Search Results for "tiny-workflow" - Page 2
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Showing 48 open source projects for "tiny-workflow"
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MetaScreener
AI-powered tool for efficient abstract and PDF screening
MetaScreener is an open-source AI-assisted tool designed to streamline the screening process in systematic literature reviews and academic research workflows. The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with... -
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DATAGEN
AI-driven multi-agent research assistant automating hypothesis
...The system coordinates multiple specialized AI agents that collaborate to perform tasks such as hypothesis generation, data collection, analysis, visualization, and report creation. Instead of requiring users to manually orchestrate each stage of a research process, the platform allows these agents to coordinate automatically and handle the workflow end-to-end. The project integrates several modern AI frameworks including LangChain, LangGraph, and large language models to manage reasoning and data processing tasks. Through this architecture, the system can combine structured data analysis with natural language reasoning to generate insights and research outputs. The platform is designed for researchers, analysts, and developers who want to accelerate data exploration and automate parts of the research lifecycle. -
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LlamaDeploy
Deploy your agentic worfklows to production
...It enables teams to move from experimental prototypes to production systems with minimal changes to existing LlamaIndex code, making it easier to operationalize AI agents. The system supports orchestrating multiple services, handling communication between agents, and managing workflow execution in distributed environments. Developers can define workflows that involve multiple steps such as data retrieval, reasoning, tool invocation, and response generation, then deploy them using the framework’s infrastructure tools. The design emphasizes scalability, modularity, and fault-tolerant execution so that agent systems can run reliably in production environments. -
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oterm
the terminal client for Ollama
Oterm is an open-source terminal client designed to provide a lightweight command-line interface for interacting with large language models through the Ollama ecosystem. The tool allows users to chat with local AI models directly from the terminal without needing a graphical interface or web application. Its interface is designed to be simple and intuitive, enabling developers to launch conversations quickly using a single command. Oterm supports persistent chat sessions that store... -
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OM1
Modular AI runtime for robots
OM1 is an open-source AI platform designed to build autonomous agents capable of interacting with digital environments and completing complex tasks. The project focuses on creating a modular architecture where language models can coordinate with external tools, APIs, and knowledge sources to accomplish multi-step objectives. Instead of operating as simple conversational systems, OM1 agents can plan actions, retrieve information, and execute tasks across different services. The framework... -
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VLMEvalKit
Open-source evaluation toolkit of large multi-modality models (LMMs)
VLMEvalKit is an open-source evaluation toolkit designed for benchmarking large vision-language models that combine visual understanding with natural language reasoning. The toolkit provides a unified framework that allows researchers and developers to evaluate multimodal models across a wide range of datasets and standardized benchmarks with minimal setup. Instead of requiring complex data preparation pipelines or multiple repositories for each benchmark, the system enables evaluation... -
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CodeGen
Open-source model for program synthesis
CodeGen is a family of open-source large language models designed specifically for program synthesis and code generation tasks. Developed by Salesforce Research, the models are trained on large datasets containing both natural language and programming language content. This allows them to translate natural language descriptions into functional code across a variety of programming languages. CodeGen supports multi-turn program synthesis, meaning it can generate complex programs through a... -
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MING
A large-scale model of medical consultation in Chinese
MING is an open-source medical large language model designed for intelligent medical consultation and question answering in Chinese. The project focuses on building a healthcare-focused conversational system capable of responding to medical questions, analyzing case descriptions, and guiding diagnostic reasoning. It is trained using medical instruction tuning so that the model can understand patient symptoms and respond with structured explanations and clinical suggestions. One of its... -
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Shell-AI
LangChain powered shell command generator and runner CLI
Shell-AI is an open-source command-line interface utility that allows users to generate and execute shell commands using natural language prompts. Instead of requiring users to remember complex command syntax, the tool lets them describe their intent in plain English and automatically suggests commands that accomplish the task. The system is powered by large language models and integrates with frameworks such as LangChain to interpret user requests and translate them into executable shell... -
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E2B Desktop Sandbox
E2B Desktop Sandbox for LLMs. E2B Sandbox
...The platform provides isolated virtual machines where applications can be executed safely without affecting the host system. Each sandbox runs independently and can be configured with custom dependencies or tools required by an AI agent or automation workflow. The system allows developers to programmatically create and control these virtual desktops through SDKs available in languages such as Python and JavaScript. Within a sandbox, developers can launch applications like browsers, editors, or other software that an AI agent may need to interact with. This approach is particularly useful for building AI agents capable of interacting with graphical environments or performing tasks such as browsing, testing software, or automating workflows. -
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Claude Code Skills & Plugins Hub
270+ Claude Code plugins with 739 agent skills
...The repository functions as a marketplace-style collection of hundreds of plugins and specialized skills that enable Claude Code to perform complex development, automation, and operational tasks. These plugins cover a wide range of domains including DevOps automation, security testing, API debugging, infrastructure management, and AI workflow orchestration. The project also includes orchestration patterns and best practices that guide how multiple AI agents or tools can collaborate effectively in software development workflows. Developers can install plugins through a package-style plugin system and integrate them with their Claude Code environment using standardized commands. -
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
...The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. Developers define data transformations and AI operations using computed columns on tables, allowing pipelines to evolve incrementally as new data or models are added. The framework supports multimodal content including images, video, text, and audio, enabling applications such as retrieval-augmented generation systems, semantic search, and multimedia analytics. -
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Lagent
A lightweight framework for building LLM-based agents
Lagent is a lightweight open-source framework designed to help developers build autonomous agents powered by large language models. The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with... -
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DocETL
A system for agentic LLM-powered data processing and ETL
...Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
...It also supports flexible embeddings, making it easier to choose different embedding models depending on domain requirements, latency targets, or accuracy goals. The overall workflow aims to minimize hallucinations by grounding outputs in retrieved material and then applying structured reasoning over that evidence before generating a final report. -
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promptmap2
A security scanner for custom LLM applications
...The project supports both white-box and black-box testing, which means it can either run tests directly against a known model and system prompt configuration or attack an external HTTP endpoint without internal access. Its scanning workflow uses a dual-LLM architecture in which one model acts as the target being tested and another acts as a controller that evaluates whether an attack succeeded. The repository emphasizes broad coverage, including test rules for prompt stealing, jailbreaks, harmful content generation, hate-related outputs, social bias, and distraction attacks. ... -
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Google Workspace MCP Server
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms
Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google... -
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LangChain-ChatGLM-Webui
Automatic question answering for local knowledge bases based on LLM
...By leveraging the LangChain framework, the platform allows developers to integrate tools such as vector databases, document loaders, and prompt chains into the chatbot workflow. The web interface simplifies the process of running and experimenting with ChatGLM models locally or on servers without requiring extensive command-line configuration. -
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Canopy
Retrieval Augmented Generation (RAG) framework
...The system provides a complete pipeline for transforming raw text data into searchable embeddings, storing them in a vector database, and retrieving relevant context for language model responses. It is designed to handle many of the complex components required for a RAG workflow, including document chunking, embedding generation, prompt construction, and chat history management. Developers can use Canopy to quickly build chat systems that answer questions using their own data instead of relying solely on the pretrained knowledge of the language model. The framework includes a built-in server and command-line interface that allow users to experiment with RAG pipelines and compare outputs between retrieval-augmented responses and standard LLM responses. -
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ToRA
Tool-integrated Reasoning LLM Agents
...This approach allows the model to reason step by step in natural language and then execute precise calculations or code through tool calls, creating a hybrid reasoning workflow. The framework was designed to address known weaknesses of large language models in mathematical problem solving and formal reasoning tasks. Training data includes tool-use trajectories that teach the model when to reason verbally and when to delegate tasks to specialized tools. -
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Firefly LLM
A large model training tool that supports training large models
Firefly is an open-source framework designed to simplify the training and fine-tuning of large language models through a unified and configurable workflow. The project provides a comprehensive environment where developers can perform tasks such as model pre-training, instruction tuning, and preference optimization using widely adopted machine learning techniques. Its architecture supports both full-parameter training and parameter-efficient strategies like LoRA and QLoRA, making it suitable for environments with limited computational resources. ... -
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ReAct Prompting
Synergizing Reasoning and Acting in Language Models
ReAct is an open-source research project that demonstrates a prompting and reasoning framework designed to improve the problem-solving capabilities of large language models. The project implements the methodology described in the research paper “ReAct: Synergizing Reasoning and Acting in Language Models,” which combines reasoning traces with action-based interactions. Instead of generating answers in a single step, models using the ReAct approach produce intermediate reasoning steps and... -
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mindflow
AI-powered CLI git wrapper, boilerplate code generator, chat history
I-powered CLI git wrapper, boilerplate code generator, chat history manager, and code search engine to streamline your dev workflow. The ChatGPT-powered swiss army knife for the modern developer! We provide an AI-powered CLI git wrapper, boilerplate code generator, code search engine, a conversation history manager, and much more! Configure the model used for generating responses by running mf config and selecting either GPT 3.5 Turbo (default) or GPT 4. In order to use GPT 4, you'll need to have special access to the API. ...
