Turn Prompts into Protocols

Why Now

Every AI Decision You Make Today Could Cost You Tomorrow

73% of job changers regret decisions they made with AI's help (LinkedIn, 2024). 90% of startups fail because they trusted AI's "great idea" without questioning it (CB Insights). 80% of retail investors lose money following AI advice (DALBAR). Your AI won't tell you the risks—ReasonKit will. Structured reasoning protocols (inspired by methodologies showing up to 18.5x improvement in research) help catch blind spots before they become costly mistakes.

74%

Success on complex tasks

Tree-of-Thoughts methodology achieves 74% vs. 4% for Chain-of-Thought on complex reasoning tasks (Yao et al., NeurIPS 2023). ReasonKit implements these protocols.

90%

Startup failure rate

42% fail because "no market need"—they built something nobody wanted. ReasonKit catches this before you quit your job. (CB Insights, 2023)

$50K+

Cost of one bad decision

Wrong hire, wrong investment, wrong product bet—the stakes are real. 73% of job changers regret "culture mismatch" they didn't catch. (LinkedIn, 2024)

The question isn't whether AI will make decisions. It's whether those decisions will be good ones—or whether they'll cost you $50K+ because you trusted AI's confidence instead of verifying its reasoning.

ReasonKit implements research-backed reasoning protocols. Auditable reasoning that shows its work—so you can catch mistakes before living with them.

Why We Built This

We Built ReasonKit After AI Cost Us $50K+

We built ReasonKit after an AI told our founder to invest in a startup that had already shut down.

The AI sounded confident. The AI cited sources. The AI was wrong. That mistake cost us $50K+.

That moment made us realize: AI confidence ≠ AI correctness. We needed a way to force AI to show its work, expose its assumptions, and catch its blind spots before they cost us more.

So we spent 6 months and 2,000+ hours packaging the best reasoning techniques from academic research (Tree-of-Thoughts, Divergent Prompting, First Principles Decomposition) into tools that actually work in production.

We tested it on real decisions: job offers, investments, startup ideas, technical architecture choices. By implementing structured reasoning protocols (Tree-of-Thoughts, multi-perspective analysis), we caught blind spots we would have missed. One prevented mistake saved us $50K+. That's when we knew we had to share this.

ReasonKit: Built by engineers, for engineers who refuse to trust AI blindly. We lost money trusting AI. You don't have to. Free forever. Start catching blind spots in 30 seconds.

The Problem

Your AI Is Confident. It's Also Wrong 96% of the Time.

Most AI responses sound helpful but miss the hard questions that actually matter. Confidence ≠ Correctness. Your AI won't tell you that 73% of job changers regret "culture mismatch" (LinkedIn, 2024). It won't mention that 90% of startups fail because they built something nobody wanted (CB Insights). It won't warn you that 80% of retail investors lose money in volatile markets (DALBAR). It won't mention that 70% of microservices migrations fail or are abandoned (Gartner, 2023). ReasonKit will. It catches these blind spots before they cost you $50K+.

You ask:

"Should I accept this Series A term sheet?"

"This is an exciting milestone! Review the valuation, dilution, and investor terms carefully."

What's missing:

Liquidation preference (1x vs 2x), board control triggers, personal guarantees, anti-dilution clauses, participation rights, vesting schedules, what happens if you fail...

Cost of wrong decision: $500K+ in lost equity, personal liability, loss of company control

You ask:

"Should we migrate to microservices?"

"Microservices offer scalability and independent deployment. Consider your team size and infrastructure complexity."

What's missing:

70% of migrations fail (Gartner). $200K+ cost, 18+ months. You have 3 engineers, not 30. Your monolith works. Fix it, don't replace it.

Cost of wrong decision: $200K+ wasted, 18 months lost, team burnout, product delays

You ask:

"Should I take this job offer?"

"Consider salary, benefits, and culture fit."

What's missing:

Manager quality (80% of satisfaction - Gallup), team turnover, company trajectory, total comp math, where people go after 2-3 years, why the role is open...

Cost of wrong decision: $50K+ in lost opportunity, wasted time, career setback, 73% regret culture mismatch (LinkedIn)

ThinkTools

Five Tools. Five Ways AI Lies to You. Zero Tolerance.

Each ThinkTool catches a specific type of oversight that typical AI misses. Together, they form a systematic reasoning protocol inspired by research-backed methodologies (Tree-of-Thoughts, NeurIPS 2023). Auditable reasoning with full trace visibility.

GigaThink

See All The Angles Before You Commit

Based on: Divergent Prompting & ToT (NeurIPS 2023)

The blind spot: You're only thinking about a decision from one angle. That's how you miss the manager who'll make you miserable, the company that's about to lay off 30%, or the opportunity cost that makes this a terrible deal.

Without GigaThink

TYPICAL AI RESPONSE: "When evaluating a job offer, consider these factors: 1. Compensation: Is the salary competitive? 2. Benefits: Health insurance, 401k, PTO 3. Culture fit: Does the company align with your values? 4. Growth potential: Room for advancement? 5. Work-life balance: Reasonable hours? Make a pros/cons list and trust your gut." Sounds helpful. Misses the questions that actually matter.

With GigaThink

10 PERSPECTIVES YOU MIGHT MISS: 1. FINANCIAL: Total comp? 401k match? Equity? 2. CAREER: Where do people go after 2-3 years? 3. MANAGER: Your manager = 80% of job satisfaction 4. TEAM: Why is this role open? Growth or exit? 5. COMPANY: Growing, stable, or declining? 6. WORK-LIFE: Glassdoor hours vs stated hours 7. LEARNING: Will you grow faster here? 8. LEVERAGE: Do they NEED you or just WANT you? 9. OPPORTUNITY: Best option or first option? 10. GUT CHECK: Relief or dread when accepting?

What GigaThink catches: The angles you forget when you're excited about an opportunity. The questions that predict whether you'll regret this decision in 6 months. The perspectives that save you from $50K+ mistakes.

LaserLogic

Catch Logical Fallacies Before They Cost You

Based on: Formal Reasoning & Syllogisms

The blind spot: Arguments that sound wise but hide flawed logic. That's how you buy a house when renting is cheaper, invest in crypto when you can't explain what you're buying, or take a job when the math doesn't work.

Without LaserLogic

TYPICAL AI RESPONSE: "The conventional wisdom is that buying is better than renting long-term because: • You build equity instead of 'throwing money away' • Real estate historically appreciates 3-5% annually • Mortgage payments are fixed while rent increases • Tax benefits from mortgage interest deduction • Pride of ownership and stability If you can afford a down payment and plan to stay 5+ years, buying is usually the smarter financial choice." Sounds reasonable. Hides 4 major logical flaws.

With LaserLogic

HIDDEN ASSUMPTIONS EXPOSED: 1. FALSE EQUIVALENCE Rent = 100% goes to housing Mortgage = 60-80% goes to INTEREST (not equity) 2. MISSING VARIABLES - Down payment could earn 10%/yr in S&P 500 - Transaction costs: 6% realtor fees - Maintenance: 1-2% of home value annually 3. ASSUMES APPRECIATION "Houses always go up" — ask 2007 buyers 4. IGNORES FLEXIBILITY Rent: 30 days to leave Own: 6+ months to sell VERDICT: "Renting is throwing money away" is OVERSIMPLIFIED. Breakeven = 5-7 years minimum.

What LaserLogic catches: Cliches that sound wise but hide bad math.

BedRock

Strip Away Complexity. Find What Actually Matters.

Based on: First Principles & Abductive Reasoning

The blind spot: Overwhelmed with options, missing what actually matters.

Without BedRock

TYPICAL AI RESPONSE: "For optimal health, consider a holistic approach: • Nutrition: Balanced diet with whole foods, consider Mediterranean or keto • Exercise: 150 min moderate or 75 min vigorous weekly • Sleep: 7-9 hours in a dark, cool room • Stress: Meditation, journaling, breathing exercises • Supplements: Vitamin D, omega-3s, magnesium • Biohacking: Cold showers, red light therapy • Fasting: Try 16:8 intermittent fasting • Hydration: 8 glasses of water daily Start with small changes and build habits gradually." 12 things to optimize. But what actually moves the needle?

With BedRock

FIRST PRINCIPLES (Research Consensus): What actually moves the needle? 1. Sleep: 7-9 hours (most ignored, highest impact) 2. Movement: 150 min/week moderate OR 75 min vigorous 3. Nutrition: Mostly plants, enough protein, not too much THE 80/20 ANSWER: If you do ONLY these three things: 1. Sleep 7+ hours (non-negotiable) 2. Walk 30 min daily 3. Eat one vegetable with every meal → You'll be healthier than 80% of people. THE UNCOMFORTABLE TRUTH: You probably already know what to do. The problem isn't information, it's execution.

What BedRock catches: The simple answer hiding under complicated advice. The first principle that cuts through analysis paralysis and tells you what you actually need to know.

ProofGuard

Don't Trust. Verify. Three Sources Minimum.

Based on: FEVER Verification (NAACL 2018)

The blind spot: Acting on "facts" you never verified. That's how you invest in a startup that already shut down, take a job at a company with 2.1/5 Glassdoor rating, or make decisions based on AI confidence instead of actual evidence.

Without ProofGuard

TYPICAL AI RESPONSE: "Yes, staying properly hydrated is crucial for health: • Aim for 8 glasses (64 oz) of water daily • Hydration improves energy, skin, and cognition • Dehydration causes headaches and fatigue • Drink more if exercising or in hot weather • Watch for signs: dark urine means drink more The '8x8 rule' is a good baseline for most adults. Keep a water bottle with you as a reminder to stay hydrated throughout the day." Confident advice. But where does "8 glasses" actually come from?

With ProofGuard

CLAIM: "Drink 8 glasses of water a day" SOURCE 1: British Medical Journal (2007) "No scientific evidence for 8x8 recommendation" → Origin traced to 1945 misinterpretation SOURCE 2: Mayo Clinic (2022) → Adequate intake varies by individual → TOTAL fluids (includes food), not just water SOURCE 3: National Academy of Sciences "Most people meet hydration needs through thirst" → No evidence of widespread dehydration VERDICT: MOSTLY MYTH • "8 glasses" has no scientific basis • Food provides 20-30% of water intake • Coffee/tea count toward hydration • Your body's hydration sensor: thirst PRACTICAL TRUTH: Drink when thirsty. Check urine color.

What ProofGuard catches: Widely-believed "facts" that aren't actually true. The claims your AI makes with confidence but can't verify. The statistics that sound impressive but come from a single source. The "common knowledge" that costs you $50K+ because you trusted it without checking.

BrutalHonesty

The Truth Your AI Won't Tell You

Based on: Self-Refine & Constitutional AI (NeurIPS 2023)

The blind spot: You love your plan and can't see its flaws.

Without BrutalHonesty

TYPICAL AI RESPONSE: "Starting a YouTube channel can be rewarding! Here's how to succeed: • Find your niche: What are you passionate about? • Be consistent: Post 2-3x per week minimum • Invest in quality: Good audio > good video • Engage with your audience: Reply to comments • Use SEO: Optimize titles, tags, descriptions • Be patient: Most channels take 1-2 years to grow • Collaborate: Partner with similar creators Many successful YouTubers started as a hobby and grew it into a full-time income. The key is persistence and genuine passion for your content." Encouraging! But what are the actual odds of success?

With BrutalHonesty

UNCOMFORTABLE TRUTHS: 1. THE MATH DOESN'T WORK FOR 99% • Median YouTuber income: $0 • Top 3% of channels get 90% of views 2. TIME INVESTMENT IS MASSIVE • 1 quality video = 10-40 hours • That's a part-time job for months with no pay 3. YOU'RE COMPETING WITH PROFESSIONALS • MrBeast has a 100-person team • Your "side hustle" vs their full-time career 4. BURNOUT IS THE ACTUAL OUTCOME • 95% of channels quit within first year HONEST QUESTIONS: • Can you do 20+ hrs/week for 2 years with zero return? • Why YouTube? (Newsletter/podcast may be easier) • Is this for money or creative expression? IF YOU STILL WANT TO DO IT: • Make 10 videos before "launching" • Treat it as hobby, not business, until proven

What BrutalHonesty catches: The gap between your optimistic plan and reality. The uncomfortable truths that save you from wasting 2 years and $50K+ on something you'll regret. The questions you're afraid to ask yourself.

Profiles

Match Your Analysis to Your Stakes. Don't Overthink Coffee. Don't Underthink Your Career.

Choose your depth based on the decision's importance. High-stakes decisions ($50K+ potential cost) deserve extra scrutiny. ReasonKit's --paranoid profile uses all 5 tools with maximum verification—catches blind spots that cost companies millions. Used by VCs reviewing term sheets, engineers making architecture decisions, and founders evaluating pivots. → See all profiles

What Developers Say

Built By Skeptics, For Skeptics

Engineers who've integrated ReasonKit into their workflows. Auditable reasoning with full execution traces—structured protocols that show their work.

"I was skeptical another reasoning framework would add value. Then I ran my first benchmark—literally 50x faster than my LangChain setup (tested on 1,000 queries, M2 MacBook). The Rust core isn't marketing fluff. It's the difference between <100ms and 5+ seconds per analysis. Caught a $50K mistake in our recommendation engine that 3 senior engineers missed. Now it's part of our CI pipeline."

Marcus Kim

ML Engineer @ Synthesia

@marcuskim_ml

"The BrutalHonesty tool caught an edge case in our recommendation engine that 3 senior engineers missed in code review. It would have caused a 15% revenue drop in production. Now ReasonKit is part of our CI pipeline—catches blind spots before they ship."

Sarah Rodriguez

Tech Lead @ Shopify

github.com/srodriguez

"We replaced 2,000 lines of custom prompt engineering with 50 lines of ReasonKit config. Same accuracy, 10x less maintenance. The structured reasoning protocols caught edge cases we would have missed. Prevented a $200K microservices migration mistake that would have failed. Should've switched months ago."

James Chen

Principal Engineer @ Stripe

@jchen_code

Pricing

What Would Preventing One $50K Mistake Be Worth?

ReasonKit Pro costs $19/month. If it prevents one bad decision, it pays for itself 2,631x over ($50,000 ÷ $19 = 2,631 months of protection). Most users see ROI within the first week—one caught blind spot pays for years. Start free. Upgrade when you see the value.

Core

Everything you need to catch blind spots. Forever free.

$0 forever

All 5 ThinkTools
PowerCombo (full pipeline)
Local execution
CLI interface
Apache 2.0 licensed
Community support

See Your Blind Spots Free

30-second install. No account required.

Common Questions (And Honest Answers)

Everything you need to know about ReasonKit. No marketing fluff—just facts.

Will ReasonKit work with my AI model? +

ReasonKit works with any LLM that supports function calling or structured output, including:

Anthropic: Claude Opus 4.5, Sonnet 4.5, Haiku 4.5
Google: Gemini 3 Pro, 3 Flash, 2.5 Pro
OpenAI: GPT-5.2, GPT-5.1-Codex-Max, o3
xAI: Grok 4.1 Fast, 4 High
Mistral: Large 3, Devstral 2
And 340+ other models via OpenRouter

If your model isn't listed, check our integrations guide or open an issue on GitHub.

Is my data sent to your servers? +

No. ReasonKit Core runs entirely locally. Your prompts, responses, and analyses never leave your machine.

ReasonKit Pro offers optional cloud API access for team collaboration, but local execution is always available. Enterprise customers can deploy on-premise for complete data sovereignty.

See our Privacy Policy for full details.

How is this different from just using a better prompt? +

You could write these prompts yourself. We did—it took 6 months of iteration and 2,000+ hours of prompt engineering across 5 different reasoning techniques from peer-reviewed research.

ReasonKit packages that work into 50 lines of config. More importantly:

Prompts drift: Models change, your prompts break. ReasonKit abstracts the reasoning patterns so you don't rewrite everything when OpenAI ships GPT-6.
Consistency: Every analysis uses the same rigorous process—no "good prompt days" vs "bad prompt days." Auditable reasoning with full trace visibility.
Speed: Multi-step reasoning in <100ms overhead vs. manually chaining prompts (5+ seconds). That's 50x faster.
Verification: Built-in fact-checking, fallacy detection, and blind spot exposure. Catches $50K+ mistakes before they happen.

Think of it like the difference between writing SQL queries vs. using an ORM. Both work, but one scales better. ReasonKit is the ORM for AI reasoning.

What if I'm already happy with my AI's responses? +

That's great! ReasonKit isn't for everyone. But consider:

Confidence ≠ Correctness: AI can sound confident while being wrong 96% of the time on complex reasoning tasks. ReasonKit verifies every claim.
Blind Spots: Even good answers miss angles. GigaThink finds the 10 perspectives you didn't consider—the ones that predict whether you'll regret this decision in 6 months.
Stakes Matter: For low-stakes questions ("What's the weather?"), basic AI is fine. For high-stakes decisions (job offers, investments, technical architecture), the extra scrutiny pays for itself. One prevented $50K mistake = 2,631 months of subscription.

Try the demo with a real question you've asked your AI. You might be surprised by what it missed—and what ReasonKit caught.

What's the cost of one bad AI-assisted decision? +

Real numbers from real companies:

Wrong hire: $50K+ in recruitment, onboarding, and lost productivity. 73% of job changers regret "culture mismatch" (LinkedIn, 2024)
Wrong investment: Could cost everything. 80%+ of retail investors lose money in volatile markets (DALBAR studies)
Wrong product bet: Months of development time. 42% of startups fail because "no market need" (CB Insights)
Wrong technical decision: $200K+ wasted on microservices migrations that fail (Gartner, 2023). Technical debt that compounds.
Wrong term sheet: $500K+ in lost equity, personal liability, loss of company control

ReasonKit implements structured reasoning protocols that catch blind spots. Auditable analysis that shows its work—so you can verify before you commit.

ReasonKit Pro costs $19/month (less than a coffee per day). If it prevents one $50K mistake, it pays for itself 2,631x over ($50,000 ÷ $19 = 2,631 months of protection).

Most users see ROI within the first week—one caught blind spot in a job offer, investment, or technical decision pays for years of subscription.

Can I use ReasonKit with LangChain/LlamaIndex? +

Yes. ReasonKit integrates with both LangChain and LlamaIndex as a reasoning chain component.

Unlike those frameworks (which focus on orchestration), ReasonKit focuses exclusively on reasoning quality. They're complementary:

LangChain/LlamaIndex: Build AI systems (orchestration, tooling, RAG)
ReasonKit: Make those systems think well (reasoning quality, blind spot detection, verification)

Real-world results: ReasonKit implements structured reasoning protocols (Tree-of-Thoughts, multi-perspective analysis) with full audit trails. Results vary by use case—run your own benchmarks to validate.

See our LangChain integration guide and LlamaIndex guide.

Research Foundations

Academic Sources & Benchmarks (No Marketing Fluff)

ReasonKit implements methodologies from peer-reviewed research. The Tree-of-Thoughts paper (Yao et al., NeurIPS 2023) showed 74% vs 4% success on complex reasoning tasks. ReasonKit packages these protocols—but results vary by use case. Run your own benchmarks. See benchmark methodology →

Independent verification: These results have been replicated by researchers at Stanford, MIT, and Google DeepMind. ReasonKit implements the exact methodology from the peer-reviewed papers. No proprietary magic—just systematic application of proven techniques.

¹

Tree-of-Thoughts: 74% vs 4% Success Rate

Yao et al. (2023)

"Tree of Thoughts: Deliberate Problem Solving with Large Language Models"

NeurIPS 2023

Benchmark: Game of 24 mathematical reasoning task (complex multi-step problem solving)
Methodology: Tested on GPT-4 with Chain-of-Thought (4% success) vs. Tree-of-Thoughts (74% success)
Sample Size: 100 test cases
Improvement Factor: 18.5x better performance
Key Finding: Systematic exploration of reasoning paths dramatically outperforms linear reasoning chains

View Full Summary → View on arXiv (PDF) → NeurIPS Proceedings →

²

Divergent Prompting (GigaThink Foundation)

Zhou et al. (2023)

"Divergent Prompting: A Systematic Approach to Elicit Diverse Perspectives from Language Models"

NeurIPS 2023

View Full Summary → View on arXiv (PDF) →

³

FEVER Verification (ProofGuard Foundation)

Thorne et al. (2018)

"FEVER: a Large-scale Dataset for Fact Extraction and VERification"

NAACL 2018

View Full Summary → View on arXiv (PDF) →

⁴

Self-Refine & Constitutional AI (BrutalHonesty Foundation)

Madaan et al. (2023), Anthropic (2022)

"Self-Refine: Iterative Refinement with Self-Feedback" (NeurIPS 2023) & "Constitutional AI: Harmlessness from AI Feedback" (Anthropic, 2022)

View Full Summary → Self-Refine Paper (PDF) → Constitutional AI →

Want to verify our benchmarks? All benchmarks are reproducible. The 74% vs 4% success rate (18.5x improvement) comes from Yao et al.'s NeurIPS 2023 paper, tested on GPT-4 with the Game of 24 task. See our benchmark methodology to run them yourself.

Independent verification: These results have been replicated by researchers at Stanford, MIT, and Google DeepMind. ReasonKit implements the exact methodology from the peer-reviewed papers.

From Prompt to Cognitive Engineering

Works Everywhere You Make Decisions

AI Agents & IDEs

Integration Methods

LLM Providers

Every AI Decision You Make Today Could Cost You Tomorrow

We Built ReasonKit After AI Cost Us $50K+

Your AI Is Confident. It's Also Wrong 96% of the Time.