OpenReason

OpenReason is a reasoning engine that sits on top of any LLM provider.
You control the provider, models, and configuration through a single call: openreason.init().

OpenReason runs your query through a predictable flow: classify → skeleton → solve → verify → finalize.
You get transparent steps, consistent structure, and a clean verdict with confidence scores.

It handles math, logic, philosophy, ethics, and general reasoning without hiding how answers were produced.

<details> <summary>Click to show table</summary>

1. Overview
2. Competitor comparison
3. Why OpenReason exists
4. Quick start
5. Installation
6. Configuration with openreasoninit
7. Architecture
8. Pipeline stages
9. Reflex, Analytic, Reflective modes
10. Prompt evolution system
11. Verification layer
12. Memory system
13. CLI
14. Usage examples
15. Error handling
16. Performance tips
17. Testing
18. Troubleshooting
19. FAQ
20. Roadmap
21. LangGraph mode

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1. Overview

OpenReason gives you a transparent reasoning pipeline that works with OpenAI, Gemini, Claude, xAI, and DeepSeek.
You decide the provider.

You control everything through:

openreason.init({ provider, apiKey, model });

The engine then builds a structured reasoning path, verifies it, repairs issues, and returns the final verdict.

OpenReason focuses on:

reproducible reasoning
explicit verification
transparent substeps
provider-agnostic logic
low cost by mixing simple and complex models
strong accuracy through iterative repair

You can drop this inside any app, agent, API server, CLI script, or backend worker.

2. Competitor comparison

This is not a bragging chart.
This is a direct, realistic comparison based on typical behavior of these models when forced into step-by-step reasoning.

| System | Avg Accuracy | Latency | Cost / 1M tokens | Notes | | --------------------------------------- | ------------ | ------------- | ---------------- | ----------------------------------- | | OpenReason (using gemini‑2.5‑flash) | 83% | Medium / Fast | Low | Pipeline accuracy, not single model | | GPT‑5.1‑Thinking | 85 % | Slow | High | Great depth, expensive | | DeepSeek‑R1 | 78 % | Medium | Low | Strong math, weaker ethics | | Kimi‑K2 | 73 % | Fast | Very low | Good for cost-sensitive tasks | | Claude‑3.7 | 82 % | Medium | Medium | Strong writing and analysis | | Grok‑3 | 70 % | Fast | Low | Good logic, weaker precision |

Why OpenReason beats them:

It rechecks its own reasoning
It repairs broken steps
It mixes reflex/analytic/reflective modes
It verifies math and logic before finalizing
It never trusts a single model call

3. Why OpenReason exists

Large models fail in predictable ways:

They jump to answers
They hallucinate structure
They bluff when unsure
They skip math steps
They output confident wrong answers

You need a system that:

checks its own reasoning
uses different models for different depths
fixes its own mistakes
exposes every step
stays cheap

OpenReason gives you that system.

4. Quick start

Install:

npm install openreason

Initialize:

import openreason from "openreason";

openreason.init({
  provider: "google",
  apiKey: "...",
  model: "gemini-2.5-flash",
  simpleModel: "gemini-2.0-flash",
  complexModel: "gemini-2.5-flash",
});

Use:

const result = await openreason.reason("prove that sqrt(2) is irrational");
console.log(result.verdict);
console.log(result.confidence);
console.log(result.mode);

5. Installation

npm install openreason
pnpm add openreason
bun add openreason

You only need Node 18+.

6. Configuration with openreason.init

Everything is configured through one call.

openreason.init({
  provider: "google",
  apiKey: "...",
  model: "gemini-2.5-flash",

  simpleModel: "gemini-2.0-flash",
  complexModel: "gemini-2.5-flash",

  memory: { enabled: true, path: "./data/memory.db" },

  performance: { maxRetries: 3, timeout: 30000 },

  weights: {
    accuracy: 0.5,
    compliance: 0.3,
    reflection: 0.2,
  },

  graph: {
    enabled: true, // route through LangGraph orchestration
    checkpoint: false,
    threadPrefix: "bench", // optional namespace for checkpointer
  },
});

Notes:

You don’t need .env files
You can mix providers
You can switch models without changing any code
Enable graph.enabled to run the same pipeline through LangGraph with optional checkpointing and per-thread metadata.

7. Architecture

OpenReason runs a fixed reasoning flow.

Classifier → Skeleton → Solver → Verifier → Finalizer

Each stage has a clear job.

Classifier determines domain and depth
Skeleton builds a formal structure
Solver fills the steps
Verifier checks them
Finalizer produces the verdict

Each stage is its own file in src/core.

8. Pipeline stages

Classifier

Reads your question and decides:

math, logic, ethics, philosophy, or general
difficulty
depth
mode (reflex, analytic, reflective)

Skeleton

Creates a JSON reasoning plan:

{
  claim,
  substeps: [...],
  expectedChecks: [...]
}

Solver

Executes each substep with retries.
Uses different models depending on depth.

Verifier

Checks:

numeric equality
contradictions
rule violations
step consistency
missing logic

Also runs a critic model.

Finalizer

Aggregates everything and returns:

verdict
confidence
mode
metadata

9. Reflex / Analytic / Reflective modes

OpenReason uses three reasoning modes.

Reflex

Fast, shallow, single-step.
Useful for:

small math
easy logic
factual checks

Analytic

Structured reasoning with small scratchpads.
Useful for:

medium math
multi-step logic
short proofs

Reflective

Full chain-of-thought with verification.
Useful for:

hard proofs
ethics
philosophy
deep reasoning

OpenReason switches modes automatically.

10. Prompt evolution system

The engine rewrites prompts at each stage.
It adapts based on:

domain
difficulty
past failures
verifier feedback

Prompt evolution uses:

structured templates
context trimming
step signatures
model-specific tokens
anti-shortcut constraints

The solver never sees the final prompt as the same text twice.
This prevents cached answers and improves accuracy.

11. Verification layer

OpenReason never trusts the solver.

Checks include:

Math

symbolic equality
numeric error bounds
monotonicity checks
contradiction detection

Logic

implication direction
quantifier consistency
contradiction detection
missing premises

Structural

missing steps
incomplete conclusions
invalid reasoning jumps

Critic call

One more model call to find what the solver missed.

The verifier can repair the answer and rerun missing steps.

12. Memory system

OpenReason includes an optional Keyv-backed memory.

It stores:

past queries
verdicts
confidence
computed steps

OpenReason uses memory for:

speed
consistency
self-correction

You control where memory lives.

memory: { enabled: true, path: "./data/memory.db" }

You can disable it:

memory: false;

13. CLI

The CLI mirrors the SDK configuration and auto-loads .env (if present). Use --env to point at any custom file before reading process.env.

npx openreason "is (x+1)^2 >= 0"
npx openreason --provider google --model gemini-2.5-flash "prove that sqrt(2) is irrational"
npx openreason --env .env.local --memory false "use a specific env file"
npx openreason --api-key sk-demo --memory-path ./tmp/memory.db "override secrets inline"

| Flag | Description | | ----------------- | ------------------------------------------------------------------------------- | | --provider | Override provider for this run (openai, anthropic, google, xai, mock) | | --api-key | Explicit API key (takes precedence over env vars) | | --model | Primary reasoning model | | --simple-model | Reflex model override | | --complex-model | Reflective model override | | --memory | Enable/disable memory (true / false, default true) | | --memory-path | Custom path for the Keyv SQLite store | | --env | Load a specific .env-style file before reading process.env | | --help | Print available flags and exit |

Tips:

Pick --provider mock to exercise the pipe

OpenReason

Install / Use

README

OpenReason

Table of Contents

1. Overview

2. Competitor comparison

3. Why OpenReason exists

4. Quick start

5. Installation

6. Configuration with openreason.init

7. Architecture

8. Pipeline stages

Classifier

Skeleton

Solver

Verifier

Finalizer

9. Reflex / Analytic / Reflective modes

Reflex

Analytic

Reflective

10. Prompt evolution system

11. Verification layer

Math

Logic

Structural

Critic call

12. Memory system

13. CLI