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Showing 18 open source projects for "i86bi-linux-l3-adventerprisek..."
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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OpenClaw
Your own personal AI assistant. Any OS. Any Platform.
OpenClaw (formerly Clawdbot/Moltbot) is an open-source, self-hosted autonomous AI assistant designed to run on user-controlled hardware and bridge conversational natural language with real-world task execution, effectively acting as a proactive digital assistant rather than a reactive chatbot. It lets you send instructions through familiar messaging platforms like WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and more, and then interprets those instructions to carry out actions such... -
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LobeHub
Workspace to find, build, and collaborate with AI agents
LobeHub is an all-in-one workspace designed to help humans and AI agents collaborate, grow, and evolve together. It treats AI agents as true teammates rather than one-off tools, enabling deeper context, continuity, and productivity. Users can build personalized agent teams that understand their workflows, preferences, and goals over time. LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features,... -
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Langroid
Harness LLMs with Multi-Agent Programming
Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a... -
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Claw Code
AI agent harness for AI coding agents
Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured,... -
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Pi Agent
AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI
Pi is an open-source AI agent toolkit and coding agent framework designed to help developers build, run, and extend intelligent AI-powered workflows. Developed by Earendil Works, the project includes a coding agent CLI, unified multi-provider LLM API, agent runtime, terminal UI library, and web UI components. Pi supports multiple AI providers such as OpenAI, Anthropic, and Google through a single consistent API layer, making it easier to integrate different models into applications and... -
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Oh My OpenAgent
The best agent harness
Oh My OpenAgent is a large-scale, open-source agent orchestration framework that aims to provide a fully unified and extensible environment for AI-powered software development and automation. It builds on the idea that no single model is sufficient, instead enabling coordinated use of multiple models for reasoning, creativity, speed, and cost efficiency within a single workflow. The system is designed as a comprehensive agent harness where tasks are automatically decomposed, delegated, and... -
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Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in... -
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KaibanJS
JS-native framework for building and managing multi-agent systems
JavaScript-native framework for building multi-agent AI systems. Multi-agent AI systems promise to revolutionize how we build interactive and intelligent applications. However, most AI frameworks cater to Python, leaving JavaScript developers at a disadvantage. KaibanJS fills this void by providing a first-of-its-kind, JavaScript-native framework designed specifically for building and integrating AI Agents. Harness the power of specialization by configuring AI agents to excel in distinct,... -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition... -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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OpenAI Symphony
Symphony turns work into isolated, autonomous implementation runs
Symphony is an open-source framework designed to transform project tasks into autonomous implementation runs managed by AI coding agents. It allows teams to manage and prioritize work while the system automatically assigns coding agents to complete tasks. Instead of directly supervising AI agents, engineers can oversee higher-level workflows and project outcomes. Symphony integrates with project management tools to detect new tasks and initiate isolated environments where agents implement... -
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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. LazyCodex also emphasizes... -
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GELab-Zero
GUI Exploration Lab. One of the best GUI agent solutions
GELab-Zero is an open-source “GUI Agent” framework aiming to automate interactions with graphical user interfaces (GUIs), combining both the agent model and all supporting infrastructure — including inference, input orchestration, and GUI automation logic — in a plug-and-play package that runs locally, without cloud dependencies. The idea is to let developers or users harness an AI agent that can simulate clicking, typing, reading UI elements, and interacting with apps in a human-like way... -
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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. A defining part of the project is that the agent can write or extend missing helper functions during a task, which is why the repository... -
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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... -
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Excalibur
Excalibur is a highly opinionated agent harness
Excalibur is an experimental or utility-oriented project that appears to focus on enabling structured execution, control, or enhancement of workflows within AI or development environments. The system likely provides tools for managing tasks, orchestrating processes, or enhancing decision-making capabilities in automated systems. Its design suggests an emphasis on control and precision, allowing users to define how tasks are executed and monitored. It may include abstractions for handling... -
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Harness-1
Ultra Recipe for Training Long-Horizon Search Agents
Harness-1 is a 20B search agent trained with reinforcement learning inside a stateful retrieval harness. It is designed for long-horizon search tasks where the model must search, inspect documents, curate evidence, verify claims, and decide when enough evidence has been gathered. The harness externalizes search state, including candidate documents, evidence links, verification records, and budget-aware context. This lets the policy focus on higher-level decisions instead of trying to keep... -
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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... -
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superplan-plugin
Superplan is a CLI-first execution system.
Superplan is an execution system for AI agents. It turns planning into actual execution inside your repository. Instead of vague plans, chat history, or TODO lists, Superplan forces work into clear, step-by-step tasks that agents can execute, track, and resume at any time. The CLI is designed for agents to follow, not for humans to run manually. You define the work; your agent executes it through Superplan's structured runtime.
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