this is f*cking brilliant... a 16-year-old CS student in China figured out how to run 300 Grok Bots for less than $0.02 per bot per week. the same swarm could cost $4,500 a week. he eliminated pointless status checks, stopped rereading entire chat histories, and killed endless retry loops. now most bots only wake up when there's actual work to do. Here 20 GitHub repos he used to make the setup cheaper: 01 Crawl4AI → convert raw web pages into lean Markdown before parsing, stripping out bloated UI elements and navigation menus before your bot ingests them github.com/unclecode/crawl4a… 02 Firecrawl → extract clean Markdown or structured JSON from websites so your pipeline ingests only the exact data points required github.com/firecrawl/firecra… 03 Chonkie → chunk massive documents into surgical segments so retrieval injects relevant snippets instead of entire files github.com/feyninc/chonkie 04 FlagEmbedding → rerank vector retrieval candidates before generation, isolating high-signal context and dropping noisy matches from prompt payloads github.com/FlagOpen/FlagEmbe… 05 LLMLingua → distill long context windows prior to inference, retaining critical constraints and entities while cutting token overhead github.com/microsoft/LLMLing… 06 RTK → truncate verbose terminal and CLI outputs before appending them to memory, preventing raw build logs from saturating context space github.com/rtk-ai/rtk 07 GPTCache → store and serve semantically identical completions locally instead of executing redundant model passes github.com/zilliztech/GPTCac… 08 Portkey Gateway → route requests through a centralized proxy to cache responses and eliminate duplicate API hits across concurrent runs github.com/Portkey-AI/gatewa… 09 Prefect → checkpoint pipeline states and serve cached step outputs when inputs haven't changed, preventing redundant workflow restarts github.com/PrefectHQ/prefect 10 Mem0 → index and retrieve discrete episodic facts so agents load targeted state instead of replaying full interaction logs github.com/mem0ai/mem0 11 Graphiti → maintain dynamic relationships and facts inside a temporal graph, retrieving bounded context rather than raw event transcripts github.com/getzep/graphiti 12 requests-cache → cache outbound HTTP payloads with expiration rules, letting tools reuse fresh responses without triggering duplicate web calls github.com/requests-cache/re… 13 Hishel → enforce HTTP-native caching and conditional validation in scraping tools, skipping unchanged data during crawl routines github.com/karpetrosyan/hish… 14 APScheduler → run lightweight Python polling on a schedule and batch low-priority updates, waking the model only on actionable triggers github.com/agronholm/apsched… 15 Celery → offload polling and background orchestration to distributed workers, reserving LLM compute strictly for execution steps github.com/celery/celery 16 PyBreaker → trip circuit breakers when external tools fail repeatedly, halting execution cascades before runaway retries drain your budget github.com/danielfm/pybreake… 17 LiteLLM → establish hard budget ceilings and spend-tracking across model providers, killing agent threads that breach cost limits github.com/BerriAI/litellm 18 RouteLLM → dynamically offload baseline tasks to fast, lightweight models while reserving frontier reasoning engines for edge cases github.com/lm-sys/RouteLLM 19 Semantic Router → classify and route user intents via local vector spaces, skipping expensive model planning steps entirely github.com/aurelio-labs/sema… 20 Phoenix → trace invocation graphs, audit token bloat across nested calls, and benchmark downstream quality before shipping optimizations github.com/Arize-ai/phoenix wire the stack together via CLI hooks, MCP servers, or custom middleware. enforce cost boundaries by funneling model traffic through a gateway or smart router audit the primary cost sinks first: token-heavy context, repetitive research loops, zero-op polling, and unchecked failure cycles clean inputs before ingestion → cache data before generation → verify local state → sever runaway loops benchmark realized output against cumulative infrastructure burn, accounting for the entire execution harness the full course below shows how to put the workflow together ↓