Lumibot vs TradingAgents
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TradingAgents helped prove that people want multi-agent financial research
workflows. It is a strong research/demo project for showing how analyst,
trader, and risk-style agents can reason together.

Lumibot is different because the AI team runs inside a Python trading framework
that already supports backtests, broker objects, orders, positions, artifacts,
and live execution paths.

Where TradingAgents Fits
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TradingAgents is useful when you want to study or prototype a multi-agent LLM
financial research flow. Its core appeal is the agent structure: analysts,
debate, and portfolio-style decision making.

Where Lumibot Fits
******************

Use Lumibot when you want that kind of agent structure to become a strategy you
can backtest, inspect, paper trade, and connect to supported brokers.

Lumibot supports:

- deterministic Python strategies and AI-agent strategies in the same framework
- AI trading teams with read-only research agents and trading-permission agents
- backtest artifacts such as charts, orders, trade files, logs, traces, and
  tearsheets
- paper and live broker paths for supported brokers
- BotSpot hosted backtests, broker connections, deployment, monitoring, alerts,
  audit history, MCP tools, and kill-switch controls

Short Version
*************

TradingAgents is a strong multi-agent research framework. Lumibot is the
practical strategy framework when you want AI trading teams that can be
backtested and operated with real broker paths.