io.github.tierproxy/python-sdk

Official

by tierproxy · Python

tierproxy

Multi-provider proxy infrastructure for AI/ML. Geo, sessions, cost-aware routing, usage tools.

Multi-provider proxy infrastructure with geo-routing, session management, and cost-aware routing for AI/ML workloads.

Topics

ai-mlcrawl4ailangchainllamaindexmcpopenapiopentelemetryproxypythonsdkweb-scraping

README.md8 KBView on GitHub

tierproxy — Python SDK

🚧 Preview release. Gateway is not yet generally available. Join the waitlist at hello@tierproxy.com. SDK is functional but tierproxy doctor against a live gateway requires invitation.

Premium multi-provider proxy infrastructure for AI/ML pipelines. Built for engineers who measure cost, latency, and success rate twice — and write Python.

Install

bash

pip install tierproxy

Quickstart — five-second flavor

python

import tierproxy
r = tierproxy.get("https://example.com", country="US")
print(r.text)

That's it. (Set TIERPROXY_API_KEY env var first.)

Three lines, persistent session

python

from tierproxy import TierProxy
with TierProxy() as g:
    print(g.me.get().client_id)
    r = g.get("https://example.com", country="US", session_id="s1")

Auto-pick the cheapest healthy upstream every request

python

g = TierProxy(routing="cheapest")  # also: "fastest", "most_reliable", "balanced"
g.get("https://example.com")     # picks via /v1/health/upstreams under the hood

Cost guardrails

python

g = TierProxy(
    monthly_budget_usd=200.0,    # raises BudgetExceededError before going over
)

Power-user knobs

python

import httpx
from tierproxy import TierProxy
from tierproxy.retry import RetryPolicy

g = TierProxy(
    api_key="tp_live_...",
    base_url="https://my-self-hosted-gw:8444",
    timeout=10.0,
    retry_policy=RetryPolicy(max_retries=5, retry_on_status=frozenset({500, 502})),
    http_client=httpx.Client(verify=False),  # custom transport
    user_agent_suffix="my-app/2.3",          # attribution
)

Raw modes (Playwright, curl, etc.)

python

from tierproxy import ProxyURL

p = ProxyURL(api_key="tp_live_...", country="US", mode="username_encoding")
print(p.http_url())  # http://customer-tp_live_...-cc-US:x@gw.tierproxy.com:443

Error handling

Every SDK error inherits from tierproxy.TierProxyError and carries a request_id for support escalation:

python

from tierproxy import TierProxy, RateLimitError
import time

with TierProxy() as g:
    try:
        resp = g.get("https://example.com/page")
    except RateLimitError as e:
        time.sleep(e.retry_after or 5)
        resp = g.get("https://example.com/page")

See Errors reference for the full HTTP-status-to-exception mapping.

AI agent integration

The SDK exposes its response models as JSON Schema and as pre-built tool definitions for Anthropic Claude and OpenAI function-calling:

python

import anthropic
from tierproxy import TierProxy, schemas

with TierProxy() as gw:
    anthropic.Anthropic().messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=schemas.anthropic_tools(),
        messages=[{"role": "user", "content": "How much quota is left?"}],
    )

See the AI integration guide and the MCP server in examples/mcp_claude_desktop.md.

How tierproxy compares

	tierproxy	Smartproxy SDK	Bright Data SDK	Oxylabs SDK	DataImpulse
Multi-provider routing	✅	❌	❌	❌	❌
Client-side smart selector (cost-aware)	✅	❌	❌	❌	❌
Live usage streaming (SSE)	✅	❌	❌	❌	❌
MCP server (Claude/Cursor/Cline)	✅	❌	❌	❌	❌
OpenTelemetry built-in	✅	❌	❌	❌	❌
Sync + async parity	✅	partial	partial	partial	partial
AI/ML framework examples shipped	8	0	1	0	0
Type-safe (Pydantic v2 + mypy strict)	✅	❌	❌	partial	❌
OpenAPI 3.1 spec	✅	❌	❌	❌	❌
Pip-installable CLI (`tierproxy doctor`)	✅	❌	❌	❌	❌
Per-request cost attribution (lazy)	✅	❌	❌	❌	❌
JA3/JA4 TLS fingerprint rotation	✅	❌	❌	❌	❌
Rate-limit learning + auto-failover	✅	❌	❌	❌	❌
License	Apache 2.0	proprietary	proprietary	proprietary	proprietary

Features

Five-second quickstart — import tierproxy; tierproxy.get(url, country="US")
Layered API — five integration levels from one-liner to power-user knobs
Smart routing — routing="cheapest" auto-picks healthy upstream per request
Cost guardrails — monthly_budget_usd= refuses requests that would exceed budget
Per-request cost attribution — client.cost_for(resp) returns USD; lazy 30s cache, no per-request overhead
Client-side response caching — cache_ttl=300, cache_max_response_size=262144 LRU with size cap
Multi-provider auto-failover — auto_failover=True retries with next-best upstream on 429/5xx
Rate-limit learning — client.rate_limits.get() surfaces gateway-aggregated 429s per target domain
JA3/JA4 TLS rotation — per-upstream fingerprint randomization (gateway side; see tls-fingerprint guide)
Cookie persistence — cookies stick to session_id across multi-step crawls
Streaming responses — client.get(url, stream=True) returns iterator (large files, SSE)
Live SSE stream — for delta in g.usage.stream() tails month-to-date bytes
MCP server — tierproxy-mcp exposes proxy as tools to Claude/Cursor/Cline
8 framework integrations — LangChain, LlamaIndex, Crawl4AI, Playwright, Firecrawl, Browser-Use, CrewAI
OpenTelemetry opt-in — pip install tierproxy[otel] for distributed tracing
Geo + sticky sessions — countries, cities, 1-1440min session pins
Dual URL syntax — headers (httpx/requests) AND username-encoding (Playwright)
Type-safe end-to-end — Pydantic v2 models, mypy strict, full IDE autocomplete

See examples/ for LangChain/LlamaIndex/Crawl4AI/Playwright and examples/levels.py for a runnable demo of every level.

Use with your favorite AI/agent framework

Framework	Example	Notes
LangChain	`with_langchain.py`	RAG document loaders through proxy
LlamaIndex	`with_llamaindex.py`	SimpleWebPageReader through proxy
Crawl4AI	`with_crawl4ai.py`	Playwright crawler + tierproxy
Firecrawl (hot)	`with_firecrawl.py`	Self-hosted Firecrawl + residential IPs
Browser-Use (hot)	`with_browser_use.py`	LLM-driven autonomous browser
CrewAI (hot)	`with_crewai.py`	Multi-agent scraper crew + cost-aware routing
Playwright	`with_playwright.py`	Direct Playwright with tierproxy
MCP (Claude/Cursor/Cline/Windsurf) (unique)	`mcp_claude_desktop.md`	Native tool integration via `tierproxy-mcp`

MCP server (Claude Desktop / Cursor / Cline / Windsurf)

bash

pip install tierproxy[mcp]

Then add to your MCP client config:

json

{
  "mcpServers": {
    "tierproxy": {
      "command": "tierproxy-mcp",
      "env": { "TIERPROXY_API_KEY": "tp_live_..." }
    }
  }
}

Now your AI assistant can call fetch_url(url, country="US"), inspect health and usage, and route through the cheapest healthy upstream — no glue code, no httpx imports, no boilerplate.

Install

Configuration

Environment variables

TIERPROXY_API_KEYrequiredsecret

tierproxy API key (tp_live_... or tp_test_...). Sign up at https://tierproxy.com.

Arguments

--specrequireddefault tierproxy[mcp]

claude_desktop_config.json

{
  "mcpServers": {
    "python-sdk": {
      "command": "uvx",
      "args": [
        "tierproxy",
        "tierproxy-mcp"
      ],
      "env": {
        "TIERPROXY_API_KEY": "<YOUR_TIERPROXY_API_KEY>"
      }
    }
  }
}