Yggaaronjmars/MiroShark
↗ GitHubUniversal Swarm Intelligence Engine
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Safety Rating A
MiroShark appears to be a legitimate open-source multi-agent simulation project. The only notable finding is the use of a hardcoded default Neo4j password ('miroshark') in Docker setup examples, which is a common documentation pattern but poses a risk if deployed without change. No malicious code patterns, obfuscated logic, dependency vulnerabilities, or prompt injection attempts were identified in the repository content. The embedded Ethereum wallet address is a standard open-source donation practice. Overall the project is safe, with the default credential note warranting minor caution for production deployments.
ℹAI-assisted review, not a professional security audit.
AI Analysis
MiroShark is a universal swarm intelligence engine and multi-agent simulation platform built in Python. It ingests documents (press releases, policy drafts, financial reports) and generates hundreds of AI agents with unique personas that simulate public reaction across three concurrent platforms: Twitter, Reddit, and a Polymarket-style prediction market. The system builds a Neo4j knowledge graph from uploaded documents, generates grounded agent personas (with optional LLM-powered web enrichment for public figures), runs cross-platform simulations with sliding-window memory and per-agent belief state tracking, and produces analytical reports via a ReACT agent. It supports local inference via Ollama, cloud APIs (OpenRouter, OpenAI, Anthropic), and Claude Code CLI, and is a community-extended fork of MiroFish incorporating an offline Neo4j+Ollama storage layer and the CAMEL-AI OASIS simulation engine.
Use Cases
- Simulating public reaction to press releases or corporate announcements before publication
- Testing draft regulations or policy documents against a simulated public
- Generating synthetic social media sentiment and prediction market signals from financial news
- Exploring narrative outcomes for creative writing by feeding characters into a simulation
- Analyzing how opinion shifts propagate across simulated social networks over time
Tags
Security Findings (2)
The README and .env.example show a default Neo4j password of 'miroshark' used consistently across all setup options. While this is an example credential in documentation, it is hardcoded as the default in Docker run commands and docker-compose, increasing the risk that users deploy with this known credential unchanged.
A cryptocurrency wallet address (0xd7bc6a05a56655fb2052f742b012d1dfd66e1ba3) is embedded in the README as a donation address. This is not a security threat but is noted for transparency.
Project Connections
MiroFish
→MiroShark is explicitly built on MiroFish by 666ghj (Shanda Group), extending it with a Neo4j+Ollama offline storage layer, cross-platform simulation (Twitter/Reddit/Polymarket), and additional LLM provider support. It credits MiroFish directly and shares the OASIS/CAMEL-AI simulation engine.
skyclaw
→Skyclaw provides a persistent autonomous AI agent runtime with swarm intelligence ('Many Tems') capabilities. MiroShark's large-scale persona simulations could feed scenarios or signals into persistent agents managed by Skyclaw, or Skyclaw's multi-agent infrastructure could serve as a deployment target for MiroShark agents.
clawvault
→ClawVault provides structured persistent memory and knowledge graph primitives for AI agents. MiroShark's per-agent belief states and round memories could be persisted and queried via ClawVault's markdown-native knowledge graph, enhancing long-term simulation continuity.
code-review-graph
→Both projects use knowledge graphs as a foundational data layer (Neo4j in MiroShark, SQLite in code-review-graph). The graph construction and semantic search patterns are complementary, and MiroShark's RAG pipeline could benefit from similar blast-radius and context-engineering techniques.
OpenJarvis
→OpenJarvis provides a local-first agent framework with support for multiple local inference backends (Ollama, vLLM, etc.). MiroShark's local Ollama mode for high-volume simulation rounds aligns directly with OpenJarvis's on-device inference philosophy, and OpenJarvis evaluation primitives could be used to benchmark MiroShark simulation quality.