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MiroFish
MiroFish turns uploaded material and a scenario question into a simulation populated by AI-generated agents.
Its workflow builds a knowledge graph, creates agent profiles, runs social interactions, and produces a report you can question further. The repository includes a web interface, a Python backend, and source and Docker setup paths. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A simulation built from documents
Supply seed material and describe the scenario. MiroFish extracts entities and relationships, generates personas, and uses the OASIS engine to simulate social activity among those agents.
Why it stands out
Follow agents as well as the report
The workflow includes Twitter-like and Reddit-like simulated environments. After a run, you can ask questions of individual simulated agents or the ReportAgent, rather than stopping at one written answer.
Availability
Public code with service dependencies
The repository declares an AGPL-3.0 license and documents source and Docker deployment. Its current configuration requires a model API and Zep Cloud; running the application on your machine does not make the whole workflow local.
Why it matters
What makes it useful
A writer exploring a story ending can give the system the existing story and a question about what happens next. MiroFish generates character profiles and interactions before producing its report. This lets the writer explore a scenario through several generated perspectives and then ask a simulated character about its response. Those responses are part of the constructed scenario, not observations of real people.
What to know
Where it fits
The documented sequence is graph building, environment setup, simulation, report generation, and further conversation. The backend accepts PDF, Markdown, and plain-text uploads. Developers can run the frontend and backend from source or use the documented Docker path. The README also links a prepared online demonstration, which provides a way to look at the workflow before setting up your own run.
Notable points
What stands out
The ReportAgent has tools for searching the graph and interviewing simulated agents. Its report can therefore draw on relationships in the supplied material and responses from the agents, rather than only summarizing the original upload. The simulation also has configured rounds and platform actions, such as posts, comments, and likes. These are actions inside the simulated environments.
Before using
What to review
Source setup specifies Node.js 18 or newer, Python 3.11 or 3.12, and uv. A model API compatible with the OpenAI SDK and a Zep Cloud API key are required.
The current configuration explicitly rejects a custom Zep API URL. Seed text and graph information are processed through external services, so check the chosen model provider and Zep Cloud before uploading material.
The README warns about high model consumption and suggests starting with fewer than 40 simulation rounds. Run size and provider settings matter when estimating service usage.
The project markets itself as a prediction engine. A generated report does not establish that the simulated population represents real people or that its outcome will occur. Read the seed material, persona choices, and simulation settings alongside the output.
Reader fit
Who may find it relevant
Relevant to writers exploring fictional scenarios and builders studying how documents, memory, agent profiles, and social-simulation rules fit together. A reader can inspect the prepared demo and public code; running a new scenario requires managing the application and its service credentials.
Editorial note
Why LifeHubber lists it
We list MiroFish because its workflow makes the construction of an AI scenario visible: source material becomes relationships, those relationships inform agents, and their simulated activity feeds a report. That helps a reader ask where an apparent conclusion came from. Before taking a polished report at face value, a builder can look at the population and rules that produced it, rather than treating many generated voices as independent evidence.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Check the claims in a generated report.
A simulated conversation can suggest questions without establishing what happened or will happen. Continue with a practical method for checking an AI answer against its sources.
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