This AI Board of Directors Is Here

Ask ChatGPT for a market-entry strategy, and it will answer like a seasoned consultant: structured plan, clear timelines, confident tone. Under the hood, though, every large language model is a bundle of biases, missing data, and skewed training. One model overweights US tech blogs, another leans on EU regulations, a third barely saw anything past 2022.

Modern LLMs also train on human preference data that rewards sounding sure of themselves. The system gets scored higher when it “commits” to an answer, even when the underlying probability is shaky. That optimization loop quietly bakes hallucinations into the user experience as long as they look plausible.

Ask a single model whether to expand into Germany or Brazil and it might spin a compelling narrative based on outdated GDP figures or misread tax incentives. A founder who treats that as gospel can misallocate millions, hire the wrong team, or pick a fatally flawed pricing strategy. The confidence is free; the correction later is not.

Finance teams already use LLMs to summarize 10‑Ks, compare ETFs, or draft options strategies. One wrong assumption about tax treatment or margin requirements, expressed in polished language, can push a retail investor into leverage they do not understand. In a corporate treasury, the same error scales into eight‑figure risk exposure.

Research workflows look safer, but they are just as fragile. A model that fabricates a citation in oncology or misstates a regulatory clause in HIPAA can derail months of work. When the system never says “I don’t know” with the same force it says “Do this,” you get decision theater: something that feels rigorous but rests on sand.

High‑stakes environments usually avoid single points of failure: aircraft have redundant sensors, banks use multiple risk models, security teams run red‑team and blue‑team drills. Treating one LLM as a single source of AI truth breaks that pattern entirely. As organizations lean on AI for strategy, law, and money, betting everything on one model’s unverified confidence stops being a productivity hack and starts looking like negligence.

An AI boardroom already exists, and Andrej Karpathy calls it llm-llm-council. Instead of trusting a single model, you spin up a panel of heavyweights—GPT-4, Claude, Gemini, Grok—ask them the same question, and force them to argue it out. A fifth “chairman” model then fuses their work into one battle-tested answer.

Karpathy is not some random GitHub tinkerer. He co-founded OpenAI, ran Tesla’s Autopilot and AI efforts, and helped shape modern deep learning culture with his Stanford teaching and YouTube lectures. When someone with that résumé open-sources a new pattern for using LLMs, the industry pays attention.

Despite YouTube titles calling this “OpenAI’s NEW LLM llm-council,” llm-llm-council is Karpathy’s independent open-source project. No OpenAI branding, no official product page, no enterprise upsell. It’s a lightweight Python repo that anyone can wire up using providers like OpenRouter and tools like Cursor.

Core idea: treat models like opinionated experts, not oracles. Each model receives your prompt in parallel and writes its own answer without seeing the others. That first pass intentionally preserves their different biases—GPT-4 might go broad and formal, Claude might go careful and nuanced, Gemini might lean into structured reasoning, Grok might be more irreverent or edgy.

Then the gloves come off. In an anonymized review phase, every model reads all the other answers without knowing who wrote what, ranks them for accuracy and insight, and flags weak spots. A critique phase pushes harder, calling out hallucinations, missing risks, or unjustified leaps—exactly the kind of probing you expect from a real board.

Finally, a designated chairman model digests the entire debate and produces a single synthesized response. It pulls the best arguments, rejects the consensus hallucinations, and surfaces edge-case concerns that one model alone might bury. You pay roughly 5x the tokens of a single call but get an order-of-magnitude reduction in obvious errors.

For founders and operators, that tradeoff is a no-brainer for high-stakes decisions—hiring, expansions, big campaigns—where one overconfident model answer can quietly cost six figures.

Inside llm-llm-council, every question turns into a four-act debate, not a single-shot answer. Andrej Karpathy’s open-source workflow treats models like ChatGPT, Claude, Gemini, and Grok as sparring partners, then forces them to converge on one battle-tested response. Think of it as structured argument, automated.

Stage one is “first opinions.” Your prompt fans out to four different frontier models at once, each generating its own answer in parallel, in isolation. No one sees anyone else’s work, so Claude can’t copy GPT-4, and Grok can’t riff on Gemini’s tone.

You can open those raw answers side by side, like tabs in a browser, and see how differently each system thinks. One model might push aggressive growth, another might flag compliance risk, a third might obsess over user experience. That spread is the whole point: diverse biases on display before they get corrected.

Stage two is where the peer pressure kicks in. All the answers go back to the same models, but now anonymized so no one knows which response came from which brand. GPT-4 is just “Answer B,” Claude is “Answer D,” and so on.

Each model must: - Rank all responses for accuracy and usefulness - Flag anything misleading or incomplete - Justify why one answer beats another

Anonymity matters. Without logos attached, models can’t lean on reputation or their own prior wording, and Karpathy’s design forces them to judge content, not identity. This is algorithmic peer review, not fan service.

Stage three turns those rankings into detailed critiques. Models now pick apart each other’s logic, pointing out hallucinated facts, missing edge cases, and hand-wavy math. If one answer invents a market size or glosses over regulatory risk, another model calls it out explicitly, often line by line.

Stage four hands everything to a “chairman” model you choose—often the system you trust most for synthesis. That chairman reads the original prompt, all four answers, every ranking, and every critique, then writes a single consolidated response that steals the best arguments and discards the broken ones. The result feels less like one model guessing and more like a 5-person committee that just spent 10 minutes arguing.

Karpathy’s repo, LLM llm-council works together to answer your hardest questions - GitHub, exposes this whole pipeline in a few hundred lines of Python. No agents, no loops—just a linear, inspectable debate chamber for your hardest calls.

More AI models do not just mean more compute; they mean more distinct personalities at the table. GPT-style models often excel at broad reasoning, code, and highly structured plans. Claude tends to be more cautious, verbose, and nuanced, with strong long-form writing and safety instincts. Gemini plugs directly into Google’s ecosystem, leaning on search, Docs, and Gmail context. Grok brings a faster, more irreverent style tuned for X’s real-time data firehose.

Those differences are not marketing blurbs; they come from different training data, architectures, and safety policies. One model might have stronger coverage of scientific papers, another of legal contracts, another of product reviews and forums. When the llm-llm-council fans a question out to four of them at once, you are sampling four different slices of the internet and four different design philosophies.

Single-model workflows quietly create AI groupthink. You ask GPT, it gives a confident strategy; you tweak the prompt and it mostly reinforces its first idea because it shares the same weights, the same blind spots. With a llm-council pattern, you get deliberate disagreement by design. One model may push aggressive growth, another may flag regulatory risk, a third may question whether the problem framing is wrong.

Think of a real boardroom. You do not want four ex-CFOs who all came from the same bank. You want: - A CFO obsessed with cash flow - A CMO who understands brand and customers - A COO who cares about operations and risk - A contrarian investor who asks, “What breaks if we’re wrong?”

llm-llm-council recreates that mix. GPT might play the ambitious strategist, Claude the risk officer, Gemini the data-driven analyst, Grok the contrarian. Their anonymous critique phase forces them to poke holes in each other’s arguments without deference to brand or reputation.

That friction surfaces risks and opportunities a solo model will almost always miss. One might hallucinate a regulation; another calls it out and cites conflicting evidence. One might ignore churn; another centers it as the core threat. By the time the chairman model synthesizes a final answer, you are acting on a plan that has survived four rounds of attack, not one model’s best guess.

Cursor quietly blows up the hardest part of using llm-llm-council: the setup. Instead of wrestling with Python, virtual environments, and CLI flags, you open an editor that behaves like ChatGPT wired directly into your codebase. Cursor wraps a full IDE around a natural-language interface, so you describe what you want and it writes, edits, and runs the scaffolding for you.

Start by grabbing Andrej Karpathy’s llm-llm-council repo from GitHub. You can paste the URL into Cursor’s “Open from GitHub” flow or clone it locally and open the folder. Either way, Cursor immediately indexes the project so its built‑in model understands every file, dependency, and config.

From there, the setup prompt is almost comically simple: open a new chat in Cursor and type something like, “Set this up for me.” Cursor parses the README, checks the Python files, and generates a step‑by‑step plan. You see it propose commands, edit config files, and explain what it’s doing in plain English before it touches anything.

Instead of you hunting Stack Overflow for errors, Cursor auto-writes the shell commands and runs them in its integrated terminal. It installs Python, pulls in packages with pip, and resolves missing libraries. If something fails, you highlight the error, tell Cursor “fix this,” and it patches the code or updates the install instructions.

Under the hood, llm-llm-council needs API access to multiple frontier models like GPT‑4, Claude, Gemini, and Grok. OpenRouter solves that in one shot. Rather than juggling four or five different vendor dashboards and keys, you create a single OpenRouter API key and plug it into the llm-llm-council config file.

OpenRouter then acts as a unified gateway. The llm-council code calls one endpoint, and OpenRouter routes each request to the right model—Anthropic, OpenAI, Google, or xAI—based on the model IDs you list. Swapping GPT‑4.1 for Claude 3.5 Sonnet becomes a one‑line config change instead of a full rewrite.

For non‑developers, that combo—Cursor plus OpenRouter—turns a research‑grade orchestration pattern into something you can spin up over lunch. You type instructions, Cursor translates them into working code, and OpenRouter exposes a menu of cutting‑edge models without vendor lock‑in. Powerful multi‑model AI orchestration stops being a lab toy and starts looking like a tool any operator, founder, or analyst can actually use.

Big strategic bets are where an llm-llm-council earns its keep. Before you wire $50,000 into a new marketing campaign, you feed the brief, target audience, and success metrics into your AI board and ask for a go/no-go recommendation. Each model attacks the plan from a different angle—market saturation, channel risk, creative fatigue, cash-flow impact—then the chairman model fuses that into a single, ranked list of scenarios with expected upside, downside, and failure modes.

Instead of a generic “optimize ROAS” answer, you can demand specifics: “Identify 5 ways this campaign could fail, assign a probability to each, and propose mitigation steps under a $5,000 test budget.” The llm-llm-council surfaces ideas like running a 10% audience split-test, delaying spend until a product update ships, or renegotiating agency terms. You get something closer to a pre-mortem from four virtual CMOs, not a vibe-based thumbs up.

Crisis mode is where single-model advice breaks fastest. When sales suddenly drop 18% week-over-week, a lone chatbot tends to latch onto the first plausible narrative—seasonality, pricing, “the economy.” An llm-llm-council instead generates multiple competing hypotheses, each backed by specific data you should pull: CRM cohorts, ad platform logs, inventory events, even changes in onboarding flows.

You might end up with a short, testable list like: - Attribution bug after an analytics update - Silent churn spike from a new paywall - Search ranking loss on 3 core keywords - Fulfillment delays in one region

The chairman model then prioritizes those theories by impact and ease of verification, effectively handing your ops team a triage playbook instead of a hunch.

Document review turns the llm-llm-council into a tireless, slightly ruthless lawyer. Upload a 40-page SaaS contract or an MSA draft and instruct the models to flag every asymmetric risk, vague obligation, and uncapped liability, then cross-compare their findings. Because the critique phase runs anonymously, no model soft-pedals a concern to align with a more “authoritative” peer.

One model might catch a sneaky auto-renewal clause, another a data-processing loophole, a third an indemnity time bomb; the chairman rolls that into a redline checklist you can hand to real counsel. For a deeper technical breakdown of how this consensus pattern works, VirtusLab’s GitHub All-Stars #10: llm-llm-council – AI Consensus mechanism dissects the architecture and trade-offs.

Sticker shock hits fast: an llm-llm-council query runs about 5x the cost of asking a single model. Every question fans out to multiple LLMs, plus a chairman model that synthesizes the final answer, so you pay for several API calls instead of one.

In real money, that still rounds to pocket change. A complex strategic prompt with four frontier models and a synthesis pass typically lands in the $0.05–$0.20 range, depending on context length and which GPT, Claude, Gemini, or Grok tiers you pick via OpenRouter.

Compare that to what you’re actually deciding. Spending 10 cents to stress-test a $50,000 ad campaign, a $250,000 hire, or a $2 million product launch is not a cost; it’s an insurance policy. If the llm-llm-council catches even one major blind spot per year, the ROI moves into thousand‑ or million‑percent territory.

Human second opinions look very different on a balance sheet. A mid-tier strategy consultant might bill $200–$500/hour; big-firm partners run into four figures. A single “quick sanity check” meeting can chew through $3,000 in fees and internal salaries before anyone opens a slide deck.

llm-llm-council gives you: - Multiple independent model opinions - Anonymous cross‑critique and ranking - A synthesized, battle‑tested recommendation

All for less than the cost of a single printed color brochure.

That changes how often you ask for a second opinion. You don’t reserve the llm-llm-council for once‑a‑quarter board topics; you can run it on every pricing change, vendor contract, or risky UX tweak without blinking at the meter. The constraint becomes attention, not budget.

Viewed through a CFO’s eyes, llm-llm-council is just another line item in “cloud services,” but functionally it behaves like an always‑on advisory firm. The twist: your “retainer” might be $10–$50/month in API spend, not five or six figures.

AutoGPT-style AI agents promised autonomous digital workers: give them a goal, then watch them plan, browse, and execute. Reality has been messier. Those systems often spiral into loops, re-plan endlessly, or stall on trivial subtasks because their core architecture encourages wandering rather than converging.

llm-llm-council comes from the opposite direction. Instead of a free-roaming agent, it runs as a tightly controlled Directed Acyclic Graph (DAG): a fixed sequence of stages with no way to circle back. Every run follows the same linear path—first opinions, peer review, critique, then chairman synthesis—so you get repeatable behavior rather than emergent chaos.

Traditional agents like AutoGPT, BabyAGI, or CrewAI lean on cyclical loops. They repeatedly ask themselves things like “What should I do next?” and “Did I complete the task?” That feedback loop can generate impressive demos, but it also leads to classic failure modes: re-opening the same tabs, rewriting similar plans, or burning tokens on side quests.

llm-llm-council removes that uncertainty by banning loops entirely. Each model speaks once in the first-opinion phase, then moves into clearly defined roles: reviewer, critic, or chairman. No agentic “self-reflection” step decides to spin up new subtasks or call external tools, which dramatically cuts the surface area for weird, hard-to-debug behavior.

That design choice signals a different purpose. AutoGPT-style stacks aim at autonomous task execution: write the code, send the email, scrape the site, update the CRM. llm-llm-council focuses on high-stakes cognition and decision support: What strategy should we pursue? Where are the hidden risks? Which assumption breaks first?

Think of it as a thinking engine, not a doing engine. You feed it a $50,000 campaign plan, a contentious M&A move, or a risky hiring roadmap. It responds with a pressure-tested, multi-model verdict you can take into a board meeting, not a half-finished to-do list scattered across APIs.

Agents may still run the playbook once you decide what to do. llm-llm-council’s job is making sure the playbook itself doesn’t suck.

AI orchestration quietly became the real 2025 story. While everyone chased bigger frontier models, developers started wiring multiple specialized LLMs together—one for research, one for critique, one for synthesis—into repeatable workflows. Karpathy’s llm-llm-council drops directly into that trend as a reference design for how multi-model “brains” should actually collaborate.

Instead of a single API call to “the best model,” tools like Cursor are evolving into full-blown AI orchestrators. Your prompt no longer maps to one request; it triggers a graph of steps: fan out to GPT-4.1, Claude 3.5 Sonnet, Gemini 2.0, Grok; run anonymous peer review; then hand everything to a chairman model for synthesis. Cursor handles the plumbing—context packing, retries, logging—while llm-llm-council defines the playbook.

Karpathy’s vision looks less like a chatbot and more like a new operating system layer. Apps will route tasks to whichever model is best at that micro-skill: code refactoring to a coding-optimized model, policy analysis to a safety-tuned model, summarization to a cheap fast model. The user just sees one answer; under the hood, half a dozen LLMs negotiated it.

That only works if developers can swap models as ruthlessly as they swap npm packages. API aggregators like OpenRouter have become critical infrastructure, exposing a single endpoint that can talk to GPT-4.1, Claude 3.5, Gemini, Grok, and dozens more. If Anthropic ships a Claude 4.0 that beats GPT-4.1 on reasoning, a config change can reroute llm-llm-council’s chairman role in minutes.

Vendor-agnostic routing also protects teams from lock-in as models iterate on a 4–6 week cadence. A llm-council that used GPT-4.1, Claude 3.5, Gemini 2.0, and Grok 2 in January might use entirely different versions by March, with no code changes. For developers exploring adjacent patterns—like swarm-style multi-agent systems—resources such as LLM llm-council - Swarms map out similar orchestration ideas.

Monolithic “one model to rule them all” systems start to look clumsy next to this. The most capable AI products of the next few years will hinge less on a single towering model and more on how intelligently they choreograph many smaller experts into a coherent, reliable whole.

You already have an AI boardroom waiting; the only missing piece is your first question. A properly configured llm-llm-council turns vague, high-stakes uncertainty into a single, battle-tested answer that has survived four rounds of critique, ranking, and synthesis. Instead of gambling on one model’s vibe, you get a plan that has been stress-tested from multiple directions.

Pick one decision that actually makes your stomach tighten a little. Not “what should I tweet,” but something like “Should we hire a $180,000 head of sales?” or “Do we kill this product line that brings in $40,000 a month?” or “Do we spend $50,000 on this brand campaign?” That unresolved tension is exactly what a multi-model llm-council is built to attack.

Now run that single question through your own llm-llm-council. Use Cursor’s no-code-style setup, plug in OpenRouter, and wire up at least three models—GPT, Claude, Gemini, maybe Grok—plus a chairman model you trust for synthesis. Give the system your real constraints: budget, timeline, team capacity, and downside risk.

Then watch the workflow play out: parallel first opinions, anonymous peer rankings, ruthless critique, and a final chairman answer that explicitly calls out risks, failure modes, and contingencies. You are not asking “What should I do?” once; you are forcing multiple expert systems to argue about what you should do, and then justify it.

This is the actual shift: from passively accepting the first confident paragraph your favorite chatbot spits out to actively moderating a structured debate among specialized models. One path leaves you exposed to a single model’s blind spot. The other gives you something closer to a board packet: dissent, edge cases, and a defensible decision you can point to when the stakes—and the invoices—arrive.

What is the LLM Council?

It's an open-source tool by Andrej Karpathy that uses multiple AI models (like GPT-4, Claude, and Gemini) to answer, critique, and rank each other's responses to generate a single, highly reliable final answer.

Is the LLM Council free to use?

The llm-council software itself is free and open-source. However, you will incur minor costs for API calls to the language models, which are managed through a service like OpenRouter on a pay-per-use basis.

Do I need to know how to code to set it up?

No. Using a tool like Cursor AI, you can set up the LLM Council with natural language commands, completely avoiding the need to write code or use a traditional terminal.

How is this different from AI agents like AutoGPT?

LLM Council follows a linear, predictable workflow (question -> debate -> synthesis -> answer) designed for high-stakes decision-making. AI agents often use cyclical loops for task execution, which can be less reliable and prone to getting stuck.