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OpenDecider
Open decision models that route AI agents without LLM calls
Agent workflows often call an LLM just to decide where a request goes next. OpenDecider answers in one forward pass with a calibrated confidence, sending unsure cases to a fallback. Works with LangGraph, CrewAI, Agno, LlamaIndex, ADK and MCP. Apache-2.0.
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.
It filters your YouTube feed with opendecider-nano running entirely in your browser. You hide the kinds of video you choose, or topics in your own words ("Hide videos about crypto"). There's no server, account or API key, and it works offline after one download.
Feedback on what it hides wrongly is the most useful thing you can send me.
Update: two of the three "coming next" items have shipped.
✅ Guardrails (0.5.0). A prompt guard against jailbreaks and prompt injection, plugged into each framework's own hook. The default guard model, opendecider-small-td, catches as many attacks as Laya's guard with about half the false alarms.
✅ TypeScript client (0.6.0). npm i @opendecider/client. Typed decisions, the router and the guard, with tools for the Vercel AI SDK and Mastra. It has no runtime dependencies and publishes with npm provenance, and each version goes live only after a maintainer approves it with 2FA.
🔜 nano in the browser. We're building this now: opendecider-nano as ONNX, with WebGPU and WebAssembly backends. It already runs in Chrome; we're checking every quantised build against the full benchmarks before it ships.
If you're putting guardrails or routing into a TypeScript or browser app, what would you want from it? Feedback is very welcome.
About OpenDecider on Product Hunt
“Open decision models that route AI agents without LLM calls”
OpenDecider was submitted on Product Hunt and earned 2 upvotes and 3 comments, placing #112 on the daily leaderboard. Agent workflows often call an LLM just to decide where a request goes next. OpenDecider answers in one forward pass with a calibrated confidence, sending unsure cases to a fallback. Works with LangGraph, CrewAI, Agno, LlamaIndex, ADK and MCP. Apache-2.0.
OpenDecider was featured in Productivity (663.5k followers), Open Source (69k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 350.6k products, making this a competitive space to launch in.
Who hunted OpenDecider?
OpenDecider was hunted by Manjunath Janardhan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Manjunath Janardhan