Which is more popular, LiteLLM or WFGY?

LiteLLM has more GitHub stars (60,079 vs 1,791).

Which is more actively developed, LiteLLM or WFGY?

LiteLLM had more commits in the last 90 days (13,238 vs 358).

Should I use LiteLLM or WFGY?

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

LiteLLM vs WFGY

Side-by-side comparison of two AI agent tools

Short answer

LiteLLM is growing faster: +2,982 GitHub stars in the last 30 days vs +17 for WFGY.
Pick LiteLLM for: open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface. Pick WFGY for: wFGY is an open-source AI Troubleshooting Atlas for RAG, agents, and real-world AI workflows.

From GitHub data refreshed daily.

LiteLLMfree

Open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface

WFGYfree

WFGY is an open-source AI Troubleshooting Atlas for RAG, agents, and real-world AI workflows. Includes the 16-problem map, Global Debug Card, and WFGY 3.0. ⭐ Star to help more builders find this repo.

Metrics

	LiteLLM	WFGY
Stars	60.1k	1.8k
Star velocity /mo	3.0k	16.894736842105264
Commits (90d)	13.2k	358
Releases (6m)	10	2
Overall score	0.9321427193766948	0.5812544173052958

Pros

+统一API接口设计，一套代码兼容100多个不同的LLM提供商，大幅简化多模型切换和对比测试
+内置企业级功能如成本追踪、负载均衡、安全防护栏，为生产环境提供完整的AI治理解决方案
+既提供Python SDK又提供独立的代理服务器部署模式，适合不同规模和架构的项目需求

+专门针对AI系统设计的故障排除框架，覆盖RAG、代理和工作流等核心场景
+开源项目拥有活跃社区支持，GitHub上已获得1684颗星的认可
+提供结构化的问题图和全局调试卡，将复杂的AI调试过程系统化和标准化

Cons

-作为中间层抽象，可能无法完全利用某些模型提供商的独特功能和高级参数配置
-依赖网络连接和第三方API稳定性，增加了系统的复杂度和潜在故障点
-对于简单的单模型应用场景可能存在过度设计，增加不必要的依赖和学习成本

-专业性较强，需要一定的AI系统基础知识才能充分利用
-针对性工具，主要适用于AI相关问题，不适合通用软件调试
-文档和学习资料可能需要时间消化理解

Use Cases

•AI应用开发中需要对比测试多个LLM模型性能，快速切换不同提供商而无需重写代码
•企业级AI服务需要统一的成本监控、访问控制和负载均衡管理多个模型调用
•构建AI代理或聊天机器人时需要根据用户需求和成本考虑动态选择最适合的模型

•RAG系统性能调优和准确性问题诊断，如检索质量差、答案不准确等问题排查
•AI代理行为异常调试，包括决策逻辑错误、工具调用失败等问题定位
•复杂AI工作流故障排除，如多步骤管道中断、数据流问题和集成错误分析

View LiteLLM Details View WFGY Details

FAQ

Which is more popular, LiteLLM or WFGY?: LiteLLM has more GitHub stars (60,079 vs 1,791).
Which is more actively developed, LiteLLM or WFGY?: LiteLLM had more commits in the last 90 days (13,238 vs 358).
Should I use LiteLLM or WFGY?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.