tmc/langchaingo

LangChain for Go, the easiest way to write LLM-based programs in Go

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Summary Information

Updated 52 minutes ago
Added to GitGenius on September 6th, 2026
Created on February 18th, 2023
Open Issues & Pull Requests: 413 (+0)
GitHub issues: Enabled
Number of forks: 1,146
Total Stargazers: 9,670 (+0)
Total Subscribers: 81 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.7 days
Mean response time: 57.8 days
90th percentile: 188.8 days
Tracked items: 133

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 94% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 5% of issues opened in the past year have been closed. Three people close 71% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 134
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 560 days
Stale 30+ days: 132
Stale 90+ days: 129

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

LangChain Go is a Go language implementation of LangChain that enables developers to build applications with large language models through composable components.

The tool addresses the need to work with LLMs in Go by providing a language-native port of the LangChain framework. It allows developers to compose LLM-based functionality through modular building blocks, supporting integration with various models and services. The approach mirrors LangChain's design philosophy of breaking down complex LLM applications into reusable, chainable components that can be combined to create sophisticated workflows.

Developers building LLM applications in Go should consider this tool if they want to leverage the LangChain ecosystem without leaving the Go language. It suits projects that need to integrate language models into Go backends, microservices, or standalone applications. The project provides documentation and an API reference alongside example implementations demonstrating common patterns like working with specific model providers and building chat applications.

The project is actively transitioning toward community-driven development, with maintainers explicitly seeking additional contributors and maintainers through its Discord community. The codebase maintains examples that demonstrate practical usage patterns, and the project has received attention from the broader Go and LLM communities through published articles and tutorials covering integration with different model providers.