SylphAI-Inc/AdalFlow

Health v2 · maintenance only

Adoption not part of the score

4211 stars · 387 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

AdalFlow is a PyTorch-like Python library for building and auto-optimizing LLM application workflows such as chatbots, RAG pipelines, and agents. It provides composable components (generators, embedders, retrievers, tools) plus trainers and optimizers that tune prompts and few-shot demos automatically.

Use cases

• build a RAG pipeline in python
• auto-optimize llm prompts
• build a chatbot with tool calling
• create an ai agent framework
• optimize few-shot examples for llm tasks
• evaluate llm applications
• build question answering over documents

When to choose

• you want a code-first, PyTorch-like framework for LLM workflows
• you need automatic prompt or few-shot demo optimization
• you're building RAG, chatbots, or agents in Python with multi-provider LLM support
• you want built-in tracing, evaluation, and training loops for LLM pipelines

When to avoid

• you need a no-code or UI-driven LLM builder
• you want a lightweight single-call SDK rather than a full workflow framework
• your stack is not Python
• you need production orchestration features like queues or scaling out of the box

Facets

library · maturity active

agent-framework rag llm-inference prompt-engineering machine-learning nlp sdk large-language-models machine-learning developer-tools python cross-platform llm-workflows auto-optimization text-grad retrievers pytorch-like few-shot-optimization tracing structured-output retrieval-augmented-generation ai-agents natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="SylphAI-Inc/AdalFlow")