bytedance/deer-flow Alternatives

when you want a LangChain-native harness with the same sub-agent architecture and tighter LangChain ecosystem integration.

The batteries-included agent harness.

when you want a lightweight, one-line-install harness with self-evolving memory and multi-channel support, trading away sandboxes and heavier infrastructure for simplicity.

Open-source super AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, multi-channel. Lightweight, extensible, one-line install. (formerly chatgpt-on-wechat)

when you want a minimal, dependency-light multi-agent framework with OpenAI-native tooling, adding your own memory/sandbox layers.

A lightweight, powerful framework for multi-agent workflows

when you prefer Google's code-first agent toolkit with built-in evaluation and deployment controls.

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

when you want a memory-first, self-improving harness with deep-research capabilities and EverOS integration.

The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.

when you need graph-orchestrated workflows, Dify DSL transpilation, and protocol switching instead of a monolithic harness.

Graph-Orchestrated Agent Loop — a production-grade framework on LangGraph. Combine workflow graphs and agent loops, transpile Dify DSL to runnable code, swap wire protocols (Dify/OpenAI).

when you work in a JVM codebase and need distributed long-running agents in Java.

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

when you need a Rust-core agent framework for performance-critical, secure multi-agent workflows that still exposes Python bindings.

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.

when you prefer a TypeScript-based agent management platform for JS/TS teams.

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

when you need an event-driven, LLM-agnostic multi-agent framework 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.

when you need a JVM-based agentic workflow platform with strong enterprise/commercial support.

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

when you want to orchestrate existing CLI agents (Claude Code, Codex) via a meta-harness rather than building custom sub-agents from scratch.

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.

when you only need search/research agent capabilities, not general-purpose coding/creation.

II-Researcher: a new open-source framework designed to aid building search / research agents

when you specifically need a deep-research agent with Alibaba's optimizations and don't need coding/creation features.

Tongyi Deep Research, the Leading Open-source Deep Research Agent

when you want a mature, self-hostable deep-research agent with broad LLM provider support and a simple setup.

An autonomous agent that conducts deep research on any data using any LLM providers