Introduction
"Don't predict individuals — simulate the swarm."
This is article No.43 in the "One Open Source Project a Day" series. Today's project is MiroFish (GitHub).
The standard playbook for prediction tools is: collect data → train a model → output a number. But this approach has a fundamental blind spot: models are static, while the real world emerges from dynamic interaction. Public sentiment, market behavior, policy responses — these are collective phenomena that emerge from countless individual interactions. You can't fit a public opinion storm with linear regression.
MiroFish takes a different approach entirely: instead of fitting, it re-enacts. By running thousands of virtual "people" through a simulated platform, it lets group behavior evolve naturally and extracts the trajectory as a prediction. This isn't predicting numbers — it's predicting stories.
56k+ Stars, 8.6k+ Forks — one of the most-watched projects in the multi-agent simulation space. The author is 666ghj (BaiFu), a student at Beijing University of Posts and Telecommunications with support from Shanda Group, and also the creator of BettaFish (40.5k Stars). Together, the two projects form a complete "data collection → simulation prediction" pipeline.
What You'll Learn
- MiroFish's core philosophy: why swarm simulation is closer to truth than statistical prediction
- The five-stage simulation pipeline: from knowledge graph construction to deep interactive reporting
- GraphRAG's role in simulation: injecting domain knowledge into agents
- Zep Cloud cross-session memory: giving agents a persistent sense of history
- "God-mode" variable injection: runtime what-if analysis
Prerequisites
- Basic understanding of Multi-Agent Systems (MAS)
- Python basics (optional, for understanding configuration logic)
- Interest in public opinion analysis or trend forecasting
Project Background
What Is It?
MiroFish is a swarm intelligence prediction engine that builds a virtual society populated by thousands of AI agents, simulates how a real crowd would respond to a given topic, and generates a structured trend forecast report from the emergent behavior.
The name is inspired by the emergent phenomenon of collective intelligence — just as a school of fish (Fish) can form patterns far exceeding individual capability, human social dynamics emerge from individual interactions into collective outcomes that no individual planned.
The core problem it solves:
Traditional approach:
Historical data → Statistical model → "Next month's sales will be X"
Problem: Can't explain why; can't handle black-swan events
MiroFish approach:
Seed knowledge + Multi-agent simulation → Group behavior evolution
→ "Under these conditions, the crowd will respond like this"
Advantage: Explainable, intervable, supports what-if analysis
About the Author
- GitHub: 666ghj
- Background: Student at Beijing University of Posts and Telecommunications; supported by Shanda Group
- Sister project: BettaFish (40.5k ⭐) — collects sentiment data from 30+ platforms, feeding into MiroFish as the data source
- Vision: Complete loop: BettaFish collects → MiroFish simulates and predicts
Project Stats
- ⭐ GitHub Stars: 56,400+
- 🍴 Forks: 8,600+
- 📦 Latest Version: v0.1.2
- 📄 License: AGPL-3.0 (copyleft; SaaS deployments must open-source modifications)
- 🌐 Language breakdown: Python 57.6% + Vue.js 41.2%
- 🤝 Core dependencies: CAMEL-AI OASIS, camel-ai 0.2.78, Zep Cloud 3.13.0, GraphRAG, PyMuPDF
Key Features
The Core: Five-Stage Simulation Pipeline
MiroFish runs through five strictly sequential stages:
Stage 1: Graph Building
Seed documents (PDF/URL) → PyMuPDF extraction → GraphRAG knowledge graph
Output: Domain knowledge graph (entities + relationships)
Stage 2: Environment Setup
CAMEL-AI OASIS initializes virtual platforms
Each agent is assigned a Zep Cloud long-term memory
Knowledge graph context injected into agent context
Stage 3: Parallel Simulation
Dual-platform simultaneous run (for confidence validation)
Thousands of agents interact across N rounds
Agent behavior driven by LLM + constrained by persona profiles
Stage 4: Report Generation
Aggregate simulation trajectories
LLM summarizes collective behavior patterns
Generate structured trend report
Stage 5: Deep Interaction
User can ask natural-language questions about the report
Supports "God-mode" variable injection (runtime what-if)
RAG retrieves simulation records to answer questions
GraphRAG: Injecting Domain Knowledge
MiroFish uses GraphRAG instead of standard RAG — and the reason is intuitive:
# Standard RAG: document → vector → retrieve similar chunks
# Problem: can only answer "what are the facts," can't reason about relationships
# GraphRAG: document → entity extraction → relationship graph → graph traversal
# Advantage: can reason "A affects B, what does B do to C?"
In simulation, agents need to understand complex causal chains (e.g., "Policy X influences Group Y's behavior"). GraphRAG's graph structure handles this kind of relational reasoning far better than vector retrieval alone.
Zep Cloud: Agents That Remember
Every agent gets its own Zep Cloud memory space:
# Each agent has persistent memory
agent_memory = ZepMemory(
session_id=f"agent_{agent_id}",
zep_client=zep_client
)
# Between simulation rounds, agents can "remember" prior interactions
# This makes agent behavior coherent and more true to real human cognition
This solves a classic problem in multi-agent simulation: if agents "forget" everything between rounds, their behavior lacks continuity and prediction credibility drops significantly.
"God-Mode" Variable Injection
One of MiroFish's most distinctive features — injecting external variables at runtime:
# Example: "What if a competitor suddenly cuts prices by 20%?"
god_mode_injection = {
"event": "competitor_price_cut",
"magnitude": -0.20,
"timing": "round_15",
"affected_agents": "all_consumer_agents"
}
# After injection, the simulation responds in real-time to this "external shock"
# Output: Group sentiment shift + behavioral evolution trajectory
This transforms MiroFish from a "what will happen" tool into a "what happens if I do this" decision support tool.
Dual-Platform Parallel Simulation
MiroFish runs the same simulation on two independent virtual platforms simultaneously:
Platform A ─── Results A ──┐
├── Confidence scoring + combined report
Platform B ─── Results B ──┘
- Both platforms converge → high-confidence conclusion
- Platforms diverge → flagged as "uncertainty zone," users cautioned
BettaFish + MiroFish: The Complete Pipeline
[BettaFish] [MiroFish]
Weibo / Twitter / Reddit / ... → Seed data → Knowledge graph
Sentiment data collection → Agent initialization
30+ platforms → Simulation → Report → Prediction
Together, the two projects form a full chain: from real-world data collection to future trend forecasting.
Quick Start
Requirements: Python 3.10+, Node.js 16+, Docker (recommended)
# Clone
git clone https://github.com/666ghj/MiroFish.git
cd MiroFish
# Configure environment
cp .env.example .env
# Edit .env with:
# - OPENAI_API_KEY (or compatible API)
# - ZEP_API_KEY (Zep Cloud account)
# - GRAPHRAG_API_KEY
# Option 1: Docker one-command start (recommended)
docker-compose up -d
# Option 2: Manual startup
pip install -r requirements.txt
cd frontend && npm install && npm run build && cd ..
python app.py
Visit http://localhost:5000 to access the web UI.
Your first simulation:
1. Upload seed documents (PDF or enter URLs)
2. Configure simulation parameters (agent count, rounds, topic)
3. Click "Start Simulation"
4. Wait ~10-30 minutes (depends on agent count and API speed)
5. Review the generated trend report; ask follow-up questions in natural language
Deep Dive
System Architecture
┌──────────────────────────────────────────────────────┐
│ Frontend Layer (Vue.js) │
│ Config / Progress Monitor / Report Viewer / Chat │
└──────────────────────┬───────────────────────────────┘
│ REST API
┌──────────────────────▼───────────────────────────────┐
│ Backend Layer (Flask + Python) │
│ Five-stage pipeline orchestration / God-mode ctrl │
└──────┬───────────────┬─────────────────┬─────────────┘
│ │ │
┌──────▼──────┐ ┌─────▼──────┐ ┌──────▼─────────────┐
│ Knowledge │ │ Simulation │ │ Memory Layer │
│ Layer │ │ Layer │ │ Zep Cloud │
│ GraphRAG │ │ CAMEL-AI │ │ Per-agent memory │
│ Knowledge │ │ OASIS │ │ Cross-round │
│ graph │ │ Dual-plat │ │ persistence │
│ PyMuPDF │ │ parallel │ └────────────────────┘
└─────────────┘ └────────────┘
CAMEL-AI OASIS: The Simulation Engine
CAMEL-AI OASIS is MiroFish's simulation core, purpose-built for social simulation:
from oasis import Environment, Agent, Platform
# Initialize virtual platform
platform = Platform(
name="simulated_weibo",
max_agents=5000,
interaction_rules=InteractionConfig(
max_posts_per_round=10,
follow_probability=0.3
)
)
# Create agents with varied personas
agents = [
Agent(
id=i,
persona=PersonaProfile(
age=random.randint(18, 65),
occupation=random.choice(occupations),
political_lean=random.gauss(0, 1),
activity_level=random.uniform(0.1, 1.0)
),
memory=ZepMemory(session_id=f"agent_{i}"),
knowledge_context=graphrag_context
)
for i in range(5000)
]
# Launch simulation
env = Environment(platform=platform, agents=agents)
results = env.run(rounds=50, topic="EV subsidy policy")
GraphRAG Knowledge Graph Construction
from graphrag import GraphRAGPipeline
# Build knowledge graph from seed documents
pipeline = GraphRAGPipeline(
input_dir="./seed_documents",
output_dir="./knowledge_graph"
)
pipeline.run()
# Example output:
# Entities: [Tesla, BYD, Subsidy Policy, Consumer Groups, ...]
# Relations: [Tesla → competes → BYD]
# [Subsidy Policy → stimulates → consumer purchase intent]
# [Purchase intent → drives → market share]
# Inject knowledge graph into agent context
context = pipeline.query("Key factors in the EV market")
Report Generation: From Trajectories to Insights
def generate_report(simulation_results):
"""Generate trend report from simulation trajectories"""
# 1. Aggregate statistics
sentiment_evolution = aggregate_sentiment(simulation_results)
opinion_clusters = cluster_opinions(simulation_results)
key_events = detect_tipping_points(simulation_results)
# 2. LLM synthesis
report_prompt = f"""
Based on the following simulation data, generate a trend analysis report:
- Sentiment evolution curve: {sentiment_evolution}
- Opinion clusters: {opinion_clusters}
- Key tipping points: {key_events}
Analyze: collective final stance, key driving factors, likely evolution paths
"""
report = llm.complete(report_prompt)
# 3. Dual-platform consistency check
confidence = calculate_confidence(
results_platform_a=simulation_results["platform_a"],
results_platform_b=simulation_results["platform_b"]
)
return Report(content=report, confidence=confidence)
Frontend: Vue.js Visualization
The Vue.js frontend provides a full visualization suite:
- Simulation progress monitor: Real-time stage progress and agent activity levels
- Knowledge graph explorer: Interactive entity-relationship graph from GraphRAG
- Sentiment heatmap: Group emotional distribution evolving across simulation rounds
- Report reader: Highlighted, annotatable report with natural language follow-up chat
Why AGPL-3.0?
AGPL-3.0 is stricter than GPL in a specific way:
MIT/Apache: Modifications can stay closed source (can commercialize without sharing)
GPL: Distributions of modified code must be open-sourced
AGPL: Even network services (SaaS) using modified code must open-source modifications
→ MiroFish with AGPL means: if you build a cloud service on it, you must open-source
your changes. Personal research and local use have no restrictions.
Resources
Official
- 🌟 GitHub: https://github.com/666ghj/MiroFish
- 🐟 Sister project: BettaFish (sentiment collection, 40.5k ⭐)
- 📄 OASIS framework: https://github.com/camel-ai/oasis
- 🧠 Zep Cloud: https://www.getzep.com
- 📊 GraphRAG: https://github.com/microsoft/graphrag
Related Technologies
- CAMEL-AI: https://github.com/camel-ai/camel — multi-agent framework foundation
- PyMuPDF: PDF document parsing
- Docker: Recommended deployment method
Summary
Key Takeaways
- Swarm simulation replaces statistical fitting: MiroFish doesn't fit historical data — it re-enacts group interaction. The output is a behavioral evolution story, not a point prediction
- GraphRAG knowledge enhancement: Graph-structured knowledge representation gives agents domain-level causal reasoning, not just fact retrieval
- Zep Cloud cross-round memory: Solves simulation continuity, making agent behavior authentically follow human cognitive patterns
- God-mode what-if analysis: Runtime variable injection turns a prediction tool into a decision support tool
- Dual-platform confidence mechanism: Consistency-based confidence scoring is more honest about uncertainty than a single prediction
- BettaFish + MiroFish closed loop: Complete pipeline from real-world data collection to future trend forecasting
Who Should Use This
- Researchers: Scholars in public opinion dynamics, social simulation, and computational social science
- Product managers / market analysts: Decision-makers who need to anticipate market reactions and run scenario analysis
- AI engineers: Developers studying multi-agent simulation architectures, GraphRAG applications, and agent memory systems
- Independent researchers: Explorers interested in swarm intelligence emergence and using AI to understand complex social systems
A Question Worth Sitting With
MiroFish represents a new paradigm for AI prediction: from "model fitting" to "world simulation". When we re-enact the real world with multi-agent systems, we're asking a deeper question — reality is fundamentally the emergent result of countless agents in dynamic equilibrium. Perhaps truly understanding that emergence requires simulation of comparable complexity.
A school of fish has no central director, yet forms spectacular collective patterns. Is the trajectory of public opinion shaped by the same invisible hand?
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