for news/blogs, less frequent for static docs).
More current than static search indexes (Google's index may be weeks old for some content); crawl frequency is optimized for AI agents rather than human search UX.
specialized vertical search with domain-specific indexes
Medium confidenceProvides dedicated search indexes optimized for specific content verticals: code (GitHub, Stack Overflow, documentation), people (professional profiles, bios), companies (structured company data with fields like founding year, CEO, funding), news (news-specific ranking), and general web. Each vertical uses domain-specific ranking signals and structured metadata extraction tailored to that content type. Queries can specify a vertical via type parameter to constrain search scope.
Maintains separate, domain-optimized indexes for code, people, companies, and news rather than a single general-purpose index. Each vertical uses ranking signals specific to that domain (e.g., GitHub stars for code, professional network signals for people, company registration data for companies) enabling higher precision than general web search.
Provides dedicated code search comparable to GitHub's native search but integrated into a single API, and company/people search with structured output that general search engines (Google, Bing) do not offer natively.
token-efficient page content retrieval with highlights
Medium confidenceRetrieves full HTML/text content of web pages indexed by Exa and optionally generates token-efficient highlights (key excerpts) that summarize page content without requiring full page processing by downstream LLMs. Highlights are pre-computed during indexing and returned as a separate field, reducing token consumption for LLM processing. Full contents are returned as raw text suitable for RAG pipelines or LLM context windows.
Pre-computes and caches token-efficient highlights during indexing, allowing downstream LLMs to consume summarized content without full-page processing. Highlights are returned as a separate field, enabling cost-conscious applications to choose between full content and summaries on a per-page basis.
More efficient than fetching raw HTML and processing with LLMs (saves tokens and latency) and cheaper than calling separate summarization APIs; highlights are pre-computed rather than generated on-demand, reducing per-request latency.
web event monitoring with configurable cadence
Medium confidenceSets up persistent monitors that track changes to specified web pages or search queries at configurable intervals (daily, weekly, or custom). When changes are detected, returns new/updated content matching the monitor criteria. Internally maintains a state machine tracking page versions and diffs, triggering notifications when content changes exceed a threshold. Useful for tracking competitor websites, news about specific topics, or monitoring for new research publications.
Maintains persistent query monitors with state tracking across multiple check intervals, returning only new/changed results rather than full result sets. Enables long-running monitoring workflows without requiring external scheduling infrastructure or database state management.
Simpler than building custom monitoring with external schedulers and state stores; integrated into Exa API so no separate infrastructure needed. Cheaper than running continuous crawlers for specific URLs.
web-grounded answer generation with streaming responses
Medium confidenceGenerates natural language answers to queries by first retrieving relevant web content via search, then using an internal LLM to synthesize answers grounded in retrieved sources. Supports streaming responses for progressive answer delivery. Internally chains search → retrieval → LLM generation, with optional citation of source URLs. Answers are streamed token-by-token, enabling real-time display in user interfaces.
Integrates search, retrieval, and LLM-based answer generation into a single streaming API endpoint, eliminating the need for application developers to orchestrate multiple API calls. Streaming responses enable progressive answer delivery without waiting for full synthesis.
Simpler than building custom search + LLM chains with LangChain/LlamaIndex; single API call vs. multiple orchestrated calls. Streaming support enables better UX than non-streaming alternatives (Perplexity, Brave) in real-time interfaces.
native tool calling integration with major llm providers
Medium confidenceProvides pre-built function calling schemas compatible with OpenAI, Anthropic, and other LLM providers' native tool-calling APIs. Exa search functions are registered as tools that LLMs can invoke directly, with automatic parameter marshaling and response formatting. Integrates with LangChain, LlamaIndex, CrewAI, and Vercel AI SDK for seamless agent integration without custom wrapper code.
Provides pre-built, provider-native tool schemas (OpenAI function_calling, Anthropic tool_use) that eliminate custom wrapper code. Integrations with LangChain, LlamaIndex, CrewAI, and Vercel AI SDK provide one-line setup vs. manual schema definition.
Faster integration than building custom tool wrappers; native schemas ensure compatibility with LLM provider updates. Pre-built integrations in popular frameworks reduce boilerplate vs. generic HTTP client approaches.
enterprise custom indexing and zero-data-retention compliance
Medium confidenceProvides enterprise customers with ability to create custom web indexes (e.g., internal documentation, proprietary data sources) and configure data retention policies including Zero Data Retention (ZDR) where queries and results are automatically purged after processing. Enables compliance with data privacy regulations (GDPR, HIPAA) and security requirements. Custom indexes are maintained separately from public web index and can be restricted to authorized users.
Offers Zero Data Retention (ZDR) option where queries and results are automatically purged post-processing, enabling compliance with strict data privacy regulations. Custom indexes allow enterprises to search proprietary data sources without exposing them to public web index.
Unique among search APIs in offering explicit ZDR compliance option; most competitors (Google, Bing, Perplexity) retain query data for analytics. Custom indexing enables private data search without building separate infrastructure.
mcp (model context protocol) server integration for claude and compatible clients
Medium confidenceImplements Exa search as an MCP server, enabling Claude (and other MCP-compatible clients) to invoke Exa search natively without custom tool definitions. MCP is a standardized protocol for LLM-to-tool communication, allowing Claude to discover and call Exa search functions as if they were built-in capabilities. Requires running an MCP server process that bridges Claude's requests to Exa API.
Implements Exa search as a standardized MCP server, enabling Claude to invoke search as a native capability without custom tool definitions. MCP is a provider-agnostic protocol, allowing the same server to work with multiple LLM clients.
More standardized than provider-specific tool calling APIs; same MCP server works with Claude, open-source LLMs, and future MCP-compatible clients. Avoids lock-in to OpenAI or Anthropic tool calling syntax.
batch processing and bulk search with volume discounts
Medium confidenceSupports batch submission of multiple search queries in a single API call, with per-request pricing that decreases at volume (e.g., $7/1k requests at standard tier, lower rates for enterprise). Enables cost-efficient bulk research workflows where hundreds or thousands of searches are executed asynchronously. Results are returned as an array, suitable for data pipeline processing.
Supports batch submission of multiple queries with volume-based pricing discounts, enabling cost-efficient bulk research workflows. Pricing scales from $7/1k requests (standard) to lower enterprise rates, incentivizing high-volume usage.
More cost-efficient than per-query APIs for bulk research; volume discounts reward high-volume users. Batch processing reduces per-request overhead vs. individual API calls.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Metaphor, ranked by overlap. Discovered automatically through the match graph.
Web Search MCP
** - A server that provides local, full web search, summaries and page extration for use with Local LLMs.
Hotbot
HotBot is an AI-powered search engine that provides users with fast and personalized search results....
Exa API
Neural search API — meaning-based search, full content retrieval, similarity search for AI agents.
Tavily API
Search API for AI agents — clean web content, answer extraction, designed for RAG and LLM apps.
Kagi Search
Premium ad-free search engine with AI summarization.
Perplexity Pro
Advanced AI research agent with deep web search.
Best For
- ✓AI agent developers building real-time research loops
- ✓LLM application builders needing web grounding with predictable latency SLAs
- ✓Teams building autonomous systems where search latency directly impacts response time
- ✓Data enrichment pipelines requiring web-sourced structured data
- ✓AI agents performing multi-step research tasks (competitive analysis, market research, due diligence)
- ✓Non-technical users building research workflows via API without writing custom parsing logic
- ✓Developer tool builders needing focused code/documentation search
- ✓Research applications requiring specific content sources
Known Limitations
- ⚠Instant mode (<180ms) sacrifices result quality for speed; may miss relevant results in favor of indexed popularity
- ⚠Index freshness varies by content type; not suitable for breaking news or real-time data not yet crawled
- ⚠Cannot access paywalled, private, or authenticated content; limited to publicly indexed web
- ⚠Deep search mode (5-60s) may exceed typical LLM context window timeouts in synchronous request patterns
- ⚠Deep search latency (5-60s) makes it unsuitable for real-time synchronous request patterns; best used in async/batch workflows
- ⚠Structured output schema is not pre-defined; output format depends on query complexity and may vary across similar queries
Requirements
Input / Output
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Language model powered search.
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