Stop Using LangChain
LangChain alternative with 2 dependencies and async-native architecture from the start.
Minimalistic, provider-agnostic, AI python library
LangChain alternative, but LiteLLM and LlamaIndex already cover this.
Python developers building RAG systems
LiteLLM · LlamaIndex · Haystack
The library intentionally exposes only the functionality common across providers to avoid provider-specific parameters.
Libraries like LangChain provide many integrations but often rely on many abstractions, heavy use of kwargs, and complex code that can be difficult to customize.
Features: - Sync and async APIs - LLM calls: invoke and stream (temperature, reasoning level) - Response metadata: answer, token usage, stop reason - RAG documents: retrieval, reranking, embeddings - Chat history: conversation store - Common error handling across providers - Providers: OpenAI, Anthropic, Google, AWS
Retry logic is left to the user (see README). Agent functionality is not supported yet.
LangChain alternative with 2 dependencies and async-native architecture from the start.
Lifecycle-aware security pipeline, not point tools—shared context from ingress through output.
Yet another LLM orchestration layer over LiteLLM + Pydantic when DSPy and LangChain dominate.
RAG library with serve command, but Langchain, LlamaIndex, and Verba already dominate.
Yet another LLM SDK when async-openai and llm-chain already exist.
Spec compiler approach is interesting but GitHub Spec Kit and Kiro already cover this.