Agentes de IA
TradingAgents
Multi-agent LLM framework that mirrors a trading firm: analysts, researchers, trader and risk team
★ 110,3 mil estrelasApache-2.0
Ver o guia →langroid é um projeto de agentes de IA de código aberto de langroid (langroid/langroid no GitHub). Tem 4.112 estrelas no GitHub e 415 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 8 de outubro de 2026.
Langroid: a principled Python framework for building LLM multi-agent applications (from CMU/UW researchers)
Sketch which agents you need (chat, document Q&A, SQL) and which LLM each will use.
Install from PyPI, adding extras for your vector store or database.
Create ChatAgents, attach tools, wrap them in Tasks, and wire the message flow.
Run the task graph, inspect the conversation, and refine prompts and tools.
ChatAgents (LLM + tools + optional vector store) wrapped in Tasks that collaborate via message exchange.
OpenAI models, local models, OpenRouter, and gateway providers through one configuration approach.
A clean abstraction for tool use and structured extraction from LLM output.
DocChatAgent for RAG over documents and SQLChatAgent for natural-language database queries.
Convert tools from any MCP server into Langroid ToolMessages automatically.
Qdrant built in, with pgvector, Milvus, Weaviate, and Pinecone available as extras.
Clone the project and enter its directory (library users can install from PyPI instead).
git clone https://github.com/langroid/langroid.git
cd langroidInstall the package, adding extras for your stack if needed.
pip install langroidFollow the README examples to create a ChatAgent, wrap it in a Task, and run a two-agent exchange with your LLM key or local model configured.
# see README examples for the exact agent/task codeResearchers from Carnegie Mellon University and the University of Wisconsin–Madison lead Langroid, with community contributors.
Yes, MIT-licensed. LLM usage costs depend on the provider or local models you configure.
Only for hosted LLMs; local models (for example via Ollama-style endpoints) work without keys.
Langroid deliberately avoids LangChain and centers on a small actor-style abstraction — Agents wrapped in Tasks exchanging messages — aiming for code that is easier to reason about than graph-heavy frameworks.
Run `pip install langroid`, configure an LLM, and follow the README's first ChatAgent/Task example.
Multi-agent LLM framework that mirrors a trading firm: analysts, researchers, trader and risk team
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.