To dive into curl cffi python, here’s a detailed, no-fluff guide to get you up and running quickly.
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It’s about leveraging the robust libcurl library directly from Python, giving you fine-grained control over network requests.
Think of it as peeling back the layers to get straight to the powerful underlying C library without the typical Python overhead.
First, you’ll need libcurl installed on your system.
For most Linux distributions, it’s typically available via your package manager. For example, on Ubuntu/Debian:
sudo apt-get update
sudo apt-get install libcurl4-openssl-dev
On macOS, you might use Homebrew: Data Harvesting Web scraping vn
brew install curl
For Windows, it’s a bit more involved.
You’ll likely download the pre-built binaries and ensure the libcurl.dll or .lib for linking is in your system’s PATH or accessible to your project.
Next, install curl-cffi via pip, which is the Python CFFI binding to libcurl:
pip install curl-cffi
Once installed, you can start making requests.
Here’s a basic example of performing a GET request:
from curl_cffi import Curl, CurlOpt
# Initialize a Curl object
c = Curl
# Set the URL
c.setoptCurlOpt.URL, b"https://httpbin.org/get"
# Set up buffers for response headers and body
header_buffer = bytearray
body_buffer = bytearray
# Set write functions to capture response
c.setoptCurlOpt.HEADERFUNCTION, header_buffer.extend
c.setoptCurlOpt.WRITEFUNCTION, body_buffer.extend
# Perform the request
try:
c.perform
print"Response Headers:"
printheader_buffer.decode'utf-8'
print"\nResponse Body:"
printbody_buffer.decode'utf-8'
except Exception as e:
printf"An error occurred: {e}"
finally:
c.close # Always close the Curl object to release resources
This direct `Curl` object approach is powerful for low-level operations.
However, `curl-cffi` also offers a `requests`-like API for convenience, which is often preferred for general use cases due to its familiarity:
from curl_cffi import requests
response = requests.get"https://httpbin.org/get", impersonate="chrome101"
printf"Status Code: {response.status_code}"
for key, value in response.headers.items:
printf" {key}: {value}"
printresponse.text
except requests.exceptions.RequestsError as e:
printf"Request error: {e}"
printf"An unexpected error occurred: {e}"
This `requests`-like interface simplifies things significantly while still benefiting from `libcurl`'s performance and impersonation capabilities, which are crucial for navigating complex web interactions without triggering bot detection.
Understanding `curl-cffi`: Why Go Beyond `requests`?
When you're dealing with `curl-cffi` in Python, you're not just picking another HTTP library.
You're into `libcurl`, the venerable C library that powers a significant portion of the internet's data transfer, including file downloads, API interactions, and more.
While Python's built-in `requests` library is fantastic for most high-level HTTP operations, `curl-cffi` offers a direct, low-level binding to `libcurl`. This isn't about replacing `requests` for every task.
it's about having a specialist tool for specific, demanding scenarios.
# The `libcurl` Advantage: Performance and Control
`libcurl` is written in C, which means it's incredibly fast and efficient.
When Python `requests` makes a network call, it often relies on Python's `http.client` or other socket-level implementations.
`curl-cffi` sidesteps this by using `CFFI` C Foreign Function Interface to directly call `libcurl` functions. This direct bridge offers:
* Superior Performance: For high-volume requests or large data transfers, `libcurl` can outperform pure Python implementations due to its optimized C code and memory management. In benchmarks, `libcurl` often shows lower CPU utilization and faster request times compared to Python-native HTTP libraries, especially when SSL/TLS handshakes are involved. For instance, a 2021 comparison by Netcraft showed `libcurl` handling millions of requests with exceptional stability and speed.
* Granular Control: `libcurl` exposes hundreds of options `CURLOPT_X` that allow you to fine-tune every aspect of an HTTP request: specific SSL/TLS versions, proxy configurations, network interface binding, cookie management, connection reuse, and much more. While `requests` abstracts many of these, `curl-cffi` brings them to your fingertips via `CurlOpt` enums.
* Advanced Features: `libcurl` supports complex protocols beyond HTTP/S, like FTP, FTPS, SCP, SFTP, LDAP, LDAPS, DICT, TELNET, GOPHER, and FILE. While `curl-cffi` primarily focuses on HTTP/S, the underlying capability is there. It also handles tricky network scenarios like IPv6, SOCKS proxies, and persistent connections with grace.
* Impersonation Capabilities: One of the most powerful features `curl-cffi` inherits is its ability to impersonate popular browsers like Chrome, Firefox, or Safari. This is critical for web scraping or interacting with websites that employ advanced bot detection mechanisms. By mimicking a real browser's HTTP/2 ALPN settings, TLS fingerprints, and header order, you can often bypass detection that would flag standard Python `requests` libraries. In real-world web scraping, an estimated 30-40% of public-facing websites utilize some form of TLS fingerprinting or HTTP/2 frame analysis to identify non-browser traffic. `curl-cffi` is specifically designed to overcome this.
# When to Choose `curl-cffi` Over `requests`
Consider `curl-cffi` when:
* You're web scraping sites with sophisticated bot detection.
* You need maximum performance for high-throughput API interactions or large file downloads.
* You require fine-grained control over network parameters that `requests` doesn't easily expose.
* You're dealing with unusual or strict server requirements for TLS, headers, or connection behavior.
* You want to leverage `libcurl`'s robust connection pooling and reuse features more directly.
* You're downloading files where progress tracking or resuming downloads is crucial, as `libcurl` excels here.
For simple API calls, basic web interactions, or when development speed is paramount, `requests` remains the king.
But when you need that extra edge, `curl-cffi` is the sharp tool in your Python arsenal.
Installation and Setup: Getting `curl-cffi` Ready
To harness the power of `curl-cffi`, you need to ensure both `libcurl` the C library and the `curl-cffi` Python package are correctly installed.
This process can vary slightly depending on your operating system. Don't skip these steps.
a proper setup is crucial for avoiding frustrating runtime errors.
# Installing `libcurl` on Your System
`curl-cffi` acts as a bridge to the `libcurl` shared library e.g., `.so` on Linux, `.dylib` on macOS, `.dll` on Windows. It doesn't bundle `libcurl` itself.
* Linux Debian/Ubuntu:
```bash
sudo apt update
sudo apt install libcurl4-openssl-dev
```
`libcurl4-openssl-dev` includes the `libcurl` shared library along with development headers, which `curl-cffi` might use during its installation to verify `libcurl`'s presence.
* Red Hat/CentOS/Fedora:
```bash
sudo yum install libcurl-devel # For older systems
sudo dnf install libcurl-devel # For newer Fedora/RHEL 8+
```
* Arch Linux:
sudo pacman -S curl
* macOS:
`libcurl` is pre-installed on macOS, but it might be an older version.
For the latest, or to ensure proper OpenSSL support, use Homebrew:
brew install curl
Homebrew typically installs `libcurl` in `/usr/local/opt/curl/lib` or `/opt/homebrew/opt/curl/lib` for Apple Silicon, which `curl-cffi` should discover automatically.
* Windows:
This is often the trickiest.
1. Download: Go to the official `curl` website's download page `https://curl.se/windows/`. Look for "Win64 - Generic" or "Win32 - Generic" depending on your Python interpreter's architecture. Download the `zip` file that includes `DLL`s e.g., `curl-8.x.x_x-win64-mingw.zip`.
2. Extract: Extract the contents of the `zip` file. You're looking for `libcurl.dll` and potentially `libcurl.lib` if you were linking C code directly.
3. Placement:
* Recommended: Place `libcurl.dll` in a directory that's already in your system's `PATH` environment variable e.g., `C:\Windows\System32`, though less common for third-party DLLs.
* Project-specific: Place `libcurl.dll` directly in the same directory as your Python script, or in a subfolder accessible to it.
* Environment Variable: Add the directory containing `libcurl.dll` to your system's `PATH` environment variable. This makes it discoverable by any application.
*Note: Ensure the architecture of `libcurl.dll` 32-bit or 64-bit matches your Python installation. A mismatch will lead to errors.*
# Installing the `curl-cffi` Python Package
Once `libcurl` is in place, installing the Python binding is straightforward using `pip`:
This command downloads and installs the `curl-cffi` package from PyPI.
During installation, `curl-cffi` will attempt to locate your system's `libcurl` shared library.
If it fails, you might see errors about `libcurl` not being found.
In such cases, double-check the `libcurl` installation steps, especially the `PATH` setup on Windows.
# Verifying the Installation
After installation, run a simple test to ensure everything is working:
response = requests.get"https://www.google.com"
printf"Successfully connected to Google. Status Code: {response.status_code}"
printf"Error during verification: {e}"
If this script runs without errors and prints a status code expected `200` or `301/302`, your `curl-cffi` setup is complete! If you encounter `DLL not found` Windows, `shared object not found` Linux, or `dylib not found` macOS errors, it's almost certainly an issue with `libcurl`'s path or availability.
Making Basic Requests: The `requests`-like API
One of the most appealing features of `curl-cffi` is its optional `requests`-like API.
For developers accustomed to Python's popular `requests` library, this provides a familiar and intuitive way to interact with `libcurl`'s power without deep into the CFFI layer.
It bridges the gap between low-level control and high-level convenience, making it an excellent choice for a wide range of tasks, particularly web scraping.
# The `requests` Module in `curl_cffi`
`curl_cffi.requests` mirrors much of the API of the standard `requests` library.
This means you can use familiar functions like `get`, `post`, `put`, `delete`, etc., along with similar arguments for headers, data, JSON, parameters, and timeouts.
The key difference lies under the hood: instead of Python's default HTTP stack, `curl-cffi` leverages `libcurl`.
# GET Requests
Fetching data is the most common operation.
# Basic GET request
response = requests.get"https://httpbin.org/get"
print"Response Body first 200 chars:\n", response.text
# With query parameters
params = {"name": "curl-cffi", "version": "1.0"}
response_with_params = requests.get"https://httpbin.org/get", params=params
print"\nResponse with parameters:"
printresponse_with_params.json # httpbin returns JSON for GET requests
Notice the `response.text` and `response.json` methods, which are direct parallels to the `requests` library.
# POST Requests
Sending data to a server is equally straightforward.
You can send form-encoded data, JSON, or raw bytes.
import json
# Sending form-encoded data dictionary
data_form = {"username": "user123", "password": "securepassword"}
response_form = requests.post"https://httpbin.org/post", data=data_form
print"\nPOST with form data:"
printresponse_form.json
# Sending JSON data dictionary passed to 'json' argument
data_json = {"product": "widget", "quantity": 5, "price": 99.99}
response_json = requests.post"https://httpbin.org/post", json=data_json
print"\nPOST with JSON data:"
printresponse_json.json
# Sending raw bytes
raw_payload = b"this is raw text payload"
response_raw = requests.post"https://httpbin.org/post", data=raw_payload, headers={"Content-Type": "text/plain"}
print"\nPOST with raw bytes:"
printresponse_raw.text # httpbin sometimes returns raw if content-type is not application/json
# Handling Headers, Cookies, and Timeouts
These essential features work as expected.
# Custom Headers
headers = {
"User-Agent": "MyCustomApp/1.0",
"Accept-Language": "en-US,en.q=0.9",
"X-Custom-Header": "Value"
}
response_headers = requests.get"https://httpbin.org/headers", headers=headers
print"\nResponse with custom headers:"
printresponse_headers.json
# Cookies
# requests automatically handles cookies received in 'Set-Cookie' headers.
# To send cookies:
cookies = {"mycookie": "myvalue", "sessionid": "abc123def"}
response_cookies = requests.get"https://httpbin.org/cookies", cookies=cookies
print"\nResponse with sent cookies:"
printresponse_cookies.json
# Timeout
# Set a timeout for the entire request connection + read
# If the request takes longer than 2 seconds, a Timeout exception is raised.
try:
response_timeout = requests.get"https://httpbin.org/delay/3", timeout=2
print"\nTimeout test should not be reached if timeout occurs:"
printresponse_timeout.text
except requests.exceptions.Timeout:
print"\nRequest timed out as expected after 2 seconds."
The `requests`-like API makes `curl-cffi` incredibly approachable while still giving you the underlying power of `libcurl`. This is where its true value shines for day-to-day web interactions where performance and resilience against bot detection are critical.
For instance, in a real-world scenario, using `curl-cffi` with `impersonate` discussed next and `timeout` can reduce connection failures by up to 15% on tricky sites compared to standard `requests`, simply due to `libcurl`'s robust error handling and connection management.
Advanced Features: Impersonation and Beyond
This is where `curl-cffi` truly differentiates itself from other Python HTTP libraries.
While standard libraries often struggle with modern web security measures, `curl-cffi` provides sophisticated tools, especially "impersonation," to bypass common bot detection techniques.
It's a must for web scraping and interacting with services that are highly protective against automated access.
# Browser Impersonation
Many websites use advanced bot detection systems that analyze not just headers and user agents, but also the nuances of the underlying TCP/TLS handshake and HTTP/2 frames.
This is known as "TLS fingerprinting" like Ja3, Jarm or "HTTP/2 fingerprinting" like Akamai's bot manager. Standard Python libraries typically have predictable fingerprints that are easily identified and blocked.
`curl-cffi` leverages `libcurl`'s capabilities to precisely mimic the network characteristics of real web browsers.
This is achieved through the `impersonate` argument in its `requests`-like API.
# Impersonate Chrome 101 on a GET request
response_chrome = requests.get"https://www.google.com", impersonate="chrome101"
printf"Impersonating Chrome 101. Status Code: {response_chrome.status_code}"
printf"Headers: {response_chrome.headers.get'Server'}"
# Impersonate Firefox 99 on a POST request
data = {"key": "value"}
response_firefox = requests.post"https://httpbin.org/post", json=data, impersonate="firefox99"
printf"\nImpersonating Firefox 99. Status Code: {response_firefox.status_code}"
printf"Headers from POST: {response_firefox.json}"
# Impersonate Safari 15.5
response_safari = requests.get"https://example.com", impersonate="safari15_5"
printf"\nImpersonating Safari 15.5. Status Code: {response_safari.status_code}"
printf"Impersonation Request error: {e}"
printf"An unexpected error occurred during impersonation: {e}"
# List of available impersonation profiles:
# 'chrome99', 'chrome100', 'chrome101', 'chrome104', 'chrome107', 'chrome110', 'chrome116', 'chrome120', 'chrome99_android', 'chrome101_android'
# 'edge99', 'edge101'
# 'firefox99', 'firefox104'
# 'safari15_3', 'safari15_5', 'safari16_0'
# 'opera85', 'opera90'
# 'ios_chrome101', 'ios_firefox101'
# 'okhttp', 'curl_8_0' useful for mimicking the curl command line tool itself
How Impersonation Works:
When you specify `impersonate="chrome101"`, `curl-cffi` configures `libcurl` to:
* Use the exact TLS handshake parameters cipher suites, elliptic curves, extensions, and their order that Chrome 101 would use.
* Send HTTP/2 settings frames and pseudo-headers in the precise order and with the exact values that Chrome 101 would.
* Set the User-Agent header to match Chrome 101's default.
* Potentially adjust other subtle network behaviors.
This level of detail makes your Python script's requests incredibly difficult to distinguish from a real browser, significantly improving success rates against anti-bot solutions like Cloudflare's Bot Management, PerimeterX, Akamai, and reCAPTCHA.
Studies show that correctly implemented TLS fingerprinting can block over 80% of unsophisticated bot traffic.
# Proxies and Authentication
`curl-cffi` fully supports various proxy types, including HTTP, HTTPS, SOCKS4, SOCKS4A, and SOCKS5, with or without authentication.
This is crucial for maintaining anonymity or bypassing geographical restrictions.
# Example with an HTTP proxy
http_proxy = "http://username:password@your_proxy_address:port"
# Example with a SOCKS5 proxy
socks5_proxy = "socks5://your_socks_proxy:port"
# Using an HTTP proxy
response_proxy = requests.get"https://httpbin.org/ip", proxies={"http": http_proxy, "https": http_proxy}
printf"\nResponse from HTTP proxy: {response_proxy.json}"
# Using a SOCKS5 proxy if you have one configured
# response_socks5 = requests.get"https://httpbin.org/ip", proxies={"http": socks5_proxy, "https": socks5_proxy}
# printf"Response from SOCKS5 proxy: {response_socks5.json}"
# Basic Authentication
# The 'auth' argument takes a tuple of username, password
response_auth = requests.get"https://httpbin.org/basic-auth/user/passwd", auth="user", "passwd"
printf"\nBasic Auth Status Code: {response_auth.status_code}"
printf"Basic Auth Content: {response_auth.text}"
printf"Proxy/Auth Request error: {e}"
printf"An unexpected error occurred during proxy/auth: {e}"
# File Uploads and Downloads
`curl-cffi` handles file operations efficiently.
For uploads, you can use the `files` argument similar to `requests`. For downloads, `libcurl`'s stream-like capabilities are inherently efficient.
import os
# --- File Upload ---
# Create a dummy file for upload
dummy_file_path = "dummy_upload.txt"
with opendummy_file_path, "w" as f:
f.write"This is a test file for upload."
with opendummy_file_path, "rb" as f:
files = {"upload_file": f}
# httpbin.org/post handles file uploads
response_upload = requests.post"https://httpbin.org/post", files=files
print"\nFile Upload Response:"
printresponse_upload.json # 'files' key shows the uploaded content
printf"File upload error: {e}"
printf"An unexpected error occurred during file upload: {e}"
if os.path.existsdummy_file_path:
os.removedummy_file_path # Clean up dummy file
# --- File Download ---
download_url = "https://www.example.com/some_large_file.zip" # Replace with a real large file for testing
output_file_path = "downloaded_file.html" # Using .html for example.com
# For large files, stream=True is crucial to avoid loading entire content into memory
with requests.getdownload_url, stream=True as r:
r.raise_for_status # Raise an exception for bad status codes
total_length = r.headers.get'content-length'
downloaded = 0
if total_length is not None:
total_length = inttotal_length
with openoutput_file_path, 'wb' as f:
for chunk in r.iter_contentchunk_size=8192: # Iterate over chunks
if chunk: # filter out keep-alive new chunks
f.writechunk
downloaded += lenchunk
if total_length:
# Simple progress indicator for large files
progress = downloaded / total_length * 100
printf"\rDownloading: {progress:.2f}%", end=''
printf"\nFile downloaded successfully to {output_file_path}"
printf"File download error: {e}"
printf"An unexpected error occurred during file download: {e}"
if os.path.existsoutput_file_path:
# os.removeoutput_file_path # Uncomment to clean up downloaded file
pass # Keep the file for inspection
The `stream=True` and `iter_content` pattern is vital for efficient large file handling, preventing memory exhaustion.
`libcurl` handles the underlying streaming beautifully.
In summary, `curl-cffi` goes beyond typical HTTP client capabilities, offering a robust solution for challenging web interactions.
Its impersonation features alone can elevate your scraping and automation projects from being easily detected to blending in seamlessly with real browser traffic.
Low-Level `Curl` Object Usage: Unlocking Raw Power
While the `requests`-like API in `curl-cffi` is convenient, the true strength and flexibility of the library lie in direct interaction with the `Curl` object. This gives you direct access to the vast array of `libcurl` options `CURLOPT_*`, allowing for incredibly fine-tuned control over every aspect of your network requests. This is where you can implement highly specialized scenarios, optimize for performance, or troubleshoot complex connection issues.
# Initializing and Setting Options
The core of using the low-level API involves:
1. Creating an instance of `curl_cffi.Curl`.
2. Setting options using `c.setoptCurlOpt.OPTION_NAME, value`.
3. Performing the request with `c.perform`.
4. Cleaning up with `c.close`.
from curl_cffi import Curl, CurlOpt, lib
import io
# 1. Set the URL
c.setoptCurlOpt.URL, b"https://httpbin.org/get"
# 2. Configure a User-Agent string
# This is a basic header, not full impersonation like the 'requests' API 'impersonate' argument
c.setoptCurlOpt.USERAGENT, b"MyCustomCurlApp/1.0 Python curl-cffi"
# 3. Enable verbose output useful for debugging
# This will print detailed information about the request/response to stderr
c.setoptCurlOpt.VERBOSE, 1
# 4. Handle response body and headers
# libcurl works with write functions that receive chunks of data.
# We provide Python callables methods of bytearray or custom functions to handle this.
response_body = bytearray
response_headers = bytearray
# Define a write function for the body
c.setoptCurlOpt.WRITEFUNCTION, response_body.extend
# Define a write function for the headers
c.setoptCurlOpt.HEADERFUNCTION, response_headers.extend
# 5. Perform the request
# 6. Get status code and error info
http_code = c.getinfoCurlOpt.HTTP_CODE
total_time = c.getinfoCurlOpt.TOTAL_TIME
print"\n--- Low-Level Curl Request Details ---"
printf"HTTP Status Code: {http_code}"
printf"Total Time Taken: {total_time:.3f} seconds"
print"\nResponse Headers:\n", response_headers.decode'utf-8'
print"\nResponse Body first 500 chars:\n", response_body.decode'utf-8'
printf"An error occurred during low-level curl operation: {e}"
# Always close the Curl object to release system resources
c.close
# Advanced `CurlOpt` Examples
The `CurlOpt` enum contains hundreds of options. Here are a few common and powerful ones:
* `CurlOpt.PROXY` and `CurlOpt.PROXYUSERNAME`/`CurlOpt.PROXYPASSWORD`: For proxy configuration.
```python
c = Curl
c.setoptCurlOpt.URL, b"https://httpbin.org/ip"
c.setoptCurlOpt.PROXY, b"http://your_proxy_address:port"
c.setoptCurlOpt.PROXYUSERNAME, b"proxyuser"
c.setoptCurlOpt.PROXYPASSWORD, b"proxypass"
# ... perform and close
* `CurlOpt.TIMEOUT` and `CurlOpt.CONNECTTIMEOUT`: Setting request timeouts. `TIMEOUT` is total, `CONNECTTIMEOUT` is for connection phase only.
c.setoptCurlOpt.URL, b"https://httpbin.org/delay/5"
c.setoptCurlOpt.TIMEOUT, 3 # Total timeout in seconds
# c.setoptCurlOpt.CONNECTTIMEOUT, 10 # Connection timeout in seconds
# ... perform, expect timeout error, and close
* `CurlOpt.COOKIEFILE` and `CurlOpt.COOKIEJAR`: For persistent cookie management. `COOKIEFILE` reads cookies from a file, `COOKIEJAR` writes them to a file after the request.
c.setoptCurlOpt.URL, b"https://httpbin.org/cookies/set?foo=bar"
c.setoptCurlOpt.COOKIEJAR, b"my_cookies.txt" # Save cookies to this file
c2 = Curl
c2.setoptCurlOpt.URL, b"https://httpbin.org/cookies"
c2.setoptCurlOpt.COOKIEFILE, b"my_cookies.txt" # Load cookies from this file
c2.perform
# ... read response, should see 'foo=bar'
c2.close
* `CurlOpt.FOLLOWLOCATION`: Automatically follow HTTP 3xx redirects.
c.setoptCurlOpt.URL, b"https://httpbin.org/redirect/1"
c.setoptCurlOpt.FOLLOWLOCATION, 1 # Enable redirects
# ...
* `CurlOpt.HTTPHEADER`: Manually setting custom request headers. This requires a list of byte strings.
c.setoptCurlOpt.URL, b"https://httpbin.org/headers"
headers =
b"X-Custom-Header: MyValue",
b"Another-Header: AnotherValue"
c.setoptCurlOpt.HTTPHEADER, headers
# Error Handling with `CurlCode` and `CurlInfo`
When using the low-level API, error handling often involves checking `libcurl`'s return codes.
`c.perform` will raise a `CurlE` exception if a `libcurl` error occurs.
You can also get more detailed info using `c.getinfo`.
from curl_cffi import Curl, CurlOpt, CurlE
c.setoptCurlOpt.URL, b"http://nonexistent.domain.xyz" # Intentional bad URL
except CurlE as e:
printf"Curl error caught: {e}"
# e.args contains the CurlCode enum value
# You can compare it with CurlCode.CURLE_UNSUPPORTED_PROTOCOL, etc.
if e.args == CurlE.PEER_FAILED_VERIFICATION:
print"SSL certificate validation failed."
elif e.args == CurlE.COULDNT_RESOLVE_HOST:
print"Could not resolve host."
printf"libcurl error code: {e.args}"
printf"General error: {e}"
The low-level `Curl` object provides maximum power and flexibility, making `curl-cffi` an incredibly potent tool for complex networking tasks that require precise control beyond what typical HTTP libraries offer.
This deep integration is particularly beneficial when you're troubleshooting network issues, working with custom protocols, or optimizing for very specific performance profiles where microseconds matter.
Concurrency with `CurlMulti`: Parallel Requests
For tasks involving many network requests, performing them sequentially can be a major bottleneck.
`curl-cffi` provides a powerful solution for this: the `CurlMulti` interface.
This allows you to manage multiple `Curl` objects concurrently, significantly speeding up data fetching, especially when dealing with many different endpoints or pages.
`CurlMulti` leverages `libcurl`'s highly optimized multi-interface, which efficiently handles I/O multiplexing waiting for data on multiple sockets without requiring separate threads for each request.
# The Power of `CurlMulti`
The `CurlMulti` object acts as a container for individual `Curl` objects.
You add `Curl` handles to it, and then `CurlMulti` takes over the responsibility of polling these handles for I/O readiness, performing transfers as data becomes available. This is non-blocking and highly efficient.
Key advantages:
* Reduced Latency: By performing requests in parallel, you don't wait for one request to complete before starting the next.
* Efficient Resource Usage: `libcurl` optimizes socket and connection reuse across multiple handles within the `CurlMulti` context.
* Scalability: Easily manage hundreds or even thousands of concurrent requests.
* No Threading Overhead for basic I/O: While you can use `CurlMulti` with Python's `threading` or `asyncio`, its core efficiency comes from `libcurl`'s internal event loop, not from Python threads for each network operation.
# Basic `CurlMulti` Example
Let's fetch data from multiple URLs concurrently.
from curl_cffi import Curl, CurlOpt, CurlMulti, CurlMultiE
import time
urls =
b"https://httpbin.org/delay/1", # Introduce a small delay
b"https://httpbin.org/get",
b"https://httpbin.org/user-agent",
b"https://httpbin.org/ip",
# Dictionary to store responses indexed by original URL
responses = {}
# Create a CurlMulti object
m = CurlMulti
# Add individual Curl handles to the multi object
active_handles = {} # To keep track of which Curl object belongs to which URL
for url in urls:
c.setoptCurlOpt.URL, url
# Store response body for each handle
body_buffer = bytearray
c.setoptCurlOpt.WRITEFUNCTION, body_buffer.extend
c.body_buffer = body_buffer # Attach buffer to Curl object for later retrieval
m.add_handlec
active_handles = url # Store the original URL for mapping
print"Starting concurrent requests..."
start_time = time.time
# Loop until all transfers are complete
running_handles = lenurls
while running_handles:
# Perform a "tick" on the multi handle. This will do I/O on active handles.
# The 'perform' method returns the number of still running handles.
ret, running_handles = m.perform
if ret != CurlMultiE.OK:
# Handle potential errors from multi.perform
printf"CurlMulti error during perform: {ret}"
break
# If nothing is running, but we expect more, we need to wait for activity
if running_handles > 0:
# Wait for activity on any of the sockets associated with the handles.
# This is a blocking call, but only for up to 1 second or less if activity occurs.
ret, num_fds = m.wait1000 # Wait up to 1000 milliseconds
if ret != CurlMultiE.OK:
printf"CurlMulti error during wait: {ret}"
break
# Get results from completed transfers
while True:
# m.info_read returns a tuple: msgs_in_queue, messages_list
# messages_list contains information about completed transfers
ret, msgs = m.info_read
printf"CurlMulti error during info_read: {ret}"
if not msgs: # No more messages in the queue
for msg in msgs:
# msg is a tuple: message_type, curl_handle, result_code
# curl_handle is the CFFI pointer to the Curl object
# result_code is a CurlCode indicating success or failure of that specific transfer
# You need to retrieve the original Python Curl object from its C handle
# curl_cffi provides m.get_handle_by_c_handle for this.
c_obj = m.get_handle_by_c_handlemsg
url = active_handles.getc_obj.handle, b"UNKNOWN_URL".decode'utf-8'
if msg == CurlMultiE.OK:
responses = c_obj.body_buffer.decode'utf-8'
printf"Completed: {url} - Size: {lenc_obj.body_buffer} bytes"
else:
error_code = c_obj.getinfoCurlOpt.HTTP_CODE # Or another relevant error info
printf"Failed: {url} - Error Code: {msg} HTTP: {error_code}"
# Remove the handle from the multi object and close it
m.remove_handlec_obj
c_obj.close
del active_handles # Remove from tracking
end_time = time.time
printf"\nAll requests completed in {end_time - start_time:.3f} seconds."
# Print some responses
for url, content in responses.items:
printf"\nResponse for {url}:"
printcontent + "..." # Print first 100 chars
Explanation of the `CurlMulti` Loop:
1. `m.perform`: This is the core function. It tells `libcurl` to do as much work as possible on all currently active handles. It sends data, receives data, handles redirects, etc. It returns a `CurlMultiE` status code and the number of still-running handles. You need to keep calling `perform` until `running_handles` is zero.
2. `m.waittimeout`: If `perform` has done all it can for now i.e., it's waiting for network I/O, `wait` tells `libcurl` to block wait until there's activity on any of its sockets, or until the `timeout` in milliseconds expires. This prevents busy-waiting.
3. `m.info_read`: After `perform` completes handles, their results are queued. `info_read` retrieves messages from this queue. Each message indicates a completed transfer success or failure and provides the `Curl` handle that finished.
4. Cleanup: It's crucial to `remove_handlec_obj` from the `CurlMulti` and `c_obj.close` the individual `Curl` object once it's done to free resources.
# Considerations for Scalability
* Resource Limits: Be mindful of your system's open file descriptor limits, especially if you plan to run thousands of concurrent requests. Each `Curl` handle consumes a file descriptor for its socket.
* Error Handling: Implement robust error handling for individual `Curl` transfers within the `CurlMulti` loop.
* Callbacks: For more complex scenarios, you might use `CurlOpt.PROGRESSFUNCTION` or `CurlOpt.XFERINFOFUNCTION` with the low-level `Curl` objects inside `CurlMulti` to get real-time transfer progress.
* Network Stability: While `CurlMulti` is efficient, network instability can still lead to timeouts or failures. Implement retry logic if necessary.
The `CurlMulti` interface is the backbone for high-performance, concurrent networking with `curl-cffi`. It provides a flexible and efficient way to manage a large number of requests without the complexities of managing threads or asyncio tasks directly for each individual network operation, as `libcurl` handles the heavy lifting in its optimized C core.
Troubleshooting and Best Practices
Even with powerful tools like `curl-cffi`, you might encounter issues.
Knowing how to troubleshoot and following best practices can save you hours of debugging.
This section covers common pitfalls, debugging techniques, and general advice for robust network operations.
# Common Issues and Solutions
1. `libcurl` not found error e.g., `OSError: The specified module could not be found` or `Error loading shared library libcurl.so.4`:
* Cause: `curl-cffi` cannot locate the `libcurl` shared library on your system.
* Solution:
* Linux/macOS: Ensure `libcurl` and its development packages `libcurl4-openssl-dev` on Debian/Ubuntu, `libcurl-devel` on RHEL/CentOS are correctly installed. Check your package manager.
* Windows: This is common. Make sure `libcurl.dll` matching your Python's architecture - 32-bit or 64-bit is:
* In a directory listed in your system's `PATH` environment variable.
* In the same directory as your Python script.
* In your `PythonXX\DLLs` directory less recommended.
* Verification: Try running a simple `curl` command in your terminal `curl --version`. If it works, the library is likely installed correctly on your system, and the issue might be with Python's access to it.
2. `requests.exceptions.RequestsError: Error Code: 60 Peer certificate cannot be authenticated with given CA certificates`:
* Cause: SSL certificate verification failed. This can happen if:
* The server's certificate is invalid or expired.
* Your system's CA certificate bundle is outdated.
* You're using a proxy that intercepts SSL traffic MITM proxy and its certificate isn't trusted.
* Update CA Certs: On Linux, `sudo apt-get install ca-certificates` or `sudo update-ca-certificates`.
* Explicitly Trust CA if necessary: If you're using a corporate proxy or self-signed certs, you might need to specify a `verify` argument path to CA bundle or, as a *last resort* and only for testing/trusted environments, disable verification `verify=False`. Disabling verification is a security risk for production.
* Check Time: Ensure your system's clock is correct. Certificate validity depends on accurate time.
3. Hanging Requests or Timeouts:
* Cause: Network issues, unresponsive servers, or insufficient timeout settings.
* Set `timeout`: Always use the `timeout` parameter in `requests.get/post` or `CurlOpt.TIMEOUT` for low-level `Curl`. A reasonable default is 5-10 seconds for connection, and another 10-30 seconds for reading, depending on the expected response size.
* Check Connectivity: Can you reach the URL with a standard browser or `curl` command?
* Proxy Issues: If using a proxy, ensure it's functional and correctly configured.
4. Bot Detection Issues even with `impersonate`:
* Cause: While `impersonate` is powerful, advanced bot detection uses multiple signals e.g., JavaScript execution, mouse movements, IP reputation, headless browser detection.
* Rotate `impersonate` profiles: Try different browser versions `chrome101`, `firefox99`, etc..
* Use Proxies: Combine `impersonate` with residential or high-quality datacenter proxies to avoid IP blacklisting.
* Cookie Management: Ensure you're handling cookies correctly session cookies are crucial. `curl-cffi` handles them automatically with the `requests`-like API.
* Rate Limiting: Implement delays between requests to mimic human browsing patterns. `time.sleep` is your friend.
* Consider Headless Browsers: For very complex JavaScript-heavy sites, a tool like Playwright or Selenium might still be necessary, though they are much heavier on resources.
# Debugging Techniques
1. Verbose Output `CurlOpt.VERBOSE`:
* When using the low-level `Curl` object, `c.setoptCurlOpt.VERBOSE, 1` is invaluable. It prints detailed `libcurl` communication TLS handshakes, headers sent/received, redirect logic to `stderr`. This gives you deep insights into what `libcurl` is doing.
* For the `requests`-like API, you can enable verbose mode by setting the `CURL_CFFI_VERBOSE=1` environment variable before running your script:
export CURL_CFFI_VERBOSE=1 # Linux/macOS
# or on Windows: set CURL_CFFI_VERBOSE=1
python your_script.py
2. Inspect Response Objects:
* Always inspect `response.status_code`, `response.headers`, and `response.text` or `response.json` to understand the server's reply.
* `response.url` shows the final URL after redirects.
* `response.request.headers` shows the exact headers sent.
3. Compare with `curl` Command Line:
* If a request fails in Python, try to replicate it using the command-line `curl` tool.
* Example: `curl -v -H "User-Agent: MyCustomUserAgent" "https://example.com/api"`.
* If the command-line `curl` works, it helps narrow down the problem to your Python code or `curl-cffi`'s configuration.
# Best Practices
1. Resource Management Always `close`:
* When using the low-level `Curl` object, always call `c.close` in a `finally` block to ensure resources are released, even if errors occur.
* The `requests`-like API handles this automatically, but if you delve into the `Curl` object directly, remember to clean up.
* For `CurlMulti`, ensure you `remove_handle` and `close` each `Curl` object once its transfer is complete.
2. Error Handling:
* Use `try...except requests.exceptions.RequestsError` for high-level errors with the `requests`-like API.
* For low-level `Curl` operations, catch `CurlE` exceptions.
* Implement retry logic for transient network errors e.g., 5xx server errors, temporary connection issues.
3. Be a Good Netizen:
* Respect `robots.txt`: Check `robots.txt` before scraping. Not doing so can lead to IP blocks and is unethical.
* Rate Limiting: Don't hammer servers with too many requests too quickly. Implement `time.sleep` between requests or use a library for rate limiting.
* User-Agent: Always set a descriptive `User-Agent` even if impersonating. Don't use default "Python-requests" or similar that clearly identifies you as a script.
4. Use Context Managers for `requests` if applicable:
* While `curl_cffi.requests` doesn't strictly require `with` statements like some other libraries, it's a good general Python practice for resources.
* For streaming downloads, `with requests.geturl, stream=True as r:` ensures the connection is closed when the block exits.
5. Environment Variables:
* Be aware that `libcurl` itself can be influenced by environment variables like `HTTP_PROXY`, `HTTPS_PROXY`, `NO_PROXY`. Ensure these are not unintentionally interfering with your `curl-cffi` requests, especially if you're specifying proxies programmatically.
By understanding these common issues, utilizing debugging tools, and adhering to best practices, you can build robust and reliable network applications with `curl-cffi`, leveraging its unique capabilities to the fullest.
Comparison with Other Python HTTP Libraries
Python's ecosystem offers a rich variety of HTTP client libraries, each with its strengths and use cases.
While `requests` is the de facto standard for general-purpose HTTP interactions, and `httpx` brings async capabilities, `curl-cffi` carves out its niche by directly integrating with `libcurl`. Understanding these distinctions is crucial for choosing the right tool for your specific needs.
# `requests` Synchronous/Blocking
* Pros:
* Ease of Use: Extremely user-friendly API, very intuitive for most common HTTP operations.
* Readability: Code is clean and easy to understand.
* Rich Ecosystem: Large community, extensive documentation, and many third-party integrations.
* Pythonic: Fully written in Python, integrates well with other Python libraries.
* Cons:
* Synchronous by default: Blocks the main thread until a response is received, which can be inefficient for concurrent requests without threading or multiprocessing.
* No Native Async: Requires external libraries like `requests-futures` or manual threading for concurrency.
* Limited Low-Level Control: Abstracts away many network details. fine-tuning TCP/TLS is difficult.
* Bot Detection: Its predictable TLS/HTTP/2 fingerprints are often easily detected by advanced anti-bot systems.
* Best For:
* Simple API integrations.
* General web browsing/data fetching where anti-bot measures are minimal.
* Beginners and rapid prototyping.
# `httpx` Synchronous & Asynchronous
* Async Support: Natively supports `async/await`, making it excellent for high-concurrency applications without thread overhead.
* HTTP/2 Support: First-class support for HTTP/2.
* Type Hinting: Modern codebase with good type hints.
* Sync & Async API: Provides both synchronous and asynchronous interfaces, allowing flexibility.
* HTTP/1.1 and HTTP/2 Protocol Negotiation: Handles this intelligently.
* Performance relative to `libcurl`: While fast for Python, it's still a pure Python implementation for networking, potentially slower than `libcurl` for very high-volume or specific network conditions.
* TLS Fingerprinting: Shares similar limitations with `requests` regarding detectable TLS/HTTP/2 fingerprints.
* Newer Ecosystem: Community and integrations are growing but not as vast as `requests`.
* Asynchronous web applications e.g., FastAPI, Aiohttp.
* High-concurrency API requests in an async context.
* Modern Python projects prioritizing async.
# `curl-cffi` Synchronous/Blocking, `libcurl`-powered
* `libcurl` Performance: Inherits the speed and efficiency of the C-based `libcurl` library.
* Advanced Control: Access to hundreds of `libcurl` options for fine-grained network tuning.
* Browser Impersonation: Uniquely capable of mimicking real browser TLS/HTTP/2 fingerprints to bypass advanced bot detection. This is its killer feature.
* Robustness: `libcurl` is battle-tested and known for its reliability in complex network environments.
* Multi-Interface `CurlMulti`: Excellent for managing many concurrent requests efficiently without explicit Python threading.
* Dependency on `libcurl`: Requires `libcurl` to be installed on the system, which can be a hurdle, especially on Windows.
* Steeper Learning Curve low-level API: While the `requests`-like API is easy, utilizing the raw `Curl` object requires understanding `libcurl` concepts and options.
* Synchronous Core: The direct `Curl` object is blocking. While `CurlMulti` helps with concurrency, it's not a native `asyncio` solution out of the box though it can be integrated.
* Smaller Python Community: Less documentation and fewer examples than `requests`.
* Web scraping of highly protected websites.
* Applications requiring maximum performance and low-level network control.
* When bypassing bot detection TLS/HTTP/2 fingerprinting is a primary concern.
* When working with a very large number of concurrent requests that can benefit from `libcurl`'s multi-interface.
# Feature Comparison Table
| Feature | `requests` | `httpx` | `curl-cffi` |
| :----------------------- | :------------------ | :------------------ | :------------------ |
| Backend | Python sockets | Python sockets | `libcurl` C |
| Async Support | No via add-ons | Yes native | No direct. `CurlMulti` for concurrency |
| HTTP/2 | No | Yes | Yes via `libcurl` |
| TLS Fingerprinting | Detectable | Detectable | Mimics Browsers |
| Low-Level Control | Limited | Moderate | Extensive `CURLOPT` |
| Proxy Support | Basic | Basic | Advanced all types, auth |
| Cookie Handling | Automatic | Automatic | Automatic `requests`-like |
| Dependency external| None | None | `libcurl` system |
| Performance | Good | Very Good | Excellent |
| Learning Curve | Easy | Moderate | Moderate requests-like / Advanced low-level |
# Conclusion on Choice
* For most everyday Python HTTP tasks, especially if you're not dealing with highly protected websites or extreme performance requirements, stick with `requests`. Its simplicity and vast community support are unbeatable.
* If your project is built with `asyncio` or you need native HTTP/2 support in an asynchronous context, `httpx` is your go-to.
* If you are engaged in web scraping, need to bypass sophisticated anti-bot measures, require absolute maximum network performance, or need very fine-grained control over network protocols, `curl-cffi` is the superior choice. It's a specialized tool that excels in challenging network environments by leveraging the robustness of `libcurl`.
Understanding these distinctions helps you make an informed decision, ensuring you pick the most appropriate tool for your specific project's needs.
Integrating `curl-cffi` with Asynchronous Python e.g., `asyncio`
While `curl-cffi`'s core `Curl` object and `CurlMulti` operate synchronously from Python's perspective they block until `libcurl` completes its operation or waits for I/O, it's entirely possible and often desirable to integrate them into an `asyncio` application.
This allows you to combine the low-level power and impersonation capabilities of `curl-cffi` with the non-blocking concurrency model of `asyncio`.
The key is to run the synchronous `curl-cffi` operations in a separate thread, typically using `loop.run_in_executor`. This prevents the blocking `curl-cffi` calls from freezing your main `asyncio` event loop.
# The `run_in_executor` Pattern
The `asyncio` event loop has a `run_in_executor` method designed precisely for this.
It takes a callable your synchronous function and its arguments, schedules it to run in a `ThreadPoolExecutor` by default, and returns a Future that your `asyncio` code can `await`.
import asyncio
from concurrent.futures import ThreadPoolExecutor
from curl_cffi import Curl, CurlOpt, CurlMulti, CurlMultiE, requests
# --- Example 1: Using requests-like API with run_in_executor ---
async def fetch_url_async_requestsurl, impersonate_profile=None:
"""Fetches a URL using curl_cffi.requests in a separate thread."""
printf" Starting async fetch for {url} requests-like..."
loop = asyncio.get_running_loop
# Run the synchronous requests.get call in the default thread pool executor
response = await loop.run_in_executor
None, # Use the default ThreadPoolExecutor
lambda: requests.geturl, impersonate=impersonate_profile, timeout=10
printf" Finished async fetch for {url} status: {response.status_code}."
return response.text # Return first 100 chars
except requests.exceptions.RequestsError as e:
printf" Error fetching {url}: {e}"
return None
except Exception as e:
printf" Unexpected error for {url}: {e}"
# --- Example 2: Using low-level Curl object with run_in_executor ---
def _sync_fetch_low_levelurl_bytes, user_agent_bytes:
"""Synchronous function for low-level Curl operations."""
body_buffer = bytearray
c.setoptCurlOpt.URL, url_bytes
c.setoptCurlOpt.USERAGENT, user_agent_bytes
c.setoptCurlOpt.WRITEFUNCTION, body_buffer.extend
c.setoptCurlOpt.TIMEOUT, 10 # Set timeout for the low-level Curl
c.perform
return body_buffer.decode'utf-8'
printf" _sync_fetch_low_level error for {url_bytes.decode}: {e}"
finally:
c.close
async def fetch_url_async_low_levelurl, user_agent:
"""Fetches a URL using low-level Curl in a separate thread."""
printf" Starting async fetch for {url} low-level..."
# Pass byte strings to the synchronous function
result = await loop.run_in_executor
None,
_sync_fetch_low_level,
url.encode'utf-8',
user_agent.encode'utf-8'
printf" Finished async fetch for {url} low-level, result: {result is not None}."
return result
# --- Main Async Function to Orchestrate ---
async def main:
start_time = time.time
urls_requests =
"https://httpbin.org/delay/2",
"https://httpbin.org/user-agent",
"https://httpbin.org/headers"
urls_low_level =
"https://httpbin.org/ip",
"https://httpbin.org/status/200"
tasks_requests =
fetch_url_async_requestsurl, impersonate_profile="chrome101"
for url in urls_requests
tasks_low_level =
fetch_url_async_low_levelurl, f"MyAsyncCurlApp/{i}"
for i, url in enumerateurls_low_level
all_tasks = tasks_requests + tasks_low_level
results = await asyncio.gather*all_tasks
printf"\n All tasks completed in {time.time - start_time:.2f} seconds."
for i, res in enumerateresults:
printf"Result {i+1}: {res if res else 'Failed'}"
if __name__ == "__main__":
asyncio.runmain
Key Takeaways for Async Integration:
1. Separate Synchronous Logic: Encapsulate your `curl-cffi` calls whether `requests`-like or low-level `Curl` object within regular synchronous Python functions. These are the functions that will be run in the executor.
2. `loop.run_in_executorNone, callable, *args`: This is the core of the integration.
* `None` means use the default `ThreadPoolExecutor`. You can also provide your own custom executor if you need more control over thread management.
* `callable` is your synchronous function.
* `*args` are the arguments to pass to your synchronous function.
3. `await` the Future: `run_in_executor` returns a `Future` object. You `await` this Future in your `async` function. The `asyncio` event loop will continue processing other tasks while the executor thread handles the blocking HTTP request.
4. Error Handling: Implement `try...except` blocks around your `await loop.run_in_executor` calls to catch exceptions that might arise from the `curl-cffi` operations in the separate thread.
5. Data Types: Remember that `libcurl` often prefers byte strings for URLs, headers, and body data. If you're using the low-level `Curl` object directly, ensure you encode your Python strings to bytes e.g., `url.encode'utf-8'` before passing them to the synchronous function that will interact with `CurlOpt`. The `requests`-like API handles this conversion for you.
# When to Use `CurlMulti` vs. `run_in_executor`
* `run_in_executor`: Ideal for integrating `curl-cffi` into an existing `asyncio` application where you have a mix of async and blocking I/O, or when you only have a moderate number of concurrent `curl-cffi` requests e.g., dozens to a few hundred. It's simpler to set up for individual blocking calls.
* `CurlMulti`: While `CurlMulti` itself is synchronous, it is highly optimized for running *many* `libcurl` operations concurrently *within a single thread*. If your primary task is making thousands of highly concurrent HTTP requests, and you're willing to manage the `CurlMulti` loop explicitly potentially within its own dedicated thread if integrating with `asyncio`, it can be more resource-efficient than spawning a new thread for every single request. However, integrating `CurlMulti` cleanly into `asyncio` requires more complex patterns e.g., running the `CurlMulti` loop in a dedicated thread and using `asyncio.Queue` for results. For most common async integrations, `run_in_executor` is sufficient and simpler.
By combining the powerful `libcurl` features offered by `curl-cffi` with `asyncio`'s efficient concurrency model, you can build high-performance, robust, and anti-detection-resilient web applications in Python.
Frequently Asked Questions
# What is `curl-cffi` in Python?
`curl-cffi` is a Python library that provides CFFI C Foreign Function Interface bindings to `libcurl`, the popular C-based client-side URL transfer library.
It allows Python developers to leverage the powerful and battle-tested `libcurl` directly from Python, offering a high-performance and feature-rich alternative to other HTTP client libraries.
# Why should I use `curl-cffi` instead of Python's built-in `requests` library?
You should consider `curl-cffi` when you need superior performance, granular control over network requests, or, most importantly, the ability to bypass advanced bot detection mechanisms.
`curl-cffi` can mimic real browser TLS and HTTP/2 fingerprints, making your requests much harder to distinguish from legitimate browser traffic, unlike standard `requests` which often gets flagged.
# How do I install `curl-cffi`?
First, ensure `libcurl` the C library is installed on your operating system e.g., `libcurl4-openssl-dev` on Ubuntu, `brew install curl` on macOS, or download DLLs on Windows. Then, install the Python package using pip: `pip install curl-cffi`.
# Does `curl-cffi` support both synchronous and asynchronous operations?
`curl-cffi`'s core `Curl` object and `CurlMulti` interfaces are synchronous blocking. However, you can easily integrate `curl-cffi` into `asyncio` applications by running its blocking operations in a separate thread pool using `loop.run_in_executor`, which allows `asyncio` to continue processing other tasks concurrently.
# What is "impersonation" in `curl-cffi`?
Impersonation in `curl-cffi` refers to its unique ability to mimic the exact network characteristics like TLS handshake parameters, HTTP/2 settings, and header order of real web browsers such as Chrome, Firefox, or Safari.
This makes your automated requests appear as if they are coming from a genuine browser, helping to bypass sophisticated bot detection systems.
# Can `curl-cffi` handle proxies and authentication?
Yes, `curl-cffi` fully supports various proxy types HTTP, HTTPS, SOCKS4/5 and authentication mechanisms e.g., Basic Auth, proxy authentication. You can configure these using the `proxies` argument in the `requests`-like API or via `CurlOpt.PROXY`, `CurlOpt.PROXYUSERNAME`, etc., in the low-level `Curl` object.
# How do I enable verbose debugging output in `curl-cffi`?
For the low-level `Curl` object, use `c.setoptCurlOpt.VERBOSE, 1`. For the `requests`-like API, set the environment variable `CURL_CFFI_VERBOSE=1` before running your Python script e.g., `export CURL_CFFI_VERBOSE=1` on Linux/macOS or `set CURL_CFFI_VERBOSE=1` on Windows.
# Is `curl-cffi` faster than `requests`?
Generally, yes.
Because `curl-cffi` leverages the highly optimized C library `libcurl` directly, it often offers superior performance, especially for high-volume requests, large data transfers, and scenarios involving complex SSL/TLS handshakes, compared to Python-native HTTP implementations.
# What is `CurlMulti` and when should I use it?
`CurlMulti` is `curl-cffi`'s interface to `libcurl`'s multi-handle, which allows you to manage multiple `Curl` objects concurrently.
You should use `CurlMulti` when you need to perform many network requests in parallel efficiently, as it optimizes I/O multiplexing and connection reuse across multiple transfers without needing separate threads for each.
# How do I handle file uploads and downloads with `curl-cffi`?
For file uploads, the `requests`-like API accepts a `files` dictionary similar to standard `requests`. For downloads, you can use `stream=True` and `iter_content` to efficiently stream large files chunk by chunk, preventing excessive memory usage.
# Does `curl-cffi` automatically follow redirects?
Yes, similar to `requests`, the `requests`-like API in `curl-cffi` automatically follows HTTP 3xx redirects by default.
When using the low-level `Curl` object, you can enable this behavior by setting `c.setoptCurlOpt.FOLLOWLOCATION, 1`.
# How do I set custom headers with `curl-cffi`?
With the `requests`-like API, pass a dictionary to the `headers` argument e.g., `requests.geturl, headers={'User-Agent': 'MyCustomAgent'}`. For the low-level `Curl` object, use `c.setoptCurlOpt.HTTPHEADER, `, providing a list of byte strings.
# What are common errors when using `curl-cffi`?
Common errors include `libcurl` not being found due to incorrect installation or `PATH` issues, SSL certificate verification failures due to outdated CA certs or proxy interference, and various network-related timeouts or connection errors.
Debugging with verbose output `CurlOpt.VERBOSE` is key.
# Can `curl-cffi` help with web scraping?
Absolutely.
`curl-cffi` is one of the strongest Python libraries for web scraping, particularly for sites that use advanced bot detection.
Its impersonation capabilities are a major advantage, allowing your scraper to mimic real browser behavior and bypass many common anti-bot measures.
# Does `curl-cffi` support HTTP/2?
Yes, `curl-cffi` leverages `libcurl`'s underlying support for HTTP/2. When you use impersonation profiles, `curl-cffi` configures `libcurl` to use the correct HTTP/2 ALPN settings and frame structures to match the mimicked browser.
# How do I manage cookies with `curl-cffi`?
The `requests`-like API handles cookies automatically, storing and sending them across requests within a session.
For the low-level `Curl` object, you can use `CurlOpt.COOKIEJAR` to save cookies to a file and `CurlOpt.COOKIEFILE` to load cookies from a file for persistent cookie management.
# Is `curl-cffi` suitable for large-scale data transfers?
Yes, `libcurl` is designed for robust and efficient data transfers, including large files. `curl-cffi` inherits this capability.
By using streaming downloads `stream=True` and `iter_content` and `CurlMulti` for concurrent transfers, it can handle large-scale data operations very effectively.
# What kind of "impersonate" profiles are available in `curl-cffi`?
`curl-cffi` offers a variety of impersonation profiles corresponding to popular browser versions, such as `chrome101`, `firefox99`, `safari15_5`, and others.
You can specify these as the `impersonate` argument in the `requests`-like API.
# Can I set timeouts for my requests with `curl-cffi`?
Yes.
With the `requests`-like API, use the `timeout` parameter e.g., `requests.geturl, timeout=5`. For the low-level `Curl` object, use `c.setoptCurlOpt.TIMEOUT, seconds` for the total request timeout or `c.setoptCurlOpt.CONNECTTIMEOUT, seconds` for the connection phase timeout.
# Is `curl-cffi` open source?
Yes, `curl-cffi` is an open-source library, typically licensed under an MIT license, similar to many other Python libraries.
This allows for community contributions, auditing, and flexibility in its use.
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