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Showing 10 open source projects for "problem a"
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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1
pua
Your AI has been placed on a PIP. 30 days to show improvement
...The project frames the AI as an underperforming senior engineer under evaluation, introducing a strong motivational and behavioral prompt layer that encourages proactive problem-solving and initiative. Rather than adding new technical capabilities, PUA focuses on shaping how the AI approaches tasks, pushing it toward more assertive, autonomous, and results-driven outputs. It is designed to influence decision-making patterns, encouraging the AI to take ownership of tasks, iterate aggressively, and prioritize delivering meaningful outcomes within constrained timeframes. -
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CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI... -
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Agency Agents
A complete AI agency at your fingertips
...It is designed around the idea of “agency,” where each agent has a defined role, responsibility, and interaction pattern within a larger system. The framework enables developers to define workflows where agents communicate, delegate tasks, and share context, creating a distributed problem-solving environment. It supports modular design, allowing agents to be composed into reusable systems that can be adapted across different use cases. The project emphasizes clarity in agent responsibilities, which helps reduce ambiguity and improve reliability in multi-agent workflows. It also provides mechanisms for managing interactions, ensuring that communication between agents remains structured and predictable. ... -
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The Fable Method
How Claude Fable 5 worked, distilled into skills
The Fable Method is a structured workflow for improving how AI agents reason, act, verify, and report. It converts observed problem-solving habits into explicit steps that different language models can follow. The core process classifies the request, defines completion criteria, gathers primary evidence, chooses one recommendation, makes the smallest correct change, and verifies the result. Four included skills cover planning, execution, judging completed work, and generating domain-specific adapters. ... -
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5
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 automation. ... -
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Tutti
Where people and agents build in tune
Tutti is an open-source shared workspace for people working with multiple AI agents. It is built to reduce the copy-paste handoff problem between tools such as Claude Code, Codex, Canvas, and other agent systems. The workspace connects context, files, app outputs, conversations, and running tasks so agents can build on each other’s work. Users can reference previous conversations, files, tasks, and app results directly instead of re-explaining the project state. Tutti also includes an app center for design, images, documents, presentations, and other workflow outputs. ... -
7
Clawith
OpenClaw for Teams
...The system likely emphasizes integration with external tools and services, allowing agents to extend their capabilities beyond internal reasoning. Its architecture suggests support for multi-agent collaboration, enabling distributed problem-solving and task delegation. It may also include monitoring and control features to ensure that agent behavior remains aligned with user goals. The project reflects a broader trend toward building AI systems that act as autonomous operators rather than passive assistants. Overall, Clawith serves as a foundation for building advanced, action-oriented AI workflows. -
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ClawTeam
ClawTeam: Agent Swarm Intelligence (One Command → Full Automation)
...Instead of relying on a single agent, the system allows a leader agent to spawn and coordinate multiple specialized sub-agents, each responsible for different aspects of a problem. These agents communicate, share insights, and dynamically adapt their strategies based on real-time feedback, creating a form of collective intelligence. The framework supports a wide range of use cases, including software development, machine learning research, financial analysis, and content production. It is designed to work with various AI tools and command-line agents, making it highly flexible and extensible. ... -
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EdgeChains
EdgeChains.js is Full-Stack GenAI library
...At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure. -
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10
AutoGroq
Revolutionizes the way users interact with Autogen
...By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency. AutoGroq was born out of the realization that the traditional approach to building AI agents was backwards. Instead of creating agents in anticipation of problems, AutoGroq uses the syntax of the users' needs as the basis for constructing the perfect AI team. It's how we wished Autogen worked from the very beginning. ...
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