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Showing 20 open source projects for "test"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
...GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI). -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
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AWS CodeDeploy Agent
Host Agent for AWS CodeDeploy
...AWS CodeDeploy fully automates your software deployments, allowing you to deploy reliably and rapidly. You can consistently deploy your application across your development, test, and production environments whether deploying to Amazon EC2, AWS Fargate, AWS Lambda, or your on-premises servers. The service scales with your infrastructure. -
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TinyCog
Cognitive architecture with a focus on self-models
TinyCog is a cognitive architecture (perception, action, reasoning, planning, and language) with a focus on representing "self-models" (physical, capabilities, intentions, social, historical, "cognitive stream") in order to implement social reasoning and collaborative planning in a "hunter domain" test bed. We use Prolog, RDF Triples, Description Logic and a HTN-Planner as base technologies. -
Loan management software that makes it easy.Bryt Software is ideal for lending professionals who are looking for a feature rich loan management system that is intuitive and easy to use. We are 100% cloud-based, software as a service. We believe in providing our customers with fair and honest pricing. Our monthly fees are based on your number of users and we have a minimal implementation charge.
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TDD MAS Toolkit
A set of powerful tools to perform TDD on MAS based on JADE
The TDD MAS Toolkit provides a set of tools to perform Test Driven Development of MAS based on Jade. The toolkit allows developers to set up simulation based and test case scenario based testing, providing powerful insights of the MAS under construction and assuring that the interation protocols and agent interaction in general are been enacted as expected. -
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DisChoco
DisChoco: Distributed Constraint Reasoning Solver
DisChoco is a Distributed Constraint Reasoning Solver. Several Distributed Constraint Reasoning algorithms are implemented (like ABT, AFC, Adopt, ...). Users may easily implement and test their algorithms with DisChoco. contact: wahbi[at]users[dot]sourceforge[dot]net -
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TuCSoN
TuCSoN tuple-based coordination middleware
TuCSoN (Tuple Centres Spread over the Network) is a tuple-based middleware for the coordination of distributed systems – including multi-agent systems, mobile agents and pervasive systems. -
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april
Simulating worlds in a computer
...The code is structured in several layers: - april-core simply defines the library and the objects - april-gui builds on the concepts in april-core, giving a visual interpretation to the concepts - AprilDream is a GUI program that uses april-gui objects to display a World in evolution. The code base also contains a test suite based on [Google test](http://code.google.com/p/googletest/) and a set of ever-growing commented examples. -
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Sentence Composer
Compose the writings semi-automatically
...SAMPLE: Input sentence was "Learning algorithm" The prefix was "transductive learning" Learning algorithm. Transduction, or transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases. The simplest realization for transductive inference is the method of k-nearest neighbors. Learning to learn learns its own inductive bias based on previous experience. ...AND SO ON.... Enjoy the program ! -
Dun and Bradstreet Risk Analytics - Supplier IntelligenceRisk, procurement, and compliance teams across the globe are under pressure to deal with geopolitical and business risks. Third-party risk exposure is impacted by rapidly scaling complexity in domestic and cross-border businesses, along with complicated and diverse regulations. It is extremely important for companies to proactively manage their third-party relationships. An AI-powered solution to mitigate and monitor counterparty risks on a continuous basis, this cutting-edge platform is powered by D&B’s Data Cloud with 520M+ Global Business Records and 2B+ yearly updates for third-party risk insights. With high-risk procurement alerts and multibillion match points, D&B Risk Analytics leverages best-in-class risk data to help drive informed decisions. Perform quick and comprehensive screening, using intelligent workflows. Receive ongoing alerts of key business indicators and disruptions.
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Siafu simulates individual agents and their context, from home to city-wide scenarios. As a developer, you use the API to write your simulation for the purposes of data-set generation, test or visualization, optionally hooking it to your own application.
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This project aims to test Distributed Artifitial Intelligence in a urban traffic light control. GlobalSyncLocal is used to interact with the SUMO simulator through the TraSMAPI.
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openEAR is the Munich Open-Source Emotion and Affect Recognition Toolkit developed at the Technische Universität München (TUM). It provides efficient (audio) feature extraction algorithms implemented in C++, classfiers, and pre-trained models on well-known emotion databases. It is now maintained and supported by audEERING. Updates will follow soon.
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The aim of the project is to provide a testing tool that would reduce the cost associated with the regression testing process. The tool provides test case subset selection so that the user need not run the redundant test cases.
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An open source research test bed for investigating decision-making in groups comprising people, computers and a mix of these two.
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"Java Artificial Intelligence Markup Language PAD" is a tool that manages ProgramD AI (on local or remote machines) and AIML files with real-time previews and it provides a network support to test AI capabilities over many network protocols.
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JAgent is a multi-agents system test and development platform. It provides a framework and GUIs helping to create and test collective strategies in a multi-agents system.
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This package contains code for the game of EasyGo, a modified version of the original game of go. This package provides a simple API so that the users can code and test their own players. Thus one can not only play, but also program their own players.
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In this project we develop algorithms that can solve a cooperative problem between four agent types, and their goal is to repair a surface time-efficiently, and a multi-agent simulator application to parametrize, animate, and test the solution.
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It's an implementation of the A* algorithm together with a grid processor which pre-processes 8-bit bpm graphic files using a variable resolution and an SDL user interface to test it.
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videonetwork
A simulator for self-organizing video replication and more
...The transport path of content is exploited to replicate content and limited storage space is handled by clean-up mechanisms. We implemented a simulator to show the algorithm's performance and to test its behavior under real circumstances. It is easily extendable to support several new scenarios or changes to the algorithm (see replication and client models).
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