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)	2 (OpenAI, Anthropic BYOK)
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)	✅ (/v1/batch/scrape)
Crawl + extract	❌ (library expects you to handle crawl)	✅ (/v1/crawl with extraction)
Multi-step workflows	✅ (graph pipelines)	⚠️ (compose via HTTP)
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)	85 ms (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) + LLM key + server
Cost (managed)	N/A (no official managed)	$13–$49/mo (credits)

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.
Cons: Less flexible for multi-step workflows, structured queries only.

Head-to-head

Decision area	ScrapeGraphAI	fastCRW
LLM provider choice	✅ 8+ (litellm)	⚠️ OpenAI + Anthropic
Simplicity (hello-world)	✅ Python import	⚠️ HTTP + JSON schema
Self-host weight	⚠️ ~200 MB+ (Python runtime)	✅ ~8 MB (Rust binary)
Cold start	⚠️ 1–3 seconds	✅ 85 ms
API-first design	❌ (Python library)	✅ (REST)
Multi-step workflows	✅ (graph pipelines)	⚠️ (manual orchestration)
Batch scrape+extract	⚠️ (DIY loop)	✅ (/v1/batch/scrape)
Crawl + extract	❌ (crawl separately)	✅ (/v1/crawl)
Language-agnostic	❌ (Python only)	✅ (REST API)
Cost (free)	✅ (library + BYOK)	✅ (AGPL-3.0 + BYOK)
Cost (scale, 1k scrapes/mo)	~$5–20 (LLM)	~$15–30 (managed) 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

1. Multi-provider LLM flexibility. You want Gemini, Groq, Ollama, or OpenRouter, not just OpenAI/Anthropic.
2. Graph-based workflows. Your extraction logic is multi-step (fetch → parse → extract → validate → transform).
3. Python-centric ML pipeline. You're already running transformers, RAG, LangChain. SGA fits naturally.
4. Free managed extraction. No managed offering; you run SGA + pay LLM provider directly.

Stay on ScrapeGraphAI if: multi-provider support or graph complexity is essential.

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. 85 ms cold start vs 1–3 seconds.
5. Crawl + extract in one call. /v1/crawl with per-URL extraction.
6. Model Context Protocol server. Claude Code, Cursor, Windsurf.
7. Managed option preferred. fastCRW managed plans ($13–$49/mo) vs SGA free library.

Switch to fastCRW if: simplicity, speed, and API-first design beat LLM provider choice.

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 roadmap

fastCRW currently supports OpenAI + Anthropic. Planned (no firm date):

• Ollama (local, free)
• OpenRouter (proxy, broader provider support)

If you need Gemini, Groq, or other providers now, stay on ScrapeGraphAI.

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.