> ## Documentation Index > Fetch the complete documentation index at: https://docs.parallel.ai/llms.txt > Use this file to discover all available pages before exploring further. # Migrate to Parallel Search > Move from Exa, Tavily, SERP APIs, or built-in model search to the Parallel Search API
For AI agents: a documentation index is available at [https://docs.parallel.ai/llms.txt](https://docs.parallel.ai/llms.txt). The full text of all docs is at [https://docs.parallel.ai/llms-full.txt](https://docs.parallel.ai/llms-full.txt). You may also fetch any page as Markdown by appending `.md` to its URL or sending `Accept: text/markdown`.
This guide maps the request you make today to its Parallel Search equivalent. Parallel Search takes keyword `search_queries`, plus an optional natural language `objective`, and returns ranked, LLM-optimized excerpts in a single call. `search_queries` is the only required field, so most migrations are a one-field change. Picking a [mode](/search/modes) is optional; requests default to `advanced`. | Coming from | Closest Parallel equivalent | | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------ | | Exa `instant` or `fast` | Search with `mode: "turbo"` | | Exa `auto` | Search with `mode: "basic"` | | Exa `deep-lite` or `deep` | [Task API](/task-api/task-quickstart) or [Chat API](/chat-api/chat-quickstart) (synthesized output) | | Exa `deep-reasoning` | [Task API](/task-api/task-quickstart) | | Tavily `fast` or `ultra-fast` | Search with `mode: "turbo"` | | Tavily `basic` | Search with `mode: "basic"` | | Tavily `advanced` | Search with `mode: "advanced"` | | SERP API | Search with `mode: "turbo"` or `"basic"` (one call, ranked excerpts) | | Built-in model search (e.g. OpenAI `web_search`) | Search as a function tool, any mode ([OpenAI](/integrations/openai-tool-calling), [Anthropic](/integrations/anthropic-tool-calling)) | ## Start with one field Exa, Tavily, and SERP APIs all center on a single query string. The fastest migration is to pass that string as one entry in `search_queries`. That alone is a complete, valid request, since `mode` is optional and defaults to `advanced`: ```json theme={"system"} { "search_queries": ["latest developments in solid state batteries"] } ``` For the lowest latency and cost, add `"mode": "turbo"`. For better results, also add an optional `objective`, a natural language description of your full intent, and split the query into 2-3 short keyword queries: ```json theme={"system"} { "objective": "Latest developments in solid state batteries. Focus on manufacturing progress and commercialization timelines.", "search_queries": ["solid state battery progress", "solid state battery commercialization"], "mode": "turbo" } ``` See [Best Practices](/search/best-practices) for how to write objectives and queries. ## From Exa | Exa parameter | Parallel equivalent | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | | `query` | `search_queries` (the only required field); optionally add an `objective` describing your full intent | | `type: "instant"` or `"fast"` | `mode: "turbo"` | | `type: "auto"` | `mode: "basic"` | | `type: "deep-lite"` or `"deep"` | Use the [Task API](/task-api/task-quickstart) or [Chat API](/chat-api/chat-quickstart) (both return synthesized output) | | `type: "deep-reasoning"` | Use the [Task API](/task-api/task-quickstart) | | `numResults` | `advanced_settings.max_results` | | `additionalQueries` | Add them as extra entries in `search_queries`; Parallel searches multiple queries in one call | | `includeDomains` / `excludeDomains` | `advanced_settings.source_policy.include_domains` / `exclude_domains` | | `startPublishedDate` | `advanced_settings.source_policy.after_date` (YYYY-MM-DD) | | `userLocation` | `advanced_settings.location` (ISO 3166-1 alpha-2 country code) | | `contents.text` (full page text) | Search returns compressed excerpts, not full page bodies. For full page contents, use the [Extract API](/extract/extract-quickstart) | | `category: "company"` or `"people"` | Consider [Entity Search](/findall-api/entity-search) | | `outputSchema` / `summary` | Structured, synthesized outputs are the [Task API](/task-api/task-quickstart) or [Chat API](/chat-api/chat-quickstart) | Before and after: ```bash Exa theme={"system"} curl https://api.exa.ai/search \ -H "x-api-key: $EXA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "query": "latest developments in solid state batteries", "type": "fast", "numResults": 10, "contents": { "text": { "maxCharacters": 1500 } } }' ``` ```bash Parallel theme={"system"} curl https://api.parallel.ai/v1/search \ -H "x-api-key: $PARALLEL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "search_queries": ["latest developments in solid state batteries"], "mode": "turbo" }' ``` Excerpts are on by default and sized dynamically, so most requests need no excerpt configuration at all. Add `advanced_settings` knobs only when you have a specific product constraint. Restrictive settings can reduce result quality. ## From Tavily Tavily's `search_depth` tiers map one-to-one onto Parallel modes. | Tavily parameter | Parallel equivalent | | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `query` | `search_queries` (the only required field); optionally add an `objective` describing your full intent | | `search_depth: "fast"` or `"ultra-fast"` | `mode: "turbo"` | | `search_depth: "basic"` | `mode: "basic"` | | `search_depth: "advanced"` | `mode: "advanced"` | | `max_results` | `advanced_settings.max_results` | | `include_domains` / `exclude_domains` | `advanced_settings.source_policy.include_domains` / `exclude_domains` | | `start_date` | `advanced_settings.source_policy.after_date` (YYYY-MM-DD). Tavily `time_range` (relative, e.g. `week`) has no direct equivalent; convert to an absolute date first | | `country` | `advanced_settings.location` (ISO 3166-1 alpha-2; convert Tavily's country name to a code) | | `topic: "news"` or `"finance"` | State the focus in your `objective` (for example "recent news about..." or "latest financial results for...") | | `include_answer` | Search returns excerpts for your model to reason over. For grounded completions, use the [Chat API](/chat-api/chat-quickstart) | | `include_raw_content` | Full page contents are the [Extract API](/extract/extract-quickstart) | Before and after: ```bash Tavily theme={"system"} curl https://api.tavily.com/search \ -H "Authorization: Bearer $TAVILY_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "query": "latest developments in solid state batteries", "search_depth": "fast", "max_results": 10 }' ``` ```bash Parallel theme={"system"} curl https://api.parallel.ai/v1/search \ -H "x-api-key: $PARALLEL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "search_queries": ["latest developments in solid state batteries"], "mode": "turbo" }' ``` Parallel authenticates with an `x-api-key` header rather than `Authorization: Bearer`, so a request that keeps the Tavily-style Bearer header returns 401. ## From a SERP API A SERP API returns a ranked list of links and snippets, but you must make subsequent fetches to extract usable information. Parallel Search combines everything into a single call. Instead of links you fetch and process, you get ranked, compressed excerpts sized for a model, so there is no fetch round and no chunking or ranking to build yourself. If a workflow still needs a specific page's full contents, use the [Extract API](/extract/extract-quickstart). You search, then fetch and process each result in your own code: ```python theme={"system"} import os, requests from bs4 import BeautifulSoup # split_into_chunks() and rank_by_relevance() are helpers you write and maintain query = "solid state battery developments" # Step 1: search returns links + snippets, no page content hits = requests.get("https://serpapi.com/search", params={ "q": query, "api_key": os.environ["SERPAPI_KEY"], }).json()["organic_results"][:10] # Step 2: fetch every result URL yourself, one at a time pages = [] for h in hits: try: html = requests.get(h["link"], timeout=10).text except requests.RequestException: continue # dead links, timeouts, bot blocks: your problem to handle text = BeautifulSoup(html, "html.parser").get_text(" ", strip=True) # (a scraper that returns markdown lets you skip this HTML parsing) pages.append(text) # Step 3: chunk and rank locally so it fits the model context chunks = [c for p in pages for c in split_into_chunks(p)] context = rank_by_relevance(chunks, query)[:15] ``` Parallel does it in one call: ```python theme={"system"} import os from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) # one call returns ranked, model-ready excerpts search = client.search(search_queries=["solid state battery developments"], mode="turbo") context = [ex for r in search.results for ex in r.excerpts] ``` You search, then fetch each result: ```bash theme={"system"} # Step 1: search returns links + snippets, no page content curl "https://serpapi.com/search?q=solid+state+battery+developments&api_key=$SERPAPI_KEY" # Step 2: fetch each result URL for its content (repeat for every link), # then strip HTML, chunk, and rank in your own code curl -L "https://" curl -L "https://" ``` Parallel does it in one call: ```bash theme={"system"} curl https://api.parallel.ai/v1/search \ -H "x-api-key: $PARALLEL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "search_queries": ["solid state battery developments"], "mode": "turbo" }' ``` ## From built-in model search "Built-in model search" splits into two cases that migrate differently. **Building on the API.** If you call a provider's hosted search tool in your own app (for example OpenAI's `web_search` in the Responses API), the provider runs the search inside its own walls. Migrate by registering Parallel Search as a function tool your app executes, which gives you mode control (Turbo today), the excerpts, and the provider choice. ```python theme={"system"} def search_web(search_queries: list[str], objective: str | None = None) -> dict: return parallel_client.search( search_queries=search_queries, objective=objective, mode="turbo", ) ``` The model decides when to search. Your handler decides how. Full walkthroughs with the tool schema and the tool-result loop live on the [OpenAI](/integrations/openai-tool-calling), [Anthropic](/integrations/anthropic-tool-calling), and [Ollama](/integrations/ollama-tool-calling) pages. **Inside the ChatGPT app (no code).** If you rely on web search in ChatGPT itself rather than building on the API, add Parallel as a connector through the [Search MCP](/integrations/mcp/search-mcp). ## What you parse now Parallel returns a `results` array. Each item has a `url`, `title`, `publish_date`, and `excerpts` (the compressed, relevant snippets your model reads), with a top-level `warnings` field worth checking on early calls: ```json theme={"system"} { "results": [ { "url": "https://...", "title": "...", "publish_date": null, "excerpts": ["..."] } ], "warnings": null } ``` If your current integration expects a field Search does not return, here is where that capability lives: | If your integration expects | With Parallel Search | Next step | | -------------------------------------------------------------------------------- | ----------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | | a relevance `score` to sort on | results come back already ranked | preserve the returned order; there is no per-result score | | full page text (Exa `text`, Tavily `raw_content`) | you get compressed excerpts, not full page bodies | use the [Extract API](/extract/extract-quickstart) for full contents | | a synthesized answer or report (Tavily `include_answer`, Exa `summary` / `deep`) | Search returns excerpts for your model to reason over | use the [Chat API](/chat-api/chat-quickstart) for grounded answers, or the [Task API](/task-api/task-quickstart) for multi-step research | | a list of people or companies (Exa `category`) | Search retrieves pages and excerpts across the web | use [Entity Search](/findall-api/entity-search) | | `images` | not returned | — | ## Verify your migration Before moving production traffic, run your top queries in the [Playground](https://platform.parallel.ai/play/search) and start with the mapped mode. On your first calls, check the response `warnings` field for any parameters that were adjusted or ignored, and confirm [Pricing](/getting-started/pricing) and [Rate Limits](/getting-started/rate-limits) for your volume.