AI Agents

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

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.

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.

Build stateful, multi-actor AI agent applications with graph-based workflows

LangGraph is an open-source framework for building stateful, multi-actor applications using LLMs. It models agent workflows as directed graphs, enabling complex control flows, human-in-the-loop patterns, and persistent state for production agents.

Data framework for building RAG and agentic AI applications

LlamaIndex (formerly GPT Index) is an open-source data framework for building LLM-powered applications. It provides data connectors, indexing, retrieval, and query engines purpose-built for RAG, agents, and knowledge assistants.

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.

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.

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.

Framework for orchestrating role-playing, autonomous AI agents

CrewAI is an open-source framework for orchestrating multi-agent systems where agents take on roles, share goals, and collaborate to complete complex tasks. It enables building autonomous teams of AI agents with minimal boilerplate.