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Deprecated: The Completions V1 API has been deprecated in favor of Completions v2.Key differences from V1:
  • New endpoint: /ai/v2/chat/completions
  • Smart routing with auto_routing: true
  • Provider selection with model_family
  • Theology-aware responses with tradition
View V2 Guide | Migration Steps
Tool calling is supported in both V1 and V2, but the Tool Use Guide now focuses on V2. Please migrate to V2 for the latest tool calling features and examples.
Standard chat completions—like OpenAI’s chat/completions endpoint—are quickly becoming the industry default for building conversational AI. Whether you’re developing a chatbot, virtual assistant, or AI-powered automation, here’s why sticking with the standard just makes sense.
The Gloo AI Completions API allows you to build with many of the leading open-source models and ensures that it has Human Flourishing alignment and guardrails for any use.

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Why Use Standard Chat Completions?

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Familiar Developer Experience

If you’ve used tools like OpenAI, Anthropic, or other LLM providers, you’ve likely already encountered the chat format: a sequence of messages between system, user, and assistant. 
[
  { "role": "system", "content": "You are a helpful assistant." },
  { "role": "user", "content": "How do I reset my password?" }
]
This simple, message-based interface has become the standard across APIs, making it easier to build, test, and scale without needing to relearn a custom format for every provider.

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Plug-and-Play with Tools and SDKs

Standard chat completions are supported out-of-the-box by:
  • Popular SDKs (OpenAI, LangChain, LlamaIndex, etc.)
  • Prompt engineering tools
  • Debugging/observability dashboards
  • Prompt versioning platforms
  • Orchestration frameworks
That means less boilerplate and more productivity.

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Easily Portable Across Models

Using a standard chat format lets you switch between:
  • OpenAI GPT and o-series
  • Anthropic Claude
  • Google Gemini
  • Open-source chat models like DeepSeek, LLaMA, or Mistral
…all without rewriting your app logic. Standardization lets you benchmark models, compare outputs, and even build model-agnostic fallbacks or ensembles.

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Better Alignment with Human Intent

The chat format encourages natural interaction flow, including:
  • Clarification (multi-turn)
  • Function/tool calling
  • Role-based prompting (e.g. system as context setter)
It also makes features like memory, tool use, and retrieval-augmented generation (RAG) feel native, rather than bolted on.

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Built-In Support for Advanced Features

Using chat completions gives you access to:
  • Function/tool calling
  • JSON mode
  • Temperature/top_p control
  • Streaming support
  • Multi-turn context windows
These features are essential for reliable, context-aware, and real-time applications.

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Supported by the Ecosystem

From hosting providers to vector databases, the whole AI tooling ecosystem now expects the standard chat format. It’s the easiest way to stay compatible and future-proof your codebase.

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TL;DR: Why Use Standard Chat Completions?

BenefitDescription
FamiliarCommon API shape across many providers.
PluggableCompatible with SDKs and tools.
PortableEasy to swap or compare models.
PowerfulSupports advanced features like tool calling.
Ecosystem-ReadyPlays nicely with the AI stack.
Human FlourishingBuild with AI and Human Flourishing capabilities at its core with the world’s leading open source models.
Ready to build? The chat format is your foundation for production-ready AI apps—backed by best practices, trusted by industry, and ready to scale.