Slim | ai-costguard

AI agents don't warn you before the invoice.
ai-costguard blocks the call before tokens leave.
Node.js. $0 to install.

[Slim | ai-costguard @SlimAssiliX]

Week 1: $127
Week 2: $891
Week 3: $6,240
Week 4: $18,400
No pages. No errors. Every health check green.
Then someone opened the invoice.
$47,000.

Four agents had been talking to each other in a loop for 11 days.
Thread:

[Slim | ai-costguard @SlimAssiliX]

@rauchg "agent as the new browser" is a clean frame.
browsers had bandwidth caps.
the agent has no equivalent cost ceiling baked in by default

[Slim | ai-costguard @SlimAssiliX]

@rauchg mcp-handler downloads going up means more agents making more tool calls.
the cost chart always lags the npm chart by about 30 days

[Slim | ai-costguard @SlimAssiliX]

@shubgaur built the best returns experience on the planet, now building the bot that generates the volume to need it

[Slim | ai-costguard @SlimAssiliX]

@patrickc @link an agent that loops was a billing problem but an agent that can purchase anywhere is a different category of risk entirely

[Slim | ai-costguard @SlimAssiliX]

@shardara @vercel does the cost check block before the call fires or after the last one already ran? Because as you know that's the line between protection and accounting

[Slim | ai-costguard @SlimAssiliX]

@Netanel_baruch @vercel the agent retried 3 times into a rate limit wall before you even saw that error and that's exactly step 4

[Slim | ai-costguard @SlimAssiliX]

@shardara @vercel that's the right place for it , most count tokens there but not estimated cost, which is the number that actually hurts when you're mixing models mid-pipeline ?

[Slim | ai-costguard @SlimAssiliX]

@shardara @vercel does the token limit check block before the call fires or after it returns?
Because that's the gap between pre-call protection and spend observability

[Slim | ai-costguard @SlimAssiliX]

@AnthropicAI what stopped it at 48 hours, a hard timer or someone watching?

[Slim | ai-costguard @SlimAssiliX]

@shardara @vercel Step 4 is simply a hard budget or retry cap that fires before the agent runs, not after it's already in prod looping like the one i am building right now .
You know , right now deploy is instant but the guardrails are someone else's problem

[Slim | ai-costguard @SlimAssiliX]

@RoundtableSpace content moderation without a cloud API still needs a device that won't throttle when the agent fires it 40 times mid-loop

[Slim | ai-costguard @SlimAssiliX]

@XFreeze design the workflow once, give it to the whole team but if the workflow has a cost problem, you've now given that to the whole team too

[Slim | ai-costguard @SlimAssiliX]

@ClaudeDevs full conversation and context intact.
does that include what the session already spent, or does the budget counter reset on resume?

[Slim | ai-costguard @SlimAssiliX]

@XFreeze a six-bot game studio with memory and recurring routines is also six simultaneous billing events.
none of the playbooks have a chapter on that

[Slim | ai-costguard @SlimAssiliX]

@vercel the trace shows you what the agent tried on a stalled payment but not what it already billed before it stalled

[Slim | ai-costguard @SlimAssiliX]

@OpenRouter 13.8x more tokens at a lower price per token.
did the average user's total bill actually go down, or did Jevons take that too ؟

[Slim | ai-costguard @SlimAssiliX]

@testingcatalog "task orchestrator" is a polite name for "the thing that decides how many agents run and for how long."
nobody's shipping the part that decides when enough is enough

[Slim | ai-costguard @SlimAssiliX]

@VaibhavSisinty open source trading punches with frontier is the benchmark story.
self-hosting 49B active parameters with no per-token bill is the agent story

[Slim | ai-costguard @SlimAssiliX]

@RoundtableSpace 350 bots running 24/7.
the market can be flat for a week but the API bill won't be