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MiroShark Simulation

MiroShark is a multi-agent social simulation engine. Describe any scenario in plain English - MiroShark spins up a network of AI agents with distinct personas and belief systems, runs them through multiple rounds of interaction and belief propagation, and returns a behavioral analysis of how the scenario plays out.

Use it for: market narrative modeling, social dynamics simulation, regulatory impact analysis, community reaction forecasting.

How It Works

MiroShark runs on a dedicated high-memory backend. The pipeline has four stages:

1. Knowledge Graph Construction

MiroShark parses the scenario and builds a knowledge graph - a structured representation of entities, relationships, and claims. This gives all agents shared factual grounding.

2. Persona Generation

Agent personas are generated with:

  • Role - trader, analyst, retail investor, whale, developer, media, skeptic

  • Initial belief state - how strongly they hold each claim in the knowledge graph

  • Influence weight - how much other agents are affected by their assertions

  • Information access - which agents see which signals first

3. Belief Propagation

Over multiple rounds, agents exchange signals, update beliefs based on neighbor influence, and form or revise positions. This models how information spreads, narratives form, and consensus or dissent emerges - similar to real social dynamics.

4. Analysis Output

After rounds complete, MiroShark returns:

  • Consensus narrative - what the majority converged on

  • Dissent clusters - minority belief groups that held different views

  • Signal strength - how quickly and strongly beliefs propagated

  • Agent behavior summary - which persona types were most influential

  • Round-by-round action log (Twitter and Reddit simulated activity)

Infrastructure

MCP Tools

miroshark_simulate

Start a new simulation. Returns a simulation_id - the simulation runs asynchronously in the backend.

Parameter
Type
Required
Description

scenario

string

yes

What to simulate - plain English, any topic

Agent count and number of rounds are determined automatically by MiroShark based on scenario complexity. A 24-hour scenario generates ~48 rounds; a 7-day scenario generates ~168 rounds.

Example:

Returns a simulation_id. Pass it to miroshark_status to track progress.

miroshark_status

Poll simulation progress and retrieve results when complete.

Parameter
Type
Required
Description

simulation_id

string

yes

ID from miroshark_simulate

Status flow:

  • preparing - building knowledge graph and generating agent personas

  • running - belief propagation rounds in progress, actions firing

  • complete - full results available

When running, the response shows current round, total rounds, and action counts:

Poll every 30-60 seconds. Preparation typically takes 3-5 minutes before rounds start.

miroshark_stop

Stop a simulation that is currently preparing or running.

Parameter
Type
Required
Description

simulation_id

string

yes

Simulation ID to stop

Example Scenarios

BTC price milestone:

Regulatory event:

Market crash:

Narrative spread:

Macro impact:

Tips

  • Specific scenarios give better results - include timeframes, numbers, and named entities

  • Poll with patience - agent preparation takes 3-5 minutes before running starts; first rounds can be slow to initialize

  • You don't need to wait for completion - Round 10-20 is usually enough for a meaningful snapshot

  • Combine with vault - use vault_save to store results for later reference or comparison

  • Combine with ask_noel - after getting results, ask Noel to interpret them in context of current market conditions

  • Use miroshark_stop if a simulation is taking too long or you want to start a new scenario

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