LiteLLM vs text-extract-api

Side-by-side comparison of two AI agent tools

Short answer

  • text-extract-api has had no commit in 9 months; LiteLLM is actively maintained (13,191 commits in the last 90 days).
  • LiteLLM is growing faster: +2,991 GitHub stars in the last 30 days vs +17 for text-extract-api.
  • Pick LiteLLM for: open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface. Pick text-extract-api for: local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON.

From GitHub data refreshed daily.

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

text-extract-apiopen-source

Local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON

Metrics

LiteLLMtext-extract-api
Stars60.0k3.2k
Star velocity /mo3.0k16.825396825396826
Commits (90d)13.2k0
Releases (6m)100
Overall score0.93721791024362280.2208723259857172

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
  • +完全本地化处理，无外部依赖，确保数据隐私和安全性
  • +支持多种先进OCR策略（LLaMA Vision、EasyOCR等），识别精度极高
  • +集成分布式队列和缓存机制，支持大规模文档批量处理

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
  • -需要安装多个依赖组件（Docker、Ollama），初始设置较为复杂
  • -本地运行PyTorch模型需要较大计算资源和存储空间

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
  • •医疗机构将MRI报告、病历等医疗文档转换为结构化数据
  • •企业财务部门处理发票、合同等文档并自动移除敏感信息
  • •法律机构批量数字化和分析大量合规文档或法律条文
View LiteLLM DetailsView text-extract-api Details

FAQ

Which is more popular, LiteLLM or text-extract-api?
LiteLLM has more GitHub stars (60,036 vs 3,183).
Which is more actively developed, LiteLLM or text-extract-api?
LiteLLM had more commits in the last 90 days (13,191 vs 0).
Should I use LiteLLM or text-extract-api?
Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.