8 Best rigging Alternatives in 2026 (Open Source)

rigging — Lightweight LLM Interaction Framework. Unlike heavyweight frameworks like LangChain, Rigging combines Pydantic structured parsing with unstructured text seamlessly, using LiteLLM connection strings for zero-config model switching — designed for production simplicity over framework complexity

These 8 open-source tools do the same job. They are ordered by how closely they match rigging, with live GitHub data so you can see which projects are actively maintained.

1. 1. Pydantic AI

   AI Agent Framework, the Pydantic way

   What sets it apart: Unlike LangChain (heavy abstraction, runtime errors) or CrewAI (multi-agent focus), Pydantic AI is built by the Pydantic team to deliver FastAPI-level type safety with dependency injection, durable execution, and composable capabilities — catching errors at write-time rather than runtime.

   Best for: Python developers who value type safety and want a FastAPI-like experience for building production AI agents; Teams already using Pydantic who want structured, validated LLM outputs with minimal boilerplate

   ⭐ 20.3krigging vs Pydantic AI →Full profile →

2. 2. Instructor

   structured outputs for llms

   What sets it apart: Simplest path from LLM text to validated Pydantic objects with automatic retries — vs raw JSON mode or Guardrails (heavier, validator-focused)

   Best for: Extracting structured JSON data from any LLM reliably; Building type-safe LLM integrations with validation; Replacing manual JSON parsing and error handling

   ⭐ 14.0krigging vs Instructor →Full profile →

3. 3. llama-cpp-agent

   The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured ou

   What sets it apart: Enabled function calling and structured output from any local LLM through grammar-based guided sampling, making capabilities previously exclusive to fine-tuned models available to all llama.cpp-compatible models — now deprecated

   Best for: Getting structured output from local LLMs without fine-tuning; Building function-calling agents with open-source models locally

   ⭐ 659rigging vs llama-cpp-agent →Full profile →

4. 4. LLMFlows

   LLMFlows - Simple, Explicit and Transparent LLM Apps

   What sets it apart: Explicit, transparent LLM pipeline framework with full traceability — no hidden prompts or calls, complete visibility into every component

   Best for: transparent-llm-app-development; building-traceable-llm-pipelines; learning-llm-orchestration

   ⭐ 708rigging vs LLMFlows →Full profile →

5. 5. Lagent

   A lightweight framework for building LLM-based agents

   What sets it apart: vs LangChain/CrewAI: PyTorch-inspired design with intuitive layer composition, dual sync/async interfaces, and built-in session-isolated memory for concurrent agent workloads

   Best for: Multi-agent workflows with iterative self-refinement; Research with InternLM/Qwen models and custom agents

   ⭐ 2.3krigging vs Lagent →Full profile →

6. 6. Langroid

   Harness LLMs with Multi-Agent Programming

   What sets it apart: vs LangChain/CrewAI: Actor-model-inspired multi-agent framework from CMU/UW-Madison researchers, praised for intuitive Agent-Task abstractions, lightweight design, and production use at companies like Nullify - no dependency on LangChain

   Best for: Building multi-agent systems with clean Agent-Task abstractions; Teams wanting an intuitive, lightweight alternative to LangChain; Research applications with complex agent collaboration patterns

   ⭐ 4.1krigging vs Langroid →Full profile →

7. 7. simpleaichat

   Python package for easily interfacing with chat apps, with robust features and minimal code complexity.

   What sets it apart: vs LangChain / LlamaIndex: radically minimal ChatGPT wrapper optimized for token efficiency — create chat sessions in 2 lines of code, with async multi-session support and no framework overhead

   Best for: Developers wanting the simplest possible ChatGPT integration in Python; Cost-conscious applications needing token-optimized workflows; Building async multi-chat applications with minimal code

   ⭐ 3.5krigging vs simpleaichat →Full profile →

8. 8. LLM

   Access large language models from the command-line

   What sets it apart: vs direct API calls: Swiss-army-knife CLI that unifies 100+ LLMs behind one command, with automatic SQLite logging, embeddings, schemas, and a rich plugin ecosystem

   Best for: Power users who want LLM access from the terminal; Quick prototyping and experimentation with multiple LLM providers; Building CLI-based LLM workflows with conversation history

   ⭐ 12.6krigging vs LLM →Full profile →