Search Results for "complex"
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77 projects for "complex" with 2 filters applied:
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Claude Code
Claude Code is an agentic coding tool that lives in your terminal
Claude Code is an intelligent agentic coding assistant that lives in your terminal and understands your entire codebase. It helps developers code faster by executing routine tasks, explaining complex code snippets, and managing git workflows—all via natural language commands. Claude Code integrates seamlessly into your terminal, IDE, or GitHub by tagging @claude to interact with your code context. The tool is designed to simplify development by automating repetitive work and providing instant clarifications on code behavior. ... -
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Open Multi-Agent
One runTeam() call from goal to result
Open Multi-Agent is a flexible framework designed to enable the creation and coordination of multiple AI agents working together to solve complex tasks through collaboration. It focuses on distributing responsibilities across specialized agents, each handling a specific part of a problem, such as planning, execution, or validation. The system emphasizes modularity, allowing developers to define agent roles, communication protocols, and workflows. It supports iterative collaboration, where agents exchange information and refine outputs collectively. ... -
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Claude Skills
Public repository for Agent Skills
...Each Skill lives in its own directory with a SKILL.md file containing metadata and instructions, and can include supplemental scripts or assets that the agent uses to perform complex operations when relevant. -
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VoltAgent
Open Source TypeScript AI Agent Framework
...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 that acts as this essential toolkit. It simplifies the development of AI agent applications by providing modular building blocks, standardized patterns, and abstractions. Whether you're creating chatbots, virtual assistants, automated workflows, or complex multi-agent systems, VoltAgent handles the underlying complexity, allowing you to focus on defining your agents' capabilities and logic. -
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MiroFlow
Agent framework that enables tool-use agent tasks
MiroFlow is a high-performance open-source framework designed for building intelligent AI agents capable of solving complex reasoning and research tasks. The system introduces a hierarchical architecture that organizes components into control, agent, and foundation layers, allowing developers to manage agent orchestration and tool interactions in a structured manner. One of the core innovations of MiroFlow is its use of agent graphs, which enable flexible orchestration of multiple sub-agents and tools in order to complete complex workflows. ... -
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Graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw)
Graphify is a data visualization and transformation tool designed to convert structured or semi-structured data into graph-based representations, enabling better understanding of relationships and dependencies. It focuses on building visual models such as nodes and edges that represent entities and their connections, making complex datasets easier to interpret. The system likely supports dynamic updates, allowing graphs to evolve as data changes or new inputs are introduced. It is particularly useful in domains such as network analysis, knowledge graphs, and system architecture visualization. The architecture emphasizes flexibility, enabling users to customize how data is mapped and displayed. ... -
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Understand Anything
Turn any codebase into an interactive knowledge graph
Understand-Anything is an AI-driven tool designed to help users deeply understand any topic by generating structured explanations, summaries, and breakdowns. It focuses on transforming complex or unfamiliar subjects into clear, step-by-step explanations that are easier to grasp. The system leverages language models to provide layered insights, allowing users to explore topics at different levels of detail. It is particularly useful for learning, research, and quick comprehension of new concepts across various domains. ... -
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LabClaw
Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
LabClaw is an open-source AI experimentation and agent orchestration platform designed to help developers build, test, and iterate on complex autonomous workflows in a controlled and modular environment. It provides a framework for composing multiple tools, prompts, and execution steps into structured pipelines that can be reused and evaluated across different scenarios. The system emphasizes experimentation, allowing users to run multiple variations of agent workflows, compare outputs, and refine performance over time. ... -
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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. -
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clawchief
Turn your OpenClaw into a Chief of Staff
clawchief is an agent orchestration and management layer designed to coordinate and control multiple AI agents within structured workflows, acting as a central authority that assigns tasks, monitors execution, and ensures coherence across complex operations. The system is built around the idea of hierarchical control, where a “chief” agent oversees subordinate agents and directs their activities based on high-level objectives. This approach allows for more predictable and organized multi-agent behavior compared to decentralized systems. The architecture likely includes task planning, delegation logic, and feedback loops that enable iterative refinement of outputs. ... -
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LazyCodex
The one and only agent harness for complex codebases
LazyCodex is an agent harness for using Codex on complex software projects. It is designed to add structure around AI coding sessions through memory, planning, execution, verification, skills, hooks, routing, and diagnostics. The project helps developers move beyond one-off prompts by giving the agent a more organized workflow inside a codebase. It supports project memory so context can persist across sessions and decisions do not need to be repeatedly reintroduced. -
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Wondel.ai Skills
Agent skills for Claude Code and other agentskills-compatible agents
...These skills can cover a wide range of domains, including development, automation, and problem-solving, allowing agents to extend their capabilities dynamically. The system emphasizes modularity, enabling skills to be reused and combined into more complex workflows. It also supports compatibility across multiple AI tools and platforms, making it a versatile resource for developers. The repository serves as both a toolkit and a marketplace of capabilities that can be installed into agents. Overall, Skills enables AI systems to become more specialized and effective by equipping them with reusable expertise modules. -
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Harness
A meta-skill that designs domain-specific agent teams
...Its approach emphasizes modularity and reuse, allowing generated agents and skills to be applied across different projects. Overall, Harness acts as an automation layer for building and managing complex agent ecosystems. -
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MiroThinker
MiroThinker is an open source deep research agent
MiroThinker is an open-source deep research AI agent designed to perform complex reasoning, information gathering, and predictive analysis tasks. The system focuses on enabling long-horizon research workflows by allowing the agent to interact repeatedly with external tools, search systems, and data sources while refining its reasoning through iterative steps. Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that involve searching for information, analyzing evidence, and synthesizing conclusions. ... -
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AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides... -
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
...The framework includes a collection of prebuilt skills and workflows that have been tested in real-world scenarios, providing a strong starting point for developers. It emphasizes continuous improvement, where each interaction contributes to better performance in future tasks. The system also integrates agent teams, allowing complex workflows to be distributed across specialized components. Overall, Pro-workflow transforms AI-assisted coding into an adaptive and evolving process that improves with use. -
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Codex plugin for Claude Code
Use Codex from Claude Code to review code or delegate tasks
...It may also include mechanisms for managing execution context, permissions, and tool access, ensuring that generated code can be safely applied. This makes it particularly useful for complex development pipelines where multiple agents or tools need to collaborate. -
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CC Workflow Studio
Accelerate Claude Code/GitHub Copilot
...The “Edit with AI” feature lets users refine workflows through natural language feedback, effectively combining the visual editor with powerful language models to reduce manual friction in complex logic construction. -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
PokeeResearchOSS provides an open-source, agentic “deep research” model centered on a 7B backbone that can browse, read, and synthesize current information from the web. Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. ... -
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AgentField
Build and run AI agents like microservices
...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. It also introduces strong identity and governance mechanisms, such as cryptographic identities and policy enforcement, ensuring that agents can operate securely. -
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Oh My OpenAgent
The best agent harness
...It emphasizes openness and flexibility, allowing developers to integrate different providers and avoid dependency on any single ecosystem or vendor. The framework includes robust tooling for managing agent workflows, monitoring execution, and integrating external tools, making it suitable for complex, production-level use cases. It also fosters a strong community-driven development approach, with features evolving in real time. -
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AG2
Framework for building and orchestrating multi-agent AI systems
AG2 is an open source framework designed to support the creation and coordination of multiple AI agents working together to solve complex tasks. It provides abstractions that allow developers to define agents with distinct roles, responsibilities, and communication patterns, enabling collaborative problem-solving workflows. AG2 focuses on making multi-agent systems more accessible by simplifying how agents are configured, connected, and executed. It includes mechanisms for agent-to-agent interaction, task delegation, and iterative reasoning, which are essential for building advanced AI-driven applications. ... -
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OpenAI Agent Skills
Skills Catalog for Codex
OpenAI Agent Skills is an open-source repository that serves as a broad catalog of agent skills designed to extend the capabilities of OpenAI Codex and other AI coding agents. It organizes reusable, task-specific workflows, instructions, scripts, and resources into modular skill folders so that an AI agent can reliably perform complex tasks without repeated custom prompting, making agent behavior more predictable and composable. Each skill is defined with clear metadata and instructions organizing how an AI assistant should complete specific tasks ranging from project management to code generation and documentation assistance. The repository supports community contributions, allowing developers to add new skills or update existing ones to keep the catalog relevant and practical for evolving use cases. -
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Obsidian Skills
Agent skills for Obsidian
...By providing formal descriptions of patterns, conventions, and workflows common to Obsidian users, the skills empower AI tools to give more relevant suggestions, generate content that adheres to user conventions, or execute complex multi-step operations that respect the knowledge graph and file relationships. -
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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
...Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability.
