agency
langchaingo
| agency | langchaingo | |
|---|---|---|
| 3 | 8 | |
| 379 | 3,116 | |
| 4.2% | - | |
| 8.3 | 9.8 | |
| 27 days ago | 7 days ago | |
| Go | Go | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
agency
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Agency: Pure Go LangChain Alternative
I would, at the very least, wrap the errors being returned inside the process function https://github.com/neurocult/agency/blob/14b14e50a7570189388...
Or, I suppose the user must handle exception behavior in their custom `OperationHandler`
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🕵 Agency: The Go Way to AI. Part 1
Recognizing the need for a Go-friendly tool that’s simple yet powerful, we developed Agency. This Go library, designed with a clean approach, matches Go's strengths in a static type system and high performance. It's our answer to bringing easy-to-use, efficient AI capabilities to Go developers.
langchaingo
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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Build a Serverless GenAI solution with Lambda, DynamoDB, LangChain and Amazon Bedrock
This use-case here is a similar one - a chat application. I will switch back to implementing things in Go using langchaingo (I used Python for the previous one) and continue to use Amazon Bedrock. But there are few unique things you can explore in this blog post:
- LangChain for Go, the easiest way to write LLM-based programs in Go
- Langchaingo – LangChain in Idiomatic Go
- Agency: Pure Go LangChain Alternative
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Building LangChain applications with Amazon Bedrock and Go - An introduction
langchaingo is the LangChain implementation for the Go programming language. This blog post covers how to extend langchaingo to use foundation model from Amazon Bedrock.
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Zep: A long-term memory store for LLM apps, written in Go
Langchain Go is being actively developed https://github.com/tmc/langchaingo
What are some alternatives?
langroid - Harness LLMs with Multi-Agent Programming
yao - :rocket: A performance app engine to create web services and applications in minutes.Suitable for AI, IoT, Industrial Internet, Connected Vehicles, DevOps, Energy, Finance and many other use-cases.
langchain - 🦜🔗 Build context-aware reasoning applications
go-openai - OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
humanscript - A truly natural scripting language
zep - Zep: Long-Term Memory for AI Assistants.
yay - 🐹 interact with openai api from command line
TaskEaseGPT - (WIP) A user-friendly, AI-powered task manager emphasizing efficient work over planning. Streamlines workflow with intelligent task generation & execution. Boost your productivity today!
go-gpt3 - OpenAI ChatGPT, GPT-3, DALL·E, Whisper API wrapper for Go [Moved to: https://github.com/sashabaranov/go-openai]
langchaingo-amazon-bedrock-llm - Amazon Bedrock extension for langchaingo