Search Results for "teams"
Sort By:
Showing 86 open source projects for "teams"
View related business solutions-
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.
-
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
1
GoClaw
GoClaw is OpenClaw rebuilt in Go
...It is best suited for teams that want to deploy AI agent teams at scale while keeping governance, isolation, and operational visibility built into the system. -
2
Tribe AI
Low code tool to rapidly build and coordinate multi-agent teams
Low code tool to rapidly build and coordinate multi-agent teams. Have you heard the saying, 'Two minds are better than one'? That's true for agents too. Tribe leverages on the langgraph framework to let you customize and coordinate teams of agents easily. By splitting up tough tasks among agents who are good at different things, each one can focus on what it does best. This makes solving problems faster and better. -
3
Agent Control
Centralized agent control plane for governing runtime agent behavior
...The repository includes SDKs, a server, telemetry components, examples, and integrations for common agent frameworks. It is especially useful for teams building customer-facing, internal, or enterprise agents that need scalable runtime guardrails. -
4
Harness
A meta-skill that designs domain-specific agent teams
Harness is a meta-skill framework designed to automate the creation and orchestration of domain-specific AI agent teams within Claude Code environments. Instead of manually defining agents and their behaviors, it generates specialized agents, assigns them roles, and produces the skills they need to execute tasks effectively. The system focuses on scaling agent-based workflows by dynamically assembling teams tailored to specific problems. It enables structured collaboration between agents, allowing them to divide work and operate in coordinated pipelines. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
5
Activepieces
Open Source AI Automation
Activepieces is an open-source automation tool designed to build workflows that connect different apps and services without requiring extensive programming knowledge. It’s tailored for technical and non-technical users alike, enabling teams to automate repetitive tasks using a visual editor and a large library of pre-built connectors. Activepieces can be self-hosted or used via a cloud deployment, making it flexible for teams of all sizes. It supports integrations with popular services like Slack, Google Sheets, and Discord, and allows users to create custom pieces to suit unique needs. ... -
6
Kanwas
Shared context board for teams and agents
Kanwas is an open-source shared context board built for teams and AI agents working together in the same workspace. It gives people and agents a common canvas where documents, evidence, decisions, notes, tasks, embeds, and outputs can live side by side. Instead of scattering context across chats, documents, and disconnected tools, Kanwas turns messy collaborative work into a shared visual environment that both humans and agents can read and update. -
7
Future AGI
Open-source platform for evaluating, observing, and improving LLM
Future AGI is an open-source, end-to-end platform for evaluating, observing, protecting, and improving AI agent applications. It is built for teams that need more than basic tracing, combining evaluations, simulations, datasets, guardrails, gateway routing, and optimization in one feedback loop. The platform helps developers detect hallucinations, measure agent quality, monitor production behavior, and use evaluation results to improve prompts or workflows over time. It supports both cloud and self-hosted deployment models, making it useful for teams with different privacy, infrastructure, and compliance needs. ... -
8
Coze Loop
Next-generation AI Agent Optimization Platform
...The platform also includes automated evaluation capabilities that assess agent performance across multiple quality dimensions such as accuracy and compliance. Its observability layer captures detailed execution traces, enabling teams to understand how inputs, prompts, and tools interact during runtime. Designed as an extensible open-source framework, Coze Loop helps teams move beyond ad-hoc prompt experiments toward structured, production-ready AI agent operations. -
9
Agent Skills
Specification and documentation for Agent Skills
...This repo serves as the canonical reference for how skills should be structured, what metadata they should include, and how an SDK can load and apply them consistently. It also includes supporting materials like guides and examples so builders can create skills that are predictable, testable, and shareable with teams. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
AIPex
AI browser automation assistant, no migration and privacy first
...Rather than treating AI as a separate helper, AIPex embeds AI capabilities into common tasks like scaffolding components, generating tests, analyzing code quality, and performing refactors, allowing developers to stay in flow while benefiting from model-assisted insights. It supports modular plugin architecture so teams can extend or customize how assistants behave based on project conventions, code standards, or tooling preferences. AIPex also includes orchestration pipelines that let teams define multi-step AI-driven transformations — for example, generating code then running validation, producing documentation, and opening change requests — all within a unified pattern. -
11
ASSERT
Requirement-driven evaluation harness for AI agents and LLM
...It can evaluate hosted models, custom agents, multi-agent systems, REST clients, and frameworks such as LangGraph, CrewAI, AutoGen, DSPy, LlamaIndex, and OpenAI Agents SDK. ASSERT is designed to close the gap between what a system is supposed to do and what evaluation actually measures. It is useful for responsible AI teams, product teams, and developers who need traceable, spec-aligned testing. -
12
MemClaw
Persistent memory for AI agent fleets (OSS)
...Its architecture goes beyond a simple vector database by adding rules about who can store, retrieve, and share each memory. caura-memclaw is best suited for teams building AI agents that need long-term memory, controlled sharing, compliance awareness, and safer cross-agent coordination. -
13
Rewriting Project Claw Code
Ensure consistency and alignment between different codebases
...Overall, Claw Code Parity helps teams maintain cohesion across their codebases while improving development efficiency and quality assurance. -
14
Superagent
Superagent protects your AI applications
...Superagent is designed for low-latency production environments and works with any major LLM provider. It enables teams to prove compliance with modern AI security and regulatory standards. -
15
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, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together. -
16
Bytebot
Bytebot is an AI desktop agent that automates computer tasks
...Bytebot can be embedded into editors, terminals, or CI/CD pipelines to provide contextual recommendations, highlight quality issues, and propose fixes based on understanding of code and project context. The framework is often extensible, allowing teams to define custom commands, workflows, or plug-ins that connect to internal APIs, coding standards, or proprietary repositories. -
17
kagent
Kubernetes native framework for building AI agents
Kagent is a Kubernetes-native framework for building, deploying, and operating AI agents as first-class cloud-native workloads. It models core agent concepts declaratively using Kubernetes custom resources, so teams can manage agents similarly to other platform components via YAML, controllers, and standard cluster workflows. In kagent’s design, an “Agent” represents a system prompt plus a set of tools and other agents, along with an LLM configuration, making the agent definition portable and repeatable across environments. It supports multiple model providers through a dedicated configuration resource, allowing teams to switch providers or run mixed environments while keeping the agent spec stable. ... -
18
PentAGI
Perform penetration testing tasks
...It leverages agent-based architecture and AI reasoning to chain together tools and strategies in a way that mimics experienced human testers. The project is built to be modular and extensible so researchers and red teams can customize behavior or integrate additional tools as needed. By focusing on autonomous decision-making in cybersecurity contexts, PentAGI represents part of the broader trend toward AI-assisted offensive security automation. -
19
Vibe-Trading
Vibe-Trading: Your Personal Trading Agent
...The platform integrates multiple data sources, including equities, crypto, and derivatives, with automatic fallback mechanisms. 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. -
20
Chatbox
The Ultimate AI Copilot on Your Desktop
...Built with an ergonomic UI, it’s optimized for long sessions, prompt experimentation, and everyday productivity. The app supports rich formatting, streaming responses, and advanced prompting to help you get clearer, more useful outputs. For individuals and teams alike, Chatbox serves as a powerful desktop copilot that blends simplicity with flexibility. -
21
Sublayer
A model-agnostic Ruby Generative AI DSL and framework
Sublayer is a platform that enables developers to build and deploy machine learning models with ease, focusing on simplifying the ML lifecycle from development to production. -
22
GSD 2
A powerful meta-prompting, context engineering
...It supports modular configurations, allowing users to adapt the build process to different project requirements. The project also emphasizes efficiency, enabling faster iteration and deployment cycles. It is particularly useful for teams looking to standardize their workflows and reduce friction in development pipelines. Overall, gsd-2 functions as a productivity tool for managing complex build and deployment processes. -
23
Alook
The collaboration layer for your AI workforce
...The platform connects local agents to external communication channels while keeping the actual agents and codebase running on the user’s own machine. It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. Alook also emphasizes memory and traceability, so agents can remember past decisions, learn preferences, and keep a record of instructions and replies. Its main value is turning separate AI coding tools into an organized, always-on operating system for agent-based work. -
24
OpenAI Symphony
Symphony turns work into isolated, autonomous implementation runs
...Each run generates proof of work such as CI results, pull requests, code reviews, and analysis to validate the completed task. By automating execution and verification, Symphony helps engineering teams scale development workflows with minimal manual oversight. -
25
pg-aiguide
MCP server and Claude plugin for Postgres skills and documentation
...It focuses on improving developer productivity by reducing errors and providing context-aware suggestions. The project also includes documentation and examples that help users understand best practices in database design and querying. It is particularly useful for teams working heavily with PostgreSQL who want to leverage AI tools effectively. Overall, pg-aiguide enhances database-related AI workflows by providing structured, domain-specific knowledge.