#structured-output

Production-ready AI Business OS — 46 autonomous AI Employees, Graph RAG (Neo4j), multi-channel, BYOK across 17 LLM providers plus custom/local endpoints (Ollama), automatic fallback. 8 libs (Python+Java). Self-hosted, MIT licensed, guardrailed autonomy for regulated industries.

Loom is a high-performance, graph-based AI workflow engine for Go. It allows you to build complex agentic systems by defining multi-step workflows as directed graphs, seamlessly connecting them to any large language model, and persisting execution state for long-running or interrupted tasks.

Headless AI form filling and conversational refinement — shared across the fleet

A Go engine for talking to LLMs that doesn't give a shit which one. Streaming, tool loops, history that actually fits the context window, retries that don't repeat themselves, and typed structured output. Swap OpenAI for Anthropic by changing one line. Library, not a framework: you own the loop.

Controls for .NET MAUI & Blazor

Опис репозиторію не опубліковано.

Go agent runtime for tool-using, code-executing, multi-provider agents with MCP support, coding-agent CLI integrations, structured output, summarization, and parallel tool execution.

Rust builders for xgrammar structural_tag tool-calling constraints

The governance layer LLM proxies promised as a TypeScript library you can read. Every call runs through one governed pipeline: rate limit → spend caps (per-user + global circuit breaker) → cache → schema-validated provider failover → usage log → LLM-judge No sidecar proxy to deploy.

Host-agnostic LLM agent execution on the OpenAI Agents SDK: model fallback, streaming, structured-output extraction, self-correction, and tracing — behind a small injected provider seam.