DecisionLoop: Python agent framework swaps LLM calls for a small decision model, claims better accuracy at lower cost
AgitatedReveal07 · reddit · 2026-10-13
A Reddit user unveiled DecisionLoop, a Python agent framework where a small decision model handles intent detection, tool selection, argument generation, and control flow instead of calling an LLM for every decision.
- Early tests across three use cases (order support, a flight desk, a banking desk) showed better tool-call accuracy and lower cost and latency than a LangChain LLM agent built on a 12B model, though results varied with smaller models; the author stresses these are preliminary, not a proper benchmark.
- Plans include a multi-agent architecture with a router delegating requests to specialist agents.
- The author is seeking feedback on architecture, real-world use cases, stress-test scenarios, and fair benchmarking against stronger LLMs.
More from coding & agent
- Dev: vibe coders with Claude can't yet beat 15-year principal engineers — but he expects yes within a year — facontidavide · 2026-10-13
- Open-Source MCP Security Gate Blocks Prompt Injections in Under 5ms — EstablishmentTough18 · 2026-10-13
- Open-source Claude skill writes working prompts on first try for 18+ AI tools — nikola_mr64990 · 2026-10-13
- When does an internal AI agent tool deserve to become a standalone app? — Moist_Deal_9995 · 2026-10-13
- Humanizer, 56k stars: strips AI-writing tells using Wikipedia's checklist — sujingshen · 2026-10-13
- Tencent open-sources Agent Memory: local SQLite memory layer cuts tokens 61% — rohan_mehta_charts92 · 2026-10-13