What is Parallel?
Parallel is an AI web search API platform that gives AI agents and LLM-powered applications accurate, real-time access to the open web. It returns cross-referenced, evidence-backed results with source provenance on every output. The platform covers a broad range of web intelligence tasks including deep research, structured data extraction, web monitoring, and data enrichment. Developers can access it through a REST API, Python SDK, TypeScript SDK, MCP server, or CLI.
Features & Benefits
- Search API: deliver real-time web search results built for AI agents and LLMs. Outputs include dense excerpts and native markdown from an index of billions of pages.
- Deep Research API (Task API): run multi-hop research queries across the web. Returns long-form, cross-referenced outputs with source attribution for every claim.
- Data Extraction (Extract API): pull structured data from any web page. Returns clean, machine-readable output from target URLs.
- Dataset Builder (FindAll API): find and compile web entities that match a natural language description. Returns results as a structured table.
- Web Enrichment: take an existing dataset and add fresh web-sourced columns. Define output fields in natural language and get enriched rows back.
- Web Monitoring (Monitor API): track any topic or event on the web continuously. Delivers snapshots and event streams when changes are detected.
- Chat API: integrate conversational AI web search into products. Returns grounded responses with web context.
- MCP Integration: connect AI agents and tools directly via the Model Context Protocol. Supports environments like Cursor and Claude Code.
- Python and TypeScript SDKs: integrate the AI web search API into existing codebases using official client libraries.
- Flexible Compute Budget: scale cost per query up or down based on task complexity. Higher budgets unlock deeper research passes.
- Evidence-Based Outputs: attach provenance and source verification to every atomic result returned by the AI web search API.
What can Parallel do?
- Add web search to an AI agent
- Run deep research with an API
- Extract structured data from web pages
- Monitor the web for specific events
- Enrich a dataset with live web data
- Build a structured dataset from web search
- Find companies matching a search criteria
- Give an LLM real-time web access
- Search the web with source citations
- Run multi-hop research queries programmatically
- Integrate web search via MCP
Real-World Applications
AI product teams building research or intelligence features may find the AI web search API useful for grounding LLM outputs in live web data. A team building a financial research assistant, for example, can call the deep research endpoint to pull cross-referenced facts on a company or sector without managing their own crawling infrastructure.
Sales and go-to-market tools can pull the FindAll and enrichment APIs to build targeted prospect lists from the web. A sales intelligence platform might define criteria in natural language, get back a structured table of matching businesses, and then enrich each row with fresh data on product releases or company status pulled directly from the web.
Operations and monitoring workflows may integrate the Monitor API to track changes across any set of web sources. A compliance team, for instance, could watch for regulatory updates across multiple jurisdictions and receive event streams the moment relevant changes appear.
Developer teams embedding the AI web search API into agentic systems can use the MCP server to connect their agents to Parallel without writing custom integration code. The CLI and SDKs make it straightforward to plug the API into existing Python or TypeScript pipelines for automated web research at scale.
