Coding agents · AI agent
Twinny: review, pricing, security and alternatives
Twinny is an open-source AI agent published by twinnydotdev, in the coding agents family. Twinny brings a coding agent, completion, chat, edits and reviews to VS Code using local or hosted language models. Its repository has 3,678 stars and its last commit is from October 16, 2026.
| Adoption (stars) | 19/40 | |
|---|---|---|
| Activity (commits, releases) | 25/30 | |
| Framework (licence, control, models) | 25/30 |
What Twinny does
Twinny belongs to the coding agents family: they read a repository, edit files, run the tests and fix things until everything passes, in a terminal or an editor. On our grid it ranks #26 of the 74 projects of this family listed in the directory.
You use it from: code editor (IDE). Usable models: local models (Ollama, llama.cpp, vLLM…). It can run on your own machine or server.
Who is Twinny for?
It is aimed at developers and technical teams who want to hand off part of the work on their codebase while keeping the final review. Typical tasks people give it:
- fix a bug from an issue or an error message
- write or extend tests
- migrate code to a new library version
- explain an unfamiliar codebase
It is a good candidate if you want the AI right inside your code editor, your data must stay on your machines, and you want the option of a local model, with no API fees.
Access and human control
According to its documentation, Twinny can:
- read and edit files
We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.
Twinny pricing
The code is free, under the MIT licence.
Getting started with Twinny
- Install it: the project ships as a Node.js package (install with
npm, or run directly withnpx). The exact command is in the repository README. - Pick the model: an API key from a provider, or a local model if your machine has enough graphics memory (the VRAM calculator tells you).
- Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
- Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.
Security: risk level
A coding agent runs commands on your machine: work on a Git branch, review the changes and isolate it in a container for long tasks.
Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.
Project activity and releases
Created on August 21, 2023, the twinnydotdev/twinny repository has 3,678 stars, 237 forks and 54 open issues, with 1,525 commits in total. It received changes this month.
Over the last 52 weeks: 117 commits, an average of 2.3 per week.
59 releases in total; 2 in the last twelve months. Latest release: v4.0.20, on September 22, 2026.
Twinny vs other coding agents
GitHub stars of the best-rated open-source projects in the coding agents family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.
Twinny and other coding agents at a glance
The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.
| Agent | Model | Score | Stars or entry price | Self-hostable | Human approval |
|---|---|---|---|---|---|
| Twinny | open source | 69 | 3.7K ★ | yes | – |
| OpenCode | open source | 95 | 221K ★ | yes | – |
| Cline | open source | 94 | 70.8K ★ | yes | yes |
| OpenHands | open source | 91 | 91.7K ★ | yes | – |
| Codex | open source | 89 | 130.8K ★ | yes | yes |
| Open Interpreter | open source | 89 | 68.7K ★ | yes | – |
| Qwen Code | open source | 89 | 28.7K ★ | yes | yes |
| Kilo Code | open source | 89 | 27.7K ★ | yes | yes |
How we rated it
Score: 69/100, computed only from public, verifiable data, with no subjective testing or partnership:
- Adoption 19/40: 3,678 stars on a log scale (40 points from about 200,000).
- Activity 25/30: last commit less than a month ago, 2 releases in six months, 117 commits in a year.
- Framework 25/30: licence, default human approval, self-hosting, model choice and documentation site.
The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.
Verdict
Twinny is a serious option; check the limitations below before granting it access.
Strengths
- Very active development
- Can run on your side: data under control
- Free choice of model
- Permissive licence (MIT)
Limitations
- Tied to a single model provider (local models (Ollama, llama.cpp, vLLM…))
Alternatives
Learn more
Frequently asked questions
Is Twinny free?
The software is free (MIT licence). You still pay for the model calls, unless you use a local model.
Is Twinny safe?
Our risk level: low. Give it minimum rights and approve irreversible actions.
Which models does Twinny use?
According to its documentation: local models (Ollama, llama.cpp, vLLM…).
Can Twinny be installed on your own server?
Yes, from the twinnydotdev/twinny repository. Data then stays with you, but security and updates are your job.
What are the alternatives to Twinny?
In the same family: OpenCode, Cline, OpenHands, and Codex.
Twinny or OpenCode: what is the difference?
Both belong to the coding agents family. Twinny has 3.7K stars against 221K for OpenCode. The right choice mostly depends on the access you are willing to give it.
Related articles
- Open source or proprietary AI agent: how to decideOctober 25, 2026
- AI agent vs. chatbot: what's the difference?October 24, 2026
- Autonomous AI agents: how much should you let them do?October 12, 2026
- AI agent memory: how agents remember (and forget)October 11, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score