AI Frameworks & APIs
AI frameworks and APIs are transforming how applications are built — this learning path takes you from OpenAI integration through LangChain agents, Claude prompt engineering, and open-source models.
What You’ll Learn
- OpenAI API integration with GPT-4, DALL-E, and Whisper
- Anthropic Claude for prompt engineering and tool use
- LangChain for building LLM applications with chains and agents
- DeepSeek open-source LLM integration and code generation
- Mistral AI models and Le Chat API
- Mastra framework for AI agents and workflows
Why AI Frameworks & APIs Matter
Every modern application now integrates AI capabilities. Understanding how to use these APIs effectively is essential. DodaTech’s Doda Browser uses AI for smart search and content summarization, DodaZIP leverages AI for intelligent file pattern detection, and Durga Antivirus Pro employs AI models for threat detection and malware analysis — making AI framework knowledge central to our engineering team.
flowchart LR
subgraph "AI Frameworks & APIs Learning Path"
A["OpenAI API\n(GPT-4, DALL-E, Whisper)"] --> B["Anthropic Claude\nPrompt Engineering"]
B --> C["LangChain\nChains & Agents"]
C --> D["DeepSeek\nOpen-Source LLMs"]
C --> E["Mistral AI\nModels & API"]
C --> F["Mastra\nAI Agents & Workflows"]
B --> G["Anthropic Claude API\nDeveloper Guide"]
G --> H["LangChain\nLLM Applications"]
H --> I["Mistral AI\nModels & API Guide"]
end
style A fill:#dbeafe,stroke:#2563eb
style C fill:#bbf7d0,stroke:#16a34a
Start with OpenAI API — the most widely adopted AI API — then explore Anthropic Claude for advanced prompt engineering, LangChain for building complex LLM applications, and open-source alternatives like DeepSeek and Mistral. Mastra ties it all together as a unified framework.
Recommended Path
| Order | Topic | Why Start Here |
|---|---|---|
| 1 | OpenAI API Guide | Most widely adopted AI API — essential knowledge |
| 2 | Anthropic Claude Guide | Advanced prompt engineering and safety features |
| 3 | LangChain Guide | Framework for building complex LLM applications |
| 4 | DeepSeek API Guide | Cost-effective open-source alternative |
| 5 | Mistral AI Guide | European open-source models with unique capabilities |
| 6 | Mastra AI Framework | Unified framework for AI agents and workflows |
| 7 | Anthropic Claude API — Developer Guide | Complete Claude API with tool use, vision, streaming |
| 8 | LangChain — Building LLM-Powered Applications | Chains, agents, memory, RAG, vector stores |
| 9 | Mistral AI — Models and API Guide | Mistral 7B, Mixtral, API, self-hosting, quantization |
| 10 | Vector Databases Guide | Embeddings, similarity search, Pinecone, Weaviate, RAG pipelines |
| 11 | Fine-Tuning LLMs Guide | LoRA, QLoRA, Axolotl, dataset preparation, deployment |
| 12 | AI Agents Guide | LangGraph, CrewAI, multi-agent systems, tool use, planning |
What’s Next
| Topic | Description |
|---|---|
| Start OpenAI API Guide | Begin with the most widely-adopted AI API |
| Python for Backend | Essential language for AI API integration |
| RESTful APIs | Foundation protocol for all web APIs |
Built by the developers of Doda Browser, DodaZIP, and Durga Antivirus Pro.
Pages in this section
OpenAI API Guide — GPT-4, DALL-E, and Whisper Integration
Learn OpenAI API integration: GPT-4 chat completions, streaming, function calling, embeddings, DALL-E image generation, Whisper speech-to-text, token counting, and rate limits.
✓ LiveAnthropic Claude API Guide — Prompt Engineering and Integration
Learn Anthropic Claude API integration: Messages API, system prompts, tool use, extended thinking, safety features, and comparison with OpenAI GPT-4.
✓ LiveLangChain Guide — Building LLM Applications with Chains and Agents
Learn LangChain: chains, agents, tools, memory, document loaders, vector stores, and RAG (retrieval augmented generation) for building production LLM applications.
✓ LiveMistral AI Guide — Le Chat API and Open-Source Models
Learn Mistral AI integration: Mistral Large, Small, Nemo models, Le Chat API, embedding models, function calling, and building applications with open-source French AI.
✓ LiveMastra AI Framework Guide — Building AI Agents and Workflows
Learn Mastra AI framework: building agents, workflows, tools, memory, RAG, LLM integration, deployment patterns, and production best practices.
✓ LiveAnthropic Claude API: Complete Developer Guide
Complete Anthropic Claude API developer guide — Messages API, system prompts, temperature/top_p/max_tokens, streaming, tool use/function calling, vision, prompt caching, and cost optimization.
✓ LiveLangChain: Building LLM-Powered Applications
Learn LangChain for building LLM-powered applications — chains (LLMChain, SequentialChain, RouterChain), prompt templates, output parsers, memory, document loaders, vector stores, and agents.
✓ LiveDeepSeek API: Complete Integration Guide
Complete DeepSeek API integration guide — chat completions, DeepSeek-R1 reasoning model, code generation, API parameters, streaming, cost comparison with OpenAI, and self-hosting options.
✓ LiveMistral AI: Models and API Guide
Complete Mistral AI guide — Mistral 7B, Mixtral 8x7B, Mistral Large models, API access via La Plateforme, self-hosting with Ollama/vLLM, quantization (GGUF/GPTQ), function calling, embeddings, and fine-tuning.
✓ LiveVector Databases — Embeddings, Similarity Search, Indexing & RAG Pipelines
Learn vector databases: embeddings (OpenAI, Cohere, HuggingFace), Pinecone, Weaviate, Chroma, Qdrant, Milvus, similarity search (cosine, euclidean, dot product), hybrid search, metadata filtering, RAG pipelines, and indexing methods (HNSW, IVF, PQ).
✓ LiveFine-Tuning LLMs — Full Fine-Tuning vs PEFT, LoRA, Dataset Prep, Training Frameworks & Deployment
Learn fine-tuning LLMs: full fine-tuning vs PEFT (LoRA, QLoRA, Adapters), dataset preparation (format, quality, dedup, augmentation), training frameworks (Axolotl, Unsloth, HuggingFace TRL), evaluation (perplexity, BLEU, ROUGE), deployment with vLLM/TGI/Ollama, overfitting prevention, and cost considerations.
✓ LiveAI Agents — Architecture, LangGraph, Multi-Agent Systems, Tool Use, Planning, Memory & Production Deployment
Learn AI agents: agent architecture (perception, reasoning, action), AutoGPT, CrewAI, LangGraph (state graphs, nodes, edges), multi-agent systems, tool use (function calling, custom tools), planning (ReAct, Plan-and-Execute), memory (short-term, long-term, entity), error recovery, production deployment, and safety.
✓ Live