MiroFish

A swarm intelligence prediction engine. Upload documents describing any scenario, and MiroFish simulates thousands of AI agents reacting on social media to predict how events will unfold.

Live: synth.scty.org

Fork of 666ghj/MiroFish — fully translated to English, local graph storage with embedded KuzuDB by default, Claude/Codex CLI support added.

What it does

1. Upload reality seeds — PDFs, markdown, or text files (news articles, policy drafts, financial reports, anything)
2. Describe what to predict — natural language prompt (e.g., "Predict public reaction to this policy over 60 days")
3. MiroFish builds a world — extracts entities and relationships into a knowledge graph, generates AI agent personas with distinct personalities and opinions
4. Agents simulate social media — dual-platform simulation (Twitter + Reddit) where agents post, reply, like, argue, and follow each other
5. Get a prediction report — AI analyzes all simulation data and produces findings. Chat with the report agent or interview individual simulated agents.

Changes from upstream

Area	Upstream	This fork
Language	Chinese UI + prompts	Full English (60+ files translated)
LLM providers	Alibaba Qwen only	OpenAI, Anthropic, Claude CLI, Codex CLI
Graph database	Hosted graph service	Local KuzuDB (embedded, free)
Entity extraction	Managed extraction pipeline	LLM-based extraction (uses your own model)
Auth	Requires API keys	Can use Claude Code or Codex CLI subscriptions (no separate API cost)

Quick start

Prerequisites

• Node.js 18+
• Python 3.11-3.12
• uv (Python package manager)

Setup

cp .env.example .env
# Edit .env — pick your LLM provider (see below)
npm run setup:all
npm run dev

• Frontend: http://localhost:3000
• Backend API: http://localhost:5001

Docker

cp .env.example .env
docker compose up -d --build

Docker builds the Vue frontend, serves it from the Flask app, and exposes the combined app on port 5001 inside the container.

LLM providers

Set LLM_PROVIDER in .env:

Provider	Config	Cost
claude-cli	Just set LLM_PROVIDER=claude-cli	Uses your Claude Code subscription
codex-cli	Just set LLM_PROVIDER=codex-cli	Uses your Codex CLI subscription
openai	Set LLM_API_KEY + LLM_MODEL_NAME	Pay-per-token
anthropic	Set LLM_API_KEY + LLM_MODEL_NAME	Pay-per-token

# Example: use Codex CLI (no API key needed)
LLM_PROVIDER=codex-cli

# Example: use OpenAI API
LLM_PROVIDER=openai
LLM_API_KEY=sk-...
LLM_MODEL_NAME=gpt-4o-mini

Using Codex CLI

For Docker deployments, MiroFish now routes Codex CLI traffic through a local OpenAI-compatible sidecar service at codex-proxy. The mirofish container talks to http://codex-proxy:11435/v1, and the proxy translates each /v1/chat/completions request into codex exec --skip-git-repo-check with bounded concurrency.

docker-compose.yml already wires this up for the Docker stack:

• mirofish runs with LLM_PROVIDER=openai
• LLM_BASE_URL=http://codex-proxy:11435/v1
• LLM_API_KEY=codex
• LLM_MODEL_NAME=codex
• codex-proxy uses CODEX_PROXY_WORKERS=4 by default

To use it:

cp .env.example .env
docker compose up -d --build codex-proxy
curl http://localhost:11435/health
docker compose up -d

The proxy container mounts the host Codex binary and ~/.codex auth state, so make sure Codex CLI is installed and authenticated on the host first. The legacy LLM_PROVIDER=codex-cli path remains available outside Docker as a fallback, but the proxy is the recommended Docker path because it queues requests instead of cold-starting an unbounded number of CLI subprocesses.

Architecture

frontend/          Vue 3 + Vite + D3.js (graph visualization)
backend/
  app/
    api/           Thin Flask REST endpoints (graph, simulation, report)
    core/          Workbench session, session registry, resource loader, tasks
    resources/     Adapters for projects, documents, Kuzu, simulations, reports
    tools/         Composable workbench operations (ingest, build, prepare, run, report)
    services/
      graph_storage.py     GraphStorage abstraction + KuzuDB/JSON backends
      graph_db.py          Compatibility facade over per-graph storage backends
      entity_extractor.py  LLM-based entity/relationship extraction
      graph_builder.py     Ontology → graph pipeline
      simulation_runner.py OASIS multi-agent simulation (subprocess)
      report_agent.py      ReACT agent with tool-calling for reports
      graph_tools.py       Search, interview, and analysis tools
    utils/
      llm_client.py        Multi-provider LLM client (OpenAI/Anthropic/CLI)
  scripts/         OASIS simulation runner scripts (Twitter + Reddit)

Workbench session metadata is persisted under backend/uploads/workbench_sessions/, and long-running task state is persisted under backend/uploads/tasks/.

The backend is being refactored toward a pi-style shape: one workbench session core, pluggable resource adapters, composable tools, and thin API shells.

How the pipeline works

Document upload → LLM ontology extraction → Knowledge graph (GraphStorage → KuzuDB by default)
    → Entity filtering → Agent persona generation (LLM)
    → OASIS dual-platform simulation (Twitter + Reddit subprocess)
    → Graph memory updates → Report generation (ReACT agent)
    → Interactive chat with report agent or individual agents

Acknowledgments

• MiroFish by 666ghj — original project
• OASIS by CAMEL-AI — multi-agent social simulation framework
• KuzuDB — embedded graph database

License

AGPL-3.0 License

AGPL-3.0