ScrapeGraphAI Alternative in 2026 — fastCRW (Rust API, Simpler Extraction)

Verdict

ScrapeGraphAI and fastCRW both combine scraping with LLM-based extraction. SGA is Python-centric and graph-flexible. fastCRW is Rust-native, REST-API-first, and simpler.

This page is honest: SGA wins on LLM provider choice (8+ providers). fastCRW wins on simplicity, self-hosting weight, and REST API design.

Who this page is for

Three readers:

• Using ScrapeGraphAI, want to try a faster/lighter alternative — skip to Capability matrix.
• Evaluating REST API scraper with LLM extraction — see API comparison.
• Searching "scrapegraphai alternative" — the head-to-head section is the short version.

Capability matrix

Capability	ScrapeGraphAI	fastCRW
Extraction approach	Graph-based (multi-step, conditional)	JSON schema + function_calling
LLM providers	8+ (OpenAI, Anthropic, Gemini, Groq, Ollama, OpenRouter, Vertex, Cohere via litellm)	Managed LLM (paid plans)
Extraction query format	Natural language description	JSON schema (structured)
Deployment model	Python library (pip install)	REST API (binary or container)
API	Python class methods	HTTP endpoints (/v1/scrape, /v1/crawl)
Single-URL extraction	✅	✅
Batch extraction	⚠️ (via for-loop or custom orchestration)	✅ up to 50 URLs via /v1/extract, or /v1/crawl for full-site batches
Crawl + extract	❌ (library expects you to handle crawl)	✅ (/v1/crawl with extraction)
Self-hosting	✅ (Python library, run anywhere)	✅ (AGPL-3.0 single binary)
Self-host binary size	~200 MB+ (Python + deps)	~8 MB (Rust binary)
Cold start	1–3 seconds (Python startup)	Fast local cold start (Rust binary)
REST API available	❌ (community wrappers exist)	✅ (built-in)
Markdown/HTML output	❌ (extraction only)	✅ (/v1/scrape formats: ["markdown"])
JavaScript rendering	✅ (with playwright)	✅ (auto-detect, LightPanda/Chrome)
Rate limiting	Self-managed (per-request via code)	✅ (built-in per-domain, per-second)
MCP support	❌	✅ (Claude Code, Cursor, Windsurf)
Cost (self-hosted)	Free (library) + LLM key	Free (AGPL-3.0) + server (no LLM features on self-host/FREE)
Cost (managed)	N/A (no official managed)	$13–$69/mo (credits; managed LLM on paid plans)

API Comparison

ScrapeGraphAI (Python library)

from scrapegraphai.graphs import SmartScraperGraph

graph_config = {
    "llm": {"model": "gpt-4", "api_key": "your-key"},
    "verbose": True,
}

scraper = SmartScraperGraph(
    prompt="Extract product names and prices",
    source="https://example.com/products",
    config=graph_config
)

result = scraper.run()
# result = {"products": [{"name": "...", "price": "..."}, ...]}

Pros: Natural language queries, multi-step graphs, flexible reasoning.
Cons: Requires Python, library overhead, cold start.

fastCRW (REST API)

curl -X POST http://localhost:8080/v1/scrape \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://example.com/products",
    "formats": ["json"],
    "schema": {
      "type": "object",
      "properties": {
        "products": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": {"type": "string"},
              "price": {"type": "string"}
            }
          }
        }
      }
    }
  }'

Or in Python:

import requests

response = requests.post(
    "http://localhost:8080/v1/scrape",
    json={
        "url": "https://example.com/products",
        "formats": ["json"],
        "schema": {
            "type": "object",
            "properties": {
                "products": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "name": {"type": "string"},
                            "price": {"type": "string"}
                        }
                    }
                }
            }
        }
    }
)
result = response.json()
# result["json"] = {"products": [...]}

Pros: Language-agnostic, REST API, simple schema-based extraction.

Head-to-head

Decision area	ScrapeGraphAI	fastCRW
LLM provider choice	✅ 8+ (litellm)	✅ self-host: bring your own LLM; managed cloud: managed LLM on paid plans
Simplicity (hello-world)	✅ Python import	✅ One HTTP call with a JSON schema
Self-host weight	⚠️ ~200 MB+ (Python runtime)	✅ ~8 MB (Rust binary)
Cold start	⚠️ 1–3 seconds	✅ Fast local cold start
API-first design	❌ (Python library)	✅ (REST)
Batch scrape+extract	⚠️ (DIY loop)	✅ up to 50 URLs via /v1/extract, or /v1/crawl
Crawl + extract	❌ (crawl separately)	✅ (/v1/crawl)
Language-agnostic	❌ (Python only)	✅ (REST API)
Cost (free)	✅ (library + your own LLM key)	✅ (AGPL-3.0 self-host; FREE has no LLM features)
Cost (scale, 1k scrapes/mo)	~$5–20 (LLM)	$13/mo Hobby (5k credits) or $0 (self-host + server)

The comparison is honest: SGA's graph approach is more powerful for complex logic. fastCRW's REST API is simpler for straightforward extraction.

When to choose ScrapeGraphAI

ScrapeGraphAI fits a narrow, specific case: extraction logic that needs Gemini, Groq, or Ollama specifically via litellm, deep graph-based multi-step orchestration (fetch → parse → extract → validate → transform in one call), or a team that wants scraping embedded directly as a Python library inside an existing ML pipeline.

When to choose fastCRW

1. Simpler extraction. JSON schema + LLM extraction, no graph orchestration needed.
2. REST API over library. You want to call from any language, not just Python.
3. Lightweight self-hosting. 8 MB binary vs 200+ MB Python environment.
4. Faster iteration. Fast local cold start vs 1–3 seconds of Python startup.
5. Crawl + extract in one call. /v1/crawl with per-URL extraction.
6. MCP integration. Claude Code, Cursor, Windsurf.
7. Managed option preferred. fastCRW managed plans ($13–$69/mo) vs SGA free library.

Switch to fastCRW if: simplicity, speed, and API-first design fit your workflow better than a Python-embedded graph library.

Migration path (ScrapeGraphAI → fastCRW)

Step 1: Extract your SGA extraction query

# Before: ScrapeGraphAI
prompt = "Extract product names, prices, and in-stock status"

Step 2: Convert to JSON schema

{
  "type": "object",
  "properties": {
    "products": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "price": { "type": "string" },
          "inStock": { "type": "boolean" }
        }
      }
    }
  }
}

Step 3: Deploy fastCRW

# Option A: Managed
# Sign up at fastcrw.com, get API key

# Option B: Self-host
docker run -p 8080:8080 ghcr.io/us/crw:latest

Step 4: Call fastCRW API

import requests

# Before (ScrapeGraphAI)
from scrapegraphai.graphs import SmartScraperGraph
scraper = SmartScraperGraph(prompt="...", source=url, config=config)
result = scraper.run()

# After (fastCRW)
response = requests.post(
    "http://localhost:8080/v1/scrape",
    json={"url": url, "formats": ["json"], "schema": {...}}
)
result = response.json()["json"]

Effort: ~1–2 hours for simple migrations. More if your SGA graphs are complex (may need custom orchestration in fastCRW).

LLM provider model

On fastCRW's managed cloud, LLM extraction runs on a managed LLM available on paid plans (the FREE plan has no LLM features). Self-hosting fastCRW gives you provider choice: configure your own LLM provider key (OpenAI-compatible endpoints, Azure, OpenRouter and more) via CRW_EXTRACTION__LLM__*. ScrapeGraphAI's litellm integration covers a broader provider list including Gemini, Groq, and Ollama, which matters if one of those specific providers is a hard requirement.

Related

• Firecrawl alternative — fastCRW vs the larger ecosystem.
• Jina Reader alternative — when URL→markdown is enough.
• fastCRW self-hosting guide — detailed on-prem setup.
• Building RAG with web scraping — extraction for LLM pipelines.