structured-output

LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows

Repair malformed JSON from LLMs, APIs, logs, and user input in Python.

本地监控 + AI 视觉 — LAN-based smartphone-powered AI monitoring framework with structured event output for data acquisition and analysis.

MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements OpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.

Unified Go interface for Language Model (LLM) providers. Simplifies LLM integration with flexible prompt management and common task functions.

A versatile workflow automation platform to create, organize, and execute AI workflows, from a single LLM to complex AI-driven workflows.

Python library for building with LLMs. One interface across 11 providers (OpenAI, Anthropic, Gemini, DeepSeek, Ollama and more), automatic failover, Pydantic-validated structured output, embeddings with vector search, and an AI-powered router. pip install simplerllm

Open agentic prompt-expansion harness for image and video generation, bridging polished demos, public APIs, and deployable workflows.

Turn documents into structured JSON with local-first document AI. Run 100% locally by default, with API, CLI, and Web UI.

A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline.

LLM-powered security log analyzer: detect threats & anomalies with zero regex — just declare a Pydantic schema. Real-time Telegram alerts, SIEM-ready with Elasticsearch/Kibana. Supports OpenAI, Ollama, vLLM.

Intent as Code | the workflow language for AI. One file, 4 verbs, one Rust binary. Local-first, any model, AGPL-3.0. 🦋

A Go toolkit for building AI agents and applications across multiple providers. Unified LLM client, agent framework with handoffs, tool calling, streaming, structured output, MCP integration, and cost tracking.

Validate and retry LLM outputs for ruby_llm. Describe the JSON response you expect, fall back to a stronger model when the cheaper one fails the rules, and gate CI on regressions — all as one contract object per step.

A comprehensive Go client library for the Perplexity AI API with support for chat completions, async jobs, streaming, multimodal messages, structured outputs, and web search integration

Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS.

Code from the ODSC Agentic Graph RAG workshop combining vector, FTS & graph retrieval for RAG. Includes observability and guardrails for evaluating outputs.

OpenRouter skill for AI agents: model discovery, multimodal chat, tool calling, routing, and starter templates.

Qwen3.6 vLLM Toolkit — Launcher + Templates, Optimized for 2×24 GB. vLLM launcher with an optimized hybrid chat template drawing from the best community fixes for Qwen 3.6 27B.