666ghj/MiroFish
MiroFish
A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物
Usage guide
MiroFish is an open-source project around agent-memory, financial-forecasting, future-prediction with 64,941 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating MiroFish for Python AI workflows.
- Comparing a GitHub project with 64,941 stars and current repository activity.
Pros
- MiroFish has visible GitHub traction with 64,941 stars. Topics: agent-memory, financial-forecasting, future-prediction.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the AGPL-3.0 terms fit your use case.
Production readiness
MiroFish should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
AGPL-3.0 is reported by GitHub; review the repository license before redistribution or commercial use.
MiroFish architecture preview
MiroFish's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, GitHub / Discord, and returns Assistant response / action result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://mirofish.ai
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub / Discord
Tool adapters let the runtime act outside the model through GitHub / Discord.
GitHub, Discord
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
John Forfar
PREDICT ANYTHING with 10,000+ Agents (even oil price based on next attack) - Mirofish Demo
40,106 views · 2026-03-18
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
MiroFish has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/666ghj/MiroFish.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ docker-compose.ymlAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
A Simple and Universal Swarm Intelligence Engine, Predicting Anything.
This is one of the documented reasons to evaluate MiroFish before choosing a stack.
Focus area: agent-memory
This is one of the documented reasons to evaluate MiroFish before choosing a stack.
AI Agents project comparison
Compare MiroFish with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official MiroFish setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If Docker startup fails, check port conflicts, image pull permissions, and volume paths first.
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented MiroFish example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
What is MiroFish?
MiroFish is an open-source ai agents project. A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物
How do I install MiroFish?
Start with the official README. The first detected setup step is: git clone https://github.com/666ghj/MiroFish.git.
Is MiroFish beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can MiroFish be used commercially?
GitHub detected the AGPL-3.0 repository license, which does not by itself confirm commercial permission. Review repository obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does MiroFish need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt MiroFish?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.