Add agent-openai-basic template - #262
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A lean OpenAI Agents SDK agent backend for Databricks Apps: runs locally with no database and no setup beyond a Databricks auth profile. Lakebase durability and MLflow tracing are opt-in. API-first (no bundled UI); exposes the OpenAI Responses API.
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Co-authored-by: Isaac <no-reply@databricks.com>
elainemwang
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Co-authored-by: Isaac <no-reply@databricks.com>
Co-authored-by: Isaac <no-reply@databricks.com>
Co-authored-by: Isaac <no-reply@databricks.com>
… at call time Co-authored-by: Isaac <no-reply@databricks.com>
Removes the import-time env gate; create_agent composes [*all_tools(), *memory_tools()]. memory_tools() returns [] unless AGENT_MEMORY_STORE is set, so the model is only offered memory tools when configured. Swap the import for a databricks-openai helper when it ships. Co-authored-by: Isaac <no-reply@databricks.com>
tracing._enabled is now set at startup instead of import, so the agent import chain is side-effect-free. load_dotenv can move below the imports, removing the ruff E402 suppression. Co-authored-by: Isaac <no-reply@databricks.com>
Co-authored-by: Isaac <no-reply@databricks.com>
build_mcp_servers() (the user knob) stays in agent/mcps.py; the connect()/MCPServerManager plumbing moves to mason/ with the other SDK-migration candidates. Also drops a dead json import and applies safe ruff fixes to the moved files. Co-authored-by: Isaac <no-reply@databricks.com>
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| # (local dev, no Lakebase) it's None, so the server serves in-request over the local SQLite store. | ||
| agent_server = LongRunningAgentServer( | ||
| "ResponsesAgent", | ||
| db_autoscaling_endpoint=os.getenv("LAKEBASE_AUTOSCALING_ENDPOINT"), |
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will update post local db backed LongRunningAgentServer to remove the currently required db_autoscaling_endpoint param. cc @shivam5
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will also use resume_strategy=ResumeStrategy.AGENT_SESSION after databricks/databricks-ai-bridge#467
Co-authored-by: Isaac <no-reply@databricks.com>
Enable tracing when a destination (MLFLOW_TRACKING_URI or MLFLOW_TRACING_DESTINATION) and an experiment (MLFLOW_EXPERIMENT_ID or MLFLOW_EXPERIMENT_NAME) are both present, in whichever form the user or Apps resource binding provides. MLflow resolves the specific value. Co-authored-by: Isaac <no-reply@databricks.com>
jennsun
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Aug 25, 2026
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| ``inbound``: Responses request -> agent-SDK run input. ``outbound``: agent-SDK stream events -> | ||
| Responses wire events. SDK-specific — a different harness would replace this layer. | ||
| """ |
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is there supposed to be content here?
jennsun
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Adds agent-openai-basic, a lean OpenAI Agents SDK agent backend for Databricks Apps.
uv run start-server). Sessions use local SQLite.POST /responses,POST /invocations).@function_toolfile inagent/tools/(auto-collected); add MCP servers inagent/mcps.py.trace example: https://eng-ml-inference.staging.cloud.databricks.com/ml/experiments/4023724100406459/traces?o=1653573648247579&selectedEvaluationId=tr-8b96a9e6b738b5605074bbae43e7ff6d