Firecrawl Release Notes

Today we're launching web-scale /monitor

an always-on search that watches the web and pings you or your agent the moment something comes online.

Before, /monitor only worked for single pages or websites. You named the URLs, Firecrawl diffed them on a schedule, and notified you when something changed. Now you can use that same power on the entire web.

Define a query and a goal, and Firecrawl runs the search on your schedule, dedupes results, and alerts only when new, relevant pages appear. Every run delivers what's new and meaningful to your AI agent or app, via webhook or email.

What is web-scale /monitor?

Web-scale /monitor finds new pages across the web as they appear. You define one or more search queries, a recency window, and a plain-language goal. On every check, Firecrawl:

1. Runs the queries against the web
2. Filters to results inside your searchWindow
3. Dedupes by canonical URL against prior checks
4. Judges new results against your goal (when judging is enabled)
5. Pings you or your agent when something worth acting on comes online

Pages and sites vs. the entire web

Same scheduling, goals, judging, and notification channels underneath. If you've used /monitor for page watching, the API shape will feel familiar: web monitors use a search target instead of URLs.

Create your first web monitor

A web monitor is a standard create_monitor call with a type: "search" target:

[Code examples in Python and Node.js omitted for brevity]

Tune recall and precision

Two levers do different jobs:

• queries control recall: what each search pulls in. Cast a wide enough net with relevant terms and variations, then let your goal decide what actually alerts.
• goal controls precision: which retrieved results actually alert.

Good queries read like search terms, not sentences. Use includeDomains and excludeDomains for domain scoping instead of site: operators in the query string.

Get pinged by webhook or email

Point a webhook at your agent and subscribe to monitor.page and monitor.check.completed, the same events page monitors use. When a new matching result comes online, the payload arrives with status: "new" and a judgment explaining why it matters.

Email summaries work the same way: sent only when a check surfaces new or errored results.

What you can build with web-scale /monitor

Use case | Example queries | Goal

Track regulatory and legal changes | FDA approval, clinical trial posting, SEC filing | Alert when a new approval, trial, or filing is published

Monitor competitor developments | "Acme Corp" funding OR acquisition, competitor careers site | Alert on funding rounds, new roles, or product launches

Track breaking news and events | Phase 3 trial results biotech, M&A filing | Alert when trial results or deal filings appear

Replace the DIY stack

Before web-scale /monitor, staying current on the open web meant wiring together a scheduler, a search API, deduplication logic, a relevance filter, and a notification layer, then maintaining all of it as queries and sources shift.

One endpoint replaces that stack. You define the query once; Firecrawl handles the always-on search, dedupe, classification, and delivery.

Pricing: web monitors cost 2 credits per 10 results per check, plus 1 credit per result the judge evaluates when judging is enabled. There is no separate per-monitor fee.

Try it today

Web-scale /monitor is live for all Firecrawl users.

1. Create a monitor in the dashboard
2. Read the Web Monitoring docs
3. Wire the monitor.page webhook into your agent or pipeline

If you're already watching known URLs with /monitor, add a search target alongside them: page monitors for what you know, web monitors for what hasn't been published yet.

Get started with web-scale /monitor · Read the docs

Original source

Web-scale /monitor watches the entire web for you. Define search queries and a goal, and Firecrawl pings you or your agent the moment something new comes online.

Highlights

• Always-on web search — Give /monitor search queries and a goal, and it searches the entire web on your schedule, alerting you when something new matches.
• Tune recall and precision — queries control what each search pulls in, while goal decides which of those results actually alert.
• Webhook or email delivery — Get pinged the moment something new matches, with a plain-English explanation of why it matters.
• One endpoint replaces the DIY stack — Define your query once. Firecrawl handles the search, deduplication, filtering, and alerts.

Read the full documentation here.

Original source

Firecrawl v2.11.0 ships the Firecrawl Research Index, keyless endpoint access, automatic PII redaction, a deterministicJson format, and video discovery on any page - plus much more.

Highlights

• Firecrawl Research Index — Search across 3M+ arXiv papers and the GitHub code behind them (issues, merged PRs, and READMEs, refreshed daily), fetch a paper's details or related work, and check claims against full text. It has state-of-the-art recall on arXivQA, outperforming the next best provider by 18% at comparable cost.
• Keyless access for core endpoints — Use /scrape, /search, /interact, and /parse without an API key from official MCP, CLI, and SDK clients.
• Automatic PII redaction — A new redactPII option strips personal and sensitive data like names, emails, phone numbers, addresses, and secrets out of scraped content before it's returned.
• deterministicJson format — Get structured JSON without running an LLM on every request. Firecrawl generates a reusable extractor for your schema and caches it per site, so repeat scrapes are cheaper and return consistent results.
• Video discovery on any page — The video format now finds videos on any page, not just supported providers like YouTube, returning each video's URL, title, thumbnail, duration, and more.

Read the full changelog here.

Original source

AI/ML research moves fast, and the work that matters is split between new papers and the code that implements them. Most search providers omit or misrank key papers, leaving you to review sources by hand without ever being sure you've caught everything.

Today we're launching Firecrawl Research Index, a specialized index for agents pushing the frontier of AI/ML research. It gives agents the entire AI/ML literature and the code behind it, plus the tools to turn them into answers — agents retrieve the right papers, verify claims against full text, and pull code for implementation autonomously.

On arXivQA, the index has state-of-the-art recall, 18% above the next best provider at similar cost. It also scores 0.750 MRR, meaning the correct paper lands in the top two results.

How the Firecrawl Research Index makes agentic deep research easier

Highest recall, at comparable cost

On arXivQA, the index hits 53.3% recall at $0.32 per task, against 45.4% for the next best provider. Your agent works from a fuller set of papers without paying a premium.

Aemon (YC W26), which builds autonomous AI research engineers, saw the same pattern in their own benchmark of scientific and technical retrieval systems:

"Aemon is building autonomous AI research engineer that solve hard scientific and technical problems. To do that, our systems must continuously learn from the frontier of research—papers, implementations, benchmarks, and technical discussions across the web.
We use Firecrawl Research as part of the retrieval stack behind Aemon. In our internal benchmark of scientific and technical retrieval systems, it delivered the strongest recall of any provider we tested, particularly at deeper search depths where comprehensive coverage is critical. Firecrawl consistently surfaced relevant scientific and technical sources that would otherwise have been missed."
— Ray Xu, Co-Founder, Aemon (YC W26)

Surface the right source first

On arXivQA, the index scores 0.750 MRR, meaning the correct paper lands in the top two results. Higher MRR means fewer wasted tokens before an agent finds what it actually needs.

Search millions of papers alongside their code

The index includes all 3M+ arXiv papers, plus GitHub artifacts from top research repos (issues, merged PRs, READMEs), refreshed daily so agents always stay current.

A complete toolset for research loops

The built-in toolset lets agents run research end-to-end — retrieving the right papers, verifying claims against full text, and pulling code. Agents can go from literature to implementation in one query, with no manual filtering, cross-referencing, or review required.

What you can use it for

• Power your research platform's search: Plug in the index, and your platform ships state-of-the-art search across millions of papers and the code behind them.
• Autonomous research agents: Your agent finds the most relevant papers and code for its problem, follows citations, and verifies against full text before it builds on them. An agent tuning a training run overnight could pull an optimizer from a recent paper and a stability fix from a related GitHub issue, then test both in its next experiment.
• Literature reviews and discovery: Find the relevant work on any topic, including papers published this week. Then start from your best hit and surface its references, citers, and related work, reaching papers a keyword search misses.

Methodology

We benchmarked on roughly 200 queries from alphaXiv's ArXivQA, each labeled with up to 10 ground-truth arXiv IDs. To measure recall, we let Opus 4.8 run each provider through its MCP and SKILL.md, then scored the papers it surfaced against those labels.

Availability

Firecrawl Research Index is available now in the API via /search/research, plus the CLI, MCP, and SDKs. It plugs into any harness you already run, including Codex, Claude Code, and Grok Build.

Get started with Firecrawl Research Index

Original source

Setting up a web data API usually starts with a signup form. Create an account, generate a key, paste it into an env file, write your first line of code. That's friction before you've built anything.

Today we're launching Firecrawl Keyless. Search, scrape, and interact with the web without an API key. Every developer gets 1,000 free credits a month, automatically. Sign up only when you need more.

With Firecrawl Keyless, you can:

• Search the web for live results with full-page content
• Scrape any URL for clean markdown, including JavaScript-heavy pages
• Interact with pages to click, fill forms, paginate, and navigate dynamic sites

Live today across our MCP, CLI, and API.

How Firecrawl Keyless makes building easier

Agents work without setup

For coding agents, this matters even more. Connect Claude Code, Cursor, OpenClaw, Hermes Agent, OpenCode, or any other MCP-compatible host, and the agent starts scraping and searching immediately. No human in the loop to generate a key and paste it into config.

Demos and prototypes that don't break

API keys fail at the worst moment: a hackathon demo, a workshop, a side project picked up months later. Keyless removes that class of failures for projects where 1,000 credits a month is plenty.

Try it today

Firecrawl Keyless is live across every surface:

• MCP - point any MCP-compatible client at https://mcp.firecrawl.dev/v2/mcp
• CLI - run npx firecrawl-cli@latest and start scraping
• API - call the Firecrawl REST endpoints directly, no Authorization header required

You get 1,000 free credits per month, every month. When you outgrow it, sign up and bring your own key.

Get started with Firecrawl Keyless · Read the docs

Original source

Firecrawl Research Index is a specialized index for agents pushing the frontier of AI/ML research. It has state-of-the-art recall on arXivQA, outperforming the next best provider by 18% at comparable cost.

Highlights

• Benchmark-leading performance — On arXivQA, the index hits 53.3% recall versus 45.4% for the next best provider. The index also scores 0.750 MRR, putting the right paper in the top one or two results.
• Search millions of papers alongside their code — The index includes 3M+ arXiv papers, plus GitHub artifacts from top research repos (issues, merged PRs, READMEs), refreshed daily.
• A complete toolset for research loops — Agents can retrieve papers, verify claims against full text, and pull code, going from literature to implementation with no manual review.
• Plugs into any agent harness — Works with Codex, Claude Code, Grok Build, and more.

Read the full documentation here.

Original source

Agents can easily scrape any web page with Firecrawl. But knowing the moment a page changes — and staying focused on the changes that actually matter — is trickier. Traditionally, this required writing a cron job, standing up snapshot storage, diffing old against new, building a webhook layer with retries, and filtering out the noise (ads, timestamps, session tokens).

Today we're launching Firecrawl /monitor — the whole monitoring stack in one endpoint, only pinging your agent when something meaningful changes.

What is Firecrawl Monitoring?

/monitor runs scheduled checks on pages or entire sites, and notifies your agent when something you care about changes. Simply describe what you want to monitor in plain English, and Firecrawl configures the URLs, logic, and schedule for you.

The output is a structured diff: what was added, what was removed, what changed. Your agent only ingests what changes on a page, skipping unchanged content and noisy diffs — using up to 90% fewer LLM tokens.

How does Firecrawl /monitor keep your agents current?

Know the moment a page changes

Set a cadence (every 5 minutes, hourly, daily, or a custom cron schedule) and Firecrawl checks the page for you. When a change is detected, your agent gets a notification. When nothing has changed, nothing is sent. No polling loops, no wasted checks.

Watch only what matters

You can monitor a single page or an entire site, and track the whole page or drill down to specific fields. You can also edit content filters and scrape behavior manually if you need more precise control.

See exactly what's different

Every notification includes a rich diff (what was added, what was removed, what changed), formatted for easy reading by a human or an agent. Each check also gets a permalink so you can share a specific change with a teammate or hand it directly to an agent for further processing.

Deliver changes straight to your agent

Changes fire as signed webhooks with custom headers and per-event subscriptions. Your agent only wakes for the events it cares about. Email delivery works the same way, with the diff already in the message body.

Know your costs before you commit

See the estimated monthly cost before you turn a monitor on. You know exactly what your chosen schedule will cost before you commit to it.

How the /monitor pipeline works

1. Create a monitor for one or more web pages, or an entire site
2. Firecrawl scrapes the pages on your schedule and stores a snapshot
3. On each check, the new version is diffed against the last stored snapshot
4. If something changed, Firecrawl sends a signed webhook or email with the structured diff and a permalink

What you can build with /monitor

Use case What it looks like Triggering agents on change A deep research agent monitoring dozens of sources can generate a fresh summary the moment one of them updates. Keeping RAG pipelines fresh When a watched page changes, Firecrawl pings your pipeline with the diff so you can refresh just that document instead of rescraping everything. Tracking competitors and markets Watch competitor pricing pages, product catalogs, or job listings and know within minutes when something shifts: a price drop, a new SKU, a new role. Monitoring sources you rely on Get updates for new research papers, developer docs, changelogs, financial filings, and government regulations.

Try it today

Firecrawl Monitoring is live for all Firecrawl users. Create your first monitor from the dashboard or via the API.

Get started with Firecrawl Monitoring · Read the docs

Original source

AI apps and agents built on Vercel often need web data: a page to scrape, a search to run, a dynamic site to interact with. Until now, that meant a separate account, separate billing, and manually copying API keys. Firecrawl is now a native integration on the Vercel Marketplace.

What the Vercel + Firecrawl integration sets up

The Vercel Marketplace integration handles Firecrawl account provisioning, API key management, and billing automatically. Here is what happens when you install:

• Provisions a Firecrawl team and API key automatically
• Injects FIRECRAWL_API_KEY directly into your Vercel project's environment variables (no copy-paste)
• Adds Firecrawl to your Vercel invoice, so there is no separate vendor or payment method to manage
• Gives you SSO into the Firecrawl dashboard from Vercel, with no separate login

Plan changes, API key rotation, and uninstall all work through the Marketplace the same way.

How to get started

1. Go to the Vercel Marketplace and find Firecrawl.
2. Pick a plan: Hobby, Starter, Growth, or Scale.
3. Select the Vercel project to attach it to.
4. Done. FIRECRAWL_API_KEY is live in your project environment.

What can you build with Firecrawl on Vercel?

Firecrawl gives your Vercel app three endpoints for pulling data from the web. It covers AI agents that browse the web, RAG pipelines that stay fresh, and SaaS tools that extract structured data:

• search — search the web and get full page content from results in a single call
• scrape — convert any URL into LLM-ready Markdown, structured JSON, or screenshots
• interact — scrape a page, then click, scroll, or fill forms to reach dynamic content

Here is what teams typically build:

Use case Endpoint Description RAG pipelines with fresh web data scrape Pull docs, changelogs, or any public page into LLM-ready Markdown on a schedule. RAG pipelines stay current without manual re-ingestion. Research and deep research search Returns complete page content per result. A deep research agent can search a topic, retrieve full articles, and synthesize across sources in one pass. Stanford's AI Playground processes 800 sources daily this way for real-time LLM grounding. Real-time product and news monitoring search Runs on each request so your app always reflects current data. Surface product launches, competitor updates, or breaking news as they happen. Data extraction for AI-native SaaS scrape + interact scrape returns structured JSON you can validate with Zod or Pydantic. interact handles clicks and scrolls for dynamically loaded content.

Try it today

Firecrawl is live on the Vercel Marketplace now. Install takes under a minute and your API key lands in your project environment automatically.

Install on Vercel Marketplace · Read the docs

Original source

Enter a URL, describe what you want to track, and /monitor notifies your AI agent via webhook the moment pages or sites change. Use up to 90% fewer LLM tokens by only ingesting what actually changes.

Highlights

• Set a goal in plain English — Describe what to watch, like "alert me when the Claude Code docs add new slash commands," and /monitor configures the URLs, schema, and schedule for you.
• Up to 90% fewer LLM tokens — Your agent only ingests what changes on a page, skipping unchanged content and noisy diffs.
• Any cadence, with cost upfront — 5 minutes, hourly, daily, or a custom cron schedule. The estimated monthly cost is shown before you turn a monitor on, so you know what you're committing to.
• Webhook or email delivery — Every change fires a signed webhook with custom headers and per-event subscriptions, or arrives by email with the diff in the body.
• Permalinks for every change — Diffs are first-class objects you can share with a teammate or hand straight to another agent.

Read the full documentation here.

Original source

Firecrawl v2.10 ships a new /parse endpoint, Lockdown Mode, Question and Highlights formats, and four new official SDKs (Go, Ruby, PHP, .NET) - plus a long list of reliability and security fixes.

Highlights

• /parse endpoint — Upload PDFs, Word docs, and spreadsheets up to 50 MB and get clean, LLM-ready Markdown, JSON, or summaries back. Powered by a new Rust-based engine that's up to 5x faster.
• Lockdown Mode — Set lockdown: true on /scrape to serve results exclusively from Firecrawl's index with no outbound requests and zero data retention by default. Available everywhere, including the CLI (--lockdown) and MCP.
• Question Format — Pass a natural-language prompt to /scrape and get a grounded answer back, with up to 100x fewer tokens per call.
• Highlights Format — Get back the exact sentences, code blocks, and table rows on a page that match your query, with original formatting preserved — also using up to 100x fewer tokens per call.
• Four New Official SDKs — Go, Ruby, PHP (with Laravel support), and .NET all joined the SDK family with v2 parity. The Rust SDK has been promoted to the official v2 SDK.

Read the full changelog here.

Original source

Question format

Getting an accurate answer — or pulling a specific piece of content — from a webpage with an LLM today means scraping the full page, chunking it, running it through your own model, and owning the prompt engineering yourself. Today we're launching two new formats for Firecrawl /scrape that replace all of that with one call:

• question — Pass a URL and a question, get a grounded answer back.
• highlights — Pass a URL and a query, get the exact sentences, code blocks, and tables from the page that match it back, verbatim.

Both formats are up to 100x more token-efficient than a full scrape, run on a fully managed LLM stack, and ship with prompt-injection hardening built in.

question format

question returns a focused, grounded answer drawn directly from the page — no footers, ads, or irrelevant prose crowding your context window. Pass a URL and the question you want answered:

The answer is synthesized by a managed LLM that's strictly instructed to ground its response in the page content. If the page doesn't contain the answer, the model says so rather than inventing one.

Highlights format

highlights is built for compliance, code extraction, and financial data capture — workflows where the answer is consumed as data and the source attribution needs to be unambiguous. Pass a URL and a query, and Firecrawl returns the exact text on the page that matches it:

A fine-tuned model selects line indices rather than generating prose, and Firecrawl reassembles the relevant text verbatim from sentences, code blocks, and table rows on the page:

• Consecutive sentences from the same block re-join into paragraphs
• Consecutive code lines wrap in fenced blocks with their original language preserved
• Table rows rebuild into markdown tables with headers automatically included

Nothing is rewritten or invented.

Highlights Focus Benchmark

We measure highlights on Focus: how closely a returned snippet matches the ideal passage on the page. A perfect 1.0 means the response is exactly as focused as the ground truth — lower scores reflect either missing content or responses padded with surrounding noise.

Here's how Firecrawl's Highlights format stacks up against Exa Highlights on a 10,000 URL test set:

Firecrawl scored 0.446 to Exa's 0.160 — roughly 2.8x more focused. That gap is the difference between an excerpt your agent can drop straight into a citation and one it has to clean up first.

Why these formats matter for agents

Grounded, page-faithful output

question answers strictly from page content — zero hallucinations.

highlights goes further, returning exact text drawn from the source. Whether your agent needs an accurate synthesized answer or a citable verbatim quote, both formats keep its output anchored to what's actually on the page.

Up to 100x fewer tokens per call

A standard /scrape call returns the full page as clean markdown (or HTML, JSON, screenshots, and more).

question returns just the answer.

highlights returns just the excerpts that match. For agents running dozens or hundreds of lookups, that difference compounds fast: lower inference costs, faster responses, and a leaner context window on every request.

A fully managed LLM stack

Firecrawl runs both formats on a managed model chain with automatic fallback and a production-tuned system prompt. The LLM integration, prompt engineering, and retry logic are all handled for you. Tokens and costs roll into the same Firecrawl billing and telemetry you already use for /scrape — one source of truth for usage and spend.

Hardened against prompt injection

Pages can contain text designed to hijack an LLM's behavior. Both formats are built to resist this:

• User prompts and page content are wrapped in distinct XML tags so the model can always tell them apart
• Page content is escaped with zero-width spaces so any embedded XML or instruction-like markup can't break out of its container
• The system prompt marks page content as untrusted and instructs the model to ignore and refuse any instructions embedded in the page

Try them today

question and highlights formats are live for all Firecrawl users.

Get started with Firecrawl · Read the docs

Original source

Highlights is a new format for /scrape that returns the exact sentences, code blocks, and table rows on a page that match your query, all while using up to 100x fewer tokens.

Highlights

• Citable, hallucination-free output — Nothing in the response is rewritten, translated, or hallucinated. Every sentence is provably from the source page, in the page's own words.
• Code blocks and tables preserved — Consecutive sentences from the same block re-join into paragraphs, consecutive code lines wrap in fenced blocks with their original language, and table rows rebuild into Markdown tables with headers auto-included.
• Up to 100x fewer tokens per call — Returning just the matching lines instead of the full page lowers inference costs, speeds up responses, and keeps your context window lean.

Read the full documentation here.
See the benchmark here.

Original source

Question is a format for /scrape that returns high-quality, grounded answers from any web page using up to 100x fewer tokens.

Highlights

• High-quality, grounded answers — question pulls the page content most relevant to your prompt and answers strictly from it, with zero hallucinations.
• Up to 100x fewer tokens per call — question returns just the answer, not the page, giving you significantly lower inference costs, faster responses, and a leaner agent context window on every request.
• Built for AI agents — Skip the scrape-parse-prompt pipeline. Drop precise, page-grounded answers straight into agent loops with a single call.
• Fully managed LLM stack — question runs on a managed model chain with automatic fallback and a production-tuned system prompt. Token usage and cost roll into the same billing surface as /scrape.
• Hardened against prompt injection — Page content is isolated with XML tagging and zero-width-space escaping, and the model is instructed to ignore any instructions embedded in the page.

Read the full documentation here.

Original source

Today we're shipping Lockdown Mode: Firecrawl's new cache-only scrape mode for security-sensitive workloads. It's a single flag on /scrape that serves results exclusively from Firecrawl's existing index - the request never leaves Firecrawl and nothing is retained. Available everywhere /scrape is: the API, every SDK, the CLI (--lockdown), and the MCP server.

The problem

Every live scrape is an outbound request to a third-party origin. For most workloads that's fine - but when an LLM agent decides which URLs to scrape, a prompt injection can turn that outbound request into a data exfiltration channel: sensitive context smuggled out in a path, query string, or header to a server the attacker controls. The same outbound request is also a problem for regulated teams who need every external call logged, approved, or simply prevented.

Lockdown Mode solves this with a single flag that serves results from Firecrawl's existing cache and guarantees the request never leaves Firecrawl.

What is Lockdown Mode?

Lockdown Mode is a cache-only scrape mode for the /scrape endpoint. Pass lockdown: true and Firecrawl serves the result exclusively from its existing index - no connection to the target URL, no robots.txt fetch, no search-index write, no audio transforms. Every outbound path is gated at the engine layer.

If the URL is in the cache, you get the result. If it isn't, the request returns a SCRAPE_LOCKDOWN_CACHE_MISS error rather than falling back to a live scrape. This is by design. When the URL isn't cached, you know immediately - no silent fallback to a live scrape.

It's particularly useful for regulated-industry teams, LLM agents that need guardrails, and any workflow where the URL itself is sensitive data.

How Lockdown Mode protects sensitive scrape jobs

No outbound request

All external operations are disabled at the engine layer on the same flag: HTTP engines, robots.txt fetching, search index writes, audio transforms. There's one auditable enforcement point, not a checklist of settings.

For regulated environments where outbound requests need approval or logging, this is the difference between an auditable workflow and a manual review process.

Zero data retention by default

Every lockdown request carries ZDR semantics automatically. The URL is never persisted, the response is never stored, and the scrape job is cleaned up immediately after delivery.

The standard ZDR pricing uplift is waived. Compliance and security don't cost extra.

One flag for every surface

lockdown: true works the same way across every surface Firecrawl supports:

• API - POST /scrape with { "lockdown": true }
• Python, Node, Go, Rust, Java, .NET, Ruby, PHP, Elixir SDKs
• CLI - firecrawl scrape --lockdown
• MCP server - same flag, same behavior

No version requirements, no additional configuration. If you can call /scrape, you can use Lockdown Mode.

Use cases

• Regulated-industry scraping: Healthcare, finance, legal, and government teams scraping documentation, regulatory pages, or partner sites where every outbound request needs audit or approval.
• Agent guardrails: Pin LLM agents to an already-indexed corpus so untrusted user input can't trigger outbound requests to arbitrary origins.
• Sensitive-URL workflows: Scrape URLs that themselves leak intent - competitor pages, internal hostnames, identifiers embedded in paths - without those URLs ever crossing the network.
• Deterministic replay: Serve a stable, indexed snapshot of pages back to downstream pipelines without re-hitting origins or paying live-scrape costs.

A few things to know

• Cache miss returns an error, not a fresh scrape. To serve a URL via Lockdown Mode, scrape it normally once first to seed the cache.
• Not for fresh or time-sensitive data. Lockdown returns cached results up to 2 years old. News feeds, live prices, and dashboards may return stale content.
• Conflicting options are silently ignored. actions, waitFor, custom headers, proxy configuration, and changeTracking are dropped when lockdown is active - lockdown wins at the engine layer.
• /scrape only. Lockdown Mode is not currently supported on /crawl, /map, or /search.

Try it today

Lockdown Mode is live for all Firecrawl users.
Lock down your most sensitive scrape jobs with lockdown: true.
Get started with Lockdown Mode · Read the docs

Original source

Lockdown Mode is a cache-only option for /scrape that keeps security-sensitive requests inside Firecrawl. Set lockdown: true to serve results exclusively from Firecrawl's index, with zero data retention by default.

Highlights

• No outbound request - Lockdown serves results from Firecrawl's index only and gates every outbound path, including HTTP and robots.txt.
• Zero data retention by default - URLs aren't persisted, response data isn't stored, and the scrape job is cleaned up after delivery.
• One flag, every surface - lockdown: true works the same across the API, every SDK (Python, Node, Go, Rust, Java, .NET, Ruby, PHP, Elixir), the CLI (--lockdown), and MCP.

Read the full documentation here.

Original source