Autohand Code CLI
Agent frameworksAutohand Code CLI is a terminal-native AI coding agent that plans and edits code, runs tools, and supports testing, memory, skills, and permission controls.
Choose the foundations for an agent application: model integrations, orchestration and the developer tools around them. Your team's language, workflow complexity and operating environment are useful starting points.
Autohand Code CLI is a terminal-native AI coding agent that plans and edits code, runs tools, and supports testing, memory, skills, and permission controls.
Bazed Agent Framework, aimed at empowering developers to build autonomous agent swarms without requiring deep Python ML knowledge, is facilitating the creation of sophisticated systems through TypeScript for enhanced autonomy and reliability
BindAI is a modular Python framework for building AI applications with reusable components for agents, tools, workflows, memory, knowledge retrieval, model providers, and integrations.
CAMEL (Communicative Agents for Mind Exploration of Large Language Model Society) is an open-source library designed for studying autonomous and communicative agents, facilitating research in understanding their behaviors, capabilities, and potential risks through scalable techniques and cooperative frameworks, including role-playing, with extensive documentation, examples, and datasets, while also supporting integration with open-source models as backends for diverse applications
ChatDev is a virtual software company utilizing intelligent agents to revolutionize the digital world through programming, offering a highly customizable framework and integrating innovative approaches like Experiential Co-Learning, Docker support, Git management, and Human-Agent Interaction
Anthropic's Python SDK for building agents with Claude using the Claude Code harness.
Cognee is an open-source framework aimed at simplifying data processing for large language models (LLMs) by creating knowledge graphs and data models, offering tools for information addition, knowledge creation, and similarity-based search
A Python CLI harness that lets coding agents access local tools such as Gmail, a browser, and files from their shell.
An open protocol for coordinating AI agent teams through shared Markdown task files and a chief-of-staff workflow.
A peer-to-peer protocol for serving and invoking agent skills over XMTP with encrypted communication and revocable trust.
Organize agents into crews and connect their tasks with stateful flows.
CUGA is an enterprise agent harness for configuring and deploying generalist agents across API and web workflows, with tool orchestration, safety policies, and evaluation.
DB-GPT revolutionizes database interactions using private LLM technology, enabling streamlined AI-native data app development with multi-model management, Text2SQL optimization, and fine-tuning, facilitating enterprises and developers to create bespoke applications in the Data 3.0 era
DevOpsGPT is an AI-driven software development automation solution that combines large language models with DevOps tools to convert natural language requirements into working software, enhancing development efficiency, shortening cycles, and reducing communication costs
A desktop workspace for orchestrating, monitoring, and automating multiple AI coding agents.
E2B Sandbox offers secure cloud environments tailored for AI agents and apps, facilitating long-running sessions with various tools and can be integrated with any large language model
A deterministic TypeScript workflow compiler that turns JSDoc-annotated functions into standalone workflow code you can inspect, test, and own.
Flowise simplifies the creation of applications leveraging large language models (LLMs) by providing a drag-and-drop interface for customizing AI workflows, offering easy installation, Docker support, development tools, and documentation for integrating various functionalities such as authentication, streaming, and custom tools to enhance AI agents' capabilities
Turn a shortlist of agent frameworks into a practical decision using task boundaries, recovery tests, integration needs and maintenance costs.
Read the guideSeparate task state, user preferences and source documents, then choose storage, retrieval and deletion rules for each type of information.
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