Showing 13 open source projects for "optimization"

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  • 1
    ECC

    ECC

    The agent harness performance optimization system

    ECC is an agent harness performance optimization system for AI coding tools such as Claude Code, Codex, Opencode, and similar environments. It packages rules, skills, instincts, memory behavior, security practices, and research-first development patterns into a structured framework. The project is designed to make coding agents more reliable by improving how they plan, inspect context, make changes, review work, and avoid unnecessary mistakes.
    Downloads: 5 This Week
    Last Update:
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  • 2
    AutoAgent AI

    AutoAgent AI

    Autonomous harness engineering

    ...It operates through a loop of testing, analyzing failures, and refining the agent’s configuration to maximize a scoring metric. The framework uses a single-file agent harness combined with structured tasks and evaluation suites to guide optimization. It runs inside Docker for safe execution and reproducibility. This approach shifts agent development from manual design to automated optimization. The system is particularly useful for building domain-specific agents that need continuous performance improvement.
    Downloads: 1 This Week
    Last Update:
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  • 3
    SwiftUI Agent Skill

    SwiftUI Agent Skill

    SwiftUI agent skill for Claude Code, Codex, and other AI tools

    ...The system includes a comprehensive review process that evaluates code across multiple dimensions, including navigation, data flow, design compliance, and performance optimization. It is specifically tailored to align with current Apple development standards, including modern Swift versions and accessibility requirements like VoiceOver support. The tool integrates seamlessly with multiple AI coding environments, allowing developers to invoke it as part of their coding or review process.
    Downloads: 0 This Week
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  • 4
    CUDA Agent

    CUDA Agent

    Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

    ...Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile.
    Downloads: 0 This Week
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  • 5
    Agent Lightning

    Agent Lightning

    The absolute trainer to light up AI agents

    Agent Lightning is an open-source framework developed by Microsoft to train and optimize AI agents using techniques like reinforcement learning (RL), supervised fine-tuning, and automatic prompt optimization, with minimal or zero changes to existing agent code. It’s designed to be compatible with a wide range of agent architectures and frameworks — from LangChain and OpenAI Agent SDKs to AutoGen and custom Python agents — making it broadly applicable across different agent tooling ecosystems. Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. ...
    Downloads: 0 This Week
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  • 6
    Agent Reinforcement Trainer

    Agent Reinforcement Trainer

    Train multi-step agents for real-world tasks using GRPO

    Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ART also supports scalable training patterns, observability tools, and integration with hosted platforms like Weights & Biases, and it provides notebooks that demonstrate training on standard benchmarks and tasks.
    Downloads: 0 This Week
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  • 7
    Vibe-Trading

    Vibe-Trading

    Vibe-Trading: Your Personal Trading Agent

    ...It features a swarm-based architecture with prebuilt expert agent teams for research, trading, and risk management. Advanced backtesting engines provide statistical validation, optimization, and performance metrics. The system also includes persistent memory, enabling it to learn from past interactions and refine strategies over time. Overall, it delivers an end-to-end AI-driven trading environment for both research and execution.
    Downloads: 8 This Week
    Last Update:
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  • 8
    Phidata

    Phidata

    Build multi-modal Agents with memory, knowledge, tools and reasoning

    ...Phidata offers pre-configured templates to accelerate development and deployment, allowing users to quickly go from building agents to shipping them into production. It includes features like real-time monitoring, agent evaluations, and performance optimization tools, ensuring the reliability and scalability of AI solutions. Phidata also allows developers to bring their own cloud infrastructure, offering flexibility for custom setups. The platform provides robust support for enterprises, including security features, agent guardrails, and automated DevOps for smoother deployment processes.
    Downloads: 8 This Week
    Last Update:
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  • 9
    TapeAgents

    TapeAgents

    A framework that facilitates all stages of LLM development

    TapeAgents is a framework that facilitates all stages of the Large Language Model (LLM) agent development lifecycle, providing tools for building, testing, and deploying AI agents.
    Downloads: 0 This Week
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  • 10
    AnyTool

    AnyTool

    AnyTool: Universal Tool-Use Layer for AI Agents

    AnyTool is an open-source universal tool-use layer for AI agents that addresses the critical problem of how autonomous agents reliably interact with external tools and environments. Rather than having each agent handle tool invocation logic on its own, AnyTool provides a standardized interface and orchestrator that intelligently selects and manages tools, reduces context overhead, and improves execution reliability across diverse capabilities like web APIs, local commands, and GUI...
    Downloads: 1 This Week
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  • 11
    AutoGen

    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...
    Downloads: 4 This Week
    Last Update:
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  • 12
    BISHENG

    BISHENG

    BISHENG is an open LLM devops platform for next generation apps

    BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate.
    Downloads: 0 This Week
    Last Update:
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  • 13
    CogAgent

    CogAgent

    An open sourced end-to-end VLM-based GUI Agent

    CogAgent is a 9B-parameter bilingual vision-language GUI agent model based on GLM-4V-9B, trained with staged data curation, optimization, and strategy upgrades to improve perception, action prediction, and generalization across tasks. It focuses on operating real user interfaces from screenshots plus text, and follows a strict input–output format that returns structured actions, grounded operations, and optional sensitivity annotations. The model is designed for agent-style execution rather than freeform chat, maintaining a continuous execution history across steps while requiring a fresh session for each new task. ...
    Downloads: 0 This Week
    Last Update:
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