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fix: lint fixes
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.github/ISSUE_TEMPLATE/bug_report.md

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**Libs and packages (please complete the following information):**
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`uv pip list`
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**Additional context**
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Add any other context about the problem here.

MAINTAINERS.md

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|-----------------|---------------------------------------|
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| Tomasz Guzik | [MRGuziX](https://github.com/MRGuziX) |
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| Wojciech Rębisz | [Wojciech-Rebisz](https://github.com/Wojciech-Rebisz) |
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agents/autogen/mcp_agent/mcp_automl_template/AUTOML_DEPLOYMENT.md

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- Open the **deployment details**. Under **Inference endpoint**, copy the **external** URL (only if you enabled the external route).
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- **Deployment URL** for the MCP server is the **predict** endpoint, for example:
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- `<EXTERNAL_BASE_URL>/v1/models/<MODEL_NAME>:predict`
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- `<EXTERNAL_BASE_URL>/v1/models/<MODEL_NAME>:predict`
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where `<MODEL_NAME>` is the deployment’s **Resource name** (lowercase, no spaces). Example: `https://my-model-myproject.apps.example.com/v1/models/my-churn-model:predict`
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- **Token** (if you enabled token auth): **Projects***your project***Deployments** → expand the deployment → use the **Token secret** value as `DEPLOYMENT_TOKEN`.
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- [Red Hat AI examples – Predict Customer Churn (AutoML)](https://github.com/red-hat-data-services/red-hat-ai-examples/blob/automl_sample/examples/automl/churn_prediction_tutorial.md) – full tutorial (project, S3, pipeline, leaderboard, predictor notebook, model registry, ServingRuntime, deployment, scoring).
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- [autogluon_tabular_training_pipeline](https://github.com/LukaszCmielowski/pipelines-components/tree/rhoai_automl/pipelines/training/automl/autogluon_tabular_training_pipeline) – pipeline source (branch `rhoai_automl`).
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- [KServe V1 Protocol](https://kserve.github.io/website/docs/concepts/architecture/data-plane/v1-protocol) – inference request/response format.
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- This repo’s [README](README.md) – MCP server setup, tools, Cursor/Ollama, and `.env`.
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- This repo’s [README](README.md) – MCP server setup, tools, Cursor/Ollama, and `.env`.

agents/autogen/mcp_agent/mcp_automl_template/pyproject.toml

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[project.optional-dependencies]
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dev = [
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"pytest>=9.0.2,<10.0.0",
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"pytest>=9.0.2,<10.0.0",
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]
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[tool.setuptools]

agents/crewai/websearch_agent/.env.example

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# MLFLOW_WORKSPACE=default
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# LLM_PROVIDER=litellm
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# MLFLOW_TRACKING_AUTH= # Use Kubernetes service account for authentication (if running inside the cluster)
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agents/crewai/websearch_agent/README.md

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## Resources
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- [CrewAI Documentation](https://docs.crewai.com/)
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- [CrewAI Tools](https://docs.crewai.com/concepts/tools)
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- [CrewAI Tools](https://docs.crewai.com/concepts/tools)

agents/crewai/websearch_agent/pyproject.toml

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dependencies = [
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"fastapi>=0.135.1",
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"uvicorn[standard]>=0.41.0",
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"crewai[litellm]>=1.11.0",
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"crewai[litellm]>=1.11.0",
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"litellm!=1.82.7,!=1.82.8", # Block compromised PyPI releases
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"python-dotenv>=1.1.1,<1.2.0", # CrewAI pins ~=1.1.1
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"setuptools>=80.9.0,<82.0.0",

agents/google/adk/.env.example

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#PORT=
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## Deployment Configuration (for OpenShift/Kubernetes)
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#CONTAINER_IMAGE=
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#CONTAINER_IMAGE=

agents/langflow/simple_tool_calling_agent/flows/outdoor-activity-agent.json

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"action_name": null,
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"action_description": null,
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"access_type": "PRIVATE"
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}
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}

agents/langgraph/agentic_rag/LICENSE

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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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SOFTWARE.

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