AI Tools & Frameworks
The AI development ecosystem includes frameworks for model training, inference serving, LLM orchestration, and production deployment. This guide covers essential tools for building AI applications.
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Machine Learning Frameworks
PyTorch
Python-first framework with dynamic computation graphs, preferred by researchers. Excellent for prototyping and has become the de facto standard for LLM development.
Best for: Research, LLM fine-tuning, rapid prototyping, academic work
TensorFlow / JAX
Production-focused framework with static graphs (TensorFlow) and functional approach (JAX). Strong deployment story with TensorFlow Lite and TFX.
Best for: Production ML pipelines, mobile deployment, edge devices, Google ecosystem
ONNX Runtime
Cross-framework inference engine supporting models from PyTorch, TensorFlow, and others. Optimized for performance with hardware-specific acceleration.
Best for: Framework-agnostic deployment, performance optimization, cross-platform inference
LLM Development Frameworks
LangChain / LangGraph
Comprehensive framework for building LLM applications with chains, agents, and memory. LangGraph adds stateful workflow orchestration for complex agent systems.
- Chain composition for multi-step reasoning
- Memory systems for conversation context
- Tool integration for function calling
- Vector store abstractions for RAG
LlamaIndex
Specialized framework for data ingestion and retrieval-augmented generation (RAG). Focuses on connecting LLMs to private data sources with intelligent indexing.
Semantic Kernel (Microsoft)
Enterprise-focused SDK for AI orchestration with first-class support for .NET and Python. Emphasizes planning, plugins, and integration with Microsoft ecosystem.
Haystack
End-to-end framework for building search systems and NLP pipelines. Strong focus on RAG, document processing, and question answering at scale.
Inference Servers and Deployment
vLLM
High-throughput inference server with PagedAttention for efficient memory management. Supports continuous batching and tensor parallelism.
✓ Best throughput · ✓ OpenAI-compatible API
Text Generation Inference (TGI)
Hugging Face's production inference server with tensor parallelism, quantization, and efficient token streaming.
✓ Easy deployment · ✓ Hugging Face integration
Ollama
User-friendly local LLM runtime with simple CLI and API. Automatic model downloads and quantization management.
✓ Easiest setup · ✓ Cross-platform · ✓ CPU/GPU
llama.cpp
C++ inference engine optimized for CPU execution with GGUF quantization. Minimal dependencies, runs on diverse hardware.
✓ CPU-first · ✓ Low resource · ✓ Wide compatibility
Deployment Platforms
- Modal: Serverless GPU functions, pay-per-use pricing
- Replicate: API-first model hosting with simple scaling
- BentoML: Package and deploy ML models as APIs
- Ray Serve: Scalable model serving on Ray clusters
Orchestration & Agent Frameworks
Agent Frameworks
AutoGPT / BabyAGI: Autonomous task execution with self-prompting
CrewAI: Multi-agent collaboration with role-based systems
LangGraph: Stateful agent workflows with cyclical execution
MetaGPT: Software development agents with role specialization
Vector Databases
Essential for RAG implementations and semantic search:
- Chroma: Lightweight, embedded vector store
- Pinecone: Managed vector database with low latency
- Weaviate: Open-source with hybrid search capabilities
- Qdrant: High-performance with filtering and payload support
- pgvector: PostgreSQL extension for vector operations
Monitoring & Observability
Production LLM deployments require specialized monitoring for costs, latency, and quality.
LLM Observability Tools
- LangSmith: Debugging and monitoring for LangChain applications
- Weights & Biases: Experiment tracking and model registry
- MLflow: End-to-end ML lifecycle management
- Arize AI: ML observability with drift detection
- Phoenix (Arize): Open-source LLM evaluation and tracing
Key Metrics to Track
- Latency (p50, p95, p99)
- Tokens per second (throughput)
- Cost per request / token
- Error rates and failure modes
- Model quality metrics (relevance, coherence)
- Cache hit rates (for RAG systems)
Tool Selection Guide
By Use Case
💡 Start Simple: Begin with managed APIs (OpenAI) and LangChain. Move to self-hosted inference (Ollama, vLLM) as requirements crystallize. Add observability early to understand real-world performance.
Resources and Further Reading
On AI-Radar
Recent Framework Articles
Last updated: January 2026 | Framework landscape updated monthly