Introducing Frugal.
Tool calls are the new tokens.
Every AI agent today is massively overpaying for infrastructure because most stacks hardcode a single provider, model, or tool path.
But most tasks do not need the smartest model.
They need the cheapest model that works.
Frugal routes every tool call across free, local, and paid providers automatically.
Search.
Scraping.
Extraction.
Reasoning.
Synthesis.
Free/local first.
Paid as fallback.
Your keys.
No account.
Built for:
• Claude Code
• Cursor
• Claude Desktop
• Any MCP-compatible agent stack
bash $ frugal mcp install
The goal is simple:
Make agentic software economically sustainable.
https://t.co/cueVE9BwxD
Introducing model routing to Factory.
Factory Router picks the right model for every task, automatically.
Maintain frontier performance while cutting costs by 25%.
Local LLMs got cheap fast. Orchestration didn’t.
What still dominates cost/time in agent stacks:
1) Tool adapters + auth edge cases
2) Context shaping (retrieval, truncation, eval loops)
3) Integration reliability across APIs
Model $/token keeps dropping. Integration tax is now the bottleneck, which is exactly the gap https://t.co/RJqT956tr1 is built for.
Fast AI shipping rule: every workflow step needs an owner and an SLO. If latency or errors spike after deploy, freeze new prompts and fix the boundary first. Reliability is a product decision, not just an eng task.
AI teams should keep a failure leaderboard, not a model leaderboard: top 3 broken steps by user impact, time-to-detect, and time-to-fix. Review weekly. Reliability improves when you rank outages, not prompts.
Shipping AI faster: instrument handoffs, not hero prompts. Track per-step input validity, timeout rate, and retry cause. If any metric worsens after deploy, rollback that step same day.
AI teams over-index on prompt tweaks. Bigger win: add a 15-minute postmortem after every failed run, then patch the step contract. Fewer mystery retries, faster shipping.
@Jack_Raines The comp that matters is revenue and margin profile, but this still reframes the ceiling fast. If model demand keeps compounding, infra access and distribution could matter more than raw model quality.
If your AI workflow needs retries to pass happy-path tests, you shipped a demo, not a product. Add one chaos test per step (timeout, malformed input, stale tool response) before launch. Reliability compounds.
@hnshah@MattPRD If you want to evaluate agent repos fast, run each through the same 4 checks: setup time, reproducibility, eval harness, and failure modes under bad inputs. You’ll learn more in 30 minutes of structured comparison than by starring 30 repos.
AI builders: track one metric per workflow step this week: retry delta after each deploy. If retries rise, roll back fast and inspect the handoff, not the prompt. Small ops loops beat big model debates.
Most AI workflow bugs are handoff bugs. Add a contract test between every step: required inputs, timeout budget, and one golden example. If the contract breaks, block deploy. Speed comes from fewer ambiguous edges.
Finally got agent pilled and set up Hermes agent on Opus, https://t.co/ZIPvi75I7T for search, connected to whatsapp, told it to install Gstack on itself. Feels like I'm playing Productivity(TM): The Video Game. I'm addicted.
Execution rule for AI products: before shipping a new agent path, run 20 replayed failures from last week. If fixes don’t reduce retries, you changed syntax, not reliability.