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

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 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 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 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 uses Gemini 2.5 Flash, while the Google ADK crash course in 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 leverages Cohere Embed-4 for image embeddings, while the RAG Agent with Cohere in 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 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 demonstrates Llama 3.2 integration, while the DeepSeek Local RAG Agent in 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.

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