15 Firecrawl Alternatives, Scored for AI Agents (2026)

Steel is the highest-scoring Firecrawl alternative at an AgentReady score of 90, ahead of Browserless at 88. Jina AI ties Firecrawl itself at 87. The field runs down to Playwright at 44.

Every score below comes from an automated check of the vendor's live docs, SDKs, MCP support, and API behavior, refreshed August 2026.

#Why this list is different

Search "firecrawl alternatives" and every result on the first page is a blog post published by a company that sells a competing scraper. Each one ranks itself first. That is not a criticism of any of them, it is just what a vendor blog is for.

We sell none of these tools. StackResolve scores software for how well AI agents can use it, and we take no money from any vendor on this page. Firecrawl scores 87 here, higher than eleven of its fifteen alternatives.

#How the scoring works

Every tool gets a 0 to 100 score built from four pillars:

• Discovery: can an agent find the product and work out what it does, without a human?
• Understanding: are the docs machine-readable? Is there an OpenAPI spec, an llms.txt, structured data?
• Adoption: can an agent go from zero to a working call unattended? Or does it hit a sales call?
• Operability: does the API behave predictably? Documented errors, rate-limit headers, idempotency.

#The full ranking

Firecrawl itself is shown in italics for reference, not ranked as its own alternative.

#Steel, 90

The only tool here that scores 100 on both discovery and adoption. It ships an MCP server, an llms.txt, an OpenAPI spec, a CLI, and a Python SDK, and it documents its errors. It is also one of only three on this list where an agent can get a key without a sales call.

Its weakest pillar is operability at 76, the same band as most of the field. Pick it when you want an agent to find, evaluate, and start using a browser API with no human in the loop.

#Browserless, 88

Second overall and strongest of the group on understanding at 92. Documented errors, an OpenAPI spec, an MCP server, a CLI. The gap is adoption at 80: it is not self-serve, so an autonomous agent hits a wall at signup.

Pick it when a human does the procurement once and agents drive it afterward.

#Jina AI, 87

Perfect 100 on both discovery and understanding, the only tool on the list to manage that. It is also the only one of sixteen that returns rate-limit headers, which is the difference between an agent that backs off correctly and one that hammers you into a ban.

Adoption drags at 70 because it is not self-serve. If your agents need to read docs and reason about the API before calling it, this is the best-documented option here.

#Apify, 74

Scores 100 on discovery and drops to 63 on adoption. A large actor marketplace, an MCP server, an llms.txt, a CLI. Best when your problem is "someone has probably already built this scraper," because the marketplace usually means they have.

#Spider, 72

The most balanced of the mid-table: 81 adoption and 78 operability, with a soft 54 on understanding. Ships MCP, OpenAPI, a CLI, and a Python SDK. A reasonable pick when you care more about the thing working than about the docs reading well.

#Hyperbrowser, 67

Understanding of 92 puts its docs near the top of the list, and it documents errors. Then operability falls to 47, the second-lowest here, and it is the one tool on this list with no MCP server. Good docs, thinner production behavior.

#ScraperAPI, 66

Discovery of 100, understanding of 46. Easy to find, harder for an agent to reason about. A proxy-first product, so it fits scale-and-rotation problems better than structured extraction.

#Browserbase, 65

Discovery 100, understanding 38. Strong presence and a well-known name, with docs that are thin from a machine's point of view. Ships MCP, OpenAPI, a CLI, and a Python SDK.

#Bright Data, 62

The largest vendor here and the clearest illustration of why size and agent-readiness are different things. Discovery is 100, understanding is 23, the second-lowest on the list. Enormous proxy infrastructure behind documentation an agent struggles to parse.

#Playwright, 44

Free, open source, and last. That is not a knock on the library, it is a category difference. Playwright is code you run, not a service an agent discovers and calls. No llms.txt, no OpenAPI, and adoption of 40. If you are writing the integration by hand, none of that matters. If you want an agent to pick it up unattended, all of it does.

#What the data shows across all sixteen

Three findings stood out when we ran the numbers.

MCP is table stakes now. 14 of 16 ship an MCP server. A year ago that was a differentiator. Today its absence is the signal, not its presence.

Almost nobody is self-serve. Only Steel, ScrapingBee, and Anchor Browser let an agent get a key without a human conversation. For autonomous workflows this is the single biggest blocker in the category, and it is invisible in every feature comparison table.

Rate-limit headers are nearly extinct. One tool out of sixteen returns them. Every other agent in this category is guessing about backoff.

#How to pick

• Fully autonomous agent, no human in the loop: Steel. It is the only tool scoring 100 on both discovery and adoption.
• Docs an agent must reason over: Jina AI, at 100 understanding, with rate-limit headers nobody else has.
• Already using Firecrawl and it works: stay. At 87 with a category-leading 100 on operability, only two tools here beat it, and neither by much.
• Scraper probably already exists: Apify.
• Free and you will write the integration yourself: Playwright.

Every score on this page is live in the StackResolve registry, and you can audit any domain yourself against the same 41 checks.