AI

MiroFish: Swarm Intelligence Engine That Predicts Anything with 53k+ GitHub Stars

MiroFish has emerged as one of the fastest-growing AI projects on GitHub, reaching 53,000+ stars. This open-source swarm intelligence engine uses thousands of autonomous agents to simulate complex scenarios and predict real-world outcomes.

Tosin Akinosho

— 4 min read

Members-Only Deep Dive - This exclusive analysis is available to Decision Crafters community members.

MiroFish has emerged as one of the fastest-growing AI projects on GitHub, reaching 53,000+ stars in just weeks. This open-source swarm intelligence engine represents a fundamental shift in how we approach prediction and forecasting. Instead of relying on traditional statistical models or single-agent AI systems, MiroFish spawns thousands of autonomous agents with unique personalities, memories, and behavioral logic to simulate complex real-world scenarios. The project is actively maintained with commits as recent as April 2, 2026, and backed by Shanda Group.

What is MiroFish?

MiroFish is a next-generation AI prediction engine powered by multi-agent simulation technology. Created by the team at Shanda Group, it takes seed information from the real world—breaking news, policy drafts, financial reports, or even literary works—and automatically constructs a high-fidelity parallel digital world. Within this simulated environment, thousands of intelligent agents interact freely, forming relationships, developing memories, and undergoing social evolution.

The core innovation lies in its ability to extract collective intelligence from agent interactions. Rather than asking a single AI model to predict an outcome, MiroFish creates a digital society where agents debate, influence each other, and collectively emerge with predictions. This approach mirrors how human societies make decisions through distributed consensus and social dynamics.

The project is built on top of OASIS (Open Agent Social Interaction Simulations) from the CAMEL-AI team, providing a robust foundation for multi-agent simulation. MiroFish adds sophisticated layers for knowledge graph construction, agent persona generation, and dynamic memory management—all designed to make predictions more accurate and interpretable.

Core Features and Architecture

Graph Building & Knowledge Extraction: MiroFish begins by extracting seed information from your input materials using GraphRAG (Retrieval-Augmented Generation). It constructs entity relationship graphs and injects both individual and collective memory into the simulation environment. This ensures agents operate with contextual awareness of the scenario.

Persona Generation & Agent Configuration: The system automatically generates unique personas for each agent based on the scenario. Each agent receives distinct behavioral logic, communication patterns, and decision-making frameworks. This diversity is critical—homogeneous agents produce groupthink, while diverse agents generate emergent intelligence.

Dual-Platform Parallel Simulation: MiroFish runs simulations across both frontend and backend platforms simultaneously, allowing for real-time visualization and deep interaction. The simulation engine automatically parses prediction requirements and dynamically updates temporal memory as agents interact.

ReportAgent with Rich Toolset: After simulation completes, a specialized ReportAgent analyzes the results using a comprehensive toolkit. This agent can query the simulated world, extract patterns, and generate detailed prediction reports with supporting evidence from agent interactions.

Deep Interaction Layer: Users can chat with any agent in the simulated world post-simulation, asking follow-up questions and exploring alternative scenarios. This transforms prediction from a black-box output into an interactive exploration tool.

Docker & Multi-Language Support: MiroFish includes Docker support for easy deployment, with both Dockerfile and docker-compose.yml provided. Recent updates (April 2026) added multi-language support with i18n functionality, making it accessible to global teams.

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Getting Started

Prerequisites: You'll need Node.js 18+, Python 3.11-3.12, and the uv package manager. MiroFish requires API access to an LLM service (Alibaba Qwen-plus is recommended) and Zep Cloud for agent memory management.

Installation via Source Code: Clone the repository and configure your environment variables:

cp .env.example .env
# Edit .env with your LLM_API_KEY, LLM_BASE_URL, and ZEP_API_KEY

npm run setup:all  # Install all dependencies
npm run dev        # Start frontend (port 3000) and backend (port 5001)

Docker Deployment: For faster setup, use Docker Compose:

cp .env.example .env
docker compose up -d

The system will be available at http://localhost:3000 for the frontend and http://localhost:5001 for the API. Start with simulations under 40 rounds to manage API costs while learning the platform.

Real-World Use Cases

Public Opinion Simulation: Feed MiroFish a news event (university controversy, policy announcement) and watch how social media sentiment evolves. The system simulates thousands of users with different perspectives, generating realistic opinion trajectories. This helps communications teams predict PR outcomes before they happen.

Financial Market Prediction: Input financial reports, earnings calls, and market signals. MiroFish simulates trader behavior, institutional responses, and market dynamics to forecast price movements. Several users have built production-grade Polymarket research engines on top of MiroFish.

Literary & Creative Exploration: Upload the first 80 chapters of "Dream of the Red Chamber" and ask MiroFish to predict the lost ending. The system generates agents with character personalities from the novel and simulates how the story might conclude based on established narrative patterns.

Policy Impact Assessment: Government agencies can simulate how proposed policies affect different population segments. MiroFish models agent responses across demographics, creating a zero-risk testing ground for policy decisions before implementation.

How It Compares

vs. CrewAI (41,871 stars): CrewAI excels at role-based multi-agent collaboration for specific tasks. MiroFish is fundamentally different—it's designed for emergent simulation and prediction, not task orchestration. CrewAI agents follow explicit workflows; MiroFish agents interact freely and generate emergent behavior.

vs. AutoGen (56,800 stars): AutoGen focuses on conversation-based multi-agent systems with explicit communication protocols. MiroFish emphasizes social simulation with implicit agent interactions, memory persistence, and emergent consensus. AutoGen is better for structured workflows; MiroFish is better for unpredictable scenarios.

vs. LangGraph (part of LangChain ecosystem): LangGraph provides low-level control over agent state and transitions. MiroFish abstracts away implementation details, focusing on high-level simulation design. LangGraph requires more engineering; MiroFish prioritizes accessibility for non-engineers.

MiroFish's unique strength is its focus on emergent intelligence through social simulation. It's not trying to replace task-oriented frameworks—it's solving a different problem: prediction through collective agent behavior.

What's Next

The MiroFish roadmap includes expanded LLM provider support, enhanced visualization tools for agent interactions, and improved performance optimization for simulations with 100,000+ agents. The team is actively recruiting full-time and internship positions for developers interested in multi-agent simulation and LLM applications.

Recent updates show the project moving toward production-grade stability with security patches (April 2026) and internationalization support. The community is growing rapidly, with active Discord and social media channels. As AI prediction becomes increasingly important for business decision-making, MiroFish is positioning itself as the open-source standard for swarm intelligence simulation.

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