deepmiro

Simulate hundreds of AI agents to predict how communities react to events and policies.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

  • Create Simulation: Run a full swarm prediction. Builds a knowledge graph, generates agent personas, runs a multi-agent social media simulation, and generates a prediction report. Streams progress upda
  • Simulation Status: Check the progress of a running or completed simulation. Returns phase-aware status with entity names and action content. Phases: building_graph → generating_profiles → simulating →
  • Get Report: Generate and retrieve the prediction report for a completed simulation. If the report hasn't been generated yet, triggers generation (may take 1-3 minutes). Returns a detailed markdown ana

What data it sees

Do you need an account

No: the server works without sign-in

Simulate hundreds of AI agents to predict how communities react to events and policies. Upload any document (PDF, Markdown, text) and DeepMiro spawns a diverse swarm of AI agents that debate, share, and form opinions — then delivers a calibrated prediction report. Free and open-source.

How to connect

How to connect deepmiro to Claude

The server address to paste into Claude settings:

https://deepmiro--kakarot-joel-yy68.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect deepmiro to ChatGPT

The server address to paste into ChatGPT settings:

https://deepmiro--kakarot-joel-yy68.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect deepmiro to Cursor

The server address to paste into Cursor settings:

https://deepmiro--kakarot-joel-yy68.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (9)

Raw names from tools/list. Only developers need these.

create_simulationRun a full swarm prediction. Builds a knowledge graph, generates agent personas, runs a multi-agent social media simulation, and generates a prediction report. Streams progress updates. Returns the final report when complete.
simulation_statusCheck the progress of a running or completed simulation. Returns phase-aware status with entity names and action content. Phases: building_graph → generating_profiles → simulating → completed.
get_reportGenerate and retrieve the prediction report for a completed simulation. If the report hasn't been generated yet, triggers generation (may take 1-3 minutes). Returns a detailed markdown analysis of the simulation results.
interview_agentChat with a specific simulated agent to understand their perspective, reasoning, and predicted behavior. The agent responds in character based on their persona and simulation experience.
list_simulationsList past simulation runs with their status and metadata.
search_simulationsSearch past simulations by topic, project name, or simulation ID.
quick_predictFast, lightweight prediction without running a full simulation. Uses the LLM to simulate swarm behavior and predict outcomes. Returns in seconds. For deeper analysis, use create_simulation instead.
upload_documentUpload a document for use in simulations. LIMITS: Max 10MB, PDF/MD/TXT only. The server extracts text server-side (PyMuPDF for PDFs). Returns a document_id to pass to create_simulation. NOTE: Only works with local file paths (stdio transport). For remote/hosted mode, the client skill uploads via HTTP instead.
simulation_dataAccess raw simulation data: agent profiles, configuration, action logs, social media posts, round-by-round timeline, per-agent activity stats, and interview history. Use this to inspect what happened during a simulation.