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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🪿 LinGoose is a Go framework for building awesome AI/LLM applications.
| Date | Stars |
|---|---|
| 2026-07-31 | 835 |
| 2026-08-01 | 835 |
| 2026-08-06 | 835 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
> [!IMPORTANT]
> **Hey there, LinGoose friend 🪿**
>
> First of all, thank you for being here. LinGoose has been a fun journey and I am proud of what it became.
>
> The honest news: LinGoose is no longer under active development. Life got busy, the AI world moved fast, and I found myself wanting to build something new rather than patch something old.
>
> That something new is [Phero](https://github.com/henomis/phero) 🐜, a Go framework built from the ground up for multi-agent AI systems. Same values, better foundation, a lot more ambition.
>
> LinGoose is not going anywhere. It will stay here, stable and available. But if you are starting something new, come join the ant colony.
# 🪿 LinGoose [](https://github.com/henomis/lingoose/actions/workflows/checks.yml) [](https://godoc.org/github.com/henomis/lingoose) [](https://goreportcard.com/report/github.com/henomis/lingoose) [](https://github.com/henomis/lingoose/releases)
## What is LinGoose?
[LinGoose](https://github.com/henomis/lingoose) is a Go framework for building awesome AI/LLM applications.<br/>
- **LinGoose is modular** — You can import only the modules you need to build your application.
- **LinGoose is an abstraction of features** — You can choose your preferred implementation of a feature and/or create your own.
- **LinGoose is a complete solution** — You can use LinGoose to build your AI/LLM application from the ground up.
> **Did you know?** A goose 🪿 fills its car 🚗 with goose-line ⛽!
🚀 Support the project by starring ⭐ the repository on [GitHub](https://github.com/henomis/lingoose) and sharing it with your friends!
## Quick start
1. [Initialise a new go module](https://golang.org/doc/tutorial/create-module)
```sh
mkdir example
cd example
go mod init example
```
2. Create your first LinGoose application
```go
package main
import (
"context"
"fmt"
"github.com/henomis/lingoose/llm/openai"
"github.com/henomis/lingoose/thread"
)
func main() {
myThread := thread.New().AddMessage(
thread.NewUserMessage().AddContent(
thread.NewTextContent("Tell me a joke about geese"),
),
)
err := openai.New().Generate(context.Background(), myThread)
if err != nil {
panic(err)
}
fmt.Println(myThread)
}
```
3. Install the Go dependencies
```sh
go mod tidy
```
4. Start the example application
```sh
export OPENAI_API_KEY=your-api-key
go run .
A goose fills its car with goose-line!
```
## Reporting Issues
If you think you've found a bug, or something isn't behaving the way you think it should, please raise an [issue](https://github.com/henomis/lingoose/issues) on GitHub.
## Contributing
We welcome contributions, Read our [Contribution Guidelines](https://github.com/henomis/lingoose/blob/main/CONTRIBUTING.md) to learn more about contributing to **LinGoose**
## Blog posts and articles
- [Anthropic's Claude Integration with Go and Lingoose](https://simonevellei.com/blog/posts/anthropic-claude-integration-with-go-and-lingoose/)
- [Empowering Go: unveiling the synergy of AI and Q&A pipelines](https://simonevellei.com/blog/posts/empowering-go-unveiling-the-synergy-of-ai-and-qa-pipelines/)
- [Leveraging Go and Redis for Efficient Retrieval Augmented Generation](https://simonevellei.com/blog/posts/leveraging-go-and-redis-for-efficient-retrieval-augmented-generation/)
## Connect with the author
[](https://twitter.com/simonevellei) [](https://github.com/henomis) [
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:87779c4090c53742, topic:llm
matched fp:87779c4090c53742, topic:embeddings
matched fp:87779c4090c53742, topic:prompt
matched fp:87779c4090c53742, topic:chatgpt