scalexi-llm 0.1.36

A comprehensive multi-provider LLM proxy library with unified interface

ScaleXI LLM

A production-ready, multi-provider LLM proxy that gives you one unified API for many different model providers.

• 9 Providers: OpenAI, Anthropic (Claude), Google (Gemini), Groq, DeepSeek, Alibaba/Qwen, Grok, local Ollama, and RunPod (native API)
• 60+ Model Configurations: Pricing, limits, and capabilities encoded in a single model registry
• Structured Outputs: Pydantic schemas with intelligent fallbacks and validation
• Vision & Files: Image analysis, PDF/DOCX/TXT/JSON handling, and automatic vision fallbacks
• Web Search: Exa + SERP (Google) integration for retrieval-augmented generation (optionally restricted to a single domain)
• Fallbacks & Reliability: Provider-best and global-standard fallbacks, plus detailed error logging
• LangSmith Tracing: Optional built-in observability — set enable_tracing=True and every call is traced

This package is ideal when you want a single, consistent interface to multiple LLM vendors, with:

• Centralized configuration for models and costs
• Unified ask function (ask_llm) that works across providers
• Built-in support for web search, files, and images
• Optional local-only workflows via Ollama
• Optional LangSmith tracing with token/cost tracking

Installation

pip install scalexi_llm

Quick Example

from scalexi_llm import LLMProxy

llm = LLMProxy()

response, execution_time, token_usage, cost = llm.ask_llm(
    model_name="chatgpt-4o-latest",
    system_prompt="You are a helpful assistant.",
    user_prompt="Explain quantum computing in simple terms."
)

print(response)

Model Listing

Inspect all registered models and their metadata (provider, pricing, limits, capabilities):

import json
from scalexi_llm import LLMProxy

llm = LLMProxy(verbose=0)
models = llm.list_available_models()
print(json.dumps(models, indent=2))

Domain-Restricted Web Search

from scalexi_llm import LLMProxy

llm = LLMProxy()

response, _, _, _ = llm.ask_llm(
    model_name="gpt-5-mini",
    user_prompt="Find the admissions requirements",
    websearch=True,
    search_tool="both",
    search_domain="binbaz.org.sa"
)

LangSmith Tracing (Optional)

from scalexi_llm import LLMProxy

# pip install langsmith  (+ set LANGSMITH_API_KEY in .env)
llm = LLMProxy(enable_tracing=True)

response, exec_time, token_usage, cost = llm.ask_llm(
    model_name="chatgpt-4o-latest",
    user_prompt="What is quantum computing?"
)
# Token usage, cost, provider, and model are automatically logged to LangSmith

Features at a Glance

• One LLMProxy class for all providers
• Unified ask_llm API for text, files, images, and web search (with file/image fallbacks for providers like RunPod/Ollama)
• Pydantic-based structured outputs with retry and model fallbacks
• Vision fallback when a chosen model doesn't support images
• Token and cost accounting for every call
• Optional LangSmith tracing with zero-code setup (enable_tracing=True)
• Comprehensive test suite (provider_test.py, ollama_test.py, combined_test.py)