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This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
Traditional prediction methods (statistical models, single-agent LLMs) fail to capture emergent collective behavior arising from individual interactions. MiroFish solves this by simulating large-scale agent societies with long-term memory and behavioral logic, allowing dynamic variable injection to explore 'what-if' scenarios at zero risk. It democratizes complex social simulation, making it accessible to non-experts via natural language inputs.
Deployment reality
The available setup signal starts with: npm run setup:all. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is AGPL-3.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build applications for public opinion forecasting, policy impact analysis, financial signal prediction, and creative narrative generation (e.g., deducing lost novel endings). The ceiling includes creating fully interactive digital twins of real-world communities, enabling real-time decision rehearsal for organizations, and generating rich, emergent storylines for entertainment. The framework supports both macro-level strategic planning and micro-level playful simulations, with a live demo showcasing trending event prediction.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
666ghj/MiroFish is tracked as a Python project in the AI Agent, LLM Tool area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 71k total stars, with +0 today, +983 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 20 days ago, and the open issue queue is 126, about 0.18% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 11k forks and 71k watchers suggest how often the project is reused or followed. License signal: AGPL-3.0. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need Python teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 983. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyAGPL-3.0 is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
71k Stars
Reuse
11k Forks
Attention
71k Watchers
Maintenance
active
License
AGPL-3.0
Open issues
126
MiroFish is a next-generation AI prediction engine powered by multi-agent technology. It extracts seed information from the real world to construct a high-fidelity parallel digital world where thousands of intelligent agents interact and evolve, allowing users to inject variables and deduce future trajectories. It aims to make prediction accessible for both serious decision-making and creative simulations.
Key Features
- Multi-agent swarm intelligence with independent personalities, long-term memory, and behavioral logic - Dynamic variable injection from a 'God's-eye view' for precise future deduction - Deep interaction with simulated agents and report generation via ReportAgent
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
npm run setup:all90
Health Score
Active
Commit Activity
Nov 26, 2025
Created
Aug 3, 2026
Last push
+405
Today's growth
+405
7-day growth
+405
30-day growth
Forks
Open
Watchers
666ghj
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
Traditional prediction methods (statistical models, single-agent LLMs) fail to capture emergent collective behavior arising from individual interactions. MiroFish solves this by simulating large-scale agent societies with long-term memory and behavioral logic, allowing dynamic variable injection to explore 'what-if' scenarios at zero risk. It democratizes complex social simulation, making it accessible to non-experts via natural language inputs.
Capabilities
Developers can build applications for public opinion forecasting, policy impact analysis, financial signal prediction, and creative narrative generation (e.g., deducing lost novel endings). The ceiling includes creating fully interactive digital twins of real-world communities, enabling real-time decision rehearsal for organizations, and generating rich, emergent storylines for entertainment. The framework supports both macro-level strategic planning and micro-level playful simulations, with a live demo showcasing trending event prediction.
Bottom Line
MiroFish is ideal for researchers, decision-makers, and creative enthusiasts who want to explore complex social dynamics and future scenarios through AI-driven simulation. Those concerned about ethical implications or lacking computational resources should approach with caution. The key trade-off is between powerful emergent prediction capabilities and the risks of misuse and resource intensity.