siyuan-note/siyuan
siyuan
A privacy-first, self-hosted, fully open source personal knowledge management software, written in typescript and golang.
Usage guide
siyuan is an open-source project around ai-agent, digital-garden, electron with 44,454 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 TypeScript, 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 siyuan for TypeScript AI workflows.
- Comparing a GitHub project with 44,454 stars and current repository activity.
Pros
- siyuan has visible GitHub traction with 44,454 stars. Topics: ai-agent, digital-garden, electron.
- 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
siyuan 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.
siyuan architecture preview
siyuan's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Files / repository context, External tool adapters, and returns Assistant response / action result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://b3log.org/siyuan
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
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
External tool adapters
Tool adapters let the runtime act outside the model through External tool adapters.
tool signal
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
DPA
Siyuan notes tutorial
16,183 views ยท 2023-10-14
Install tutorial
Before you install
- Node.js and the package manager used by the project
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
siyuan 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/siyuan-note/siyuan.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ docker run -d \Adoption 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 privacy-first, self-hosted, fully open source personal knowledge man
This is one of the documented reasons to evaluate siyuan before choosing a stack.
Focus area: ai-agent
This is one of the documented reasons to evaluate siyuan before choosing a stack.
AI Agents project comparison
Compare siyuan with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official siyuan 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 siyuan example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
What is siyuan?
siyuan is an open-source ai agents project. A privacy-first, self-hosted, fully open source personal knowledge management software, written in typescript and golang.
How do I install siyuan?
Start with the official README. The first detected setup step is: git clone https://github.com/siyuan-note/siyuan.git.
Is siyuan beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can siyuan 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 siyuan 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 siyuan?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.