MiroFish Review

★6.2/10

Run multi-agent simulations from seed material and get a prediction report.

Review updated June 2026 • By The AI Way Editorial • Tested 311+ tools across the site • 5 min read

MiroFish AI Agents Autonomous Agents Open Source RAG Sandbox Self-Hosted Freemium

Read this first

Do not treat MiroFish's prediction report as a forecast you can act on without validation. It is a simulation engine, and public materials do not prove accuracy on real decisions.

Our Verdict

MiroFish earns attention because it has a sharp, weirdly memorable job: turn seed material into a multi-agent rehearsal of how a group, market, audience, or fictional world might react. The value is not ordinary research summarization; it is scenario stress-testing with agents, memory, GraphRAG, reports, and follow-up interaction. The cost is trust and setup: the online edition is still waitlist-framed, and the open-source route expects local services, credentials, and judgment about simulation limits.

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Free to start, then pay when the limits stop you.

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MiroFish official website and landing page preview

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check_circle Pros

✓Turns a vague prediction question into a staged run: graph build, environment setup, simulation, report, and follow-up chat.
✓Handles both serious and creative scenarios, from policy opinion and crisis PR to finance signals and novel endings.
✓Gives users an interactive simulated world after the report, not just a static answer.
✓Strong GitHub traction gives technical users more code, issues, forks, and history to inspect before trusting a young simulator.

cancel Cons

✕The hosted online edition is not positioned as generally launched yet; the page asks users to join a waitlist.
✕Source deployment is technical and needs Node.js, Python, uv, LLM credentials, and Zep credentials.
✕Prediction quality is hard to verify from public materials, so high-stakes decisions still need outside evidence.
✕The category is unfamiliar; many users will not know to search for swarm-intelligence prediction until the use case is explained.

Should you use it?

Best for: Scenario rehearsal where a team wants to test how agents representing audiences, investors, citizens, or fictional characters might react to a proposed event.

Skip it if: Skip it if you need a proven forecasting vendor with published accuracy, clean billing, enterprise procurement, and a non-technical hosted app today.

Is it worth the price?

Freemium

There is no public plan table or paid tier to compare. The practical cost is the open-source setup plus model usage, and long simulations can consume heavily enough that early tests should stay under 40 rounds.

One thing to know before you start

Start with a low-stakes scenario where you already know the outcome or can compare against later public reaction. If MiroFish cannot produce useful pressure points there, do not trust it on finance, policy, or crisis decisions.

What people actually use it for

Stress-test a PR response

Seed the simulation with a crisis event, audience context, and proposed response, then inspect how virtual groups and KOL-style agents react.

Explore policy reaction risk

Feed in a policy draft or public issue brief, then use the report to spot opinion clusters, likely objections, and risk points before launch.

Rehearse market sentiment

Use financial signals or market narratives as seed material, then simulate investor sentiment shifts before choosing what to investigate deeper.

Deduce a fictional ending

Build a character world from a novel or script, then let the agents test relationship changes, plot branches, and possible endings.

What does MiroFish actually do?

MiroFish is not trying to be a normal answer engine. The product makes more sense when the user has a question that depends on many actors reacting to each other: citizens responding to a policy, investors processing a signal, viewers reacting to a brand crisis, or characters moving through a fictional world. The input is seed material plus a natural-language prediction need. The output is a report and a simulated world that can still be inspected after the run, including follow-up conversations with simulated individuals.

The product's strongest detail is the pipeline. MiroFish describes graph construction from seed material, individual and collective memory injection, GraphRAG construction, entity relationship extraction, persona generation, simulation parameter injection, parallel simulation, temporal memory updates, report generation, and post-run interaction. That stack gives it a real shape. It is not just asking an LLM to guess what happens next; it tries to create a world with agents, relationships, memory, and a report agent sitting on top of the result, so the user can inspect the simulated world instead of reading one unsupported paragraph.

The caution is just as important as the promise. The online edition is still waitlist-framed, while the open-source setup asks for Node.js 18+, Python 3.11 to 3.12, uv, LLM credentials, and Zep Cloud credentials. Long runs can burn enough model usage that first tests should stay under 40 simulation rounds. That makes MiroFish exciting for experiments and early adopters, but not something to describe as a finished forecasting platform with proven decision accuracy or enterprise-grade forecasting governance for accountable teams.

What you can do with it

Upload seed material and describe the prediction question in natural language.

Extract entities, relationships, individual memory, and collective memory before running the simulation.

Build a GraphRAG layer from the source material so agents can act from structured context.

Generate personas and environment settings for a simulated world.

Run a parallel multi-agent simulation with dynamic temporal memory updates.

Produce a prediction report through a ReportAgent after the simulated events run.

Chat with simulated individuals or continue questioning the ReportAgent after the run.

Use demo scenarios around public opinion events and fiction-ending prediction.

Technical details

license: AGPL-3.0.
platform: Open-source web app with separate frontend and backend services, plus source and Docker deployment paths.
deployment: Source deployment requires Node.js 18+, Python 3.11 to 3.12, uv, LLM API credentials, and Zep Cloud credentials; Docker Compose is another setup path.
memory_layer: Individual memory, collective memory, GraphRAG construction, and dynamic temporal memory updates sit inside the simulation pipeline.
api_available: Local backend service runs on port 5001; no separate public hosted API plan was found.
model_requirement: Uses an OpenAI SDK-compatible LLM endpoint; the sample configuration points to Qwen-plus through Alibaba Bailian and warns to start with fewer than 40 simulation rounds because consumption is high.
github_language_mix: Python and Vue are the main languages in the public repository.
simulation_pipeline: Graph construction, environment setup, dual-platform simulation, report generation, and deep interaction are the named stages.

Top Alternatives to MiroFish

If MiroFish is close but still misses the job, try one of these instead.

ChatGPT

Switch to ChatGPT when you only need a quick scenario analysis or structured argument, not a multi-agent simulated world.

Rose AI

Switch to Rose AI when the problem is financial data discovery and analysis rather than simulating market behavior from seed signals.

AI-Trader

Switch to AI-Trader when the job is trading execution, signals, or copy-trading automation instead of exploratory social or market simulation.

Key Questions

What does MiroFish do?

It turns seed material and a prediction request into a multi-agent simulation, then returns a prediction report and an interactive simulated world.

Is MiroFish available as a hosted product?

Not as a clearly launched public SaaS. The online edition is framed as upcoming, and the current site asks users to join a waitlist.

Can MiroFish be self-hosted?

Yes. It supports source deployment and Docker deployment, with frontend and backend services running locally.

What do I need to run MiroFish from source?

The source path needs Node.js 18+, Python 3.11 to 3.12, uv, an OpenAI SDK-compatible LLM endpoint, and a Zep Cloud API key.

Is MiroFish a finance prediction tool?

Only partly. Finance and investment are listed scenarios, but the same product also targets policy opinion, crisis PR, marketing, social-science research, and fiction simulation.