go-llm: A Go Language LLM SDK with Unified Multi-Model Support

go-llm is a Go language SDK designed to unify access to over 200 pre-trained large language models (LLMs) from providers like OpenAI, Anthropic, and OpenRouter. It offers complete features including streaming responses, multi-modal (visual/PDF/audio) processing, embedding vector generation, function calling, and cost management, helping Go developers quickly build AI applications without dealing with diverse API differences.

With the rapid development of LLM technology, developers face the pain point of adapting to significantly different API interfaces from various providers (e.g., OpenAI's GPT series, Anthropic's Claude, OpenRouter's aggregated models). go-llm was born to solve this: it provides a unified Go SDK, allowing developers to call multiple LLMs in a consistent way.

• Multi-model unified access: Supports over 200 models from OpenAI (GPT-4/3.5), Anthropic (Claude series), OpenRouter (open-source & commercial models), enabling flexible model switching/combination without changing core logic.
• Builder API: Uses Go's builder pattern for concise, readable, maintainable chained calls, lowering the entry barrier.
• Advanced features: Function calling (for agent interactions), structured output (easy post-processing), streaming responses (real-time feedback), multi-modal processing (image/PDF), embedding generation (semantic search/clustering), audio processing.
• Cost & reliability: Built-in cost tracking and auto-retry mechanisms for production environments.

• Smart customer service: Leverages multi-turn dialogue and streaming responses for natural, fast interactions; function calls enable order queries/info modifications.
• Content generation: Produces marketing copy, technical docs, code comments; structured output suits format-specific needs.
• Knowledge base QA: Combines embedding vectors for private knowledge base retrieval and LLM-based answers.
• Multi-modal apps: Handles image understanding, PDF processing, image description generation.

• Adapter pattern: Unifies API differences by using provider-specific adapters to convert request/response formats to SDK standards, ensuring extensibility (new providers only need new adapters).
• Error handling: Provides unified error types (network errors, API limits, content filtering) for appropriate developer responses.

1. Model selection: Choose models based on task complexity (lightweight models for simple tasks to cut costs).
2. Prompt engineering: Build a prompt template library and iterate for high-quality outputs.
3. Streaming optimization: Enable streaming for user interactions but note front-end processing requirements.
4. Error handling: Implement robust error handling and fallback strategies.
5. Cost control: Use built-in tracking to analyze usage and optimize costs.

go-llm offers Go developers a feature-complete, easy-to-use LLM toolkit that simplifies multi-model access and ensures production reliability. As LLM tech evolves, such unified SDKs will become more critical, letting developers focus on business innovation instead of API differences. It is a worthy consideration for teams using/planning Go for AI app development.