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MiroFish

AI#Multi-agent#LLM#Simulation#GraphRAG#Swarm Intelligence

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// summary

MiroFish is a next-generation AI prediction engine based on multi-agent technology that constructs high-fidelity digital parallel worlds by extracting real-world seed information. Users can perform simulations within this sandbox by injecting variables, thereby precisely deducing future trajectories. The platform aims to provide decision-makers with a zero-risk testing laboratory while offering individual users a creative simulation space.

// technical analysis

MiroFish is a swarm intelligence prediction engine based on multi-agent technology, designed to simulate real-world scenarios by constructing high-fidelity parallel digital worlds. The project extracts seed information from the real world and combines it with agents possessing independent personalities, long-term memory, and behavioral logic to conduct social evolution simulations within a digital sandbox. Its core design philosophy lies in breaking through the limitations of traditional forecasting through the emergence of group interactions, providing decision-makers with a zero-risk testing environment while also offering creative simulation spaces for individual users. Technically, it integrates GraphRAG, multi-agent collaboration, and dynamic memory update mechanisms, achieving a complete closed loop from data input to the generation of in-depth interaction reports.

// key highlights

Utilizes multi-agent technology to build a high-fidelity digital sandbox, enabling dynamic deduction and prediction of complex real-world events.

Supports natural language descriptions for prediction requirements and automatically generates detailed prediction reports containing in-depth interaction analysis.

Built-in GraphRAG technology effectively extracts seed information and constructs complex relationship networks between agents.

Provides a god-view dynamic variable injection feature, allowing users to intervene in real-time during the simulation to observe different trajectories.

Supports in-depth conversations with any agent in the simulated world, enhancing the interactivity and immersion of the prediction process.

Based on the OASIS engine architecture, it possesses mature group social interaction simulation capabilities, suitable for various scenarios ranging from policy testing to literary creation.

// use cases

Macro decision simulation: Provides a zero-risk rehearsal environment for decision-makers by simulating and testing policies or public relations events.

Micro creative sandbox: Supports users in exploring various imaginative scenarios, such as deducing the endings of literary works or conducting fun simulations.

Deep interactive prediction: Enables users to obtain detailed prediction reports and deep insights by interacting with agents and reporting agents within the simulated world.

// getting started

Developers can deploy the project via source code or Docker for a quick start. First, configure the .env file by entering the LLM API Key and Zep Cloud configuration, then use npm run setup:all to install frontend and backend dependencies, and finally execute npm run dev to access the frontend interface locally for simulation.