Open-source alternatives to Wiseflow

This project is an LLM autonomous agent framework and orchestration tool designed to build goal-driven agents that automate complex workflows. It functions as a system for converting high-level objectives into a series of autonomous actions and managing the coordination of multiple specialized agents to solve multi-step problems. The framework features a tool integration layer that parses structured model outputs into executable functions and external API calls. It utilizes a non-blocking execution pipeline to manage task orchestration through recursive loops and asynchronous event handling.

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

TradingAgents-CN is a multi-agent framework designed for autonomous financial market analysis and automated trading execution. It functions as a containerized orchestrator that leverages large language models to perform complex reasoning, research, and decision-making tasks within financial environments. The platform distinguishes itself through a modular architecture that integrates diverse artificial intelligence providers and financial data sources into a unified pipeline. It provides granular control over agent behavior through prompt-driven logic configuration and multi-model orchestrati

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

This project is an autonomous software engineering platform and orchestration framework designed to manage specialized artificial intelligence agents. It provides a suite of tools for coordinating autonomous entities to execute complex development tasks, ranging from architectural planning and code reviews to performance optimization. The platform distinguishes itself through its multi-agent orchestration layer, which dynamically assigns roles based on an analysis of a project's technology stack. By utilizing a modular agent registry, the system scales capabilities across different software m

Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

gstack is an AI agent framework and development workflow system designed to automate the software development lifecycle. It coordinates specialized AI personas to manage tasks across product design, engineering management, and quality assurance, transforming product intent into technical specifications and final releases. The project is distinguished by its deep integration of headless browser automation and semantic code memory. It utilizes a persistent Chromium daemon for web scraping and visual auditing, and implements a searchable knowledge base that logs architectural decisions and repos

The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

Suna is an orchestration platform designed for the deployment, management, and governance of autonomous AI agents. It provides a centralized system for defining agent behaviors and tool integrations, enabling the automation of complex business processes through a unified interface. The platform distinguishes itself by applying infrastructure-as-code principles to AI, utilizing version-controlled repositories to manage agent configurations, skills, and guardrails. It ensures secure and predictable operations by spawning ephemeral, isolated virtual machines for every individual task, preventing

OpenFang is an operating system for LLM agents designed to orchestrate autonomous agents with built-in task scheduling, tool sandboxing, and multi-model routing. It provides a secure AI execution environment that integrates prompt injection scanning, cryptographic audit trails, and resource metering to ensure controlled processing. The platform distinguishes itself through a comprehensive security architecture, featuring fuel-metered tool sandboxing and an immutable activity audit trail based on cryptographic hash-chains. It implements high-assurance identity verification via signed manifests

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

Rivet is a visual LLM workflow designer and AI agent orchestration engine. It serves as a development environment for building retrieval augmented generation pipelines and a TypeScript library for embedding visual AI graphs and prompt logic into JavaScript applications. The system differentiates itself through a node-based editor that maps data flow between language models, vector databases, and external APIs. It provides specialized tools for prompt engineering, including interfaces for iterative prompt refinement and A/B testing to improve model response quality. The platform covers a broa

LangChain.js is a framework for building, executing, and monitoring stateful agentic applications. It provides an orchestration engine that models workflows as directed graphs, allowing developers to connect language models, data sources, and external tools into modular, multi-step processes. The platform distinguishes itself through its focus on stateful execution and human-in-the-loop control. It manages agent lifecycles by persisting execution state across threads, enabling fault tolerance and the ability to pause workflows at designated breakpoints for manual review or modification. This

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

oh-my-pi is an agentic workflow automation platform and AI coding agent orchestrator designed for autonomous software engineering. It functions as a multi-model LLM router and an LSP-integrated development environment, coordinating specialized AI agents to perform codebase analysis, automated refactoring, and complex task execution. The system distinguishes itself through the use of subagent coordination to execute parallel tasks within isolated environments and an auto-research framework for iterative experiments. It employs AST-driven structural search for code discovery and content-hash an

This project is a framework for managing generative AI services through a unified provider interface and adapter layer. It provides a standardized API for calling multiple cloud-based and locally hosted models, translating provider-specific parameters and responses into a uniform format. The system includes an agent orchestrator designed for long-running tasks, featuring state persistence for resuming runs and execution tracing to monitor decision-making processes. It integrates the Model Context Protocol to connect models to external servers and filesystems and employs a policy-based executi

auto-dev is an AI-native software engineering tool and multi-agent development platform designed to automate the entire software development lifecycle. It functions as an autonomous orchestrator that manages AI-driven coding, testing, and infrastructure configuration through declarative agent chains. The project is built on a Kotlin Multiplatform AI framework, allowing agent logic to run across diverse environments and device interfaces. The platform implements the Model Context Protocol to exchange tools and project information with external AI services. It distinguishes itself through the u

This project provides a comprehensive guide and framework for implementing autonomous AI coding assistants within local development environments. It focuses on orchestrating multi-agent teams that can plan, execute, and verify complex software engineering tasks, such as refactoring, bug resolution, and test generation, while maintaining deep awareness of project-specific context and memory. The system distinguishes itself through a robust security-first architecture that enforces granular access controls, execution isolation, and mandatory human-in-the-loop approvals for all file modification

ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a

Agent Squad is an LLM multi-agent orchestration framework designed to coordinate specialized agents to solve complex tasks. It functions as a system for managing agent teams and supervisors, utilizing a supervisor-led orchestration model to decompose large problems into manageable steps. The framework distinguishes itself through a combination of intent-based query routing and human-in-the-loop automation. It employs a hierarchical routing system to direct requests to the most appropriate agent or model, while integrating asynchronous messaging queues to route complex cases to human operators

This project is a container-native runtime designed for building, orchestrating, and executing autonomous AI agents. It provides a framework for managing multi-agent teams and complex workflows by packaging agent configurations as portable container images. By leveraging declarative configuration files, the system allows users to define agent personas, model routing, and tool access without requiring changes to application code. The platform distinguishes itself through its deep integration with container infrastructure, ensuring that agent tasks and external tools run within isolated environ

vibe-vibe is an LLM agent engineering framework and toolchain optimizer designed for orchestrating multi-agent systems. It serves as a comprehensive guide and methodology for transforming conceptual ideas into deployed applications through agentic software engineering. The project focuses on the orchestration of specialized AI agent roles with defined collaboration boundaries and iterative feedback loops. It provides frameworks for toolchain optimization, including the selection and evaluation of protocols that extend model capabilities and the design of standardized tool interfaces. The sys

Kiro is an AI-powered development tool and multi-agent workflow orchestrator. It functions as a context-aware code generator and coding assistant that transforms natural language requirements into structured implementation plans and production-grade code. The system distinguishes itself through multi-agent task decomposition, where complex requirements are broken into sequenced tasks and assigned to specialized agents. It features multi-model orchestration to select specific language models based on reasoning complexity, cost, and latency, and includes a headless command-line interface for id

Open-SWE is an asynchronous software engineering agent and orchestrator designed to automate end-to-end coding tasks and pull request reviews. It functions as a middleware framework that coordinates long-running AI operations across multiple subagents, utilizing state persistence and human-in-the-loop oversight to manage complex workflows. The system is distinguished by its use of isolated remote Linux sandboxes for secure code execution and shell command processing. It features a webhook-driven integration platform that triggers automated engineering tasks via mentions and events in GitHub,

Ruflo is an AI agent orchestration platform and workflow automation tool designed to decompose high-level goals into executable action plans. It functions as a manager for multi-agent swarms, organizing autonomous entities into collaborative topologies that utilize shared consensus to complete complex tasks. The framework distinguishes itself through a retrieval-augmented generation layer and knowledge graphs for reasoning over linked data. It incorporates a trajectory-based learning loop that analyzes previous execution paths to refine cognitive patterns and improve future reasoning accuracy