Search Results for "reliability"
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Showing 51 open source projects for "reliability"
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Upsonic
The most reliable AI agent framework that supports MCP
Upsonic is a reliability-focused AI agent framework designed for real-world applications. It enables the development of trusted agent workflows within organizations by incorporating advanced reliability features, such as verification layers and output evaluation systems. The framework supports the Model Context Protocol (MCP), facilitating integration with various tools and enhancing agent capabilities. -
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HolmesGPT
CNCF Sandbox Project
HolmesGPT is an open-source AI agent designed to help DevOps and site reliability engineering teams diagnose and resolve production incidents. The system aggregates signals from observability tools such as logs, metrics, alerts, and distributed traces, then analyzes them using large language models to identify potential root causes. Rather than requiring engineers to manually correlate large volumes of monitoring data, HolmesGPT automatically synthesizes evidence and presents explanations in natural language. ... -
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Future AGI
Open-source platform for evaluating, observing, and improving LLM
...It supports both cloud and self-hosted deployment models, making it useful for teams with different privacy, infrastructure, and compliance needs. Future AGI is especially relevant for agent-heavy products where reliability, regression testing, and safety checks matter before and after release. Its main value is turning AI agent development into a measurable engineering process instead of an informal cycle of prompting, guessing, and manual review. -
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Agent Skills for Context Engineering
A comprehensive collection of Agent Skills for context engineering
...Rather than being a single application, it packages practical guidance into skill modules that agents can load to improve planning, retrieval, memory usage, and overall reliability in real workflows. The repository emphasizes context engineering as a discipline, covering why agents fail when context gets too large, too noisy, or poorly structured, and how to mitigate those failure modes with repeatable patterns. It is designed to be used across modern agent environments that support skill folders and structured instructions, so teams can standardize how agents operate instead of relying on ad-hoc prompting. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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KeepChatGPT
Browser userscript that enhances ChatGPT reliability and usability
KeepChatGPT is an open source browser userscript designed to enhance the reliability, usability, and efficiency of the ChatGPT web interface. It runs through userscript managers and injects additional functionality directly into the page, allowing users to improve their workflow without requiring a backend service or separate application. It focuses on solving common problems experienced during AI conversations, such as session timeouts, network errors, message failures, and interruptions during long chats. ... -
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AnyTool
AnyTool: Universal Tool-Use Layer for AI Agents
...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 automation. It uses progressive filtering and adaptive orchestration to ensure the right tools are retrieved efficiently and work cohesively with agents of varying complexity, scaling to thousands of tools with self-optimizing behavior. The system also tracks tool reliability and quality, offering a safer and more predictable automation experience with persistent learning from previous executions. -
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Sprix SAGE Router
State-aware SELF/COLLABORATE/HANDOFF routing for A2A agent networks
...Routing decisions account for current progress, capability coverage, context-transfer costs, permissions, budgets, and deadlines. The system assigns remaining requirements to agents through task dependency graphs and estimates execution schedules. It learns contextual reliability and outcome information from previous execution evidence instead of relying on a single reputation score. Bounded search and replanning allow the router to compare feasible teams and revise decisions as task conditions change. -
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NVIDIA NeMo Agent Toolkit
Library for efficiently connecting and optimizing teams of AI agents
NVIDIA NeMo Agent Toolkit is an open-source framework designed to build, optimize, and manage AI agents across different development ecosystems. It provides enterprise-grade tools for improving agent performance, reliability, and observability throughout the development lifecycle. The toolkit integrates with popular agent frameworks such as LangChain, LlamaIndex, CrewAI, Microsoft Semantic Kernel, and Google ADK. Developers can monitor agent execution, trace workflows, and analyze token-level performance to identify bottlenecks and improve efficiency. ... -
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Composio
Composio equip's your AI agents & LLMs
Empower your AI agents with Composio - a platform for managing and integrating tools with LLMs & AI agents using Function Calling. Equip your agent with high-quality tools & integrations without worrying about authentication, accuracy, and reliability in a single line of code. -
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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Spec Kit
Toolkit to help you get started with Spec-Driven Development
...The toolkit provides scaffolding, prompt templates, and automation scripts that help teams maintain a clear source of truth throughout the development lifecycle. By emphasizing intent before code, Spec Kit reduces ambiguity and improves the reliability of AI-generated output. It integrates with popular AI coding tools such as GitHub Copilot and similar assistants, allowing developers to embed spec-driven practices directly into their existing workflows. Overall, the project aims to improve collaboration between humans and AI by making software development more predictable, traceable, and maintainable. -
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Harness Engineering
Field guide, and agent context bundle for harness engineering
...Its agent guide routes each task to the smallest relevant set of materials instead of loading the entire corpus. The playbooks help teams improve one representative workflow, review repository readiness, and compare changes over time. The project emphasizes reliability, security, maintainability, compatibility, authority, and cumulative organizational learning. It is designed to help agents recover intent, operate real systems, verify results, and leave future runs better prepared. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
14
Guardrails
Framework for validating and controlling LLM outputs in AI apps
...Guardrails also supports generating structured data from language models, allowing developers to enforce schemas or type constraints on responses. A companion ecosystem known as a hub provides reusable validators that can be combined into input and output guards to address different reliability and safety concerns. -
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CLI-Anything
Making ALL Software Agent-Native
...It integrates with multiple AI platforms such as Claude Code, OpenClaw, Codex, and GitHub Copilot CLI, enabling cross-platform compatibility and flexibility. CLI-Anything emphasizes structured outputs such as JSON to reduce parsing complexity and improve reliability in automation scenarios. -
16
Fli
Google Flights MCP and Python Library
Fli is a powerful Python library and command-line tool that provides direct programmatic access to Google Flights data through reverse-engineered API interactions rather than traditional web scraping. This approach enables faster, more reliable, and more stable access to flight information, avoiding the fragility associated with HTML parsing and UI changes. The library supports a wide range of flight search capabilities, including filtering by airline, departure time, number of stops, cabin... -
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Claude Codex Settings
My personal Claude Code and OpenAI Codex setup
Claude Codex Settings is a configuration-focused repository that provides curated settings, prompts, and workflow optimizations for improving AI-assisted coding environments. It is designed to help developers fine-tune how Claude and similar models behave within coding workflows, ensuring more consistent and high-quality outputs. The project emphasizes practical usability, offering ready-to-use configurations that can be directly integrated into development environments. It also includes... -
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FuzzyAI Fuzzer
A powerful tool for automated LLM fuzzing
FuzzyAI is an open-source fuzzing framework designed to test the security and reliability of large language model applications. The tool automates the process of generating adversarial prompts and input variations to identify vulnerabilities such as jailbreaks, prompt injections, or unsafe model responses. It allows developers and security researchers to systematically evaluate the robustness of LLM-based systems by simulating a wide range of malicious or unexpected inputs. -
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ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
...With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and improving markdown conversion, reflecting active community use in research flows. It’s designed to be drop-in for MCP clients, giving them typed inputs/outputs and predictable errors around a well-known academic corpus. For developers building research copilots, it removes the glue work of wiring arXiv APIs into an agent toolchain. -
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Instructor
Structured outputs for llms
...Its customizable nature permits the definition of validators and custom error messages, enhancing data validation processes. Instructor is trusted by engineers from platforms like Langflow, underscoring its reliability and effectiveness in managing structured outputs powered by LLMs. Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE. -
21
MiMo-V2.5-ASR
Robust Speech Recognition Across Languages, Dialects
...It focuses on scalability and performance, making it suitable for both research and production applications. Overall, it represents a high-performance speech recognition solution optimized for versatility and reliability. -
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OpenLIT
OpenLIT is an open-source LLM Observability tool
...Whether you're working with popular LLM providers such as OpenAI and HuggingFace, or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights including GPU performance stats for self-hosted LLMs to improve performance and reliability. This project proudly follows the Semantic Conventions of the OpenTelemetry community, consistently updating to align with the latest standards in observability. -
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Robyn
Experimental, AI/ML-powered and open sourced Marketing Mix Modeling
Robyn is an open-source, AI/ML-powered Marketing Mix Modeling (MMM) toolkit developed by Meta Marketing Science under the “facebookexperimental” GitHub umbrella. Its goal is to democratize rigorous MMM: what traditionally required expert statisticians and expensive consulting becomes accessible to any company with data. Robyn takes in historical data (spends on different marketing channels, conversions, or revenue, and optional context or organic-media variables) and uses a combination of... -
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OpenSRE
Build your own AI SRE agents. The open source toolkit for the AI era
OpenSRE is an open-source framework designed to build AI-powered Site Reliability Engineering agents that automate incident investigation and root cause analysis across modern cloud environments. It connects to observability tools, infrastructure systems, and communication platforms to gather logs, metrics, and traces in real time. When an alert is triggered, the system autonomously analyzes correlated signals, identifies anomalies, and generates structured investigation reports with probable causes and recommended actions. ... -
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ContextForge MCP Gateway
A Model Context Protocol (MCP) Gateway & Registry
...Operators can define virtual servers, wire multiple transports, and optionally enable an admin UI for management and monitoring. Packaged for quick starts via PyPI and Docker, it targets production reliability with health checks, metrics, and structured logs. The project positions itself as an integration hub so agentic apps can “connect once, use many” backends with consistent policy and lifecycle control.
