Search Results for "define" - Page 4
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Showing 187 open source projects for "define"
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LitServe
Minimal Python framework for scalable AI inference servers fast
LitServe is a minimal Python framework designed for building custom AI inference servers with full control over how models are executed and served. It allows developers to define their own inference logic, making it suitable for complex systems such as multi-model pipelines, agents, and retrieval-augmented generation workflows. Unlike traditional serving tools that enforce rigid abstractions, LitServe focuses on flexibility by letting users control request handling, batching strategies, and output processing directly in Python. ... -
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BehaviorTree.CPP
C++ behavior tree library for robotics and AI decision systems
BehaviorTree.CPP is a C++ library designed to create, manage, and execute behavior trees, a widely used model for decision-making in robotics and artificial intelligence systems. It provides a flexible and modular framework that allows developers to define complex behaviors as reusable tree structures composed of nodes. BehaviorTree.CPP emphasizes performance and real-time execution, making it particularly suitable for robotics applications where responsiveness is critical. It supports asynchronous actions, enabling long-running tasks without blocking the execution of the entire tree. ... -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation of workflows, PySpur makes it easier to debug interactions between components and identify failures in complex pipelines. ... -
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Cog
Package and deploy machine learning models using Docker containers
...It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. ... -
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SimpleTuner
A general fine-tuning kit geared toward image/video/audio diffusion
...It supports fine-tuning workflows for models such as Stable Diffusion variants and other diffusion architectures, enabling users to adapt pretrained models to specialized datasets or creative tasks. The system includes configuration-driven training processes that allow users to define datasets, model paths, and training parameters with minimal setup. SimpleTuner also emphasizes experimentation and academic collaboration, encouraging contributions and iterative improvements from the open-source community. -
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Better Agents
Standards for building agents, better
...Rather than being a full execution framework itself, Better-Agents focuses on enhancing coding assistants and agent development tools by embedding standardized guidelines into the development process. The system generates structured project files, including configuration documents that define the architecture, roles, and capabilities of the agent system. By following these conventions, developers can ensure that their agents adhere to widely accepted design patterns and operational standards. -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. ... -
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Fast MCP
A Ruby Implementation of the Model Context Protocol
...Fast-mcp provides developers with a streamlined toolkit for building MCP servers that expose application functionality to AI agents. The framework focuses on ease of use, allowing developers to quickly define tools, endpoints, and integrations that can be accessed through MCP-compatible clients. By abstracting much of the underlying infrastructure, fast-mcp enables rapid prototyping of AI-enabled applications that can interact with external systems such as databases, APIs, or file systems. The project emphasizes performance and simplicity, making it suitable for both small prototypes and production deployments. -
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LlamaDeploy
Deploy your agentic worfklows to production
...The system supports orchestrating multiple services, handling communication between agents, and managing workflow execution in distributed environments. Developers can define workflows that involve multiple steps such as data retrieval, reasoning, tool invocation, and response generation, then deploy them using the framework’s infrastructure tools. The design emphasizes scalability, modularity, and fault-tolerant execution so that agent systems can run reliably in production environments. -
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LLM Agents Papers
Must-read Papers on LLM Agents
...The repository helps readers understand how agent architectures are evolving and how they are applied in domains such as robotics, software automation, and decision-making systems. It also provides references to influential works that define the conceptual foundations of agent-based AI systems. -
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Refly
The first open-source agent skills builder
Refly is an AI-native workflow platform that democratizes automated workflow and skills creation for both technical and non-technical users by offering a visual, natural-language-driven interface. Instead of requiring code, Refly lets creators define tasks and business logic through simple “vibes,” which are compiled into structured, reusable agent skills that can be executed on engines like Claude Code, Cursor, or other supported runtimes. With a focus on making automation accessible, it provides a visual canvas and low-code components that feel similar to drag-and-drop builders but backed by powerful AI orchestration, memory handling, and integrations with external services. ... -
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SkillKit
Supercharge AI coding agents with portable skills
SkillKit is a developer-centric toolkit for constructing modular, reusable AI agent skills and integrating them into workflows, platforms, and applications with minimal overhead. It provides a set of abstractions, templates, helper utilities, and patterns that help developers define intents, actions, context handling, memory management, and multi-step logic so that skills can be built once and reused everywhere. Instead of reinventing the wheel every time a new conversational or automation feature is needed, SkillKit encourages engineers to encapsulate logic into coherent skill units that can be registered, tested, and composed together. ... -
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ZAPI
ZAPI by Adopt AI is an open-source Python library
ZAPI is a developer-centric API framework that streamlines building, testing, and deploying APIs with strong type safety and minimal boilerplate, helping teams deliver backend services faster with fewer errors. It emphasizes a declarative router and schema model that uses types to define request and response formats, providing clear contracts for frontend and backend teams while automatically generating documentation. Zapi abstracts many repetitive tasks such as validation, authentication flows, and error handling so developers can focus on business logic instead of infrastructure plumbing. It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. ... -
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OpenTinker
OpenTinker is an RL-as-a-Service infrastructure for foundation models
...The architecture supports a range of single-turn and multi-turn agentic tasks with a design that abstracts away infrastructure complexity while offering flexible Python APIs to define environments and workflows. -
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Bytebot
Bytebot is an AI desktop agent that automates computer tasks
...Bytebot can be embedded into editors, terminals, or CI/CD pipelines to provide contextual recommendations, highlight quality issues, and propose fixes based on understanding of code and project context. The framework is often extensible, allowing teams to define custom commands, workflows, or plug-ins that connect to internal APIs, coding standards, or proprietary repositories. -
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Gate22
Open-source MCP gateway and control plane for teams
Gate22 is an open-source governance and control plane for Model Context Protocol (MCP) environments that helps teams define and enforce policies about which tools and capabilities AI agents can access, how they can interact with those tools, and how usage is logged and audited. It provides a centralized layer where organizations can configure permission boundaries, role-based access, and operational constraints that govern agent behavior and tool invocation across agentic IDEs or custom agent stacks. ... -
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DeployStack
Centralized credential vault, governance, and token optimization
DeployStack is an open-source framework that helps developers and teams define and deploy production infrastructure stacks using modular, reusable templates, often with IaC (infrastructure as code) principles. It provides a structured way to compose resources such as cloud networking, compute, and managed services into coherent deployment blueprints that can be versioned and reused across projects. By abstracting common deployment patterns and capturing them as templates, Deploystack reduces duplication of effort that typically occurs when setting up stacks for different applications or environments. ... -
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Minigrid
Simple and easily configurable grid world environments
...It provides a suite of simple 2D grid-based tasks (e.g., navigating mazes, unlocking doors, carrying keys) where an agent moves in discrete steps and interacts with objects. The design emphasizes speed (agents can run thousands of steps per second), low dependency overhead, and high customizability — making it easy to define new maps, new tasks, or wrappers. It supports the Gymnasium-style environment API so that RL researchers can plug it into their existing frameworks and algorithms with minimal adaptation. Because of its simplicity, it is often used for rapid prototyping, analytic experiments, curriculum learning, or pedagogical tutorials. While it is not a full 3D simulation environment, its strength lies in enabling many environment resets and steps cheaply, which is valuable for algorithmic RL research rather than high-fidelity rendering. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. ... -
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VoltAgent
Open Source TypeScript AI Agent Framework
An AI Agent Framework provides the foundational structure and tools needed to build applications powered by autonomous agents. These agents, often driven by Large Language Models (LLMs), can perceive their environment, make decisions, and take actions to achieve specific goals. Building such agents from scratch involves managing complex interactions with LLMs, handling state, connecting to external tools and data, and orchestrating workflows. VoltAgent is an open source TypeScript framework... -
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HybridClaw
The enterprise operating layer for open agents
...It is designed to work alongside modern agent ecosystems such as OpenClaw, Claude Code, and similar agentic coding tools, providing a flexible infrastructure for managing agent behaviors, workflows, and capabilities. The project emphasizes modularity, allowing developers to define and compose “skills” or capabilities that agents can invoke dynamically, enabling more adaptive and context-aware automation. HybridClaw aims to bridge the gap between isolated AI tools and fully orchestrated agent systems by enabling communication, coordination, and shared context across multiple agents or processes. It is particularly relevant in scenarios where developers want to build complex autonomous systems that interact with codebases. -
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oh-my-agent
Portable multi-agent harness for .agents-based skills, workflows
oh-my-agent is a flexible and extensible framework designed to simplify the creation, management, and orchestration of AI agents across various tasks and environments. It builds on the idea of modular agent systems, allowing developers to define specialized roles and capabilities that can be combined into larger workflows. The framework emphasizes usability, making it easier to configure agents, assign tasks, and manage interactions without requiring deep expertise in AI system design. It likely includes support for plugins or skills, enabling agents to extend their functionality through integrations with external tools. ... -
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AgentField
Build and run AI agents like microservices
...Instead of treating agents as isolated scripts or prototypes, the system elevates them to first-class infrastructure components that can be deployed, orchestrated, and managed at scale across distributed environments. Developers define agents as typed functions, and the platform automatically handles orchestration, communication, identity, and execution, allowing agents to behave like APIs within a broader system architecture. The framework includes built-in support for asynchronous execution, long-running processes, and multi-agent coordination, enabling complex workflows that go far beyond simple prompt-response interactions. ... -
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OpenSage
An agent framework that enables AI to create their own agent
OpenSage is an emerging open-source AI agent development framework designed to automate the creation, orchestration, and evolution of intelligent agents through a self-programming paradigm. Unlike traditional agent frameworks that require developers to manually define workflows, tools, and structures, OpenSage introduces a system where large language models can dynamically generate their own agent architectures, including sub-agents, toolchains, and execution strategies. The framework is built around the concept of an Agent Development Kit (ADK), providing structured components for memory, reasoning, and task decomposition while allowing agents to iteratively improve their own design. ... -
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yek
Serialize repositories into LLM-ready context w/ smart prioritization
...Yek supports multiple directories, individual files, and glob patterns, making it flexible for different workflows. It can stream output when piped or save results to a temporary file, depending on usage. Configuration is handled through a yek.yaml file, allowing users to define ignore rules and priority settings. By consolidating code and documents into a single, ordered format, Yek simplifies preparing repositories for AI-driven analysis, debugging, or automation tasks.
