10 GitHub repos to trim Grok Bot prompts and token waste

maestro @maestroothOriginally posted 2026.10.23

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YOUR OLD GROK BOT SETUP IS THE PROBLEM we all know the new unification plan sucks... SpaceXAI just admitted the old rules you gave Grok Bot are making it pricier and dumber. 75% of your tokens burned on pointless work. a $200 plan delivering $50 worth of actual results 10 GitHub repos to fix the setup that’s wasting your tokens and dragging down your results: 01 Promptfoo → compare your current instructions against a cleaner version on the same test cases, so removing a rule becomes something you can evaluate https://github.com/promptfoo/promptfoo 02 DSPy → optimize instructions and examples against a defined quality metric, replacing endless manual prompt edits with measured experiments https://github.com/stanfordnlp/dspy 03 SAMMO → experiment with prompt structure, wording, and compression while checking whether the task still gets done correctly https://github.com/microsoft/sammo 04 PromptWizard → generate, critique, and refine candidate instructions using task examples, then test the revised prompt before adopting it https://github.com/microsoft/PromptWizard 05 DeepEval → turn recurring failures into repeatable evaluations so a shorter prompt doesn’t quietly lose requirements your workflow depends on https://github.com/confident-ai/deepeval 06 Inspect AI → evaluate complete interactions, including tool use and multi-turn behavior, to catch instructions that send the workflow down unnecessary paths https://github.com/UKGovernmentBEIS/inspect_ai 07 Langfuse → trace instrumented runs and track prompt versions, helping you connect a configuration change to repeated calls or declining output quality https://github.com/langfuse/langfuse 08 OpenLIT → monitor usage, cost, and latency in connected AI workflows so you can identify expensive steps before rewriting the whole setup https://github.com/openlit/openlit 09 OpenLLMetry → add tracing to supported model libraries and frameworks, making the sequence of calls behind an expensive result easier to inspect https://github.com/traceloop/openllmetry 10 Helicone → log and analyze API requests to find costly patterns, failed calls, and changes in spending across your instrumented workflow https://github.com/Helicone/helicone save this. the most expensive line in your setup might be the one you forgot you added

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