ReasonKit

The AI Reasoning Engine

"Designed, not Dreamed." — From Prompt to Cognitive Engineering.

Auditable Reasoning for Production AI | Rust-Native | Turn Prompts into Protocols

🚀 Quick Install

curl -fsSL https://get.reasonkit.sh | bash

Universal Installer • All Platforms • All Shells • 30 Seconds

📖 Installation Guide • 📦 Crates.io • 📚 Docs.rs

The Problem We Solve

Most AI is a slot machine. Insert prompt → pull lever → unclear, hope for coherence, at the mercy of chance.

ReasonKit is a factory. Input data → apply protocol → deeper logic, auditable result, known probabibility.

The Cost of Wrong Decisions: Without structured reasoning, AI decisions lead to financial loss and missed opportunities. Structured protocols catch errors early and prevent costly mistakes before they compound.

LLMs are fundamentally probabilistic. Same prompt → different outputs. This creates critical failures:

Failure	Impact	Our Solution
Inconsistency	Unreliable for production	Deterministic protocol execution
Hallucination	Dangerous falsehoods	Multi-source triangulation + adversarial critique
Opacity	No audit trail	Complete execution tracing with confidence scores

We don't eliminate probability (impossible). We constrain it through structured protocols that force probabilistic outputs into deterministic execution paths.

Quick Start

Already installed? Jump to Choose Your Workflow or How to Use.

Need installation help? See the Installation Guide or Installation Section below.

🤖 Choose Your Workflow

🤖 Claude Code (Opus 4.5)

Agentic CLI. No API key required.

claude mcp add reasonkit -- rk serve-mcp
claude "Use ReasonKit to analyze: Should we migrate to microservices?"

Learn more: Claude Code Integration

🌐 ChatGPT (Browser)

Manual MCP Bridge. Injects the reasoning protocol directly into the chat.

# Generate strict protocol
rk protocol "Should we migrate to microservices?" | pbcopy

# → Paste into ChatGPT: "Execute this protocol..."

Learn more: ChatGPT Integration

⚡ Gemini 3.0 Pro (API)

Native CLI integration with Google's latest preview.

export GEMINI_API_KEY=AIza...
rk think --model gemini-3.0-pro-preview "Should we migrate to microservices?"

Learn more: Google Gemini Integration • All Provider Integrations

Note: The rk command is the shorthand alias for rk.

30 seconds to structured reasoning. See How to Use for more examples.

ThinkTools: The 5-Step Reasoning Chain

Each ThinkTool acts as a variance reduction filter, transforming probabilistic outputs into increasingly deterministic reasoning paths.

📖 Full Documentation: ThinkTools Guide • API Reference

ThinkTool	Operation	What It Does
GigaThink	`Diverge()`	Generate 10+ perspectives, explore widely
LaserLogic	`Converge()`	Detect fallacies, validate logic, find gaps
BedRock	`Ground()`	First principles decomposition, identify axioms
ProofGuard	`Verify()`	Multi-source triangulation, require 3+ sources
BrutalHonesty	`Critique()`	Adversarial red team, attack your own reasoning

Variance Reduction: The Chain Effect

Result: Raw LLM variance ~85% → Protocol-constrained variance ~28%

Reasoning Profiles

Pre-configured chains for different rigor levels. See Reasoning Profiles Guide for detailed documentation.

# Fast analysis (70% confidence target)
rk think --profile quick "Is this email phishing?"

# Standard analysis (80% confidence target)
rk think --profile balanced "Should we use microservices?"

# Thorough analysis (85% confidence target)
rk think --profile deep "Design A/B test for feature X"

# Maximum rigor (95% confidence target)
rk think --profile paranoid "Validate cryptographic implementation"

Profile	Chain	Confidence	Use Case
`--quick`	GigaThink → LaserLogic	70%	Fast sanity checks
`--balanced`	All 5 ThinkTools	80%	Standard decisions
`--deep`	All 5 + meta-cognition	85%	Complex problems
`--paranoid`	All 5 + validation pass	95%	Critical decisions

See It In Action

$ rk think --profile balanced "Should we migrate to microservices?"

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ThinkTool Chain: GigaThink → LaserLogic → BedRock → ProofGuard
Variance:        85% → 72% → 58% → 42% → 28%
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[GigaThink] 10 PERSPECTIVES GENERATED                         Variance: 85%
  1. OPERATIONAL: Maintenance overhead +40% initially
  2. TEAM TOPOLOGY: Conway's Law - do we have the teams?
  3. COST ANALYSIS: Infrastructure scales non-linearly
  ...
  → Variance after exploration: 72% (-13%)

[LaserLogic] HIDDEN ASSUMPTIONS DETECTED                      Variance: 72%
  ⚠ Assuming network latency is negligible
  ⚠ Assuming team has distributed tracing expertise
  ⚠ Logical gap: No evidence microservices solve stated problem
  → Variance after validation: 58% (-14%)

[BedRock] FIRST PRINCIPLES DECOMPOSITION                      Variance: 58%
  • Axiom: Monoliths are simpler to reason about (empirical)
  • Axiom: Distributed systems introduce partitions (CAP theorem)
  • Gap: Cannot prove maintainability improvement without data
  → Variance after grounding: 42% (-16%)

[ProofGuard] TRIANGULATION RESULT                             Variance: 42%
  • 3/5 sources: Microservices increase complexity initially
  • 2/5 sources: Some teams report success
  • Confidence: 0.72 (MEDIUM) - Mixed evidence
  → Variance after verification: 28% (-14%)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
VERDICT: conditional_yes | Confidence: 87% | Duration: 2.3s
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

What This Shows:

Transparency: See exactly where confidence comes from
Auditability: Every step logged and verifiable
Deterministic Path: Same protocol → same execution flow
Variance Reduction: Quantified uncertainty reduction at each stage

Architecture

The ReasonKit architecture uses a Protocol Engine wrapper to enforce deterministic execution over probabilistic LLM outputs.

📖 Full Documentation: Architecture Guide • API Reference

Three-Layer Architecture:

Probabilistic LLM (Unavoidable)
- LLMs generate tokens probabilistically
- Same prompt → different outputs
- We cannot eliminate this
Deterministic Protocol Engine (Our Innovation)
- Wraps the probabilistic LLM layer
- Enforces strict execution paths
- Validates outputs against schemas
- State machine ensures consistent flow
ThinkTool Chain (Variance Reduction)
- Each ThinkTool reduces variance
- Multi-stage validation catches errors
- Confidence scoring quantifies uncertainty

Key Components:

Protocol Engine: Orchestrates execution with strict state management
ThinkTools: Modular cognitive operations with defined contracts
LLM Integration: Unified client (Claude, GPT, Gemini, 18+ providers)
Telemetry: Local SQLite for execution traces + variance metrics

flowchart LR
    subgraph CLI["ReasonKit CLI (rk)"]
      A[User Command<br/>rk think --profile balanced]
    end

    subgraph PROTOCOL["Deterministic Protocol Engine"]
      B1[State Machine<br/>Execution Plan]
      B2[ThinkTool Orchestrator]
      B3[(SQLite Trace DB)]
    end

    subgraph LLM["LLM Layer (Probabilistic)"]
      C1[Provider Router]
      C2[Claude / GPT / Gemini / ...]
    end

    subgraph TOOLS["ThinkTools · Variance Reduction"]
      G["GigaThink<br/>Diverge()"]
      LZ["LaserLogic<br/>Converge()"]
      BR["BedRock<br/>Ground()"]
      PG["ProofGuard<br/>Verify()"]
      BH["BrutalHonesty<br/>Critique()"]
    end

    A --> B1 --> B2 --> G --> LZ --> BR --> PG --> BH --> B3
    B2 --> C1 --> C2 --> B2

    classDef core fill:#030508,stroke:#06b6d4,stroke-width:1px,color:#f9fafb;
    classDef tool fill:#0a0d14,stroke:#10b981,stroke-width:1px,color:#f9fafb;
    classDef llm fill:#111827,stroke:#a855f7,stroke-width:1px,color:#f9fafb;

    class CLI,PROTOCOL core;
    class G,LZ,BR,PG,BH tool;
    class LLM,llm C1,C2;

Built for Production

ReasonKit is written in Rust because reasoning infrastructure demands reliability.

Capability	What It Means for You
Predictable Latency	<5ms orchestration overhead, no GC pauses
Memory Safety	Zero crashes from null pointers or buffer overflows
Single Binary	Deploy anywhere, no Python environment required
Fearless Concurrency	Run 100+ reasoning chains in parallel safely
Type Safety	Errors caught at compile time, not runtime

Benchmarked Performance (view full report • online version):

Operation	Time	Target
Protocol orchestration	4.4ms	<10ms
RRF Fusion (100 elements)	33μs	<5ms
Document chunking (10 KB)	27μs	<5ms
RAPTOR tree traversal (1000 nodes)	33μs	<5ms

Why This Matters:

Your AI reasoning shouldn't crash in production. It shouldn't pause for garbage collection during critical decisions. It shouldn't require complex environment management to deploy.

ReasonKit's Rust foundation ensures deterministic, auditable execution every time—the same engineering choice trusted by Linux, Cloudflare, Discord, and AWS for their most critical infrastructure.

Memory Infrastructure (Optional)

Memory modules (storage, embedding, retrieval, RAPTOR, indexing) are available in the standalone reasonkit-mem crate.

📖 Documentation: Memory Layer Guide • Crates.io • Docs.rs

Enable the memory feature to use these modules:

[dependencies]
reasonkit-core = { version = "0.1", features = ["memory"] }

Features:

Qdrant vector database (embedded mode)
Hybrid search (dense + sparse fusion)
RAPTOR hierarchical retrieval
Local embeddings (BGE-M3 ONNX)
BM25 full-text search (Tantivy)

Installation

Primary Method (Universal - All Platforms & Shells):

curl -fsSL https://get.reasonkit.sh | bash

📖 Full Installation Guide: docs.reasonkit.sh/getting-started/installation

Platform Support:

✅ Linux (all distributions)
✅ macOS (Intel & Apple Silicon)
✅ Windows (WSL & Native PowerShell)
✅ FreeBSD (experimental)

Shell Support:

✅ Bash (auto-detected, PATH configured)
✅ Zsh (auto-detected, PATH configured)
✅ Fish (auto-detected, PATH configured)
✅ Nu (Nushell) (auto-detected, PATH configured)
✅ PowerShell (cross-platform, PATH configured)
✅ Elvish (auto-detected, PATH configured)
✅ tcsh/csh/ksh (basic support)

Features:

🎨 Beautiful terminal UI with progress visualization
⚡ Fast installation (~30 seconds)
🔒 Secure (HTTPS-only, checksum verification)
🧠 Smart shell detection and PATH configuration
📊 Real-time build progress with ETA
🔄 Automatic Rust installation if needed

📖 Learn more: Installation Guide • Installation Audit Report

# Cargo (Rust) - Recommended for Developers
cargo install reasonkit-core

# From Source (Latest Features)
git clone https://github.com/reasonkit/reasonkit-core
cd reasonkit-core && cargo build --release

📦 Package Links: Crates.io • Docs.rs • GitHub Releases

Windows (Native PowerShell):

irm https://get.reasonkit.sh/windows | iex

Python bindings available via PyO3 (build from source with --features python).

How to Use

Command Structure: rk <command> [options] [arguments]

📖 Full CLI Reference: CLI Documentation • API Reference

Standard Operations:

# Balanced analysis (5-step protocol)
rk think --profile balanced "Should we migrate our monolith to microservices?"

# Quick sanity check (2-step protocol)
rk think --profile quick "Is this email a phishing attempt?"

# Maximum rigor (paranoid mode)
rk think --profile paranoid "Validate this cryptographic implementation"

# Scientific method (research & experiments)
rk think --profile scientific "Design A/B test for feature X"

With Memory (RAG):

# Ingest documents
rk ingest document.pdf

# Query with RAG
rk query "What are the key findings in the research papers?"

# View execution traces
rk trace list
rk trace export <id>

📖 Learn more: RAG Guide • Memory Layer Documentation

Contributing: The 5 Gates of Quality

We demand excellence. All contributions must pass The 5 Gates of Quality:

📖 Contributing Guide: CONTRIBUTING.md • Quality Gates Documentation

# Clone & Setup
git clone https://github.com/reasonkit/reasonkit-core
cd reasonkit-core

# The 5 Gates (MANDATORY)
cargo build --release        # Gate 1: Compilation (Exit 0)
cargo clippy -- -D warnings  # Gate 2: Linting (0 errors)
cargo fmt --check            # Gate 3: Formatting (Pass)
cargo test --all-features    # Gate 4: Testing (100% pass)
cargo bench                  # Gate 5: Performance (<5% regression)

Quality Score Target: 8.0/10 minimum for release.

📖 Complete Guidelines: CONTRIBUTING.md • Quality Metrics

🏷️ Community Badge

If you use ReasonKit in your project, add our badge:

[![Reasoned By ReasonKit](https://raw.githubusercontent.com/reasonkit/reasonkit-core/main/brand/badges/reasoned-by.svg)](https://reasonkit.sh)

📖 Badge Guide: Community Badges • All Variants

🎨 Branding & Design

Brand Playbook - Complete brand guidelines
Component Spec - UI component system
Motion Guidelines - Animation system
3D Assets - WebGL integration guide
Integration Guide - Complete integration instructions

📖 Online Resources: Brand Guidelines • Design System

Design Philosophy: Honest Engineering

We don't claim to eliminate probability. That's impossible. LLMs are probabilistic by design.

We do claim to constrain it. Through structured protocols, multi-stage validation, and deterministic execution paths, we transform probabilistic token generation into auditable reasoning chains.

What We Battle	How We Battle It	What We're Honest About
Inconsistency	Deterministic protocol execution	LLM outputs still vary, but execution paths don't
Hallucination	Multi-source triangulation, adversarial critique	Can't eliminate, but can detect and flag
Opacity	Full execution tracing, confidence scoring	Transparency doesn't guarantee correctness
Uncertainty	Explicit confidence metrics, variance reduction	We quantify uncertainty, not eliminate it

Version & Maturity

Component	Status	Notes
ThinkTools Chain	✅ Stable	Core reasoning protocols production-ready
MCP Server	✅ Stable	Model Context Protocol integration
CLI	🔶 Scaffolded	`mcp`, `serve-mcp`, `completions` work; others planned
Memory Features	✅ Stable	Via `reasonkit-mem` crate
Python Bindings	🔶 Beta	Build from source with `--features python`

Current Version: v0.1.5 | CHANGELOG | Releases • 📦 Crates.io • 📚 Docs.rs

Verify Installation

# Check version
rk --version

# Verify MCP server starts
rk serve-mcp --help

# Run a quick test (requires LLM API key)
OPENAI_API_KEY=your-key rk mcp

📖 Troubleshooting: Installation Issues • Common Problems

License

Apache 2.0 - See LICENSE

Open Source Core: All core reasoning protocols and ThinkTools are open source under Apache 2.0.

📖 License Information: LICENSE • License Strategy

ReasonKit — Turn Prompts into Protocols

Designed, Not Dreamed

📦 Package Links: Crates.io • Docs.rs • PyPI

reasonkit-core 0.1.6