langchain-ai/langgraph alternatives

choose it if you want an Apache-2.0 Python multi-agent harness with its own orchestration model.

Multi-Agent Harness for Production AI

choose it if you want a Python-usable agentic framework backed by a Rust core for performance.

GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.

choose it if you prefer OpenAI's agent/handoff model over a graph state machine.

A lightweight, powerful framework for multi-agent workflows

choose it if you prefer Google's code-first agent development and deployment toolkit.

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

choose it if your app is on .NET/C# and you want a Microsoft-maintained agent/LLM SDK.

Integrate cutting-edge LLM technology quickly and easily into your apps

choose it if you want Microsoft's cross-language agent orchestration instead of LangGraph.

A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

choose it if you want a TypeScript-native agent engineering framework.

AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework

choose it if you need to orchestrate external agent harnesses (Claude Code, Codex, etc.) rather than build graph state machines.

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.

choose it if you want MCP as the core protocol for agent workflow patterns.

Build effective agents using Model Context Protocol and simple workflow patterns

choose it if you want a polyglot Python/TypeScript SDK for building custom agent harnesses.

Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.

choose it if you need a Java framework for distributed, long-running agents.

Build distributed, production-grade, long-running agents.

choose it if you want a TypeScript harness runtime that turns LLM calls into working agents.

The open-source agent harness - the runtime layer that turns an LLM into a working agent.

choose it if you want an event-driven agent framework written in Go.

Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade observability.

choose it if you need event-driven multi-agent systems integrated with message brokers.

An event-driven framework designed to build and orchestrate multi-agent AI systems. It enables seamless integration of AI agents with real-world data sources and systems, facilitating complex, multi-step workflows.

a Java/enterprise visual agent workflow platform, not a code-embeddable library.

Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.

an older standalone agent management platform, not an embeddable framework.

Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.

a ready-made memory-first agent harness, not a general-purpose orchestration library.

The memory-first, self-improving agent harness built on EverOS, with MiroThinker-powered deep research and reasoning.

a visual no-code agent/workflow builder, not a library you embed.

Langflow is a powerful tool for building and deploying AI-powered agents and workflows.