Multi-provider proxy infrastructure for AI/ML. Geo, sessions, cost-aware routing, usage tools.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-tierproxy-python-sdk": {
"command": "<see-readme>",
"args": []
}
}
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Multi-provider proxy infrastructure for AI/ML. Geo, sessions, cost-aware routing, usage tools.
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🚧 Preview release. Gateway is not yet generally available. Join the waitlist at hello@tierproxy.com. SDK is functional but
tierproxy doctoragainst 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.
pip install tierproxy
import tierproxy
r = tierproxy.get("https://example.com", country="US")
print(r.text)
That's it. (Set TIERPROXY_API_KEY env var first.)
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")
g = TierProxy(routing="cheapest") # also: "fastest", "most_reliable", "balanced"
g.get("https://example.com") # picks via /v1/health/upstreams under the hood
g = TierProxy(
monthly_budget_usd=200.0, # raises BudgetExceededError before going over
)
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
)
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
Every SDK error inherits from tierproxy.TierProxyError and carries a
request_id for support escalation:
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.
The SDK exposes its response models as JSON Schema and as pre-built tool definitions for Anthropic Claude and OpenAI function-calling:
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 int