Search AI & ML Showcase

Build with leading text, image, and video models on one reliable, cost-effective API platform. Switch models without rebuilding integrations, and pay only for what you use.

Build with leading text, image, and video models on one reliable, cost-effective API platform. Switch models without rebuilding integrations, and pay only for what you use.

a natural language agent that connects to a SQL database, learns schema + domain context, and answers questions using a ReAct SQL loop.

It can: - Connect to any SQL database supported by SQLAlchemy - Introspect table/column schema automatically - Bootstrap a domain YAML from natural language description - Route tables/metrics and reason iteratively with grounded SQL evidence - Enforce read-only SQL validation before query execution

Production-grade AI agent framework built by the Pydantic team

PydanticAI is a Python framework for building production AI agents with type-safe structured outputs, dependency injection, streamed responses, and seamless integration with popular LLM APIs.

Run large language models locally with one command

Ollama enables developers to run open-source LLMs locally on macOS, Linux, and Windows with a simple CLI and REST API. It manages model downloads, quantization, and GPU acceleration automatically.

Microsoft's framework for multi-agent AI conversation and automation

AutoGen (by Microsoft) enables developers to create LLM applications using multiple agents that can converse, delegate tasks, and collaborate. It supports complex agent topologies including human proxy agents and code-executing agents.

The open-source AI-native embedding database

Chroma is an open-source, AI-native vector database designed for building LLM applications. It supports embedding storage and retrieval with first-class Python and JavaScript SDKs, and runs in-process for local development or as a hosted service.

Programming framework for algorithmically optimizing LM prompts

DSPy is a Stanford research framework that replaces manual prompt engineering with programmatic optimization. It treats prompts and LM calls as declarative modules and automatically optimizes them using optimizers like BootstrapFewShot and MIPRO.

Microsoft's SDK for integrating LLMs into apps with plugins and planners

Semantic Kernel is an open-source SDK from Microsoft that orchestrates AI models with native code, enabling developers to build AI applications that combine functions, plugins, memory, and planners in C#, Python, and Java.

High-throughput LLM inference engine with PagedAttention

vLLM is an open-source, high-performance LLM inference and serving engine. It uses PagedAttention for efficient KV-cache management, achieving 24× higher throughput than Hugging Face Transformers for production serving.

Graph-based RAG that extracts knowledge graphs for better retrieval

GraphRAG is an open-source framework from Microsoft Research that uses LLMs to extract entities and relationships into a knowledge graph, enabling global reasoning over large document corpora that traditional RAG cannot handle.

The most widely adopted framework for building LLM-powered applications

LangChain is an open-source framework for building applications powered by large language models. It provides composable building blocks for chains, agents, memory, RAG pipelines, and tool integrations across 50+ LLM providers.

Automated evaluation framework for RAG pipelines

RAGAS is an open-source framework for evaluating Retrieval-Augmented Generation (RAG) pipelines. It provides reference-free metrics like faithfulness, answer relevance, and context precision using LLM-as-judge methods.