Open source alternative to LSEG Workspace (formerly Refinitiv Eikon) and AlphaSense
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Build trading workflows with TradingAgents, an open source multi-agent LLM framework for strategy research, signal generation, and execution planning.
A quant researcher can run the same ticker and date through bullish, bearish, technical, sentiment, and risk agents to compare how each role affects the final recommendation.
You need audited live-trading infrastructure with compliance approvals, broker routing, and production risk controls already included.
A fintech team can use TradingAgents as a reference architecture for an explainable research assistant, then adapt prompts, model providers, and portfolio constraints to its own product.
Your team cannot maintain Python services, API keys, model configuration, and data-vendor integrations.
Finance and AI researchers can inspect a real multi-agent trading workflow, cite the paper, and run controlled experiments around model choice, debate rounds, and ticker history.
Your study requires certified investment recommendations or licensed proprietary market data bundled with the tool.
A technical trader can run local experiments through the CLI, review generated analyst reports, and use the decision log to compare later outcomes against prior recommendations.
You want a no-code retail trading app that connects directly to a brokerage account.
Quant teams and fintech builders often need to test market hypotheses across fundamentals, news, sentiment, technical signals, and portfolio risk, but paid market intelligence products package that work inside proprietary content, search, analytics, and workflow environments. That makes it hard to customize the research process itself, audit each agent role, or compare how different LLMs handle the same ticker.
TradingAgents addresses the research workflow gap for teams that can bring their own data, model keys, and engineering discipline. It is not a financial advice tool or a ready-made broker app; it is a framework for building, inspecting, and stress-testing multi-agent trading decisions before any production use.
TradingAgents models a trading firm as a LangGraph workflow with analyst, researcher, trader, risk management, and portfolio manager roles. Each role contributes a separate report or decision step, so teams can inspect how a ticker analysis moves from evidence to final recommendation.
The framework includes fundamentals, sentiment, news, and technical analyst agents. The sentiment analyst aggregates news headlines, StockTwits, and Reddit chatter, while the technical analyst uses indicators such as MACD and RSI to frame price action.
Researcher agents argue bullish and bearish cases before a trader forms the transaction proposal. This debate structure helps expose competing assumptions instead of collapsing the workflow into a single prompt response.
Risk management agents review volatility, liquidity, and other risk factors before a portfolio manager approves or rejects the proposed transaction. Approved proposals route to a simulated exchange, which keeps experiments separate from live brokerage execution.
TradingAgents supports OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Ollama, and Azure OpenAI. Teams can run cloud models, enterprise providers, or local Ollama models while keeping the same agent workflow.
LSEG Workspace and AlphaSense are paid research products built around licensed content, search, and analytics. TradingAgents is an Apache-2.0 Python framework for inspecting a multi-agent trading research process.
| Criterion | TradingAgents | LSEG Workspace / AlphaSense |
|---|---|---|
| License | Apache-2.0 | Proprietary |
| Self-hosting | Runs from source or Docker | Managed commercial products |
| Primary value | Customizable agent workflow | Licensed content, search, analytics, and enterprise workflows |
| Model control | Choose hosted, enterprise, OpenRouter, or Ollama models | Vendor-controlled AI features |
| Support | Community and self-managed engineering | Commercial support |
TradingAgents is the better fit when your team wants to customize the research logic itself. You can inspect analyst roles, tune debate rounds, swap model providers, run local Ollama models, and keep a decision log across ticker analyses.
LSEG Workspace or AlphaSense remains the better choice when the job depends on licensed content, managed search, vendor support, or an analyst-ready interface. TradingAgents should sit beside those data sources or replace a custom agent prototype, not pretend to be a full proprietary market intelligence terminal.
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
conda create -n tradingagents python=3.13
conda activate tradingagents
pip install .
```
```bash
cp .env.example .env # add your API keys
docker compose run --rm tradingagents
```
```bash
docker compose --profile ollama run --rm tradingagents-ollamaYes, TradingAgents is Apache-2.0 licensed and free to run from source. You still need to pay for any external LLM, market data, or infrastructure services you connect to it.
TradingAgents is documented as a research framework, not a live brokerage system. The README describes approved proposals being sent to a simulated exchange, so production trading would require separate broker integration, controls, and review.
TradingAgents supports major hosted LLM providers, OpenRouter, Azure OpenAI, and local Ollama models. The README lists the current provider catalog, and the CLI lets users choose separate deep-thinking and quick-thinking models.
Yes, TradingAgents supports Ollama for local models. The README documents OLLAMA_BASE_URL for local or remote Ollama endpoints and includes a Docker Compose profile for Ollama runs.
TradingAgents can replace part of the research orchestration workflow, but it does not replace proprietary data terminals or premium research libraries. It is best when you want open, inspectable agent logic and already have access to the data sources you need.
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