---
title: "Best Firecrawl Alternative — EnConvert API"
description: "Looking for a Firecrawl alternative that handles more than web scraping? EnConvert covers the full RAG pipeline input surface with one API and one output schema"
canonical: "https://www.enconvert.com/solutions/best-firecrawl-alternative-enconvert-api"
locale: "en"
---

# Best Firecrawl Alternative for RAG Pipeline Ingestion

If you are building a RAG pipeline, you need web page extraction that produces output ready for vector embeddings without additional preprocessing. Firecrawl handles web scraping well, but it covers one input type and returns one output schema, leaving documents, summarisation, structured extraction, and source discovery for other tools to handle. A developer who starts with Firecrawl and adds complexity to their pipeline hits the ceiling quickly and starts stitching together additional services, each with its own authentication, rate limits, and failure modes. EnConvert is the alternative: one API, one consistent output schema, six endpoints covering the full input surface an agent pipeline encounters.  

| Feature | Firecrawl | EnConvert |
| --- | --- | --- |
| Input types covered | Web pages only | Web pages, documents, files, and more across six endpoints |
| Output schema | One schema (scrape/parse) | One unified schema across every endpoint |
| Document ingestion | Not covered | Ingest endpoint converts sites and document lists to RAG-ready JSONL |
| Summarisation | Not covered | Distill endpoint returns LLM-ready summaries |
| Structured extraction | Parse only | Lookup endpoint with structured extraction |
| Source discovery | Map endpoint | Discover endpoint maps sites before spending ops |
| Pricing entry point | Free tier, then paid plans | Free: 500 ops/month, no card required |
| MCP server | Available | Listed on Anthropic MCP Registry, LobeHub, and Glama |

## Example

```python
import os

import requests

ENCONVERT_API_KEY = os.environ["ENCONVERT_API_KEY"]
PAGE_URL = os.environ["PAGE_URL"]  # the web page URL to extract

response = requests.post(
    "https://api.enconvert.com/v2/perceive",
    headers={
        "X-API-Key": ENCONVERT_API_KEY,
        "Content-Type": "application/json",
    },
    json={
        "url": PAGE_URL,
        "outputs": ["markdown"],
        "only_main_content": True,
    },
    timeout=60,
)
response.raise_for_status()
perceived = response.json()

if perceived.get("error") or "markdown" not in perceived.get("outputs", {}):
    raise RuntimeError(f"Perceive failed for {PAGE_URL}: {perceived.get('error')}")

# The markdown is a signed artifact URL (valid 15 minutes). Plain GET, no API key.
content_response = requests.get(perceived["outputs"]["markdown"]["url"], timeout=60)
content_response.raise_for_status()
clean_content = content_response.text

print(clean_content)
```

## Output

Firecrawl is a strong choice for web scraping at scale. EnConvert covers web scraping and the five other input types an agent pipeline encounters as it grows: document ingestion, summarisation, structured extraction, source discovery, and format conversion. A developer who standardises on EnConvert does not need to add tools as their pipeline expands to handle new input types.

## Get started

[Get free API key — 500 ops per month, no card required.](https://www.enconvert.com/auth?mode=signup)

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- [How to Scrape Jira with AutoGen](https://www.enconvert.com/solutions/scrape-jira-with-autogen-enconvert-api.md)
- [How to Scrape Stripe Docs with OpenAI Agents SDK](https://www.enconvert.com/solutions/scrape-stripe-docs-with-openai-agents-sdk-enconvert-api.md)