Showing 8 open source projects for "protocol test harness"

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  • 1
    Browser Harness

    Browser Harness

    Self-healing browser harness that enables LLMs to complete any task

    Browser Harness is a self-healing browser control system built to give language models direct and flexible access to a real Chrome browser through the Chrome DevTools Protocol. Its main philosophy is minimalism: instead of imposing a rigid framework, it exposes a very thin bridge so the agent can perform browser tasks with almost no abstraction in the way.
    Downloads: 17 This Week
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  • 2
    Reef

    Reef

    Continual learning infra for self-improving agents

    ...Reef also supports test-time learning scenarios where measurable task outcomes can drive iterative improvement.
    Downloads: 0 This Week
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  • 3
    Agentic Commerce Protocol (ACP)

    Agentic Commerce Protocol (ACP)

    Interaction model for connecting buyers to complete purchases

    ACP is an open, draft specification for letting buyers, their AI agents, and businesses complete purchases through a standardized interaction model. It’s maintained by OpenAI and Stripe and licensed under Apache-2.0, with the goal of being easy to adopt alongside a merchant’s existing commerce stack rather than replacing it. The repository organizes the spec as human-readable RFCs plus machine-readable OpenAPI and JSON Schema definitions, along with worked examples and a changelog so...
    Downloads: 1 This Week
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  • 4
    AutoAgent AI

    AutoAgent AI

    Autonomous harness engineering

    AutoAgent is an experimental AI framework focused on autonomous agent engineering, where a meta-agent iteratively improves another agent’s architecture without direct human intervention. Instead of manually tuning prompts or workflows, developers define high-level goals in a configuration file, and the system continuously modifies its own tools, orchestration, and logic based on benchmark performance. It operates through a loop of testing, analyzing failures, and refining the agent’s...
    Downloads: 0 This Week
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    ASSERT

    ASSERT

    Requirement-driven evaluation harness for AI agents and LLM

    ASSERT is a requirement-driven evaluation harness for AI agents and LLM applications. It turns natural-language specifications, policies, product requirements, and launch criteria into structured tests that can be reviewed, executed, scored, and improved. The pipeline derives behavior categories, generates single-turn and multi-turn test cases, runs them against a target system, and uses an LLM judge to score conversations against the stated policies.
    Downloads: 0 This Week
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  • 6
    Voyager

    Voyager

    The open harness for creative work

    Voyager is an AI-powered creative automation platform that enables users to generate, edit, and produce visual, video, audio, and design content using AI agents. The software connects AI models with professional creative applications, including Adobe Photoshop, Illustrator, Premiere Pro, After Effects, DaVinci Resolve, Blender, and Ableton Live. Users can describe creative tasks in natural language, allowing AI agents to select tools, generate assets, edit content, and complete multistep...
    Downloads: 0 This Week
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  • 7
    aictx

    aictx

    Repo-local continuity runtime for AI coding agents. Helps them continu

    AICTX helps Codex, Claude, GitHub Copilot and other coding agents continue work across sessions by preserving the last useful execution state: active work, next actions, decisions, failures, validation evidence and repo context. The continuity idea is not to add more hidden memory or dump more context into the prompt. AICTX keeps operational continuity inside the repo: - active Work State and next action; - execution summaries and handoffs; - explicit decisions; - known failures and...
    Downloads: 70 This Week
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  • 8
    OSS-Fuzz Gen

    OSS-Fuzz Gen

    LLM powered fuzzing via OSS-Fuzz

    OSS-Fuzz-Gen is a companion project that helps automatically create or improve fuzz targets for open-source codebases, aiming to increase coverage in OSS-Fuzz with minimal maintainer effort. It analyses a library’s APIs, examples, and tests to propose harnesses that exercise parsers, decoders, or protocol handlers—precisely the code where fuzzing pays off. The system integrates with modern LLM-assisted workflows to draft harness code and then iterates based on build errors or low coverage signals. Importantly, it aligns with OSS-Fuzz conventions, generating corpus seeds, build rules, and sanitizer settings so projects can plug in quickly. ...
    Downloads: 0 This Week
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