TauricResearch

I Built an AI Team That Researches Stocks and Debates Investment Decisions 🤖 What if you had an entire team of AI agents researching stocks for you? Imagine one agent analyzing stock charts, another digging through financial reports, and another tracking the latest market news. Now imagine two more AI agents arguing about whether you should buy or avoid a particular stock. And instead of simply combining their opinions, the system makes them challenge each other’s conclusions before arriving at a final investment recommendation. Sounds like something a hedge fund might build, right? Well, there’s an open-source project that does exactly this. It’s called TradingAgents, developed by I Built an AI Team That Researches Stocks and Debates Investment Decisions 🤖 , and it uses a multi-agent AI architecture to simulate how a financial research team collaborates. I recently explored this project, studied its architecture, set it up, and put the agents to work analyzing one of the most talked-about companies in the stock market: NVIDIA (NVDA). The result was interesting, but what fascinated me even more was how the agents worked together. In this article, I’ll explain how TradingAgents works, walk through its architecture, show you how to get started, and share what happened during my experiment. ___ 🎥 Full video walkthrough {% embed youtu.be %} ___ 💡 Why Use Multiple AI Agents for Stock Research? If you’ve ever researched a stock before investing, you know how overwhelming the process can become. You need to understand several things: - Financial performance: Revenue, earnings, profitability, and cash flow. - Valuation: Whether the stock is reasonably priced relative to its growth expectations. - Technical indicators: Price movements, moving averages, RSI, and MACD. - Market news: Earnings announcements, industry developments, and economic events. - Investor sentiment: How the broader market perceives the company. - Risk factors: Everything that could potentially go wrong. And here’s something investors sometimes overlook: A great company isn’t necessarily a great stock to buy at any price. A business might be growing rapidly and dominating its industry, but if its stock price already reflects unrealistic growth expectations, investing could still be risky. Now, you could ask ChatGPT or another LLM: >_“Should I buy NVIDIA stock?”_ But a single AI response might overlook important factors or lean too heavily toward one interpretation. What if we approached this differently? Instead of asking one AI model to handle everything, we could assign different research responsibilities to specialized AI agents. One focuses on financial fundamentals. Another analyzes technical indicators. Another investigates risks. Then we make them collaborate, debate, and challenge each other’s conclusions. That’s the fundamental idea behind TradingAgents. ___ 🏗️ What Is TradingAgents? TradingAgents is an open-source multi-agent framework developed by Tauric Research. It’s designed to simulate the collaborative decision-making process of a financial research and trading organization. Rather than relying on a single LLM prompt, TradingAgents coordinates multiple specialized agents through a structured workflow. The system includes: 1. Financial research analysts 2. Bullish and bearish researchers 3. A research manager 4. A trader agent 5. Risk management agents 6. A portfolio manager Each agent has a specific responsibility, and information flows between them as the research progresses. The interesting part is that the agents don’t simply generate independent reports. They share findings, debate competing investment theses, evaluate risk, and eventually produce a consolidated recommendation. Let’s understand how this works. ___ 🔍 Step 1: Four AI Analysts Research the Stock The workflow begins with four specialized research agents. Each examines the company from a different perspective. 📊 1. Market Analyst The Market Analyst focuses on technical analysis. It examines information such as: - Historical stock prices - Moving averages - Relative Strength Index (RSI) - Moving Average Convergence Divergence (MACD) - Recent price trends For example, the RSI can help identify whether a stock has experienced unusually strong buying or selling momentum. The analyst uses available technical indicators to understand what’s happening with the stock’s price behavior. 💰 2. Fundamental Analyst The Fundamental Analyst investigates the underlying business. It examines financial information such as revenue, earnings, profitability, and other available company data. Its goal is to answer questions like: Is this company financially strong? What do its fundamentals suggest about its business performance? Are there financial weaknesses investors should consider? For a company like NVIDIA, this analysis might involve examining revenue growth, margins, profitability, and the sustainability of demand for its products. 📰 3. News Analyst Financial performance alone doesn’t tell the entire story. A company’s outlook can change significantly because of new developments. The News Analyst investigates relevant information such as: - Company announcements - Earnings-related developments - Industry news - Economic events - Broader market developments For example, changes in semiconductor export restrictions or major AI infrastructure spending announcements could influence how investors evaluate NVIDIA. 🗣️ 4. Sentiment Analyst The Sentiment Analyst examines available public discussions and sentiment-related information. Its goal is to understand how investors and the broader market are discussing the company. This can provide useful context about market expectations, enthusiasm, and concerns. However, sentiment isn’t necessarily an indicator of whether a stock is fundamentally undervalued or overvalued. It’s simply another perspective. Why This Architecture Matters The four analysts can gather information in parallel. Rather than forcing a single AI agent to perform every research task, the system distributes responsibilities across specialized agents. Their findings become the foundation for the next stage. And this is where things get particularly interesting. ___ 🐂 Step 2: Bull vs. Bear — AI Agents Debate the Investment Once the analysts complete their research, their findings are passed to two additional agents. The Bull Researcher and the Bear Researcher. These agents have opposing responsibilities. The Bull Researcher The Bull Researcher builds the strongest possible argument for investing in the company. For example, a bullish NVIDIA investment thesis might emphasize: - Strong demand for AI computing infrastructure - Revenue and earnings growth - Competitive advantages - Market leadership - Long-term industry opportunities The Bear Researcher The Bear Researcher takes the opposite position. Its responsibility is to challenge the bullish argument and identify potential weaknesses. For example: - Is the stock valuation too expensive? - Are growth expectations unrealistic? - Could competition increase? - Are there regulatory or geopolitical risks? - What happens if AI infrastructure spending slows? But here’s the interesting part. These agents actually debate each other. The Bull presents an argument. The Bear challenges it. The Bull gets an opportunity to respond. And the discussion can continue through multiple configurable rounds. Instead of simply asking an LLM to list pros and cons, TradingAgents creates a structured exchange between competing investment perspectives. After the debate, a Research Manager evaluates the arguments and produces an investment plan. This approach doesn’t guarantee better investment decisions, but it provides a mechanism for examining competing interpretations before reaching a conclusion. ___ ⚖️ Step 3: The Trader and Risk Management Agents After the Research Manager produces an investment plan, the workflow moves to the Trader Agent. Despite its name, the Trader Agent in my demonstration wasn’t executing any actual trades. Instead, it translated the research into a more concrete investment proposal. That proposal then went through another review process. This time, three risk-focused agents evaluated it. Aggressive Risk Analyst Examines the investment proposal from a perspective that accepts greater risk in pursuit of potential returns. Conservative Risk Analyst Focuses on downside protection, uncertainty, and reasons to avoid excessive exposure. Neutral Risk Analyst Attempts to balance potential opportunities against the associated risks. These perspectives are used to challenge the investment proposal. Finally, the Portfolio Manager evaluates the research, proposed action, and risk discussion to produce the final recommendation. So the overall workflow looks something like this: STOCK / COMPANY | v RESEARCH ANALYSTS | +------------+------------+ | | | Market Fundamental News Analyst Analyst Analyst | | | +------------+------------+ | Sentiment Analyst | v RESEARCH DEBATE | +------+------+ | | Bull Agent Bear Agent | | +------+------+ | v Research Manager | v Trader Agent | v RISK ANALYSIS | +----------+----------+ | | | Aggressive Conservative Neutral | | | +----------+----------+ | v Portfolio Manager | v FINAL RECOMMENDATION Simplified conceptual representation of the TradingAgents workflow. The analyst stage is designed to support parallel research. This is what makes TradingAgents an interesting example of agentic AI and multi-agent orchestration. It’s not just multiple LLM calls. It’s a structured workflow in which different agents contribute information, challenge conclusions, and participate in a decision-making process. ___ 🛠️ Step 4: Installing and Setting Up TradingAgents Now that we understand the architecture, let’s look at how to run it. TradingAgents is open source, so you can inspect the implementation, modify the agents, and experiment with different configurations. 1. Clone the GitHub Repository Start by cloning the project: git clone https://github.com/TauricResearch/TradingAgents.git cd TradingAgents Open the project in your preferred code editor. I used VS Code for my demonstration. 2. Create a Python Virtual Environment I used Python 3.13 and uv for environment management. Create a virtual environment: uv venv --python 3.13 Activate it on macOS or Linux: source .venv/bin/activate This keeps the project’s Python dependencies separate from other applications installed on your machine. 3. Install the Dependencies Install the project using the dependency instructions provided in the repository. For example, when installing from the project directory with uv: uv pip install -e . The exact installation steps may change between releases, so check the repository’s latest README before proceeding. 4. Configure Your AI Model Provider TradingAgents supports configurable AI model providers. Depending on the version and configuration, you can use supported providers such as OpenAI, Google, or Anthropic. The repository includes an example environment configuration file. You can copy it: cp .env.example .env Then configure the credentials required by your selected provider. In my demonstration, I used the framework’s supported ChatGPT subscription authentication flow rather than manually configuring an API key. For versions supporting this option, the login command is: tradingagents login chatgpt Follow the authentication instructions displayed in your terminal. Note: Subscription-based authentication depends on the installed TradingAgents version and its supported integration. A ChatGPT subscription is not generally interchangeable with OpenAI API credits. 5. Launch TradingAgents Once everything is configured, launch the CLI: tradingagents The interface guides you through several configuration options. You can select: - The stock ticker - Analysis date - Output language - Research analysts - Research depth - AI model provider - LLM models - Reasoning effort For my experiment, I selected NVIDIA (NVDA) and enabled all four research analysts. I also chose the shallow research configuration to keep the demonstration relatively quick. Research depth affects how many debate and discussion rounds the agents perform. Once configured, the agents begin researching the company. ___ 🚀 Step 5: I Asked the AI Team to Analyze NVIDIA With everything running, I gave the system NVIDIA as the company to investigate. The workflow started with the research analysts gathering information. The Market Analyst examined technical indicators. The Fundamental Analyst investigated the company’s financial position. Meanwhile, the News and Sentiment Analysts collected information from their available data sources. Once the initial research was complete, the system moved through the debate and risk evaluation stages. Eventually, TradingAgents generated a detailed report containing the findings from the different agents. The report included technical analysis, financial research, sentiment observations, news analysis, and the final investment thesis. So, What Did the AI Agents Recommend? After completing the analysis, the Portfolio Manager produced its final recommendation: UNDERWEIGHT. In portfolio terminology, an underweight position generally means maintaining less exposure to an asset than a relevant benchmark or reference allocation. But I wouldn’t interpret this result as proof that NVIDIA is a bad investment. The recommendation reflects the model-generated research, available information, and configuration used during this particular run. I haven’t established whether this recommendation would outperform a benchmark or lead to better investment outcomes. And that’s an important distinction. Generating a convincing investment thesis is not the same as demonstrating predictive accuracy. What I found useful was the structured research process. Instead of receiving a short answer from a chatbot, I could examine the different research perspectives that contributed to the final decision. That makes TradingAgents particularly interesting as an investment research assistant. ___ 🧠 TradingAgents Also Has a Memory System Another feature worth exploring is the framework’s built-in memory and historical evaluation capabilities. After completing an analysis, TradingAgents can save information about the research decision in a local journal. This can include: - Stock ticker - Analysis date - Generated recommendation - Evaluation status Initially, the outcome is marked as pending. That makes sense because the system doesn’t know what will happen to the stock after the recommendation. Once sufficient trading sessions have passed, the framework can evaluate the stock’s subsequent performance against a benchmark. In the configuration I explored, the default evaluation window was five trading days. Based on the evaluation, an AI model can generate a reflection about the previous research. For example: What did the analysis get right? What risks did it overlook? Which assumptions turned out to be incorrect? What lessons might be useful for future research? Those reflections can then be retrieved as context for subsequent analyses. This creates a basic feedback mechanism. However, it’s important to understand that memory-based reflection is not the same as training or fine-tuning the underlying LLM. The framework can reuse previous lessons as contextual information, but that doesn’t automatically mean its investment predictions improve over time. Demonstrating that would require systematic evaluation. ___ 🔮 What If We Took This Project Further? After experimenting with TradingAgents, I started thinking about what else we could build using this architecture. Because the project is open source, there are several interesting directions to explore. 1. Build an AI-Powered Stock Screener Instead of analyzing one company at a time, imagine providing the system with a watchlist of 50 stocks. The agents could research each company, evaluate its investment thesis, and produce a shortlist of opportunities worth investigating further. You could then compare the companies using consistent research criteria. 2. Create a Financial Research Dashboard Another possibility is building a dashboard that tracks the agents’ research over time. For example, it could display: - Companies being monitored - Latest research recommendations - Bullish and bearish arguments - Risk assessments - Historical recommendations - Subsequent stock performance This could make it easier to compare how investment theses evolve as new information becomes available. 3. Introduce Industry-Specific AI Agents Different industries require different types of analysis. For example, we could introduce specialist agents for: Semiconductors: Manufacturing capacity, AI infrastructure demand, supply chains, and competitive positioning. Healthcare: Clinical trials, regulatory approvals, product pipelines, and reimbursement risks. Technology: Revenue growth, recurring revenue, customer retention, and competitive advantages. These agents could supplement the general research team with more specialized domain knowledge. 4. Evaluate Whether Multi-Agent Research Actually Works This is probably the most interesting direction from a research perspective. We could systematically compare: Single LLM vs. Multi-Agent Research vs. Traditional Financial Screening And evaluate metrics such as: - Research completeness - Factual accuracy - Recommendation consistency - Risk identification - Token usage and cost - Latency - Performance against relevant benchmarks We could also run historical evaluations while carefully controlling for look-ahead bias and survivorship bias. That would help answer a much more meaningful question: Do multi-agent AI systems actually improve investment research quality, or do they simply generate more elaborate reports?

I Built a Team of AI Agents That Research Stocks, Debate Each Other, and Make Investment Recommendations I’ve been experimenting with multi-agent AI systems, and recently came across an interesting open-source project called TradingAgents, developed by Tauric Research. The idea is pretty cool: instead of asking a single LLM whether a stock is worth buying, you create an entire team of specialized AI agents that work together like a financial research firm. Each agent has a specific responsibility: - Market Analyst: Studies price movements, RSI, MACD, moving averages, and other technical indicators. - Fundamental Analyst: Analyzes revenue, earnings, profitability, and company financials. - News Analyst: Tracks company announcements and market developments. - Sentiment Analyst: Examines public discussions and investor sentiment. But here’s where it gets interesting. Once these agents finish their research, their findings are passed to two more agents: a Bull Researcher and a Bear Researcher. The Bull builds the strongest possible argument for investing in the company, while the Bear challenges those arguments and highlights potential risks. They actually go through multiple rounds of debate before a Research Manager evaluates their conclusions. The resulting investment plan then passes through a Trader Agent, three Risk Analysts (aggressive, conservative, and neutral), and finally a Portfolio Manager that produces the investment recommendation. I decided to test this entire workflow on NVIDIA (NVDA). After analyzing the company’s financials, technical indicators, news, and market sentiment, the agents generated a detailed investment research report. The final recommendation? UNDERWEIGHT. What interested me wasn’t necessarily whether that recommendation was correct, but how the different AI agents collaborated, challenged each other’s assumptions, and arrived at a structured investment thesis. Another interesting feature is the framework’s built-in memory. It can store previous research decisions, evaluate subsequent stock performance, and generate reflections that can be retrieved during future analyses. Of course, this doesn’t mean the agents can consistently predict stock prices or outperform the market. The quality of the research still depends heavily on the underlying data, LLMs, and how the workflow is configured. I also made a hands-on tutorial covering: - How the TradingAgents multi-agent architecture works - How to install and configure the open-source framework - How to run the agents locally using the CLI - A complete NVIDIA stock research demonstration - How the agents generate their final investment report Full walkthrough: youtu.be GitHub: TauricResearch/TradingAgents

看到个挺有意思的开源项目: 让一群 AI 分别扮演基本面、新闻、情绪、技术分析师，吵完再交给交易员和风控拍板，最后出一份报告。适合想研究 AI 怎么分析股票的人，自己装好填个模型 key 就能跑一只票。官方也说了是研究用的，别直接照着下单哈😅 TauricResearch/TradingAgents

TradingAgents by Tauric Research: A Trading Firm of 12 LLM Agents, and What One Decision Costs --- title: "TradingAgents by Tauric Research: A Trading Firm of 12 LLM Agents, and What One Decision Costs" published: true tags: opensource, ai, finance, agents canonical_url: devpik.com --- TradingAgents is a free, open-source Python framework from Tauric Research that analyzes a stock the way a trading firm would, with a team of language-model agents instead of one prompt. Four analysts study the market, sentiment, news and fundamentals; a bull and a bear argue over their reports; a trader turns the result into a plan; three risk analysts argue over that plan; and a portfolio manager makes the call. You give it a ticker and a date, and it returns a rating and a folder of reports. It had 115,371 GitHub stars and more than 22,000 forks when we wrote this, and one sentence in the README matters more than the rest: the framework is designed for research purposes and is not intended as financial, investment or trading advice. What Is TradingAgents? The project began as a research paper, "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024, the same day the repository was created. It is built on LangGraph, licensed under Apache 2.0, and the current release is version 0.7.0, from 16 October 2026. Version 0.7.0 contains no broker integration and no code that places orders: a run ends with a rating and its reasoning, and what you do with them is up to you. That makes it a tool for studying how multi-agent LLM systems reason about markets, not a bot that trades for you, and this post treats it that way. Twelve Agents, Modeled on a Trading Firm Every role in TradingAgents is a separate agent with its own prompt, and most of them have their own data. The analysts run at the same time, and the debate starts once all four reports are in. | Role | What it does | Model tier | | --- | --- | --- | | Market Analyst | Prices and up to 8 technical indicators (moving averages, MACD, RSI, Bollinger Bands, ATR, VWMA), checked against a verified price snapshot | Quick | | Sentiment Analyst | Yahoo Finance headlines, StockTwits messages and Reddit posts, scored as one sentiment band | Quick | | News Analyst | Company news, global macro headlines, FRED economic data and Polymarket prediction markets | Quick | | Fundamentals Analyst | Balance sheet, cash flow, income statement, company overview and insider transactions | Quick | | Bull and Bear Researchers | Argue for and against the trade from the four reports | Quick | | Research Manager | Judges the debate and writes the investment plan | Deep | | Trader | Turns the plan into a buy, hold or sell proposal | Quick | | Aggressive, Conservative and Neutral Analysts | Argue over the trader's proposal from three risk appetites | Quick | | Portfolio Manager | Makes the final decision on a five-step scale | Deep | The two tiers are the main cost control. You choose a quick model and a deep model, and each can run on its own provider, so the managers could run on Claude while everything else runs on a cheaper OpenAI model. The defaults are OpenAI's GPT-6 Luna for the quick tier and GPT-6 Sol for the deep one. The final answer is one of five ratings: Buy, Overweight, Hold, Underweight or Sell. When the portfolio manager's output contains no readable rating, the code records REVIEW instead of quietly treating it as a Hold, because a decision nobody can read is not a decision to hold. Fifteen Model Calls Is the Floor for One Decision Nothing in the README says how many times a run calls a language model, and that number is what you pay for. So we counted it from the code: the graph's routing in conditional_logic.py, the analyst loop in analysts/turn.py, the default settings in default_config.py, and the project's own end-to-end test, which runs the whole graph offline with a scripted model. We read version 0.7.0 (commit e8f5503) and did not pay for a live run, so this is a count of calls, not a bill. With the default settings and all four analysts, one decision makes at least 15 model calls: | Step | Calls at minimum | Tier | | --- | --- | --- | | Market, news and fundamentals analysts | 2 each: one round of tool calls, then the report | Quick | | Sentiment analyst | 1, because its data is fetched before the model is called | Quick | | Bull and bear researchers | 2, one debate round each | Quick | | Research manager | 1 | Deep | | Trader | 1 | Quick | | Three risk analysts | 3, one risk round each | Quick | | Portfolio manager | 1 | Deep | | Total | 15 | 13 quick, 2 deep | The project's test agrees. It deliberately breaks the provider on the 12th call and describes that point as "past the analysts, before the decision", which is exactly where the count puts it: 7 analyst calls, then the bull, the bear, the research manager and the trader, so the 12th call is the first risk analyst. Three things push a real run above 15: - Tool rounds. The market analyst is told to fetch prices first, then compute indicators, then check the verified snapshot, so a model that follows the instructions needs several rounds before it writes. Each analyst may take up to 20 rounds of tool calls, plus one final call without tools when it runs out. - Debate settings. The README's own Python example sets two rounds for the bull and bear, which adds 2 calls. Two rounds of both debates make it 20. - Past decisions. Each run settles earlier decisions whose holding period has passed and writes a one-paragraph reflection on each, one more quick call per decision. Two conclusions follow. The quick model carries 13 of the 15 calls, so it decides most of the cost, and the deep model is called only twice. And backtests multiply: the README's example grid, two tickers every 7 days from 1 June to 1 August with only the market and news analysts, is 18 decisions and at least 216 model calls before the reflections. Debates Are a Setting You Choose The debates are what make TradingAgents different from asking one model for an opinion, and they are fully configurable. The bull and bear take turns until each has spoken max_debate_rounds times, then the research manager writes a plan from the transcript. The aggressive, conservative and neutral analysts do the same for max_risk_discuss_rounds rounds before the portfolio manager decides. Both default to 1. More rounds give the agents more chances to answer each other, at a cost of 2 calls per extra debate round and 3 per extra risk round. The same settings are available as environment variables for the CLI: TRADINGAGENTS_MAX_DEBATE_ROUNDS and TRADINGAGENTS_MAX_RISK_ROUNDS. You can also pass in what you already hold. Without a portfolio, the agents write guidance for a reader who applies it to their own position; with one, the trader, the risk analysts and the portfolio manager work against your actual cash and positions. The CLI takes it as a JSON file (tradingagents --portfolio my_book.json) and refuses to start when the file does not validate. Point-in-Time Data, and the One Feed That Is Not A backtest is only honest if each run sees what was public on its date. Most of TradingAgents' recent releases are about exactly that, and the README is specific about how each source behaves. Fundamentals are read as filed. US company statements come from SEC EDGAR, which records the date every figure was filed, so a run dated in the past reads the statements as they stood that day. The README's example is Apple's total assets for fiscal 2008, filed as $39.6 billion and restated to $36.2 billion in 2010. We checked it against EDGAR's public API: the figure for the period ending 27 September 2008 appears as $39.572 billion in the 10-K filed on 27 October 2009, and as $36.171 billion in the amended 10-K filed on 25 January 2010. A run dated between those two filings reads the first number. Other sources hold back what they cannot date. Yahoo Finance dates a statement by the period it covers rather than by when it was published, so a run dated in the past is told those statements are withheld. Insider trades are dated by when they happened, not when they were filed, so they are withheld for past dates too. News articles published after the analysis date are filtered out, and the project keeps regression tests for the ways that filter has leaked in the past. Social sentiment is the exception. The sentiment analyst trims StockTwits and Reddit posts to the seven days before the analysis date, so nothing from after it leaks in. The problem is the other direction. Its code says these feeds "serve recent items and are not archived, so a historical run's sentiment inputs are not point-in-time." A run dated months back finds little or nothing in its window, and the feed tells the model the window was never observed rather than reporting that nobody posted. That is presumably why the README's backtest example selects only the market and news analysts, and it is the setting to copy: leave the sentiment analyst out of anything you backtest. Memory and Backtests Score Decisions Against the Market TradingAgents keeps a memory log of its own decisions at ~/.tradingagents/memory/trading_memory.md. While the analysts of a later run work, it settles every logged decision whose holding period has passed: it fetches the realized return, and the alpha against the market's benchmark (SPY for US tickers, regional indexes such as the Nikkei 225 or Nifty 50 elsewhere), then writes a short reflection. The portfolio manager reads recent decisions on the same ticker, plus recent lessons from others, before it decides. The holding period is 5 trading days, counted from the first close after the analysis date. Version 0.7.0 moved it from the close of the analysis date itself, because a run reads news published after that day's close and could not have traded on the earlier price. To judge whether the system decides well rather than reading one run, run_backtest sweeps a grid of tickers and dates into a memory log of its own and scores each settled decision on realized alpha, grouped by rating. An interrupted sweep resumes with the same run_id, skipping the cells that already ran. Each run also writes its full state to full_states_log_<date>.json under the ticker's results folder, with the date, the final rating and the model settings. The paper reports that TradingAgents improved cumulative returns, Sharpe ratio and maximum drawdown over baseline strategies. The README is careful in the other direction: backtest results "are not guaranteed to match any published figure", and the framework is "a research scaffold for studying multi-agent analysis, not a strategy with a fixed, replicable return." Two Runs of the Same Ticker Will Not Match The README says it directly: two runs of the same ticker and date can differ, and that is expected, not a bug. Language-model sampling varies even at a fixed temperature, reasoning models vary most because their reasoning is itself sampled, and the live news and social feeds change from day to day. Some things no longer vary. The company behind a ticker is resolved before any agent runs, and the market analyst grounds every exact price and indicator claim in a verified snapshot. The README says these two changes address earlier reports of runs analyzing the wrong company or inventing price levels. To see the variation yourself, run the same ticker and date twice and save both reports. Each saved report is a folder of Markdown files, one per agent (market.md, bull.md, trader.md, decision.md and so on), plus complete_report.html as one page. Comparing the two decision.md files shows exactly where the reasoning changed. For tighter repeatability, set temperature to 0 and choose models that honor it. The current default models are reasoning-first and largely ignore temperature, so the README suggests naming a non-reasoning model for both tiers when reproducibility matters more than depth. Installing TradingAgents and Running Your First Analysis TradingAgents needs Python 3.11 or newer and an API key for at least one model provider. The CLI asks for the ticker, date, provider, models and research depth, and remembers your answers for next time. For scripts and scheduled jobs it runs without questions from flags. Docker works too (docker compose run --rm tradingagents), with an Ollama profile for local models. The provider list is long: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Kimi, Groq, NVIDIA, Azure OpenAI, AWS Bedrock, Ollama, and any OpenAI-compatible server such as vLLM or LM Studio. Since version 0.7.0 it can also run on a ChatGPT Plus or Pro plan instead of an API key, after a one-time tradingagents login chatgpt; those runs count toward the plan's usage limits. Any market Yahoo Finance covers works, using the exchange suffix: 0700.HK for Hong Kong, 7203.T for Tokyo, RELIANCE.NS for India, 600519.SS for Shanghai, and crypto pairs like BTC-USD. SEC EDGAR covers companies that file with the SEC, including foreign companies listed in the US; other listings fall back to the next data vendor. Two defaults are worth knowing. Checkpoint resume is off unless you pass --checkpoint, which lets a crashed run continue from its last completed step. And the interactive CLI checks for a newer release at most once a day in the background; it sends nothing about your runs, and TRADINGAGENTS_NO_UPDATE_CHECK=1 turns it off. Who Should Use TradingAgents, and Who Should Not TradingAgents is a good fit if you want to study how LLM agents reason about markets: researchers and students working on multi-agent systems, developers who want a well-tested LangGraph project to learn from, and quantitative tinkerers who want to see whether structured debate changes a model's calls. Its strengths are the ones that matter for research. The roles are explicit, the debates are configurable, the data is point-in-time where it can be, and it has a real backtest harness that scores decisions against a benchmark. It is the wrong tool if you are looking for something to trade your money. It places no orders, the README says it is not financial advice, two runs can disagree, and every decision costs at least 15 model calls. Treat its ratings as a research output to study, and keep the sentiment analyst out of anything you backtest. Links - GitHub: TauricResearch/TradingAgents - Full breakdown on DevPik: devpik.com FAQs What is TradingAgents? A free, open-source Python framework from Tauric Research that analyzes a stock with a team of LLM agents modeled on a trading firm: four analysts, a bull and a bear researcher, a research manager, a trader, three risk analysts and a portfolio manager. Given a ticker and a date, it returns one of five ratings (Buy, Overweight, Hold, Underweight or Sell) and a report from every agent. It is built on LangGraph and licensed under Apache 2.0. Who made TradingAgents? Tauric Research. The framework accompanies the paper "TradingAgents: Multi-Agents LLM Financial Trading Framework" by Yijia Xiao, Edward Sun, Di Luo and Wei Wang, first posted to arXiv on 28 December 2024. The GitHub repository was created the same day and had more than 115,000 stars in October 2026. Is TradingAgents free? The code is free under the Apache 2.0 license. Running it is not, unless you use local models: every decision makes at least 15 calls to the language models you configure, billed by your provider. You can run it on local models through Ollama or an OpenAI-compatible server, or on a ChatGPT Plus or Pro plan, where runs count toward the plan's usage limits. Can TradingAgents trade with real money? No. Version 0.7.0 has no broker integration and no code that places orders. A run ends with a rating and its reasoning. The README describes the framework as designed for research purposes and says it is not intended as financial, investment or trading advice. How many API calls does a TradingAgents run make? At least 15 with the default settings and all four analysts: 2 for each of the three tool-using analysts, 1 for the sentiment analyst, 2 for the bull and bear, 1 each for the research manager, trader and portfolio manager, and 3 for the risk analysts. Thirteen of those go to the quick model and two to the deep model. More tool rounds, more debate rounds and reflections on past decisions add to it; two rounds of each debate make it 20. Which models and markets does TradingAgents support? Most major providers, including OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen, GLM, MiniMax, OpenRouter, Mistral, Groq, Azure OpenAI and AWS Bedrock, plus local models through Ollama and any OpenAI-compatible server. It works with any market Yahoo Finance covers, using exchange suffixes such as .HK, .T, .NS or .SS, and with crypto pairs like BTC-USD.

이번 주 개발자라면 봐야 할 오픈소스 Top 5 +1 · GitHub 트렌딩 (10월 4주) 이번 주(10/21~10/28) GitHub 트렌딩 주간 순위 Top 5와 번외(순위 밖) 1개를, 초보자·개발자별로 어떻게 써 보면 좋은지까지 정리했어요. 6~10위는 아래 목록에, 13개 전체 설명은 롱폼에 있어요. 순위는 GitHub Trending(weekly) 페이지 순서 그대로예요(2026-10-28 06:00 KST 수집). 별 수는 수집 시점 값이에요. ▶ 전체 13개 자세히(롱폼): youtu.be 1위 cloudflare/security-audit-skill — 코딩 에이전트에게 6단계 보안 감사를 시키는 스킬(MIT). 코드는 9/14 이후 변경 없음, 별은 9/23 약 2만 → 10/28 약 5만. ⚠ 실행 검증엔 외부 네트워크를 막은 샌드박스 필요, 소스는 쓰는 모델 회사로 전송 2위 anthropics/knowledge-work-plugins — Anthropic 직무별 플러그인(Cowork용, Claude Code에서도). sales 2.1.0: 문장마다 출처·원본 링크 3위 AgriciDaniel/claude-seo — Claude Code용 SEO 점검 플러그인. v2.5.0: 서버 CVE 노출 점검(응답만 읽음), Python 3.11~3.14(3.15는 아직 안 됨). 일부 확장은 유료 외부 서비스 4위 multica-ai/andrej-karpathy-skills — Karpathy의 글을 바탕으로 다른 개발자가 만든 CLAUDE.md(원칙 4개). LICENSE 파일 없음 5위 TauricResearch/TradingAgents — 멀티 에이전트 매매 연구 프레임워크(투자 조언 아님). v0.7.1: 0 없는 날짜가 이후 공시를 읽던 버그 수정 → 예전 백테스트는 다시 돌려 보기 ▶ 6~10위 6위 Graphify-Labs/graphify — 코드·문서를 지식 그래프로(코드는 로컬, 모델 없음). ⚠ PyPI 이름은 graphifyy 7위 ruvnet/ruflo — Claude Code·Codex 위 다중 에이전트 도구. ⚠ 3.63.3 이상으로(이전엔 클론한 저장소 설정으로 AI 워커가 켜질 수 있었음) 8위 anomalyco/opencode — 모델 회사에 묶이지 않는 오픈소스 코딩 에이전트. v2.2.0. ⚠ Console에 외부 키를 저장했다면 공급자 쪽에서 폐기·재발급 9위 stablyai/orca — 여러 코딩 에이전트를 worktree별로 동시에 돌리는 데스크톱 앱, 10/21 별 10만 10위 android/skills — Google Android 팀 공식 에이전트 스킬, 30개 → 36개 ▶ 번외(순위 밖) akitaonrails/ai-memory(주간 11위) — 여러 코딩 에이전트가 같이 쓰는 장기 기억 서버. 3.2.0: 띄어쓰기 없는 한글 부분 검색, 끊긴 세션 알림. 프롬프트·도구 기록을 서버에 저장 ▶ 링크 cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ 내레이션은 AI 보이스(ElevenLabs)예요. 실화면이 없는 부분은 공식 문서에 적힌 동작을 그린 원리 모션이고, '이렇게 써 보세요' 카드는 문서에 있는 기능 범위의 활용 예예요. #GitHub트렌딩 #오픈소스 #개발자 #코딩에이전트 #SoloStack [de-DE] Die Top 5 +1 Open-Source-Projekte, die Entwickler diese Woche kennen sollten · GitHub Trending (4... Ich habe die Top 5 der wöchentlichen GitHub-Trends dieser Woche (21.–28. Oktober) sowie einen weiteren Eintrag ohne Platzierung zusammengestellt und gebe Tipps zur optimalen Nutzung für Anfänger und Entwickler. Die Plätze 6–10 sind unten aufgeführt, und ausführliche Beschreibungen aller 13 Einträge finden Sie in der Langfassung. Die Rangliste entspricht der Reihenfolge auf der GitHub-Trendseite (wöchentlich, Stand: 28.10.2026, 06:00 Uhr KST). Die Sternebewertungen basieren auf den Werten zum Zeitpunkt der Erfassung. ▶ Details zu allen 13 Einträgen (Langfassung): youtu.be Platz 1: cloudflare/security-audit-skill – Ein Skill (MIT-Lizenz), der es Coding Agents ermöglicht, sechsstufige Sicherheitsaudits durchzuführen. Der Code ist seit dem 14.09. unverändert. Die Anzahl der Sterne stieg von ca. 20.000 am 23.09. auf ca. 50.000 am 28.10. ⚠ Die Ausführungsprüfung erfordert eine Sandbox, die externe Netzwerke blockiert; der Quellcode wird an das Modellunternehmen gesendet, das ihn verwendet. 2. Platz: anthropics/knowledge-work-plugins – Jobspezifische Plugins von Anthropics (für Cowork, auch auf Claude Code verfügbar). Version 2.1.0: Quellcode und Originallink für jeden Satz. 3. Platz: AgriciDaniel/claude-seo – SEO-Check-Plugin für Claude Code. Version 2.5.0: Server-CVE-Prüfung (liest nur Antworten), Python 3.11–3.14 (3.15 wird noch nicht unterstützt). Einige Erweiterungen sind kostenpflichtige externe Dienste. 4. Platz: multica-ai/andrej-karpathy-skills – CLAUDE.md (4 Prinzipien), erstellt von einem anderen Entwickler basierend auf Karpathys Schriften. Keine Lizenzdatei. 5. Platz: TauricResearch/TradingAgents – Multiagenten-Framework für Trading-Analysen (keine Anlageberatung). v0.7.1: Fehler behoben, der dazu führte, dass Offenlegungen nach Datumsangaben mit Nullen gelesen wurden → Alte Backtests erneut ausführen ▶ 6. bis 10. Platz 6. Platz: Graphify-Labs/graphify – Code und Dokumentation als Wissensgraph (Code lokal, keine Modelle). ⚠ PyPI-Name: graphifyy 7. Platz: ruvnet/ruflo – Multiagenten-Tool basierend auf Claude Code und Codex. ⚠ Erfordert Version 3.63.3 oder höher (zuvor konnten KI-Worker über die Einstellungen des geklonten Repositorys aktiviert werden) 8. Platz: anomalyco/opencode – Open-Source-Codierungsagent, unabhängig von einem Modellunternehmen. v2.2.0. ⚠ Wenn Sie den Fremdschlüssel in der Konsole gespeichert haben, muss der Anbieter ihn verwerfen oder neu ausstellen. 9. Platz: stabilai/orca – Eine Desktop-Anwendung von worktree, die mehrere Coding-Agenten gleichzeitig ausführt; 100.000 Sterne am 21.10. 10. Platz: android/skills – Offizielle Agentenfähigkeiten des Google Android-Teams (30 → 36) ▶ Bonus (Nicht in der Rangliste) akitaonrails/ai-memory (11. Platz der Woche) – Ein Langzeitspeicherserver, der von mehreren Coding-Agenten gemeinsam genutzt wird. Version 3.2.0: Suche nach koreanischen Teilen ohne Leerzeichen, Benachrichtigungen bei Verbindungsabbrüchen. Eingabeaufforderung und Toolverlauf auf dem Server speichern ▶ Links cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ Die Sprachausgabe stammt von einer KI-Stimme (ElevenLabs). Die Abschnitte ohne Videomaterial zeigen die in der offiziellen Dokumentation beschriebenen Aktionen. Die „So verwenden Sie es“-Karten enthalten Anwendungsbeispiele innerhalb des in der Dokumentation beschriebenen Funktionsumfangs. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [en] This Week's Must-See Open Source: GitHub Trending Top 5 + 1 (Oct, Week 4) This week's GitHub Trending (weekly) Top 5 plus one repo from outside the chart, with a quick tip for beginners and for working developers on each (collected 2026-10-28 06:00 KST). 5 TradingAgents · 4 andrej-karpathy-skills · 3 claude-seo · 2 knowledge-work-plugins · Extra: ai-memory · 1 Cloudflare security-audit-skill Full rundown of all 13 repos in the long-form video: youtu.be Narration is an AI voice (ElevenLabs). [es-US] Los 5 +1 proyectos de código abierto que todo desarrollador debe ver esta semana · GitHub Trendin... He recopilado los 5 elementos más populares de GitHub de esta semana (del 21 al 28 de octubre), junto con un elemento no clasificado, e incluyo consejos sobre cómo usarlos de la mejor manera, tanto para principiantes como para desarrolladores. Los puestos del 6 al 10 se muestran a continuación, y las descripciones completas de los 13 elementos se pueden encontrar en la versión extendida. La clasificación sigue el orden exacto de la página de Tendencias de GitHub (semanal) (recopilada el 28/10/2026 a las 06:00 KST). Las calificaciones con estrellas se basan en los valores vigentes en el momento de la recopilación. ▶ Detalles de los 13 elementos (versión extendida): youtu.be 1.er puesto: cloudflare/security-audit-skill — Una habilidad (MIT) que permite a los agentes de codificación realizar auditorías de seguridad de 6 etapas. El código no ha sufrido cambios desde el 14/09. Las estrellas aumentaron de aproximadamente 20 000 el 23/9 a aproximadamente 50 000 el 28/10. ⚠ La verificación de ejecución requiere un entorno aislado que bloquee las redes externas; el código fuente se envía a la empresa modelo que lo utiliza. 2.º puesto: anthropics/knowledge-work-plugins — Plugins específicos para el trabajo de Anthropic (para Cowork, también disponibles en Claude Code). sales 2.1.0: Fuente y enlace original para cada frase. 3.er puesto: AgriciDaniel/claude-seo — Plugin de verificación SEO para Claude Code. v2.5.0: Verificación de exposición CVE del servidor (solo lee las respuestas), Python 3.11~3.14 (3.15 aún no compatible). Algunas extensiones son servicios externos de pago. 4.º puesto: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principios) creado por otro desarrollador basado en los escritos de Karpathy. Sin archivo de licencia. 5.º puesto: TauricResearch/TradingAgents — Marco de investigación de trading multiagente (no es asesoramiento de inversión). v0.7.1: Se corrigió un error por el cual se leían las divulgaciones después de fechas con ceros → Se volvieron a ejecutar las pruebas retrospectivas anteriores. ▶ 6.º a 10.º puesto 6.º puesto: Graphify-Labs/graphify — Código y documentación como un grafo de conocimiento (el código es local, sin modelos). ⚠ El nombre en PyPI es graphifyy. 7.º puesto: ruvnet/ruflo — Herramienta multiagente basada en Claude Code y Codex. ⚠ Requiere la versión 3.63.3 o superior (anteriormente, los agentes de IA podían habilitarse mediante la configuración del repositorio clonado). 8.º puesto: anomalyco/opencode — Agente de codificación de código abierto no vinculado a una empresa modelo. v2.2.0. ⚠ Si has guardado la clave externa en la consola, el proveedor debe descartarla o volver a emitirla. 9.º stablyai/orca: Aplicación de escritorio que ejecuta varios agentes de codificación simultáneamente mediante worktree. 100.000 estrellas el 21/10. 10.º android/skills: Habilidades oficiales de agente del equipo de Google Android. 30 → 36 ▶ Bonificación (Fuera de la clasificación) akitaonrails/ai-memory (11.º de la semana): Servidor de memoria a largo plazo compartido por varios agentes de codificación. 3.2.0: Búsqueda de partes en coreano sin espacios, notificaciones de sesión desconectada. Guarda el historial de sugerencias y herramientas en el servidor. ▶ Enlaces cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narración la proporciona una voz de IA (ElevenLabs). Las partes sin imágenes reales son animaciones principales que representan las acciones descritas en la documentación oficial, y las tarjetas "Prueba a usarlo así" son ejemplos de uso dentro del ámbito funcional descrito en la documentación. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [fr-FR] Top 5 +1 des projets open source à ne pas manquer pour les développeurs cette semaine · GitHub Tr... Voici le classement des 5 éléments les plus populaires de la semaine sur GitHub (du 21 au 28 octobre), ainsi qu'un élément non classé. Vous trouverez également des conseils pour une utilisation optimale, que vous soyez débutant ou développeur. Les éléments classés de la 6e à la 10e place sont listés ci-dessous. La description complète des 13 éléments est disponible dans la vidéo détaillée. Ce classement correspond exactement à celui de la page GitHub Trending (hebdomadaire) (données collectées le 28 octobre 2026 à 06h00 KST). Le nombre d'étoiles correspond aux valeurs au moment de la collecte. ▶ Détails sur les 13 éléments (vidéo détaillée) : youtu.be 1re place : cloudflare/security-audit-skill — Skill (MIT) permettant aux agents de développement de réaliser des audits de sécurité en 6 étapes. Code inchangé depuis le 14/09. Le nombre d'étoiles est passé d'environ 20 000 le 23/09 à environ 50 000 le 28/10. ⚠ La vérification de l'exécution nécessite un environnement isolé (sandbox) bloquant les réseaux externes ; le code source est envoyé à l'entreprise modèle qui l'utilise. 2e place : anthropics/knowledge-work-plugins — Plugins Anthropic spécifiques aux métiers (pour Cowork, également disponibles sur Claude Code). sales 2.1.0 : Source et lien original pour chaque phrase. 3e place : AgriciDaniel/claude-seo — Plugin de vérification SEO pour Claude Code. v2.5.0 : Vérification de l'exposition aux CVE du serveur (lecture seule des réponses), Python 3.11 à 3.14 (3.15 non pris en charge pour le moment). Certaines extensions sont des services externes payants. 4e place : multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principes) créé par un autre développeur à partir des écrits de Karpathy. Pas de fichier de licence. 5e place : TauricResearch/TradingAgents — Cadre de recherche multi-agents pour le trading (ne constitue pas un conseil en investissement). v0.7.1 : Correction d'un bug où les informations étaient lues après des dates contenant des zéros → Réexécution des anciens backtests ▶ 6e à 10e place 6e place : Graphify-Labs/graphify — Code et documentation sous forme de graphe de connaissances (code local, pas de modèles). ⚠ Nom PyPI : graphifyy 7e place : ruvnet/ruflo — Outil multi-agents basé sur Claude Code et Codex. ⚠ Nécessite la version 3.63.3 ou supérieure (auparavant, les agents IA pouvaient être activés via les paramètres du dépôt cloné) 8e place : anomalyco/opencode — Agent de codage open source indépendant de toute société de modélisation. v2.2.0. ⚠ Si vous avez enregistré la clé étrangère dans la console, le fournisseur doit la supprimer ou la réémettre. 9e stable : stablyai/orca — Application de bureau exécutant simultanément plusieurs agents de codage par arborescence de travail, 100 000 étoiles le 21/10 10e android/skills — Compétences d'agent officielles de l'équipe Google Android, 30 → 36 ▶ Bonus (Hors classement) akitaonrails/ai-memory (11e de la semaine) — Serveur de mémoire à long terme partagé par plusieurs agents de codage. Version 3.2.0 : Recherche de parties coréennes sans espaces, notifications de déconnexion. Sauvegarde de l'historique des invites et des outils sur le serveur ▶ Liens cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narration est assurée par une voix de synthèse (ElevenLabs). Les séquences sans images illustrent les actions décrites dans la documentation officielle, et les fiches « Essayez ceci » présentent des exemples d'utilisation dans le cadre fonctionnel décrit dans la documentation. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [hi] इस सप्ताह डेवलपर्स के लिए टॉप 5 +1 ओपन सोर्स · GitHub ट्रेंडिंग (अक्टूबर का चौथा सप्ताह) मैंने इस सप्ताह (21-28 अक्टूबर) के GitHub ट्रेंडिंग साप्ताहिक रैंकिंग में शीर्ष 5 आइटमों को संकलित किया है, साथ ही एक गैर-रैंक वाले आइटम को भी शामिल किया है। इसमें शुरुआती और डेवलपर दोनों के लिए इनके सर्वोत्तम उपयोग के सुझाव भी दिए गए हैं। रैंक 6-10 नीचे सूचीबद्ध हैं, और सभी 13 आइटमों का पूरा विवरण विस्तृत रूप में उपलब्ध है। रैंकिंग GitHub ट्रेंडिंग (साप्ताहिक) पृष्ठ पर दिए गए सटीक क्रम का पालन करती है (28 अक्टूबर 2026 को सुबह 06:00 KST पर संकलित)। स्टार रेटिंग संकलन के समय के मूल्यों पर आधारित हैं। ▶ सभी 13 आइटमों का विवरण (विस्तृत रूप): youtu.be पहला स्थान: cloudflare/security-audit-skill — एक कौशल (MIT) जो कोडिंग एजेंटों को 6-चरण सुरक्षा ऑडिट करने में सक्षम बनाता है। कोड 14 सितंबर से अपरिवर्तित है; 23 सितंबर को लगभग 20,000 स्टार्स से बढ़कर 28 अक्टूबर को लगभग 50,000 स्टार्स हो गए। ⚠ निष्पादन सत्यापन के लिए बाहरी नेटवर्क को ब्लॉक करने वाले सैंडबॉक्स की आवश्यकता होती है; सोर्स कोड का उपयोग करने वाली मॉडल कंपनी को सोर्स कोड भेजा जाता है। दूसरा स्थान: anthropics/knowledge-work-plugins — एंथ्रोपिक जॉब-विशिष्ट प्लगइन्स (कोवर्क के लिए, क्लाउड कोड पर भी उपलब्ध)। sales 2.1.0: प्रत्येक वाक्य के लिए सोर्स और मूल लिंक। तीसरा स्थान: AgriciDaniel/claude-seo — क्लाउड कोड के लिए SEO चेक प्लगइन। v2.5.0: सर्वर CVE एक्सपोज़र चेक (केवल रीड रिस्पॉन्स), Python 3.11~3.14 (3.15 अभी समर्थित नहीं है)। कुछ एक्सटेंशन सशुल्क बाहरी सेवाएं हैं। चौथा स्थान: multica-ai/andrej-karpathy-skills — कार्पेथी के लेखन पर आधारित एक अन्य डेवलपर द्वारा निर्मित CLAUDE.md (4 सिद्धांत)। कोई लाइसेंस फ़ाइल उपलब्ध नहीं है। पाँचवाँ स्थान: TauricResearch/TradingAgents — मल्टी-एजेंट ट्रेडिंग रिसर्च फ्रेमवर्क (निवेश सलाह नहीं)। संस्करण 0.7.1: शून्य से शुरू होने वाली तिथियों के बाद खुलासे पढ़े जाने की समस्या को ठीक किया गया → पुराने बैकटेस्ट दोबारा चलाएँ। ▶ छठा से दसवाँ स्थान छठा स्थान: Graphify-Labs/graphify — नॉलेज ग्राफ़ के रूप में कोड और दस्तावेज़ीकरण (कोड स्थानीय है, कोई मॉडल नहीं)। ⚠ PyPI नाम graphifyy है। सातवाँ स्थान: ruvnet/ruflo — क्लाउड कोड और कोडेक्स पर निर्मित मल्टी-एजेंट टूल। ⚠ इसके लिए 3.63.3 या इससे उच्चतर संस्करण आवश्यक है (पहले, क्लोन किए गए रिपॉजिटरी सेटिंग्स के माध्यम से AI वर्कर्स को सक्षम किया जा सकता था) आठवां स्थान: anomalyco/opencode — ओपन-सोर्स कोडिंग एजेंट, किसी मॉडल कंपनी से संबद्ध नहीं। संस्करण 2.2.0। ⚠ यदि आपने कंसोल में फ़ॉरेन की सहेजी है, तो प्रदाता को इसे हटाना या पुनः जारी करना होगा। नौवां स्थान: stablyai/orca — वर्कट्री द्वारा बनाया गया एक डेस्कटॉप ऐप जो एक साथ कई कोडिंग एजेंट चलाता है, 21/10 को 100k स्टार प्राप्त हुए। दसवां स्थान: android/skills — Google Android टीम के आधिकारिक एजेंट कौशल, 30 से 36 तक पहुंचे। ▶ बोनस (रैंकिंग से बाहर) akitaonrails/ai-memory (साप्ताहिक 11वां स्थान) — कई कोडिंग एजेंटों द्वारा साझा किया जाने वाला एक दीर्घकालिक मेमोरी सर्वर। संस्करण 3.2.0: बिना रिक्त स्थान वाले कोरियाई शब्दों की खोज, डिस्कनेक्टेड सेशन सूचनाएं। प्रॉम्प्ट और टूल हिस्ट्री को सर्वर पर सेव करें ▶ लिंक्स cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ यह ऑडियो-कॉम्प्रोमाइज्ड वॉइस (ElevenLabs) द्वारा प्रदान किया गया है। जिन हिस्सों में वास्तविक फुटेज नहीं है, वे आधिकारिक दस्तावेज़ में वर्णित क्रियाओं को दर्शाने वाली मुख्य गतिविधियाँ हैं, और 'इसे इस तरह इस्तेमाल करके देखें' कार्ड दस्तावेज़ में उल्लिखित कार्यात्मक दायरे के भीतर उपयोग के उदाहरण हैं। #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [id] Top 5 +1 Open Source yang Wajib Dilihat Developer Minggu Ini · GitHub Trending (Minggu ke-4 Oktober) Saya telah menyusun 5 peringkat teratas GitHub Trending Weekly untuk minggu ini (21–28 Oktober) beserta satu item yang tidak masuk peringkat, termasuk kiat-kiat tentang cara terbaik menggunakannya untuk pemula dan pengembang. Peringkat 6–10 tercantum di bawah ini, dan deskripsi lengkap dari semua 13 item dapat ditemukan dalam versi lengkapnya. Peringkat mengikuti urutan persis di halaman GitHub Trending (mingguan) (dikumpulkan pada 28 Oktober 2026 pukul 06:00 KST). Peringkat bintang didasarkan pada nilai pada saat pengumpulan. ▶ Detail tentang semua 13 (Versi Lengkap): youtu.be Peringkat 1: cloudflare/security-audit-skill — Sebuah skill (MIT) yang memungkinkan agen pengkodean untuk melakukan audit keamanan 6 tahap. Kode tidak berubah sejak 14/9; Bintang meningkat dari sekitar 20.000 pada 23/9 menjadi sekitar 50.000 pada 28/10. ⚠ Verifikasi eksekusi memerlukan sandbox yang memblokir jaringan eksternal; kode sumber dikirim ke perusahaan model yang menggunakannya. Juara 2: anthropics/knowledge-work-plugins — Plugin khusus pekerjaan Anthropic (untuk Cowork, juga tersedia di Claude Code). sales 2.1.0: Sumber dan tautan asli untuk setiap kalimat. Juara 3: AgriciDaniel/claude-seo — Plugin pengecekan SEO untuk Claude Code. v2.5.0: Pengecekan paparan CVE server (hanya membaca respons), Python 3.11~3.14 (3.15 belum didukung). Beberapa ekstensi adalah layanan eksternal berbayar. Juara 4: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 prinsip) yang dibuat oleh pengembang lain berdasarkan tulisan Karpathy. Tidak ada file lisensi. Peringkat ke-5: TauricResearch/TradingAgents — Kerangka kerja riset perdagangan multi-agen (bukan saran investasi). v0.7.1: Memperbaiki bug di mana pengungkapan dibaca setelah tanggal dengan angka nol → Jalankan ulang backtest lama ▶ Peringkat ke-6~10 Peringkat ke-6 Graphify-Labs/graphify — Kode dan dokumentasi sebagai knowledge graph (kode lokal, tanpa model). ⚠ Nama PyPI adalah graphifyy Peringkat ke-7 ruvnet/ruflo — Alat multi-agen yang dibangun di atas Claude Code dan Codex. ⚠ Membutuhkan 3.63.3 atau lebih tinggi (sebelumnya, pekerja AI dapat diaktifkan melalui pengaturan repositori yang dikloning) Peringkat ke-8 anomalyco/opencode — Agen pengkodean sumber terbuka yang tidak terikat pada perusahaan model. v2.2.0. ⚠ Jika Anda telah menyimpan kunci asing di Konsol, penyedia harus membuang atau menerbitkannya kembali 9th stablelyai/orca — Aplikasi desktop yang menjalankan beberapa agen pengkodean secara bersamaan oleh worktree, 100 ribu bintang pada 21/10 10th android/skills — Keterampilan agen resmi dari tim Google Android, 30 → 36 ▶ Bonus (Di Luar Peringkat) akitaonrails/ai-memory (Minggu ke-11) — Server memori jangka panjang yang digunakan bersama oleh beberapa agen pengkodean. 3.2.0: Pencarian bagian Korea tanpa spasi, pemberitahuan sesi terputus. Simpan riwayat perintah dan alat ke server ▶ Tautan cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ Narasi disediakan oleh suara AI (ElevenLabs). Bagian tanpa cuplikan aktual adalah gerakan prinsip yang menggambarkan tindakan yang dijelaskan dalam dokumentasi resmi, dan kartu 'Cobalah menggunakannya seperti ini' adalah contoh penggunaan dalam lingkup fungsional yang diuraikan dalam dokumentasi. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [it] I 5+1 migliori progetti open source che ogni sviluppatore dovrebbe vedere questa settimana · GitH... Ho compilato la classifica settimanale dei 5 elementi più popolari su GitHub (dal 21 al 28 ottobre), includendo anche un elemento non classificato e suggerimenti su come utilizzarli al meglio, sia per i principianti che per gli sviluppatori. Le posizioni dalla 6 alla 10 sono elencate di seguito, mentre le descrizioni complete di tutti e 13 gli elementi sono disponibili nella versione estesa. La classifica segue l'ordine esatto della pagina GitHub Trending (settimanale) (dati raccolti il ​​28/10/2026 alle 06:00 KST). Le valutazioni a stelle si basano sui valori al momento della raccolta. ▶ Dettagli su tutti e 13 gli elementi (versione estesa): youtu.be 1° posto: cloudflare/security-audit-skill — Una skill (MIT) che consente agli agenti di programmazione di condurre audit di sicurezza in 6 fasi. Codice invariato dal 14/09; Le stelle sono aumentate da circa 20.000 il 23/09 a circa 50.000 il 28/10. ⚠ La verifica dell'esecuzione richiede una sandbox che blocca le reti esterne; il codice sorgente viene inviato all'azienda modello che la utilizza. 2° posto: anthropics/knowledge-work-plugins — Plugin specifici per il lavoro di Anthropic (per Cowork, disponibili anche su Claude Code). sales 2.1.0: Fonte e link originale per ogni frase. 3° posto: AgriciDaniel/claude-seo — Plugin di controllo SEO per Claude Code. v2.5.0: Verifica dell'esposizione CVE del server (legge solo le risposte), Python 3.11~3.14 (3.15 non ancora supportato). Alcune estensioni sono servizi esterni a pagamento. 4° posto: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 principi) creato da un altro sviluppatore basandosi sugli scritti di Karpathy. Nessun file di licenza. 5° posto: TauricResearch/TradingAgents — Framework di ricerca multi-agente per il trading (non fornisce consulenza sugli investimenti). v0.7.1: Corretto un bug per cui le informazioni venivano lette anche dopo date con zeri → Eseguire nuovamente i vecchi backtest ▶ Dal 6° al 10° posto 6° posto: Graphify-Labs/graphify — Codice e documentazione sotto forma di grafo della conoscenza (il codice è locale, non ci sono modelli). ⚠ Il nome su PyPI è graphifyy 7° posto: ruvnet/ruflo — Strumento multi-agente basato sul Codice di Claude e sul Codex. ⚠ Richiede la versione 3.63.3 o superiore (in precedenza, i worker AI potevano essere abilitati tramite le impostazioni del repository clonato) 8° posto anomalyco/opencode — Agente di codifica open source non legato a un'azienda modello. v2.2.0. ⚠ Se hai salvato la chiave esterna nella Console, il fornitore deve eliminarla o riemetterla. 9° posto stablyai/orca — Un'app desktop che esegue più agenti di codifica simultaneamente tramite worktree, 100.000 stelle il 21/10 10° posto android/skills — Skill ufficiali per agenti del team Google Android, da 30 a 36 ▶ Bonus (Fuori classifica) akitaonrails/ai-memory (11° posto settimanale) — Un server di memoria a lungo termine condiviso da più agenti di codifica. 3.2.0: Ricerca di parti coreane senza spazi, notifiche di sessione disconnessa. Salvataggio della cronologia dei prompt e degli strumenti sul server ▶ Link cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ La narrazione è fornita da una voce AI (ElevenLabs). Le parti senza filmati reali mostrano i movimenti principali che illustrano le azioni descritte nella documentazione ufficiale, mentre le schede "Prova a usarlo in questo modo" sono esempi di utilizzo nell'ambito funzionale delineato nella documentazione. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack [pt-BR] Top 5 +1 projetos open source que todo desenvolvedor deve ver esta semana · GitHub Trending (4ª s... Compilei o Top 5 dos itens mais populares do GitHub desta semana (21 a 28 de outubro), juntamente com um item não classificado, incluindo dicas sobre como melhor utilizá-los, tanto para iniciantes quanto para desenvolvedores. Os itens classificados de 6 a 10 estão listados abaixo, e as descrições completas de todos os 13 itens podem ser encontradas no formato longo. A classificação segue a ordem exata da página de itens populares (semanais) do GitHub (coletada em 28/10/2026 às 06:00 KST). As classificações por estrelas são baseadas nos valores no momento da coleta. ▶ Detalhes sobre todos os 13 itens (Formulário longo): youtu.be 1º lugar: cloudflare/security-audit-skill — Uma skill (MIT) que permite que agentes de codificação realizem auditorias de segurança em 6 etapas. Código inalterado desde 14/09; O número de estrelas aumentou de aproximadamente 20.000 em 23/09 para aproximadamente 50.000 em 28/10. ⚠ A verificação de execução requer um ambiente isolado (sandbox) que bloqueie redes externas; o código-fonte é enviado para a empresa modelo que o utiliza. 2º Lugar: anthropics/knowledge-work-plugins — Plugins antrópicos específicos para o trabalho (para Cowork, também disponíveis no Claude Code). sales 2.1.0: Fonte e link original para cada frase. 3º Lugar: AgriciDaniel/claude-seo — Plugin de verificação de SEO para Claude Code. v2.5.0: Verificação de exposição a CVEs no servidor (somente leitura das respostas), Python 3.11~3.14 (3.15 ainda não suportado). Algumas extensões são serviços externos pagos. 4º Lugar: multica-ai/andrej-karpathy-skills — CLAUDE.md (4 princípios) criado por outro desenvolvedor com base nos escritos de Karpathy. Sem arquivo de licença. 5º Lugar: TauricResearch/TradingAgents — Framework de pesquisa de negociação multiagente (não é recomendação de investimento). v0.7.1: Corrigido um bug que fazia com que as divulgações fossem lidas após datas com zeros → Reexecutados os backtests antigos ▶ 6º ao 10º Lugar 6º Lugar: Graphify-Labs/graphify — Código e documentação como um grafo de conhecimento (código local, sem modelos). ⚠ Nome no PyPI: graphifyy 7º Lugar: ruvnet/ruflo — Ferramenta multiagente construída sobre Claude Code e Codex. ⚠ Requer a versão 3.63.3 ou superior (anteriormente, os workers de IA podiam ser habilitados por meio das configurações do repositório clonado) 8º Lugar: anomalyco/opencode — Agente de codificação de código aberto não vinculado a uma empresa modelo. v2.2.0. ⚠ Se você salvou a chave estrangeira no Console, o provedor deve descartá-la ou reemitir. 9º stablyai/orca — Um aplicativo para desktop que executa vários agentes de codificação simultaneamente, da worktree, 100 mil estrelas em 21/10. 10º android/skills — Skills oficiais de agentes da equipe do Google Android, 30 → 36. ▶ Bônus (Fora do Ranking) akitaonrails/ai-memory (11º da semana) — Um servidor de memória de longo prazo compartilhado por vários agentes de codificação. 3.2.0: Busca por partes em coreano sem espaços, notificações de sessão desconectada. Salvar histórico de prompts e ferramentas no servidor ▶ Links cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents akitaonrails/ai-memory ※ A narração é feita por uma voz de IA (ElevenLabs). As partes sem imagens são animações que ilustram as ações descritas na documentação oficial, e os cards "Experimente usar assim" são exemplos de uso dentro do escopo funcional descrito na documentação. #GitHubTrending #OpenSource #Developer #CodingAgent #SoloStack

Tu propio "hedge fund" de agentes IA, open source: TauricResearch/TradingAgents Analistas de técnico, noticias, sentimiento y fundamentales. Debate alcista vs bajista. Trader. Equipo de riesgo con veto. Pruébalo primero en paper trading. No es consejo de inversión. t.co

Бык против медведя: ИИ-агенты спорят, куда вложить деньги #нейросети #claudecode #вайбкодинг Ты написал «ФОНД», держи 👇 TradingAgents — команда ИИ-агентов, которая разбирает акцию и спорит перед решением. GitHub: github.com/TauricResearch/TradingAgents Научная статья: arxiv.org/abs/2412.20138 Как запустить: Нужен Python 3.11 или новее. Скачай проект: git clone TauricResearch/TradingAgents Зайди в папку: cd TradingAgents Установи: pip install . Подключи модель: ключ API любого провайдера (как его указать — в README) или локальную модель через Ollama. Можно войти своей подпиской ChatGPT Plus или Pro: tradingagents login chatgpt. Запросы тогда расходуют лимиты тарифа. Запусти: tradingagents. Выбери тикер и дату и смотри, как агенты спорят. ⚠️ Это исследовательский проект, а не финансовый совет. Результаты между запусками могут отличаться, сделки идут на симулированную биржу. Не используй его для торговли реальными деньгами. Больше гайдов по ИИ в моём ТГ: t.me/justuserai

이번 주 개발자라면 봐야 할 오픈소스 Top 10 +@ 총정리 · GitHub 트렌딩 (10월 4주) 이번 주(10/21~10/28) GitHub 트렌딩 주간 순위 Top 10과 순위 밖 3개(이번 주 페이지에 올라온 13개 전부)를 정리했어요. 레포마다 무엇을 하는지, 최근 바뀐 것, 초보자·개발자별로 어떻게 써 보면 좋은지까지 담았어요. 순위는 GitHub Trending(weekly) 페이지 순서 그대로예요(2026-10-28 06:00 KST 수집). 별 수는 수집 시점 값이에요. ▶ 챕터 0:00 오프닝 0:21 10위 android/skills 0:59 9위 Orca 1:36 8위 OpenCode 2:18 7위 ruflo 3:00 6위 graphify 3:38 5위 TradingAgents 4:24 4위 andrej-karpathy-skills 5:01 3위 claude-seo 5:43 2위 knowledge-work-plugins 6:25 번외 ① ai-memory 7:03 번외 ② T3 Code 7:33 번외 ③ FastAPI 8:11 1위 security-audit-skill 9:16 이번 주 확인할 것 ▶ Top 10 10위 android/skills — Google Android 팀 공식 에이전트 스킬. 일주일 새 30개에서 36개(v1.0.16~1.0.18): ANR 점검·수정, 온디바이스 LLM(LiteRT-LM), ML Kit 에이전트 API, Compose Ink, Glance 위젯 메모리 등 9위 stablyai/orca — 여러 코딩 에이전트를 git worktree별로 동시에 돌리는 데스크톱 앱(내 구독 사용). 9/30 이후 채팅 화면(실험)·권한 모드(Manual/Auto/Yolo)·앱 안 브라우저 클릭 표시·휴대폰 앱 개선, 10/21 별 10만. v1.4.221(10/5) Copilot 설정 파일 권한 보안 수정 8위 anomalyco/opencode — 모델 회사에 묶이지 않는 오픈소스 코딩 에이전트(75+ 공급자·로컬 모델). v2.2.0: 데스크톱 앱 개편, 데스크톱 플러그인(실험). ⚠ Console 보안 사고(10/17): Console에 외부 키를 저장했다면 공급자 쪽에서 폐기·재발급(OpenCode 발표 기준 Console 사용자의 1.4%) 7위 ruvnet/ruflo — Claude Code·Codex 위 다중 에이전트 도구(옛 claude-flow). 이번 주 릴리스 28번. ⚠ 3.63.3(10/26) 이전에는 클론한 저장소의 설정 파일로 AI 워커가 켜질 수 있었음 → 3.63.3 이상으로. Anthropic Managed Agents 도구 4개(직접 켜야 함, 예산 상한 필수, 실서비스 미검증) 6위 Graphify-Labs/graphify — /graphify로 코드·문서·SQL·PDF를 지식 그래프로. 코드는 로컬 tree-sitter 분석(모델 없음), 문서·이미지는 쓰는 모델 사용. 0.9.97: 그래프 설명 속 비밀값 가림 강화. ⚠ PyPI 이름은 graphifyy(y 두 개) 5위 TauricResearch/TradingAgents — 증권사 조직을 본뜬 멀티 에이전트 매매 연구 프레임워크(투자 조언 아님). 9/16 이후 백테스트·분석가 병렬·한국 등 벤치마크·역할별 모델 공급자·ChatGPT 구독 실행. v0.7.1: 0 없는 날짜가 이후 공시를 읽던 버그 수정 → 예전 백테스트는 다시 돌려 보기 4위 multica-ai/andrej-karpathy-skills — Karpathy의 글을 바탕으로 다른 개발자가 만든 CLAUDE.md 하나(원칙 4개). 마지막 커밋 4/20, LICENSE 파일 없음 3위 AgriciDaniel/claude-seo — Claude Code용 SEO 점검 플러그인(스킬 26·서브에이전트 19). v2.5.0: 자체 보안 감사 반영, 서버 CVE 노출 점검(응답만 읽음), Python 3.11 이상. 일부 확장은 유료 외부 서비스 2위 anthropics/knowledge-work-plugins — Anthropic 직무별 플러그인(Cowork용, Claude Code에서도). 저장소 안 22개·목록 123개. sales 2.1.0: 문장마다 출처와 원본 링크 ▶ 번외(순위 밖, 이번 주 페이지 11~13위) akitaonrails/ai-memory — 여러 코딩 에이전트가 같이 쓰는 장기 기억 서버. 3.2.0: 띄어쓰기 없는 한글 부분 검색, 끊긴 세션 알림. 프롬프트·도구 기록을 서버에 저장 pingdotgg/t3code — 내 PC의 Codex·Claude Code·Cursor·OpenCode 등을 웹·휴대폰에서 조작(내 구독 사용). 이번 주 v0.1.1~0.1.3, 한 번만 써 보려면 npx t3@latest fastapi/fastapi — ⚠ 0.160.0: 파라미터 모델 extra 설정이 같은 위치 다른 필드에도 적용 / 0.162.0: 폼에서 빠진 bool을 False로 치지 않음 → 체크박스는 기본값 False 선언 후 테스트 ▶ 1위 cloudflare/security-audit-skill — 코딩 에이전트에게 6단계 보안 감사를 시키는 스킬(MIT). 블로그의 후보 20,799 → 조치 7,245는 Cloudflare 사내 시스템 수치(이 스킬 성능 아님). 코드는 9/14 이후 변경 없음, 별은 9/23 약 2만 → 10/28 약 5만, 10/21~24 일간 트렌딩 전체 1위. ⚠ 실행 검증엔 외부 네트워크를 막은 샌드박스 필요, 한 번 실행은 여러 번 돌린 결과의 절반쯤(README), 소스는 쓰는 모델 회사로 전송 ▶ 끄는 방법·설치 - Android CLI 사용 통계: 명령마다 --no-metrics - Orca 익명 사용 데이터: onorca.dev - TradingAgents 업데이트 확인: TRADINGAGENTS_NO_UPDATE_CHECK=1 - graphify: uv tool install graphifyy → graphify install - security-audit-skill: npx skills add cloudflare/security-audit-skill --skill security-audit ▶ 관련 영상 - 지난주 트렌딩 롱폼(10월 3주): youtu.be - OpenCode 무료 모델 직접 실측: youtu.be ▶ 링크 cloudflare/security-audit-skill anthropics/knowledge-work-plugins AgriciDaniel/claude-seo multica-ai/andrej-karpathy-skills TauricResearch/TradingAgents Graphify-Labs/graphify ruvnet/ruflo anomalyco/opencode stablyai/orca android/skills akitaonrails/ai-memory pingdotgg/t3code fastapi/fastapi ※ 내레이션은 AI 보이스(ElevenLabs)예요. 실화면이 없는 부분은 공식 문서에 적힌 동작을 그린 원리 모션이고, '이렇게 써 보세요' 카드는 문서에 있는 기능 범위의 활용 예예요. #GitHub트렌딩 #오픈소스 #개발자 #코딩에이전트 #SoloStack [en] This Week's Must-See Open Source: GitHub Trending Top 10 + Extras (Oct, Week 4) A full walkthrough of this week's GitHub Trending (weekly) Top 10 in page order (collected 2026-10-28 06:00 KST), plus the three repos ranked 11-13 — what each does, what changed recently, and how beginners and working developers can put it to use. 10 android/skills · 9 Orca · 8 OpenCode · 7 ruflo · 6 graphify · 5 TradingAgents · 4 andrej-karpathy-skills · 3 claude-seo · 2 knowledge-work-plugins · 1 Cloudflare security-audit-skill Extras: ai-memory, T3 Code, FastAPI (0.160/0.162 change parameter-model extras and omitted bool form fields). Check this week: upgrade ruflo to 3.63.3+, revoke and reissue provider keys stored in OpenCode Console, test FastAPI form handling before upgrading. Narration is an AI voice (ElevenLabs).

Top 4 GitHub Repos for AI Agent Builders #shorts #hallmark #TradingAgents #Agent-Reach #cindy #ai Today's top 4 trending GitHub repos for AI agent builders (Oct 27, 2026): 1. Nutlope/hallmark — Anti-AI-slop design skill for Claude Code, Cursor, Codex — Nutlope/hallmark 2. TauricResearch/TradingAgents — Multi-agent LLM framework for financial trading — TauricResearch/TradingAgents 3. Panniantong/Agent-Reach — One CLI for agents to search Twitter, Reddit, YouTube — Panniantong/Agent-Reach 4. makecindy/cindy — Open-source AI agent that works out of the box — makecindy/cindy New list every day. Subscribe for tomorrow's top 4. #shorts #github #aiagents #claudecode #codex #cursor #llm #opensource #multiagent #agentskills #mcp #developer #coding #ai #trendingrepos #ai #aivideo #aishorts #aianimation #agenticai #llm

[개발자들의 PICK] 이번 주 깃허브 트렌딩 결산, 인기 오픈소스 TOP 10 이번 주 깃허브에서 가장 많은 별을 받은 오픈 소스 TOP 10을 10위부터 소개합니다. 소개한 저장소 링크는 고정 댓글에 있습니다. 00:00 이번 주 TOP 10 00:05 10위 안드로이드 스킬 00:51 9위 오르카 01:38 8위 오픈코드 02:15 7위 루플로 02:47 6위 그래피파이 03:32 5위 트레이딩 에이전츠 04:17 4위 카파시 스킬 04:56 3위 클로드 SEO 05:35 2위 지식 노동 플러그인 06:14 1위 클라우드플레어 시큐리티 오딧 스킬 06:55 마무리 📌 이번 주! 깃허브 트렌딩 TOP 10 저장소 10위 안드로이드 스킬 — android/skills 9위 오르카 — stablyai/orca 8위 오픈코드 — anomalyco/opencode 7위 루플로 — ruvnet/ruflo 6위 그래피파이 — Graphify-Labs/graphify 5위 트레이딩 에이전츠 — TauricResearch/TradingAgents 4위 카파시 스킬 — multica-ai/andrej-karpathy-skills 3위 클로드 SEO — AgriciDaniel/claude-seo 2위 지식 노동 플러그인 — anthropics/knowledge-work-plugins 1위 클라우드플레어 시큐리티 오딧 스킬 — cloudflare/security-audit-skill [en-US] [Developer's PICK] This Week's GitHub Trending Roundup, Top 10 Popular Open Source Projects Here are the Top 10 Open Sources with the Most Stars on GitHub this week, starting from 10th place. Links to the featured repositories are in the pinned comment. 00:00 This Week's Top 10 00:05 10th Android Skills 00:51 9th Orca 01:38 8th OpenCode 02:15 7th Luplo 02:47 6th Graphify 03:32 5th Trading Agents 04:17 4th Kapasi Skills 04:56 3rd Claude SEO 05:35 2nd Knowledge Work Plugin 06:14 1st Cloudflare Security Audit Skills 06:55 Wrap-up 📌 This Week! GitHub Trending Top 10 Repositories 10th Android Skills — android/skills 9th Orca — stablyai/orca 8th Open Code — anomalyco/opencode 7th Ruflo — ruvnet/ruflo 6th Graphify — Graphify-Labs/graphify 5th Trading Agents — TauricResearch/TradingAgents 4th Karpathy Skills — multica-ai/andrej-karpathy-skills 3rd Claude SEO — AgriciDaniel/claude-seo 2nd Knowledge Work Plugins — anthropics/knowledge-work-plugins 1st Cloudflare Security Audit Skills — cloudflare/security-audit-skill [id] [Pilihan Pengembang] Rekap Trending GitHub Minggu Ini, 10 Open Source Terpopuler Berikut adalah 10 Open Source Teratas dengan Bintang Terbanyak di GitHub minggu ini, dimulai dari peringkat ke-10. Tautan ke repositori unggulan ada di komentar yang disematkan. 00:00 10 Teratas Minggu Ini 00:05 Peringkat ke-10 Android Skills 00:51 Peringkat ke-9 Orca 01:38 Peringkat ke-8 OpenCode 02:15 Peringkat ke-7 Luplo 02:47 Peringkat ke-6 Graphify 03:32 Peringkat ke-5 Trading Agents 04:17 Peringkat ke-4 Kapasi Skills 04:56 Peringkat ke-3 Claude SEO 05:35 Peringkat ke-2 Knowledge Work Plugin 06:14 Peringkat ke-1 Cloudflare Security Audit Skills 06:55 Ringkasan 📌 Minggu Ini! 10 Repositori Terpopuler di GitHub 10 Android Skills — android/skills 9 Orca — stablyai/orca 8 Open Code — anomalyco/opencode 7 Ruflo — ruvnet/ruflo 6 Graphify — Graphify-Labs/graphify 5 Trading Agents — TauricResearch/TradingAgents 4 Karpathy Skills — multica-ai/andrej-karpathy-skills 3 Claude SEO — AgriciDaniel/claude-seo 2 Knowledge Work Plugins — anthropics/knowledge-work-plugins 1 Cloudflare Security Audit Skills — cloudflare/security-audit-skill