Tools tagged with "Structured output"

Cog packages machine learning models for researchers and developers serving them on their own infrastructure. It handles the container environment and inference API around a Python model.

Firecrawl turns web pages into content that developers can use in AI apps and agents. Its self-hosted API suits teams that want to collect web data on infrastructure they control.

SGLang is a self-hosted inference framework for teams serving AI models to applications. It supports production workloads on a single GPU or across distributed clusters, including AI agents and reinforcement learning rollouts.

FreeLLMAPI gives developers one API for experimenting with cloud models and local backends. The router runs locally, but cloud inference still sends requests to the providers you enable.

vLLM serves language models on hardware you control. It's for developers and teams building model APIs that need to handle concurrent requests while using memory and compute efficiently.

LightLLM is a self-hosted LLM inference and serving framework for developers and teams running models on their own GPU servers. Its focus is serving requests efficiently: it schedules work around predicted memory use and handles text encoding, model inference and output decoding asynchronously to improve GPU utilization.

LocalSheets is a spreadsheet for people who want their workbooks on their own disk, with optional AI through a local Ollama instance. It runs from a single HTML file in a desktop browser and works offline without an account or cloud service. The app doesn't collect analytics or tracking data.

xberg, formerly Kreuzberg, is a local document extraction engine for developers building AI search and document processing applications. It turns files into text and structured content that those applications can use.

open·kritt helps security researchers and developers investigate code for vulnerabilities through focused AI agent tasks. It gives teams control over the research prompts, workflows, model providers, and infrastructure they use to review a codebase.

CrewAI is a Python framework for developers automating work with teams of AI agents. It combines agents that make decisions with explicit workflow logic, so a process can include both autonomous work and predictable steps.

Claudio coordinates multiple Claude Code sessions on one repository. It's for developers who want agents to tackle separate coding tasks in parallel, with each session working on its own branch.

Unstract is a self-hosted, no-code platform for teams that need structured data from documents whose layouts vary. It uses LLMs to extract JSON from PDFs, images, scans and office files without building a template for each vendor or form. Its use cases include finance, insurance, healthcare and KYC document processing.

Agently is a Python AI agent framework for developers building assistants and service backends with local Ollama models or hosted model APIs. It runs within your application, without requiring a separate orchestration service. Model requests go to the endpoint you choose, so using a remote provider sends the request data outside your machine.

Kestra coordinates AI, data and infrastructure workflows for teams that need to connect tasks and track their execution. It can run existing scripts and containers, so workflows can span tools written in different languages.

LLM Farm is an app for on-device AI conversations and comparing language models on Apple hardware. It suits people who want offline chat or need to assess which model fits a project.

LM Studio runs downloaded language models for conversations and tasks on your computer. Its Bionic agent helps people create documents, write software and carry out work using local or frontier open models.

TabbyAPI is a self-hosted LLM API server for people who want to run ExLlamaV3 models on their own hardware. It’s built for personal projects and small groups; its maintainers don’t recommend it for production servers.

Flawless provides CISRE, a self-hosted AI SRE platform for teams investigating Kubernetes incidents. It connects evidence collection and diagnosis with approved changes and checks that the affected resource has recovered.

Stagehand is an open-source browser agent SDK for developers automating websites and collecting structured data in local Chrome. Model inference is separate from browser execution: direct provider calls send model context remotely, while a custom callback can connect a self-hosted model. Local runs require Chrome.

OpenMAIC is a self-hosted AI classroom platform for learners and educators building lessons from a topic or their own materials. AI teachers and classmates explain concepts and discuss questions during the lesson.

DSPy is a Python framework for developers building language model applications with local backends such as Ollama on a laptop or SGLang on a GPU server. It connects application code to those model servers, which handle inference on your hardware. It also supports cloud providers, so where model requests go depends on the backend you choose.

Adala is a Python framework for AI engineers and data scientists who want to run data-labeling and evaluation workflows in their own process. It works with local pandas DataFrames and ground-truth examples, but the documented OpenAI and OpenRouter configurations send task inputs to cloud models. Those connections need internet access and an API key.

Loop.js is an AI agent framework for developers who want repeated attempts at a task with a separate check of the result. You define the goal and completion criteria, and the engine coordinates work until the verifier accepts it or a stopping condition ends the run.

MCP Agent is a Python framework for developers building AI agents that use tools through Model Context Protocol (MCP). It handles connections to MCP servers and provides reusable workflows for coordinating agents across a task.

Ollama JavaScript is a library for developers adding model chat, generation or retrieval features to JavaScript applications. It connects those applications to an Ollama server, including one running locally.

AG2 is an open-source Python framework for developers building cooperating AI agents on their own machines or servers. Its Ollama integration lets agents use a local LLM; it also supports OpenAI, Anthropic and Gemini APIs, which send model requests to those providers. It runs on macOS, Windows and Linux under the Apache 2.0 license.

RubyLLM is an AI framework for Ruby and Rails developers building chat and agent features into their applications. Its shared API lets you switch between local and hosted models without writing a separate integration for each provider.

BAML is a programming language for developers building AI agents, with a documented Ollama client for models running locally and tracing that runs on your machine. The OllamaClient integration is documented for the canary toolchain. The standard OpenAI connection sends model requests to OpenAI; installing BAML locally does not make that connection offline.

WebLLM is an engine for developers building browser-based AI assistants and Chrome extensions. It runs language models on the user's device, so applications don't need a separate inference server to process prompts.

Prismos AI is a desktop assistant for people working with private documents and technical files. It uses local Ollama models to answer questions and retain knowledge for later conversations.

Llama Desktop runs language models on your computer for private chat and use in other apps. It's for people who want a local ChatGPT alternative and developers connecting coding agents, editors or scripts to their own models.

Pydantic DeepAgents is a terminal coding assistant and Python framework for developers who want to use or build their own AI agents. Both share a Pydantic AI foundation for carrying out tasks across files and tools.

vllm-mlx is a local LLM inference server for developers who want to connect AI apps and coding assistants to models running on an Apple Silicon Mac. One process exposes both OpenAI-compatible and Anthropic-compatible APIs. It's open source under Apache 2.0 and runs on macOS with at least 8 GB of unified memory.

Guidance is a Python library for developers who need language model responses in a defined format. It controls output during generation for applications that expect structured data or specific syntax.