Is io.github.kakarot-dev/deepmiro safe to use?

io.github.kakarot-dev/deepmiro has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.kakarot-dev/deepmiro?

io.github.kakarot-dev/deepmiro can be installed by cloning its GitHub repository and following the setup instructions in the README.

What AI clients work with io.github.kakarot-dev/deepmiro?

io.github.kakarot-dev/deepmiro is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.

io.github.kakarot-dev/deepmiro MCP Server — Score: 15/100 (F)

A swarm intelligence engine that rehearses the future.

Feed it a document. Describe a scenario. Watch hundreds of AI agents with distinct personalities, memories, and social instincts interact — and return with a prediction.

What It Does

DeepMiro extracts entities and relationships from any document — a policy draft, a market report, a chapter of a novel — and constructs a parallel digital world. Inside it, hundreds of autonomous agents form opinions, argue on simulated social platforms, shift allegiances, and produce emergent behavior that no single prompt could predict.

You get back a structured prediction report and a living world you can interrogate, agent by agent.

Input: A PDF and a question in plain language. Output: A detailed prediction report + an interactive simulation you can explore.

How It Works

Document ──► Entity Extraction ──► Agent Generation ──► Dual-Platform Simulation ──► Prediction Report
              (NER + GraphRAG)    (personas, memory,     (Twitter-like + Reddit-like     (ReportAgent with
                                   social networks)       parallel interaction)            deep analysis tools)

Phase	What happens
Graph Build	Extracts entities, relationships, and context from your documents. Builds a knowledge graph via GraphRAG.
Environment Setup	Generates agent personas with distinct personalities, beliefs, and social connections.
Simulation	Agents interact across dual platforms (Twitter-like and Reddit-like) in parallel. Dynamic memory updates each round.
Report Generation	A ReportAgent analyzes the post-simulation environment — sentiment shifts, faction formation, viral dynamics, outcome trajectories.
Deep Interaction	Chat with any agent to understand their reasoning. Query the ReportAgent for follow-up analysis.

Quick Start

1. Get an API key

2. Install

Claude Code (plugin — recommended) — one command gets you the /predict skill + the MCP server wired up:

claude plugin marketplace add kakarot-dev/deepmiro
claude plugin install deepmiro@deepmiro-marketplace
export DEEPMIRO_API_KEY=dm_your_key   # or set it in ~/.claude/settings.json

Then restart Claude Code and say /predict or predict how people will react to [scenario].

Other clients:

Client	Install
OpenAI Codex	`codex plugin install kakarot-dev/deepmiro`
Claude Desktop	Add to `claude_desktop_config.json`: `"deepmiro": {"command": "npx", "args": ["-y", "deepmiro-mcp"], "env": {"DEEPMIRO_API_KEY": "dm_xxx"}}`
ChatGPT Desktop	Settings → MCP Servers → Add → `npx deepmiro-mcp` with env `DEEPMIRO_API_KEY`
Cursor / Windsurf	Settings → MCP → Add → `npx deepmiro-mcp` with env `DEEPMIRO_API_KEY`
VS Code (Copilot)	Add to `.vscode/mcp.json`: `"deepmiro": {"command": "npx", "args": ["-y", "deepmiro-mcp"], "env": {"DEEPMIRO_API_KEY": "dm_xxx"}}`

Self-host

No API key needed. Run the engine locally and point the MCP server at it:

git clone https://github.com/kakarot-dev/deepmiro.git
cd deepmiro
cp .env.example .env    # add your LLM API key
docker compose -f docker/docker-compose.yml up -d

# Connect your AI client to the local engin

... [View full README on GitHub](https://github.com/kakarot-dev/deepmiro#readme)

io.github.kakarot-dev/deepmiro

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