v3.10.0
lf dump/lf restore. Task result evidence is now paginated, filterable, and sortable for faster loads.
What’s new
Safe code execution sandbox to run untrusted and model-generated code in an isolated environment.
Connect external agent providers — Dify, LangSmith, Claude Managed Agents, AWS Bedrock, and Azure Foundry — as models in your AI apps.
Register a placeholder model connection (
connection_type: placeholder) to stand in for a model before its real integration exists — it returns empty completions so you can set up apps and pipelines ahead of time:display_name: "Placeholder model" key: "placeholder_model" task: "chat_completion" config: connection_type: "placeholder"Re-run an evaluation against the latest versions of its models, datasets, adapters, tasks, and secrets, or keep the original versions.
CLI: back up and restore an entire AI app with
lf dumpandlf restore.CLI: run
lf test taskon specific samples via--sample-ids.CLI: export risk scorers and risk policies with
lf export risk-scorerandlf export risk-policies.SDK: write custom scorers with the new
latticeflow.core.experimental.scorersmodule, including agent scorers such asFunctionCallCoverage,DisallowedFunctionCalls,FunctionCallOutputContains, andFunctionCallOutputNotContains.Risk policy status is now shown for risk policies that haven’t been evaluated yet.
Improvements
- Task result evidence now loads faster: it is served through paginated endpoints with server-side filtering and sorting.
- Clearer error message when you try to connect to an HTTPS server over
http://. lf testnow surfaces errors immediately instead of logging them and continuing.- Renamed the evidence statistic “Used for Metrics” to “Excluded from Metrics” for clarity.
Bug fixes
- Filter order is now preserved when adding or removing filter values.
- Removed the
rnj-1-instructTogether AI model, which was no longer responding. - Fixed upload of the new task result log format.