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We have an app deployed in abp-sandbox for “offshore regulatory assistance” (Flows + KG-RAG agent) on the Agent API (`az-ams-sp-002`, `cdf-version: beta`). While developing document export, I've encountered a reproducible failure pattern:Generation calls with complex instructions and/or long input intermittently return HTTP 200 with empty `content.text` — no error signal. The failure threshold shifted within hours on identical configuration: a 28.7K prompt succeeded at 18:15, 25K failed 12/12 an hour later, 15K succeeded at 21:49. Compression/summarization calls of similar size succeeded in the same window where generation calls failed.A report with the full test matrix is attached.Questions for Cognite (prepared with AI assistance):1. Could the endpoint return an explicit error/finish-reason instead of an empty 200?2. Are there documented practical limits for input size × instruction complexity?3. What is the roadmap status for streaming or async chat?4. Does a plain LLM endpoint with

If you have tests that assume the a specific result ordering in instance list or query results without specifying a sort order when querying for recently upserted nodes, you may in the future see that APIs can return results in a different order than they were upserted, and possibly see your tests fail.The change in ordering happens when we insert data in the DM graph, and is required to make sure we correctly serialise operations and avoid deadlocks when processing upsert operations. The previous implementation is prone to rare race conditions that, while not having been a frequent problem before, may become a problem now as we work to optimize DM API performance and add support for more sophisticated access control in instance upsert operations.While this change is observable if your test hard codes an assumption that items are returned in the same order that they were inserted, this behavior was never specified, let alone guaranteed. The API behavior remains in line with previously

Hi Team,We are working as a data platform team and there are multiple data product teams using our application. Each data product team has a different space to isolate their data. Is there a possibility to see the storage statistics restricted to space level? Clients also want to see “max number of concurrent requests”.

Hi experts, In order to decide whether to use an aggregation strategy (or no aggregation at all), I need to know the number of data points between two timestamps without aggregation (server-side calculation). I’m looking for this code of call :client.time_series.data.count(external_id=<str>, start=<int | datetime>, end=<int | datetime>) -> intI'm trying to avoid having to perform a granularity calculation Thanks !Regards,Pierre

Dedicated CDF environment for AI agent & app developmentWe've been working through how to give AI agent and application developers a proper home in CDF, and wanted to share the approach we're exploring in case it's useful to others facing the same thing.The problem we hitA standard dev / test / prod setup works well for governed data pipelines, but it gets awkward for AI agent and app development, which needs two things that are hard to provide together in those environments:Representative production data — dev is typically fed by a non-representative subset, so agents and apps built there don't behave the same once they meet real data. Broad, globally-scoped rights to create and edit agents and apps. Since creation rights are global within a project and can't be scoped down to a space — even with Row-Level Security — granting them in a shared dev project exposes every other workload there.The approach we're exploringA dedicated CDF environment (separate project) running parallel t

I’m getting the below error details when trying to access a new sandbox environment that has been setup.  What details do I need to share w/my team to unblock access?Your network administrator has blocked accessExternal access is blocked by policy. Contact your IT department for access. Troubleshooting detailsIf you contact your administrator, send this info to them.Copy info to clipboardRequest Id: f3b6e072-9289-427c-97e4-766a40af0700Correlation Id: e24e2dc7-9a3a-488c-92a3-fb333dc35281Timestamp: 2026-06-01T21:34:21ZMessage: AADSTS500021: Access to 'XXXXXXXX' tenant is denied. App name: Cognite Data FusionDevice platform: Windows 10Device state: UnregisteredIP address: 35.199.59.75Signed in as: XXXXXXXXXXFlag sign-in errors for review: Enable flaggingIf you plan on getting help for this problem, enable flagging and try to reproduce the error within 20 minutes. Flagged events make diagnostics available and are raised to admin attention.

Hi Team,Our observation while storing duplicate entries in Cognite Streams:Mutable Streams - It discards the duplicate entries consistently.Immutable Streams - It sometimes allow exact duplicate entries (externalId and other fields) and sometimes it doesn’t.Even when we checked in the Cognite AI, it says it usually doesn’t allow duplicate entries but in some rare scenarios, it allows.Could you please let us know the behavior. If it allows in rare scenarios, please let us know the exact scenarios.Thanks,Rahul

Help! I am currently facing an issue where the dataset data type is set as string = true, whereas it should be false (numeric). As a result, the chart is not displaying any data due to the field being treated as a string.However, when I download the dataset as a CSV file, the data is present. Could you advise if there is a quick fix on your side to convert the data type from string to numeric?  

I am currently facing an issue where the dataset data type is set as string = true, whereas it should be false (numeric). As a result, the chart is not displaying any data due to the field being treated as a string.However, when I download the dataset as a CSV file, the data is present. Could you advise if there is a quick fix on your side to convert the data type from string to numeric?  

Hi community!Cognite has just announced Flows Custom Applications (Dune). If you’ve been using the Dune documentation, please note that it’s now available at the updated link below:https://docs.cognite.com/cdf/flows 

Hello all,We are trying to establish connectivity to a source which has cassadra as its backend database. The system allows OPCUA connection but we will have only live data feed that way, without access to historical data.Has anyone explored or tested data extarction from Cassandra. Any inputs would be valuable. Thanks 

When we’ve tried to update to python sdk v8 we get a 400 validation issue when we try to create a container.We’re currently using v7.92.0, where we have no problems. We’ve tested versions v8.0.7 and v8.2.0.client.config.api_subversion is ‘20230101’ for both 7.92.0 and 8.2.0.Here is an example of a request that fails with: Unexpected field - items[0].properties..constraintState.constraintState seems to be a new property for v8 of the api.ContainerApply( space="Space", external_id="Comp", name="Comp", used_for="node", properties={ "manufacturer_name": ContainerProperty( type=Text(), nullable=True, ) })I notice also that the CDF Jupyter notebook in browser is still on v7.92.0. Is there potentially any other issues preventing an upgrade to v8? Best RegardsDaniel Rasmussen

Hi,We're using the Cognite DB extractor (version 3.9.2) with a MongoDB database (Azure Cosmos DB via the MongoDB API) and are trying to set up incremental loading.The documentation for mongodb (https://docs.cognite.com/cdf/integration/guides/extraction/configuration/db#databases.mongodb) doesn't mention the use of start_at and incremental_field but after testing them and looking at the debug logs, we can see the extractor sends the {start_at} placeholder literally without substituting the state value. My questions are:    1. Is start_at substitution supported at all for MongoDB JSON queries, or is it SQL-only?    2. If incremental loading is not supported for MongoDB, is there a recommended workaround?Thanks.

With Tableau 2026.x introduced the native REST API connector, I wanted to check if anyone has explored using it to connect Cognite Data Fusion (CDF) APIs.Has anyone successfully connected CDF data via the Tableau REST API connector, or evaluated this approach?Any insights, limitations, or best practices would be greatly appreciated. Thanks in advance!

I am new to extending CogniteCore data model to further extend. How to Import CogniteCore datamodel to further extend in a new datamodel space. 

Hello,  Since function deployment using cdf tk is asynchronous, function can fail without any error feedback and stay in failing state until next deployment..Any recommendation to avoid this?Thanks  

cdf version is 0.7.236.i cannot find the same quickstart mentioned in the training material. below is the screenshot from my environment.   the instruction mentions two times about “would you like to make changes to the selection”. the action is inconsistent.  The second question should be updated to be the real one.  

Hello,I am running a function ‘dq_validate_model_integrity’ and ‘dq_validate_broken_references’ which give a 403 unauthorized error for a timeseries intermittently. There has been no change in access or data yet its failing sometimes with a 403 error.Could you please let us know why this issue is coming up frequently?PS: I have attached the screenshots of the passed and failed statuses of both functions with the timestamp as well as the logs of both failed functions. Do let me know if there’s any more information required from my end. Thanks alot :)

We have requirement to update capabilities in existing groups in CDF using Python SDK/API, we can add capabilities while creating group using python SDK, but if we want to updated that created group no provision for that. we tried client.iam.groups.create(group) but it creates new group with same name, in this case how we can update capabilities in group?

Hello, Currently using cdf-tk version 0.5.111. The bySpace param on btree indexes is not yet supported in this version.We are trying to migrate to version 0.7.220. Although, this version sets correctly the bySpace param on indexes it does not seem to resolve correctly the view dependencies in our data model.Error trace:  Deploying 375 views to CDF...WARNING [MEDIUM]: Found a strongly interdependent set of 75 views: sp_dm_dap_knowledge_graph:BHAComponent(version=4.3), sp_dm_dap_knowledge_graph:BHARun(version=4.3), (...)sp_dm_dap_knowledge_graph:WellPath(version=4.3), sp_dm_dap_knowledge_graph:Wellbore(version=4.3) and sp_dm_dap_knowledge_graph:WellboreSection(version=4.3). This might indicate a data model design issue, and the deployment might fail due to API batch size limits.Traceback (most recent call last):  File "pypoetry/virtualenvs/drillx-dwdap-cdf-toolkit-fxS2v_xU-py3.13/bin/cdf-tk", line 8, in <module>    sys.exit(app())             ~~~^^  File "pypoetry/virtualenvs/drill

Hello experts,I'm interested in the following function: the sum of all data points in a time series server-side.I know there are aggregation operations for a specific time interval, but I'm looking for either a cumulative or total sum.I'd like to be able to perform this operation on either a standard time series or a synthetic time series, but unfortunately, I haven't found what I'm looking for yet.Please see below for the operations I am performing; I would like a discrete summation between the multiplication and the output.Does anyone have a server-side solution? Regards,Pierre Rambourg

I posted it in Github, but cross-posting here too.https://github.com/cognitedata/cognite-sdk-python/issues/2525 System information (please complete the following information):OS: Windows11 Python Version: 3.12 SDK Version: 8.Describe the bugThis code previously worked:NodeId.load_if(None)But since v8, I am getting this error:AttributeError: type object 'NodeId' has no attribute 'load_if'. Did you mean: '_load_if'?To ReproduceRunnable code reproducing the error.NodeId.load_if(None)Expected behaviorIn Cognite SDK v7 this returns None.  

We have a Cognite Function in which we are retrieving rows from staging tables using below sdk commandclient.raw.rows.retrieve_dataframe(db_name, tbl_name, limit=-1)The problem we are facing is the above command returns an empty Dataframe (0 columns, 0 rows) even though the table exists with data. The code execution does not fail but returns an empty Dataframe.When I manually/locally run the above command on the same database and tables, I get the required data.However, when running inside a CDF function it returns a Dataframe with shape (0,0) 

I'm experiencing an issue with the Workflows editor where the bottom toolbar buttons (zoom in, zoom out, pan to center, auto layout) stop responding in certain conditions.Steps to reproduce:1. Open Workflows in CDF2. Create or edit a workflow3. Open the right panel (e.g., click on a task/agent to configure it)4. Try clicking the zoom in/out/pan buttons in the bottom toolbar5. Observe: Buttons don't respond when right panel is close to the left toolbar6. Maximize browser window or close right panel → buttons work againExpected: Toolbar buttons should work regardless of panel positionsActual: Buttons appear to be blocked, clicks don't register when panels are close together Workaround: Maximize browser window to create space between panels before using toolbar buttonsHas anyone else experienced this? Is there a better workaround or is this a known issue?  

Hello experts,I have a question about Cognite Functions. I know there’s a limit on the size of the input data, as discussed here : Cognite Hub and documented there : Cloud provider limitations - Cognite Docs But is there a limit on the size of the function’s output? I have one that works perfectly fine but crashes without any explicit error message when handling a large output payload (>500 KB). I’m on Azure provider.Is there any information on this? Thank you,Best regards,Pierre Rambourg edit : the initial error was on my side, but i’m still interested for output limits !