# What Models Are Supported by Awesome LLM Apps: Complete Provider Guide

> Discover what models Awesome LLM Apps supports including OpenAI GPT-4o, Claude 3.5, Gemini 2.0, Cohere, xAI, and open-source options via Ollama like Llama 3.2.

- Repository: [Shubham Saboo/awesome-llm-apps](https://github.com/shubhamsaboo/awesome-llm-apps)
- Tags: api-reference
- Published: 2026-02-16

---

**Awesome LLM Apps supports commercial APIs including OpenAI (GPT-4o, GPT-5), Anthropic (Claude 3.5/4.5), Google Gemini (2.0/2.5/3.0), Cohere, and xAI, plus open-source models via Ollama such as Llama 3.2, DeepSeek, and Gemma 3.**

Awesome LLM Apps is a curated collection of end-to-end applications demonstrating Retrieval-Augmented Generation (RAG), AI agents, and multi-agent systems. Understanding what models are supported by Awesome LLM Apps is essential for developers selecting the right provider for their use case, whether they need cloud-based APIs or fully local inference.

## Commercial API Providers

The repository integrates with all major commercial LLM providers through thin wrapper classes that implement a unified `generate()` interface.

### OpenAI Models

OpenAI is listed as a primary provider in the main [`README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/README.md) at the repository root. Individual applications reference specific model variants:

- **GPT-4o** – Used in voice-enabled RAG implementations
- **GPT-4** and **GPT-4-mini** – Standard text generation tasks
- **GPT-5-mini** and **GPT-5** – Next-generation reasoning tasks

The Voice RAG demo in [`voice_ai_agents/voice_rag_openaisdk/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/voice_ai_agents/voice_rag_openaisdk/README.md) explicitly instantiates `gpt-4o` for multimodal audio processing.

### Anthropic Claude

Anthropic's Claude family powers several starter agents requiring long-context reasoning:

- **Claude 3.5 Sonnet** – General-purpose agent tasks
- **Claude 4.5 Sonnet** – Enhanced reasoning for complex analysis

The AI Startup Trend Analysis agent in [`starter_ai_agents/ai_startup_trend_analysis_agent/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_startup_trend_analysis_agent/README.md) demonstrates Claude integration via the Anthropic API at line 44.

### Google Gemini

Google's Gemini models handle multimodal and agentic workflows through the Google ADK (Agent Development Kit):

- **Gemini 2.0 Flash** and **Gemini 2.5 Flash** – Fast, cost-effective inference
- **Gemini 3 Flash** and **Gemini 3 Pro** – Advanced reasoning (default in ADK crash course)
- **Gemini Image** – Vision-language tasks

The Multimodal AI Agent in [`starter_ai_agents/multimodal_ai_agent/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/multimodal_ai_agent/README.md) uses Gemini 2.5 Flash, while the Google ADK crash course in [`ai_agent_framework_crash_course/google_adk_crash_course/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/ai_agent_framework_crash_course/google_adk_crash_course/README.md) specifies Gemini 3 Flash as the default.

### Cohere

Cohere provides both generative and embedding models for RAG pipelines:

- **Command-r7b-12-2024** – Generative model for RAG agents
- **Embed-4** – Multimodal embeddings for vision RAG
- **Cohere-3.5 reranker** – Document reranking

Vision RAG in [`rag_tutorials/vision_rag/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/rag_tutorials/vision_rag/README.md) leverages Cohere Embed-4 for image embeddings, while the RAG Agent with Cohere in [`rag_tutorials/rag_agent_cohere/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/rag_tutorials/rag_agent_cohere/README.md) uses `Command-r7b-12-2024`.

### xAI

xAI's models appear in specialized financial advisory agents:

- **GPT-5 (via xAI)** – Reasoning for insurance and financial analysis

The AI Life Insurance Advisor in [`starter_ai_agents/ai_life_insurance_advisor_agent/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_life_insurance_advisor_agent/README.md) lists GPT-5 as its core model.

## Open-Source and Local Models

For privacy-sensitive or offline deployments, the repository supports self-hosted models via Ollama.

### Ollama Integration

Ollama enables local execution of open-source weights without API dependencies. Supported architectures include:

- **Llama 3.2** and **Llama 3.1** – Meta's general-purpose models
- **Gemma 3** – Google's lightweight open models
- **Qwen 3** – Alibaba's multilingual models
- **DeepSeek V2** – Reasoning-focused architecture
- **gpt-oss 20b** – Open-source GPT-style models

The Local RAG Agent in [`rag_tutorials/local_rag_agent/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/rag_tutorials/local_rag_agent/README.md) demonstrates Llama 3.2 integration, while the DeepSeek Local RAG Agent in [`rag_tutorials/deepseek_local_rag_agent/README.md`](https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/rag_tutorials/deepseek_local_rag_agent/README.md) shows DeepSeek V2 deployment.

## How Model Support Works Under the Hood

The repository's architecture decouples model providers from application logic through abstraction layers.

### Model-Agnostic Architecture

Each application imports a model wrapper class (e.g., `OpenAIChat`, `AnthropicChat`, `GoogleGeminiChat`, or `OllamaChat`) that implements a unified `generate(messages, **kwargs)` interface. This allows the same agent orchestration logic to switch providers by changing a configuration file or environment variable.

### Configuration and Environment Variables

Every demo requires environment variables for authentication:

- `OPENAI_API_KEY` – OpenAI access
- `ANTHROPIC_API_KEY` – Claude access
- `GOOGLE_API_KEY` – Gemini access
- `COHERE_API_KEY` – Cohere access
- `OLLAMA_BASE_URL` – Local Ollama endpoint (default: `http://localhost:11434`)

The underlying code reads these variables to instantiate the appropriate client class.

### Agent Orchestration Frameworks

Three main frameworks handle model interaction:

1. **OpenAI Agents SDK** – For OpenAI-centric demos requiring native tool use
2. **Google ADK** – For Gemini-centric workflows with Google Cloud integration
3. **Agno** – Vendor-neutral framework supporting any LLM including Ollama-hosted models

### Embedding and Vector Store Integration

Embeddings align with the selected provider when available:

- **OpenAI** – `text-embedding-3-small` or `text-embedding-3-large`
- **Gemini** – `embedding-001`
- **Cohere** – `embed-4` (multimodal)
- **Ollama** – Local embedding models (e.g., `nomic-embed-text`)

The vector database (Qdrant, Chroma, or Pinecone) remains constant regardless of the embedding source.

## Summary

- **Awesome LLM Apps supports 8+ major providers**: OpenAI, Anthropic, Google Gemini, Cohere, xAI, and local models via Ollama.
- **Model-agnostic architecture** uses wrapper classes with unified `generate()` interfaces, enabling provider swaps via environment variables.
- **Three orchestration frameworks** cover different use cases: OpenAI Agents SDK, Google ADK, and Agno.
- **Embedding flexibility** matches each provider's capabilities, from OpenAI's text embeddings to Cohere's multimodal Embed-4.
- **Local deployment** is fully supported through Ollama integration for Llama, DeepSeek, Gemma, and other open-source weights.

## Frequently Asked Questions

### Can I use a model not explicitly listed in the repository?

Yes. The repository's model-agnostic architecture in `awesome-llm-apps` allows you to integrate any LLM by creating a thin wrapper that implements the `generate(messages, **kwargs)` signature. Update the environment variables in your `.env` file and the corresponding README documentation to reflect the new provider's API requirements.

### How do I switch between OpenAI and local Ollama models?

Switching providers requires changing only the environment configuration and model wrapper instantiation. For OpenAI, set `OPENAI_API_KEY` and use the `OpenAIChat` wrapper. For Ollama, set `OLLAMA_BASE_URL` to `http://localhost:11434` and use the `OllamaChat` wrapper pointing to your local model (e.g., `llama3.2`). The underlying agent logic remains identical.

### Are embeddings provider-specific?

Yes, embeddings are typically sourced from the same provider as the generation model when available. The repository uses OpenAI's `text-embedding-3` series, Gemini's `embedding-001`, Cohere's `embed-4` for multimodal tasks, and Ollama-hosted embedding models like `nomic-embed-text`. However, the vector store implementation (Qdrant, Chroma, or Pinecone) is provider-agnostic and works with any embedding source.

### Which orchestration framework should I use?

Choose based on your target provider and use case. Use the **OpenAI Agents SDK** for OpenAI-centric applications requiring native function calling. Use **Google ADK** when building Gemini-centric workflows with Google Cloud integration. Use **Agno** for vendor-neutral implementations or when deploying local Ollama models, as it supports any LLM through a unified interface.