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crypticsaiyan/franklin_conviction

Analysis updated 2026-05-18

0TypeScriptAudience · developerLicense

TLDR

A simulated AI investment committee that debates a crypto token through seven agents and outputs a conviction score with position sizing.

Mindmap

mindmap
  root((Franklin Conviction Engine))
    What it does
      AI investment committee
      Conviction score output
    Tech stack
      TypeScript
      Express
      React
      Pyth prices
    Use cases
      Token research
      Free demo mode
    Audience
      Developers
      Crypto researchers

Code map

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What do people build with it?

USE CASE 1

Get a structured bull versus bear analysis of a crypto token before deciding to buy.

USE CASE 2

Run a free, no-wallet demo to see how the multi-agent debate pipeline works.

USE CASE 3

Use the conviction score and stop-loss output as a starting point for a trading plan.

What is it built with?

TypeScriptExpressReactVitePyth

How does it compare?

crypticsaiyan/franklin_conviction0xradioac7iv/tempfsabboskhonov/hermium
Stars000
LanguageTypeScriptTypeScriptTypeScript
Setup difficultymoderatemoderate
Complexity3/54/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

In plain English

Franklin Conviction Engine is a tool that assembles a simulated investment committee of AI agents to analyze a cryptocurrency token and produce a final buy/sell recommendation with suggested position sizing. Instead of a single AI answering a question, it runs seven specialized agents in a structured pipeline designed to surface disagreement and force synthesis. When you enter a token, four analyst agents run in parallel: a Bull analyst (arguing the case for buying), a Bear analyst (arguing against), a Macro analyst (looking at broader market conditions), and a Narrative analyst (examining sentiment and story). A disagreement gate then checks the gap between the Bull and Bear scores, if that gap exceeds 30 points, a premium Arbitrator agent is brought in to adjudicate. Otherwise the Arbitrator is skipped, keeping costs down. A Synthesis agent then merges all the inputs, and finally a Risk Officer issues a conviction score, entry zone, stop-loss level, position size, and time horizon. Live market prices come from BlockRun's PriceClient, which is backed by Pyth, a real-time price feed. Agents stream their outputs back to the browser as they complete, using Server-Sent Events. The tool has two modes: FREE mode, which routes all calls through zero-cost NVIDIA models and requires no API key, and SMART mode, which uses Franklin wallet routing profiles including the premium Arbitrator when needed. Each session uses a wallet key you supply yourself, nothing is stored server-side. Built with Express and TypeScript on the backend and React with Vite on the frontend. MIT licensed. The README includes a disclaimer that outputs are research scaffolding, not financial advice.

Copy-paste prompts

Prompt 1
Explain how the Bull, Bear, Macro, and Narrative agents disagree and how the Arbitrator resolves it.
Prompt 2
Walk me through running Franklin Conviction Engine in FREE mode with no wallet key.
Prompt 3
What does the Risk Officer agent output at the end of the pipeline?
Prompt 4
How do I set up the frontend and backend for this project locally?

Frequently asked questions

What is franklin_conviction?

A simulated AI investment committee that debates a crypto token through seven agents and outputs a conviction score with position sizing.

What language is franklin_conviction written in?

Mainly TypeScript. The stack also includes TypeScript, Express, React.

Who is franklin_conviction for?

Mainly developer.

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