# TradingAgents Analyst, bull, bear, risk and portfolio-manager agents argue over one ticker. *https://stockmarketstack.com/tools/tradingagents · Financial MCP Servers & AI Agents* ## Facts ### At a glance | Field | Value | | --- | --- | | Vendor | Tauric Research | | Category | Financial MCP Servers & AI Agents | | Job | infrastructure | | Website | https://github.com/TauricResearch/TradingAgents | | Pricing model | open-source | | Free tier | true | | Open source | true | | Licence | Apache-2.0 | | Self-hosted | true | | Tested hands-on | false | | Last updated | 2026-09-13 | ### Coverage | Field | Value | | --- | --- | | Asset classes | stocks, etf, crypto, forex, indices, commodities | | Markets | us, ca, uk, asia, au, global | | Works outside the US | true | | Data latency | none | | Platforms | cli, library | | AI features | recommendations | ### Interfaces | Field | Value | | --- | --- | | API | false | | Webhooks | false | | Scripting | Python | | Python | true | | Spreadsheet add-in | false | | MCP server | false | | Export | json | ### Capabilities Yes: news No: charting, screening, scanning, backtesting, automation, live_trading, paper_trading, portfolio_tracking, broker_import, tax_reporting, alerts, options_analysis *Verified: pricing 2026-09-13; capabilities 2026-09-13; coverage 2026-09-13.* ## What it is TradingAgents runs a LangGraph pipeline of language-model agents over one ticker and one date. Four analysts — market, sentiment, news, fundamentals — write reports; a bull and a bear researcher argue over them; a research manager picks a side; a trader converts the plan into a Buy, Hold or Sell with an entry price, a stop and sizing guidance; three risk debaters argue again; and a portfolio manager closes on a five-tier rating of Buy, Overweight, Hold, Underweight or Sell with an optional price target. Debate and risk rounds are configurable — one each by default, five each on the CLI's "Deep" preset — and every leg is its own model call. Apache-2.0 by the LICENSE file, roughly 105,000 stars, not archived. Last release v0.4.0 on 31 August 2026; last commit to `main` 7 September 2026, merging an untagged v0.4.2 branch, with pull requests still being closed on 13 September 2026. 179 issues and 204 pull requests are open. ## Pricing Nothing to buy. Tauric Research publishes no price page at all — `/pricing` and `/plans` both 404 on 13 September 2026 — and offers "enterprise access" only through a contact form. The bill is your own model key: `gpt-5.6` and `gpt-5.6-luna` by default, or Anthropic, Google, xAI, DeepSeek, Qwen, GLM, MiniMax, Groq, Mistral, NVIDIA, OpenRouter, Bedrock, Azure, Ollama and any OpenAI-compatible endpoint. ## Data & coverage It ships no data of its own. Prices, indicators, fundamentals and news default to yfinance and need no key; Alpha Vantage is the alternative and does; FRED macro needs a free key; Polymarket, StockTwits and Reddit's public RSS need none. Any market Yahoo covers works through the exchange suffix — `0700.HK`, `7203.T`, `AZN.L`, `RELIANCE.NS`, `600519.SS` — with the alpha benchmark chosen per market. Forex and commodity symbols normalise to Yahoo's forms, but the fundamentals leg is equity-shaped and has little to read outside stocks. ## Limitations - The output is a recommendation. The schema carries a Buy/Overweight/Hold/Underweight/Sell rating, an entry price, a stop-loss, sizing such as "5% of portfolio", and a price target. Nobody stands behind any of it; the project's own disclaimer says it is not investment advice. - No execution. The README's "sent to the simulated exchange and executed" has no code behind it — no broker, no fills, no position accounting anywhere in the repo. - Not reproducible, by the project's own account: two runs of the same ticker and date can differ, and the curated reasoning models largely ignore the temperature knob. - Not a backtester either. A historical date is point-in-time guarded — v0.4.0 fixed look-ahead in FRED vintages, social sentiment and the decision memory — but there is no portfolio simulator and no P&L, and the README says backtest results are not guaranteed to match any published figure, the paper's headline return included. - `backtrader` is a declared dependency and is imported nowhere in the package. - `pip install tradingagents` installs somebody else's package. The PyPI name is held by an unrelated third party at version 0.7.0 under MIT, advertised with the same summary line. The install is a clone plus `pip install .`, or the Docker image. - No MCP server, despite the category, and no API to call — a CLI and a Python import. ## Alternatives [FinRobot](https://stockmarketstack.com/tools/finrobot) covers similar ground and renders a formal analyst report instead of a debate transcript; [FinGPT](https://stockmarketstack.com/tools/fingpt) is the model layer rather than the harness. For market data wired into an LLM without an opinion attached, the servers in the [MCP and AI agents category](https://stockmarketstack.com/categories/mcp-ai-agents) do that job and stop there. ## FAQ ### What does a single ticker cost to run? The code is free; the language-model calls are not, and they are billed to your own key. The defaults are `gpt-5.6` for the deep-reasoning agents and `gpt-5.6-luna` for the quick ones. Neither the repo nor tauric.ai publishes a token count or a dollar figure per run, so the only honest estimate is structural — at the default depth a run is four analyst legs with tool loops, two debate turns, three risk turns and three manager/trader calls, each carrying the accumulated reports forward. The CLI's "Deep" preset sets five rounds, which is ten debate turns and fifteen risk turns instead of two and three. ### Does TradingAgents place trades? No. The README says an approved proposal "will be sent to the simulated exchange and executed", but there is no exchange, broker or order code in the repository. What a run produces is a tree of Markdown reports and one JSON state log per ticker and date. ### Will two runs of the same ticker on the same date agree? Not reliably, and the project says so itself. Its Reproducibility section notes that provider sampling is non-deterministic, that the default reasoning models largely ignore the temperature setting, and that news and social sources keep moving even when the analysis date is pinned. Company identity and the price and indicator figures are the parts now resolved deterministically. ### Is `pip install tradingagents` the right way to install it? No, and the trap is well set. The `tradingagents` name on PyPI belongs to a third party — version 0.7.0, MIT, homepage mai0313.github.io — and it carries the same one-line summary as Tauric's repo, so the page reads as authentic. It is not Tauric Research's 0.4.0 Apache-2.0 project. The documented install is to clone the repo and `pip install .`, or to run the Docker image. ## Also worth comparing - [MaverickMCP](https://stockmarketstack.com/tools/maverick-mcp.md) — Thirty-seven stock-analysis tools for any MCP client, on Yahoo data and no key. - [Vibe-Trading](https://stockmarketstack.com/tools/vibe-trading.md) — A local finance research agent with 74 MCP tools, nine backtest engines and 14 brokers. - [AI Hedge Fund](https://stockmarketstack.com/tools/ai-hedge-fund.md) — LLM investor personas score a ticker, deterministic code sizes the book, nothing trades. - [Equibles](https://stockmarketstack.com/tools/equibles.md) — Scrapes SEC, FINRA, FRED and CFTC data into your own Postgres and serves it over MCP. - [Financial Datasets](https://stockmarketstack.com/tools/financial-datasets.md) — US fundamentals and filings for agents, over REST and a first-party hosted MCP server. - [FinGPT](https://stockmarketstack.com/tools/fingpt.md) — Financial LLM adapters, instruction datasets and training notebooks, all MIT.