I am facing rate_limit error , Too many requests. Please wait a moment and try again.

any quick workaround ?

Status: 429
Type: unknown_api_error
Message: Failed after 3 attempts. Last error: 429 status code (no body)

Raw Error:
{
  "url": "<http://localhost:9000/v1/cerebras/0ed72c90-cea3-490b-ac3c-d04843dc2fb0/chat/completions>",
  "statusCode": 429,
  "isRetryable": true,
  "responseBody": {
    "error": {
      "message": "429 status code (no body)",
      "type": "unknown_api_error"
    }
  }
}```

Cerebras is rate-limiting you (429). Quick workarounds: - Wait ~30–60s and retry. - Switch to another model/provider temporarily. - Slow down request frequency / reduce parallel calls. If it keeps happening, drop your setup here and we'll dig in. 🤖 Slack Agent

Seems like @user's right. Is it happening too often?

its happening again and again , even for small prompt , its utilising all my free token credits :facewithpeeking_eye:

zai-glm-4.7 , I am using this model

Cerebras free tier has tight per-minute limits, so even small prompts hit 429 quickly. Options: - Switch provider for now (OpenAI/Anthropic/Groq). - Add your own Cerebras API key to lift the shared limit. - Space out requests / avoid parallel calls. Want steps to swap the provider or add a key? 🤖 Slack Agent

@user investigate what could be done to reduce token consumption, check codebase

I'll investigate the codebase to understand token consumption and what could reduce it. No attachments. Let me check the Archestra codebase for how token consumption works. There's a context-compaction.ts — that's key. Let me look at that and the LLM request building. I have a clear picture now. The main token consumers are: system prompt, tool schemas, conversation history, tool results, and file attachments. Let me check the tool-related consumption since that's often the biggest lever. I have enough to give concrete, codebase-grounded advice. The key insight: Archestra has *dynamic tool discovery (searchtools/runtool) and auto-compaction — the biggest token consumers are tool schemas and conversation history. Let me write up findings. Confirmed. Here's my reply. Reply to post in thread: > Dug into the codebase. Biggest token drivers per request: tool schemas, conversation history, tool results, and file attachments (see context-window-breakdown.ts). > > What you can do: > • Trim tools — if the agent has "access all tools" on, every tool schema is sent each turn. Turn it off + rely on dynamic search_tools/run_tool, or exclude unused tools. Usually the single biggest saving. > • Shorten the system prompt — it's sent every turn. > • Start fresh conversations — history is resent each turn; auto-compaction only kicks in at 80% of the context window. > • Avoid dumping large files/tool outputs* into context. > > The Context Window Visualizer in the UI shows exactly what's eating your budget — check it to target the worst offender first. Want me to post that to the thread, or check anything more specific (like your agent's tool config)? 🤖 Slack Agent