i’ve been using workers AI via the binding from my worker for many months and it‘s been great. overa

Mmr.niko.la My `wrangler.toml` defines `AUDIO_PROCESSOR` as a Durable Object, and the code u...

M

mr.niko.la•5/1/25, 6:57 AM

Capacity error, will retry: 3040: Capacity temporarily exceeded, please try again

Capacity error, will retry: 3040: Capacity temporarily exceeded, please try again

Capacity error, will retry: 3040: Capacity temporarily exceeded, please try again

Capacity error, will retry: 3040: Capacity temporarily exceeded, please try again

cf whisper worker ai. @Isaac | AS213339 is this common?

IIsaac McFadyen Is this in a Durable Object? Why are you using the `this` there?

M

mr.niko.la•5/1/25, 7:13 AM

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

T

TechOnyx•5/1/25, 8:19 AM

I recently wanted to integrate AI into my site, so I go into Cloudflare Dash > AI > Choose Workers API and create it, and then I deleted it immediately because I wanted to use the Rest API, but I can’t see that screen where you choose amongst worker and rest api, it keeps redirecting me to ai/usage

T

TechOnyx•5/1/25, 8:24 AM

Please help me, I'm locked out from the AI section, I'm unable to get my api keys and all that stuff

T

TechOnyx•5/1/25, 8:25 AM

I'm stuck on /usage page

R

rob•5/2/25, 2:44 PM

How do we know what models r the best for what use case. / modalities ? Is there like a matrix somewhere

Rrob How do we know what models r the best for what use case. / modalities ? Is there...

I

Isaac McFadyen•5/2/25, 8:26 PM

Just a matter of testing with your specific use-case

I

Isaac McFadyen•5/2/25, 8:26 PM

Specialty models like Qwen Coder will generally be better at their specific specialties than a non-specific model llike LLaMa

K

Keshav G•5/3/25, 8:08 AM

hello all
i am using runwithtools to build a rag chat app. the problem is the output message being outputted is very short, and the runwithtools doesn't respect the max_tokens. how to fix this ? kindly help

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

K

Keshav G•5/3/25, 2:23 PM

ok i sorted this issue, created a new package with these additional parameters.

now how can i get the final response and the previous tool response, to store as context for the ongoing conversation ?

Aacusti i’ve been using workers AI via the binding from my worker for many months and it...

V

Viktor•5/3/25, 5:52 PM

I logged in once, and that's it.
Sometimes it asks me which account I want to use (my login has multiple accounts connected to it), but that's like once in a week.

KKeshav G ok i sorted this issue, created a new package with these additional parameters. ...

V

Viktor•5/3/25, 6:01 PM

I honestly love everything about Cloudflare AI, except for their

ai-utils

ai-utils

ai-utils

ai-utils. I spend a lot of time troubleshooting it, it tracks token usage wrong (returns only the last ones), calls

AI.run

AI.run

AI.run

AI.run more often than necessary, etc. See #Is module @cloudflare/ai-utils stable? > I just wrote the function calling in my own code, works way better, and doesn't inflict double usage consumption. And I'd recommend you to do so, too, tbh

M

Mark•5/4/25, 2:54 AM

Does anyone still having index issue with AutoRag ?

MMark Does anyone still having index issue with AutoRag ?

I

iammac•5/4/25, 7:19 AM

Thread at #autorag

N

Nikhil Kachare•5/5/25, 9:59 AM

We recently transitioned from using the OpenAI Whisper API to Workers AI, using the @cf/openai/whisper-large-v3-turbo model. The experience has been excellent so far. One significant difference we've observed is with the language parameter: it defaults to 'en', unlike OpenAI, which automatically detects the language. Is this a feature missing from Workers AI, or am I overlooking something?

VViktor I honestly love everything about Cloudflare AI, **except** for their `ai-utils`....

K

Keshav G•5/5/25, 7:35 PM

yes, i have modified the function to return messages array for use in context later.
i have also published the changes as a npm package

K

Keshav G•5/5/25, 7:38 PM

i dont know, why the development is so ignored.. also the models are very weak
now way better open source models are there like the qwen, r1, even the llama 405b. cloudflare needs to up the game asap

A

Aiden•5/6/25, 7:23 AM

When to support qwen3

A

Aiden•5/6/25, 7:27 AM

@Cloudflare

When to support qwen3

AAiden @Cloudflare When to support qwen3

I

Isaac McFadyen•5/6/25, 2:34 PM

?pings

S

SuperHelpflare•5/6/25, 2:34 PM

Please do not ping community members for non-moderation reasons. Doing so will not solve your issue faster and will make people less likely to want to help you.

I

Isaac McFadyen•5/6/25, 2:35 PM

That is a bot but the point stands, do not try to ping people please

C

cipherfunk•5/7/25, 1:14 AM

Does the Cloudflare implementation of MeloTTS support any voice other than the default? Any language code I have tried returns the same voice. Is lang: 'EN-AU' the correct formatting?

M

matti•5/8/25, 10:05 AM

Hello! as of today all my local dev queries to cloudflare ai return the following error message, but it works in production. Has anyone noticed a similar thing?

M

matti•5/8/25, 10:05 AM

X [ERROR] InferenceUpstreamError: 10000: Authentication error

at Ai._parseError (cloudflare-internal:ai-api:107:24)
at async Ai.run (cloudflare-internal:ai-api:82:19)

B

blindChicken•5/8/25, 3:36 PM

I'm getting

Error: 8001: Invalid input

Error: 8001: Invalid input

Error: 8001: Invalid input

Error: 8001: Invalid input when I try to call tools using

@cf/meta/llama-4-scout-17b-16e-instruct

@cf/meta/llama-4-scout-17b-16e-instruct

@cf/meta/llama-4-scout-17b-16e-instruct

@cf/meta/llama-4-scout-17b-16e-instruct. If I call the model without tools it works fines, but if add the tools array it throws the error. Here is my tools array:

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

Here is the call to the model:

await AI.run( model, {  messages: messages, tools: tools });

await AI.run( model, { messages: messages, tools: tools });

await AI.run( model, {  messages: messages, tools: tools });

await AI.run( model, { messages: messages, tools: tools });

B

blindChicken•5/8/25, 3:59 PM

If I use

@cf/meta/llama-3.3-70b-instruct-fp8-fast

@cf/meta/llama-3.3-70b-instruct-fp8-fast

@cf/meta/llama-3.3-70b-instruct-fp8-fast

@cf/meta/llama-3.3-70b-instruct-fp8-fast instead of

llama-4-scout

llama-4-scout

llama-4-scout

llama-4-scout the tool call works, but then the models responds incorrectly. Has there been a change in how tools are call between llama-3 and llama-4? Should I use

Functions

Functions

Functions

Functions instead.

B

blindChicken•5/8/25, 4:06 PM

If I change it to call functions instead of tools, I get this cryptic error:

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

B

bscofield•5/8/25, 7:12 PM

Howdy! I'm getting an

AiError: 3043: Internal server error

AiError: 3043: Internal server error

AiError: 3043: Internal server error

AiError: 3043: Internal server error on some calls -- has anyone successfully debugged those?

B

blindChicken•5/8/25, 8:18 PM

I found a solution to my problem above, instead of using tool calling, I used

response_format

response_format

response_format

response_format and as result I get response as JSON that can be easily parsed and then used to call my functions. It basically provides the same result, with the exception of allowing the model to select the tool, but one could easily set a prop in the JSON for function selection. It still sucks that tool calling appears to be broken.

Bbscofield Howdy! I'm getting an `AiError: 3043: Internal server error` on some calls -- ha...

M

matti•5/8/25, 10:24 PM

I've been getting that too, I think something is off today

Mmatti I've been getting that too, I think something is off today

B

bscofield•5/8/25, 11:05 PM

I got different results when I changed models -- maybe worth a shot for you?

M

matti•5/9/25, 7:56 AM

I know yesterday I wasn't able to develop locally, I was getting auth errors all day long. That seems fixed. And I was also getting some AiError 3043 in prod on one certain flow.

D

dust•5/9/25, 8:14 AM

hi guys. I have an issue fetching the api with 401 error. i fetched with vanilla js. i am not sure this is serve side issue or client side but the browser said the error401 is coming from the server. I ran the python code with the same token and works well. i have allowed the control origin but still error. hope anyone can help.

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }

A

ajgeiss0702•5/9/25, 7:24 PM

has anyone had any success using custom LoRAs recently? Last time i tried they would just fail with a generic error for seemingly no reason

M

matti•5/10/25, 10:51 AM

Has anyone used Qwen qwq-32b : https://developers.cloudflare.com/workers-ai/models/qwq-32b/

M

matti•5/10/25, 10:52 AM

it always sends me back his thinking

M

matti•5/10/25, 10:52 AM

"I've been asked hello by the user, I think I should say hello back"

M

matti•5/10/25, 10:53 AM

is there a good way to filter it out ? (maybe just the first text until the first \n)

M

matti•5/10/25, 11:47 AM

(found the answer myself, I can remove things on the stream until the </think> marker).

A

absolutejam•5/10/25, 7:41 PM

Anyone know how to get the text-to-img models working with the Go cloudflare client? I've tried

@cf/bytedance/stable-diffusion-xl-lightning

@cf/bytedance/stable-diffusion-xl-lightning

@cf/bytedance/stable-diffusion-xl-lightning

@cf/bytedance/stable-diffusion-xl-lightning and

@cf/black-forest-labs/flux-1-schnell

@cf/black-forest-labs/flux-1-schnell

@cf/black-forest-labs/flux-1-schnell

@cf/black-forest-labs/flux-1-schnell but both produce the error:

expected destination type of 'string' or '[]byte' for responses with content-type 'image/png' that is not 'application/json'

Which is because they're returning bytes instead of a JSON object (tested with cURL).

resp, err := client.AI.Run(ctx, model, ai.AIRunParams{
    AccountID: cloudflare.F(cfAccountID),
    Body: ai.AIRunParamsBodyTextToImage{
        Prompt: cloudflare.F(prompt),
    },
})
if err != nil {
    return err
}

resp, err := client.AI.Run(ctx, model, ai.AIRunParams{
    AccountID: cloudflare.F(cfAccountID),
    Body: ai.AIRunParamsBodyTextToImage{
        Prompt: cloudflare.F(prompt),
    },
})
if err != nil {
    return err
}

resp, err := client.AI.Run(ctx, model, ai.AIRunParams{
    AccountID: cloudflare.F(cfAccountID),
    Body: ai.AIRunParamsBodyTextToImage{
        Prompt: cloudflare.F(prompt),
    },
})
if err != nil {
    return err
}

resp, err := client.AI.Run(ctx, model, ai.AIRunParams{
    AccountID: cloudflare.F(cfAccountID),
    Body: ai.AIRunParamsBodyTextToImage{
        Prompt: cloudflare.F(prompt),
    },
})
if err != nil {
    return err
}

A

absolutejam•5/10/25, 7:54 PM

Or should I report this as an issue?

C

cipherfunk•5/11/25, 5:52 PM

When will we get a video generation model? Does Cloudflare have enough GPUs to support this?

G

gabrielstuff•5/12/25, 11:13 AM

Hello! I hope you’re doing well. I’m experimenting with the Cloudflare AI-Worker and attempting to parallelize my AI calls, but I keep encountering the following error:

AiError: 3040: Capacity temporarily exceeded, please try again.

AiError: 3040: Capacity temporarily exceeded, please try again.

AiError: 3040: Capacity temporarily exceeded, please try again.

AiError: 3040: Capacity temporarily exceeded, please try again.

Is this expected behavior when scaling up request volume? If so, are there recommended strategies or configuration tweaks to improve throughput?

Any guidance would be greatly appreciated—thank you!

G

gabrielstuff•5/12/25, 11:56 AM

here is an example :

{
exception:
 
{
stack:
 
"AiError: 3040: Capacity temporarily exceeded, please try again. at Ai._parseError (cloudflare-internal:ai-api:102:24) at async Ai.run (cloudflare-internal:ai-api:82:19) at async Object.fetch (index.js:21:22)",
name:
 
"Error",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
timestamp:
 
1747050909290,
},
message:
 
"3040: Capacity temporarily exceeded, please try again.",
$workers:
 
{
truncated:
 
false,
event:
 
{
request:
 
{
url:
 
"https://classify-ai.workers.dev/",
method:
 
"POST",
path:
 
"/"
}
},
outcome:
 
"exception",
scriptName:
 
"classify-ai",
eventType:
 
"fetch",
executionModel:
 
"stateless",
scriptVersion:
 
{
id:
 
"1678a794-81e9-48fd-8d8f-43f9d95d4235"
},
requestId:
 
"93e9b9b6a8b802ad"
},
$metadata:
 
{
id:
 
"01JV25JKKAQGM3BZG6KQDG9AA7",
requestId:
 
"93e9b9b6a8b802ad",
trigger:
 
"POST /",
service:
 
"classify-ai",
level:
 
"error",
error:
 
"3040: Capacity temporarily exceeded, please try again.",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
account:
 
"f1c3db27dc01771c8c27ec175b404598",
type:
 
"cf-worker",
fingerprint:
 
"4bc40a10bc8b1073f4883173eeb61c8a",
origin:
 
"fetch",
messageTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
errorTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
}
}

{
exception:
 
{
stack:
 
"AiError: 3040: Capacity temporarily exceeded, please try again. at Ai._parseError (cloudflare-internal:ai-api:102:24) at async Ai.run (cloudflare-internal:ai-api:82:19) at async Object.fetch (index.js:21:22)",
name:
 
"Error",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
timestamp:
 
1747050909290,
},
message:
 
"3040: Capacity temporarily exceeded, please try again.",
$workers:
 
{
truncated:
 
false,
event:
 
{
request:
 
{
url:
 
"https://classify-ai.workers.dev/",
method:
 
"POST",
path:
 
"/"
}
},
outcome:
 
"exception",
scriptName:
 
"classify-ai",
eventType:
 
"fetch",
executionModel:
 
"stateless",
scriptVersion:
 
{
id:
 
"1678a794-81e9-48fd-8d8f-43f9d95d4235"
},
requestId:
 
"93e9b9b6a8b802ad"
},
$metadata:
 
{
id:
 
"01JV25JKKAQGM3BZG6KQDG9AA7",
requestId:
 
"93e9b9b6a8b802ad",
trigger:
 
"POST /",
service:
 
"classify-ai",
level:
 
"error",
error:
 
"3040: Capacity temporarily exceeded, please try again.",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
account:
 
"f1c3db27dc01771c8c27ec175b404598",
type:
 
"cf-worker",
fingerprint:
 
"4bc40a10bc8b1073f4883173eeb61c8a",
origin:
 
"fetch",
messageTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
errorTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
}
}

{
exception:
 
{
stack:
 
"AiError: 3040: Capacity temporarily exceeded, please try again. at Ai._parseError (cloudflare-internal:ai-api:102:24) at async Ai.run (cloudflare-internal:ai-api:82:19) at async Object.fetch (index.js:21:22)",
name:
 
"Error",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
timestamp:
 
1747050909290,
},
message:
 
"3040: Capacity temporarily exceeded, please try again.",
$workers:
 
{
truncated:
 
false,
event:
 
{
request:
 
{
url:
 
"https://classify-ai.workers.dev/",
method:
 
"POST",
path:
 
"/"
}
},
outcome:
 
"exception",
scriptName:
 
"classify-ai",
eventType:
 
"fetch",
executionModel:
 
"stateless",
scriptVersion:
 
{
id:
 
"1678a794-81e9-48fd-8d8f-43f9d95d4235"
},
requestId:
 
"93e9b9b6a8b802ad"
},
$metadata:
 
{
id:
 
"01JV25JKKAQGM3BZG6KQDG9AA7",
requestId:
 
"93e9b9b6a8b802ad",
trigger:
 
"POST /",
service:
 
"classify-ai",
level:
 
"error",
error:
 
"3040: Capacity temporarily exceeded, please try again.",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
account:
 
"f1c3db27dc01771c8c27ec175b404598",
type:
 
"cf-worker",
fingerprint:
 
"4bc40a10bc8b1073f4883173eeb61c8a",
origin:
 
"fetch",
messageTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
errorTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
}
}

{
exception:
 
{
stack:
 
"AiError: 3040: Capacity temporarily exceeded, please try again. at Ai._parseError (cloudflare-internal:ai-api:102:24) at async Ai.run (cloudflare-internal:ai-api:82:19) at async Object.fetch (index.js:21:22)",
name:
 
"Error",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
timestamp:
 
1747050909290,
},
message:
 
"3040: Capacity temporarily exceeded, please try again.",
$workers:
 
{
truncated:
 
false,
event:
 
{
request:
 
{
url:
 
"https://classify-ai.workers.dev/",
method:
 
"POST",
path:
 
"/"
}
},
outcome:
 
"exception",
scriptName:
 
"classify-ai",
eventType:
 
"fetch",
executionModel:
 
"stateless",
scriptVersion:
 
{
id:
 
"1678a794-81e9-48fd-8d8f-43f9d95d4235"
},
requestId:
 
"93e9b9b6a8b802ad"
},
$metadata:
 
{
id:
 
"01JV25JKKAQGM3BZG6KQDG9AA7",
requestId:
 
"93e9b9b6a8b802ad",
trigger:
 
"POST /",
service:
 
"classify-ai",
level:
 
"error",
error:
 
"3040: Capacity temporarily exceeded, please try again.",
message:
 
"3040: Capacity temporarily exceeded, please try again.",
account:
 
"f1c3db27dc01771c8c27ec175b404598",
type:
 
"cf-worker",
fingerprint:
 
"4bc40a10bc8b1073f4883173eeb61c8a",
origin:
 
"fetch",
messageTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
errorTemplate:
 
"<NUM>: Capacity temporarily exceeded, please try again.",
}
}

G

gabrielstuff•5/12/25, 12:26 PM

opened an isue : https://community.cloudflare.com/t/gemma-aierror-3040-capacity-temporarily-exceeded-please-try-again/797352

J

John Guthrie•5/12/25, 3:31 PM

Hi Cloudflare community,

I'm running into an issue while trying to create a new AutoRAG pipeline through the Cloudflare dashboard. I've successfully gone through the steps:

Connect your data source
Generate indexes
Configure retrieval and generation
However, when I reach the final step (4. Finalize), provide a name for the RAG, and choose "Create one for me" for the Service API Token (it generated AutoRAG Token - 2025-05-06), and then click the "Create" button, I immediately get the following error message:

D1 TYPE ERROR: Type 'object' not supported for value '[object Object]'

It seems like something during the finalization process is trying to pass a JavaScript object [object Object] directly into a D1 database operation where it's not supported.

Has anyone else encountered this specific D1 type error when setting up AutoRAG? Any ideas what configuration step or data aspect might be causing this, or how I can further debug it?

Thanks for any help or pointers!

i’ve been using workers AI via the binding from my worker for many months and it‘s been great. overa

Similar Threads

Similar Threads

Similar Threads