Agentic Swarms Are Here: What MiroFish Teaches Us About Building Multi-Agent Systems
Published: March 2026
A 20-year-old university student built an AI system in ten days that can simulate thousands of autonomous agents interacting on social media platforms, predicting real-world outcomes from financial markets to political events. It got 33,000 GitHub stars in two weeks and $4 million in funding within 24 hours.
That system is MiroFish. And whether you are an AI consultant, a product builder, or a business leader trying to understand where agent technology is heading, the lessons from this project are worth paying attention to.
What Is MiroFish?
MiroFish is an open-source swarm intelligence prediction engine. You feed it a source document (a news article, financial report, policy draft, or even a novel) and it builds a digital world populated by thousands of autonomous AI agents. Each agent has its own personality, memory, social history, and behavioral logic. They interact on simulated social media platforms, form coalitions, debate positions, and shift opinions. The collective dynamics that emerge from these interactions are analysed to produce structured prediction reports.
The engine is powered by OASIS (Open Agent Social Interaction Simulations) from CAMEL-AI, capable of handling up to one million simultaneous agents with 23 different social actions including posting, commenting, following, liking, and searching.
The core insight is deceptively simple: instead of predicting the future by extrapolating numbers, MiroFish builds a miniature version of the relevant social system and runs it forward at accelerated speed.
The Five-Stage Pipeline
MiroFish transforms a document into a prediction through a clearly defined process:
Stage 1: Knowledge Graph Construction. The source document is processed by GraphRAG to extract entities (people, organisations, events) and their relationships. This becomes the foundation for everything that follows.
Stage 2: Environment Setup. From the knowledge graph, thousands of agent personas are generated. Each gets a unique profile: personality, background, initial stance, social relationships. An environment configuration agent sets simulation rules.
Stage 3: Parallel Simulation. Two simulated platforms (one Twitter-like, one Reddit-like) run simultaneously. Agents interact freely, forming opinions, shifting positions, and creating emergent collective dynamics. Long-term memory is managed by Zep Cloud.
Stage 4: Report Generation. A specialised ReportAgent analyses simulation results, examining opinion shifts, coalition formations, and emergent patterns to produce a structured prediction report.
Stage 5: Deep Interaction. After the simulation, you can talk to any individual agent or the ReportAgent directly. Inject new variables and re-run scenarios to explore alternative outcomes.
What Makes Agentic Swarms Different from Single Agents
Most AI agents built today follow a simple loop: receive input, call tools, produce output. Even the sophisticated "deep agents" we discussed previously still follow this fundamental pattern, just with better planning and context management.
Agentic swarms operate on a completely different principle: emergence.
A single agent has one perspective. Ten agents have ten perspectives. A thousand agents with unique personalities, memories, and social connections produce collective behaviours that no individual agent was programmed to exhibit. Opinion polarisation happens organically. Herd effects emerge naturally. Coalition formation occurs without explicit programming.
This is not just a scaling problem. It is a fundamentally different approach to generating intelligence from AI systems.
Practical Use Cases for Agentic Swarm Systems
Financial Market Simulation
Feed MiroFish earnings reports, regulatory filings, or market sentiment data. The swarm simulates how thousands of market participants would react to different scenarios. The collective dynamics reveal potential price movements, sentiment shifts, and risk concentrations that single-model analysis would miss.
Public Policy Testing
Before implementing a new policy, simulate how different stakeholder groups would respond. A tax change might seem positive in isolation, but when thousands of agents representing different demographics interact and form coalitions around shared grievances, unexpected second-order effects emerge.
Crisis Communication Planning
Upload a crisis scenario and let the swarm simulate how public opinion would evolve across different response strategies. Which messaging approach prevents reputation damage? Where does misinformation take hold? The simulation provides evidence-based answers.
Product Launch Prediction
Simulate market reception before launching. Agents representing different customer segments interact, share opinions, and form collective judgments about your product. The resulting dynamics reveal which features drive adoption and which trigger objections.
Content and Narrative Analysis
Feed in existing content (articles, social media threads, even novels) and simulate how audiences would interact with it. The system has been used to predict the lost ending of Dream of the Red Chamber based on the first 80 chapters, treating literary analysis as a prediction problem.
The Architecture Behind the Swarm
MiroFish uses a stack that any team building agentic systems should study:
GraphRAG for knowledge extraction. Instead of flat text processing, the system builds a structured knowledge graph. This gives agents contextual grounding that raw text cannot provide.
Independent agent memories via Zep Cloud. Each agent maintains its own conversation history and evolves over time. This is what allows authentic opinion shifts during simulation.
Dual-platform simulation via OASIS. Running on two different social architectures (microblog and forum) captures different interaction patterns and prevents platform-specific biases.
ReportAgent as analytical layer. A specialised agent interprets swarm dynamics rather than relying on raw output. This pattern of "agent that watches agents" is powerful for any multi-agent system.
GraphRAG-based knowledge graphs. The structured relationships between entities give agents context that goes beyond simple document retrieval.
Lessons for Building Your Own Swarm Systems
You do not need to simulate one million agents to benefit from swarm architectures. Even small swarms of 10-50 agents can produce emergent insights that single agents cannot. Here are the principles that translate:
Give Agents Genuine Independence
The power of a swarm comes from agent autonomy. Each agent needs its own context, its own memory, and its own decision-making process. If your "multi-agent" system just routes tasks to identical agents with different prompts, you are not building a swarm. You are building a queue.
Design for Emergence, Not Control
Single-agent systems are predictable. Swarm systems are not, and that is the point. Design your agent interactions to allow unexpected collective behaviours. Rigid constraints kill emergence.
Memory Is the Multiplier
Short-lived agents forget everything. Persistent agents accumulate experience. MiroFish uses Zep Cloud for long-term memory across simulation rounds. For production systems, any persistent storage that agents can read and write works. The key insight is that memory turns a one-shot interaction into an evolving social system.
Specialised Observer Agents
MiroFish's ReportAgent is not a participant. It observes, analyses, and synthesises. This separation of participant and observer is a pattern worth copying in any multi-agent system. Agents that do the work and agents that interpret the work serve different functions.
Start Small, Scale Later
MiroFish can handle one million agents, but meaningful emergence happens with far fewer. Start with 20 agents representing different stakeholder perspectives. Add complexity only when the smaller system stops producing useful insights.
The Bigger Picture: Why Swarms Matter Now
We are moving from the era of single-purpose AI tools to the era of AI systems. The next wave of valuable AI applications will not be chatbots or coding assistants. They will be systems where multiple specialised agents collaborate, compete, and produce collective intelligence.
MiroFish demonstrates this principle in a dramatic way by simulating entire social systems. But the same patterns apply to business use cases that are far more grounded: multi-agent supply chain optimisation, collaborative research systems, competitive intelligence platforms, and automated decision support.
The question is no longer whether agentic swarms are useful. It is which problems in your business are swarm-shaped problems: problems where the answer lives not in any single perspective, but in the collective dynamics of many perspectives interacting over time.
Frequently Asked Questions
What is MiroFish?
MiroFish is an open-source swarm intelligence prediction engine that simulates thousands of autonomous AI agents interacting on social platforms to predict real-world outcomes from financial markets to political events.
Can MiroFish actually predict the future?
MiroFish does not predict the future directly. It simulates how a social system might evolve based on current conditions, allowing you to explore scenarios and identify likely dynamics. The quality of predictions depends on the quality of input data and the realism of the simulation.
What programming languages and frameworks does MiroFish use?
MiroFish uses Python for the backend with LangChain and GraphRAG, Node.js for the frontend, and the OASIS framework by CAMEL-AI for agent simulation. It requires Python 3.11-3.12 and Node.js 18+.
Do you need expensive hardware to run agentic swarms?
Not necessarily. While MiroFish at full scale requires significant compute, small swarm simulations with 20-50 agents can run on standard cloud instances. The key is managing memory and context efficiently, not raw compute power.
How is MiroFish different from other multi-agent frameworks?
MiroFish focuses specifically on social simulation and emergence-based prediction, whereas frameworks like LangChain Deep Agents or Claude Code focus on task completion. MiroFish simulates entire social systems where collective behaviour emerges from individual agent interactions.
