OpenDecider - Open decision models that route AI agents without LLM calls

Hi! I'm Manjunath, and I built OpenDecider. Why I built it: in most agent workflows I looked at, a large share of the LLM calls weren't generating anything. They were deciding: is this billing or support? Should this go to the research agent or a person? Is the answer good enough to send? Each of those decisions costs a full LLM call, adds a second or more of latency, and gives you no reliable confidence score. What it does: OpenDecider is a family of small open models trained for exactly those decisions. You give it the input, the options and a question. One forward pass returns a probability for every option, so when it's unsure it sends the case to your fallback instead of guessing. What's included: 🔹 Integrations for LangGraph/LangChain, LlamaIndex, Agno, CrewAI, Microsoft Agent Framework, Google ADK, PydanticAI and Strands, plus an MCP server for Claude Code, Cursor and Mastra. 🔹 Models from nano (milliseconds, runs on a CPU) to larger GPU models, with GGUF and MLX builds for Ollama, LM Studio and Apple Silicon. 🔹 Production pieces: a batching server with auth and Prometheus metrics, Docker images, audit hooks, fallback on errors and OpenTelemetry spans. 🔹 A runnable example for every framework, with no API key needed. Everything is Apache-2.0 with open weights, and it all runs on your own hardware, so your data never leaves it. Coming next: guardrails against jailbreaks and prompt injection in each framework's own hook, a TypeScript client, and nano running in the browser. I'd love to hear where you'd use it, and which decisions in your agents you'd hand over first. Honest feedback on what's missing is the most useful thing you can give me.