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Vikaskanturi
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llm-client

Route LLM calls through a single client across multiple providers.

Updated Apr 4, 2026
Search Tags:#llm#retry#provider-agnostic#llm-client#unified-interface#json-parsing
One-click install
npx skills add https://github.com/Vikaskanturi/java_to_python_migration_agent --skill llm-client
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-client
Source: https://github.com/Vikaskanturi/java_to_python_migration_agent/tree/main/skills_extracted/llm-client
Command: npx skills add https://github.com/Vikaskanturi/java_to_python_migration_agent --skill llm-client

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill unifies all LLM calls across the migration pipeline by routing them through a single client, ensuring consistent prompts, error handling, and provider-agnostic behavior.

Core Features & Use Cases

  • Centralized LLM interface for all agents, guaranteeing a single import path (core.llm_client.LLMClient) and uniform interaction patterns.
  • Support for multiple providers (Groq, HuggingFace, Ollama, OpenAI) with configurable models, retry logic, and JSON parsing utilities.
  • Safe, testable integration with rotation of providers and robust error handling to recover from transient failures in production environments.

Quick Start

Configure the LLM provider and credentials, then instantiate LLMClient and inject it into all agents.

Frequently Asked Questions about llm-client

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I unify LLM API calls across multiple providers like OpenAI and HuggingFace?▼

To unify LLM API calls across providers like OpenAI and HuggingFace, route requests through a single client interface. This enforces consistent prompts, provider-agnostic behavior, and centralized error handling for all agents.

What's the best way to add retry logic and backoff for LLM client requests?▼

Adding retry logic and backoff for LLM client requests is handled natively by configuring a unified client. It provides robust error handling and automatic recovery mechanisms to ensure resilience against transient production failures.

Can I switch between Groq, Ollama, and OpenAI models without changing agent code?▼

Yes, you can switch between Groq, Ollama, and OpenAI models without changing agent code. A provider-agnostic client allows safe rotation of providers and configurable models while maintaining a single import path for all agents.

How do I parse JSON responses from LLM outputs consistently?▼

To parse JSON responses from LLM outputs consistently, utilize built-in JSON parsing utilities within a unified LLM client. This guarantees uniform interaction patterns and reliable data extraction across all configured providers.

Why should I centralize LLM calls in a migration pipeline instead of using provider SDKs directly?▼

Centralizing LLM calls in a migration pipeline ensures consistent prompts and testable integration compared to using provider SDKs directly. It enforces a single import path, simplifying maintenance and providing robust error recovery.

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