feat: add openai company_agent sample

packages/uipath-openai-agents/samples/company_agent/.agent/REQUIRED_STRUCTURE.md

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+## Required Agent Structure
+
+**IMPORTANT**: All UiPath coded agents MUST follow this standard structure unless explicitly specified otherwise by the user.
+
+### Required Components
+
+Every agent implementation MUST include these two Pydantic models:
+
+```python
+from pydantic import BaseModel
+
+class Input(BaseModel):
+    """Define input fields that the agent accepts"""
+    # Add your input fields here
+    pass
+
+class Output(BaseModel):
+    """Define output fields that the agent returns"""
+    # Add your output fields here
+    pass
+```
+
+### SDK Initialization
+
+```python
+from uipath.platform import UiPath
+
+# Initialize with environment variables
+uipath = UiPath()
+
+# With explicit credentials
+uipath = UiPath(base_url="https://cloud.uipath.com/...", secret="your_token")
+
+# Or with client_id and client_secret
+uipath = UiPath(
+    client_id=UIPATH_CLIENT_ID,
+    client_secret=UIPATH_CLIENT_SECRET,
+    scope=UIPATH_SCOPE,
+    base_url=UIPATH_URL
+)
+```
+
+### Standard Agent Template
+
+Every agent should follow this basic structure:
+
+```python
+from uipath.platform import UiPath
+from pydantic import BaseModel
+
+# 1. Define Input, and Output models
+class Input(BaseModel):
+    field: str
+
+class Output(BaseModel):
+    result: str
+
+# 2. Initialize with environment variables
+uipath = UiPath()
+
+# 3. Define the main function (the main function can be named "main", "run" or "execute")
+def main(input_data: Input) -> Output:
+     pass
+```

packages/uipath-openai-agents/samples/company_agent/.claude/commands/eval.md

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+---
+allowed-tools: Bash, Read, Write, Edit, Glob
+description: Create and run agent evaluations
+---
+
+I'll help you create and run evaluations for your UiPath agent.
+
+## Step 1: Check project setup
+
+Let me check your project structure:
+
+!ls -la evaluations/ entry-points.json 2>/dev/null || echo "NEEDS_SETUP"
+
+# Check if schemas might be stale (main.py newer than entry-points.json)
+!if [ -f main.py ] && [ -f entry-points.json ] && [ main.py -nt entry-points.json ]; then echo "SCHEMAS_MAY_BE_STALE"; fi
+
+### If NEEDS_SETUP
+
+If `entry-points.json` doesn't exist, initialize the project first:
+
+!uv run uipath init
+
+Then re-run this skill.
+
+### If SCHEMAS_MAY_BE_STALE
+
+Your `main.py` is newer than `entry-points.json`. Refresh schemas:
+
+!uv run uipath init --no-agents-md-override
+
+## Step 2: What would you like to do?
+
+1. **Create new eval set** - Set up evaluations from scratch
+2. **Add test case** - Add a test to existing eval set
+3. **Run evaluations** - Execute tests and see results
+4. **Analyze failures** - Debug failing tests
+
+---
+
+## Creating an Eval Set
+
+First, create the directory structure:
+
+!mkdir -p evaluations/eval-sets evaluations/evaluators
+
+Read the agent's Input/Output schema from entry-points.json to understand the data types.
+
+### Evaluator Selection Guide
+
+| If your output is... | Use this evaluator | evaluatorTypeId |
+|---------------------|-------------------|-----------------|
+| Exact string/number | `ExactMatchEvaluator` | `uipath-exact-match` |
+| Contains key phrases | `ContainsEvaluator` | `uipath-contains` |
+| Semantically correct | `LLMJudgeOutputEvaluator` | `uipath-llm-judge-output-semantic-similarity` |
+| JSON with numbers | `JsonSimilarityEvaluator` | `uipath-json-similarity` |
+
+### Step 1: Create Evaluator Config Files
+
+**Each evaluator needs a JSON config file** in `evaluations/evaluators/`.
+
+**ExactMatchEvaluator** (`evaluations/evaluators/exact-match.json`):
+```json
+{
+  "version": "1.0",
+  "id": "ExactMatchEvaluator",
+  "name": "ExactMatchEvaluator",
+  "description": "Checks for exact output match",
+  "evaluatorTypeId": "uipath-exact-match",
+  "evaluatorConfig": {
+    "name": "ExactMatchEvaluator",
+    "targetOutputKey": "*"
+  }
+}
+```
+
+**LLMJudgeOutputEvaluator** (`evaluations/evaluators/llm-judge-output.json`):
+```json
+{
+  "version": "1.0",
+  "id": "LLMJudgeOutputEvaluator",
+  "name": "LLMJudgeOutputEvaluator",
+  "description": "Uses LLM to judge semantic similarity",
+  "evaluatorTypeId": "uipath-llm-judge-output-semantic-similarity",
+  "evaluatorConfig": {
+    "name": "LLMJudgeOutputEvaluator",
+    "model": "gpt-4o-mini-2024-07-18"
+  }
+}
+```
+
+**JsonSimilarityEvaluator** (`evaluations/evaluators/json-similarity.json`):
+```json
+{
+  "version": "1.0",
+  "id": "JsonSimilarityEvaluator",
+  "name": "JsonSimilarityEvaluator",
+  "description": "Compares JSON structures",
+  "evaluatorTypeId": "uipath-json-similarity",
+  "evaluatorConfig": {
+    "name": "JsonSimilarityEvaluator",
+    "targetOutputKey": "*"
+  }
+}
+```
+
+**ContainsEvaluator** (`evaluations/evaluators/contains.json`):
+```json
+{
+  "version": "1.0",
+  "id": "ContainsEvaluator",
+  "name": "ContainsEvaluator",
+  "description": "Checks if output contains text",
+  "evaluatorTypeId": "uipath-contains",
+  "evaluatorConfig": {
+    "name": "ContainsEvaluator"
+  }
+}
+```
+
+### Step 2: Create Eval Set
+
+**Eval Set Template** (`evaluations/eval-sets/default.json`):
+```json
+{
+  "version": "1.0",
+  "id": "default-eval-set",
+  "name": "Default Evaluation Set",
+  "evaluatorRefs": ["ExactMatchEvaluator"],
+  "evaluations": [
+    {
+      "id": "test-1",
+      "name": "Test description",
+      "inputs": {
+        "field": "value"
+      },
+      "evaluationCriterias": {
+        "ExactMatchEvaluator": {
+          "expectedOutput": {
+            "result": "expected value"
+          }
+        }
+      }
+    }
+  ]
+}
+```
+
+**Important notes:**
+- `evaluatorRefs` must list ALL evaluators used in any test case
+- Each evaluator in `evaluatorRefs` needs a matching JSON config in `evaluations/evaluators/`
+- `evaluationCriterias` keys must match entries in `evaluatorRefs`
+- Use `expectedOutput` for most evaluators
+- LLM evaluators need `model` in their config. Available models are defined in the SDK's `ChatModels` class (`uipath.platform.chat.ChatModels`):
+  - `gpt-4o-mini-2024-07-18` (recommended for cost-efficiency)
+  - `gpt-4o-2024-08-06` (higher quality, higher cost)
+  - `o3-mini-2025-01-31` (latest reasoning model)
+  - Model availability varies by region and tenant configuration
+  - Check your UiPath Automation Cloud portal under AI Trust Layer for available models in your region
+
+---
+
+## Adding a Test Case
+
+When adding a test to an existing eval set:
+
+1. Read the existing eval set
+2. Check which evaluators are in `evaluatorRefs`
+3. Add the new test to `evaluations` array
+4. If using a new evaluator, add it to `evaluatorRefs`
+
+### Test Case Template
+
+```json
+{
+  "id": "test-{n}",
+  "name": "Description of what this tests",
+  "inputs": { },
+  "evaluationCriterias": {
+    "EvaluatorName": {
+      "expectedOutput": { }
+    }
+  }
+}
+```
+
+---
+
+## Running Evaluations
+
+First, read entry-points.json to get the entrypoint name (e.g., `main`):
+
+!uv run uipath eval main evaluations/eval-sets/default.json --output-file eval-results.json
+
+**Note:** Replace `main` with your actual entrypoint from entry-points.json.
+
+### Analyze Results
+
+After running, read `eval-results.json` and show:
+- Pass/fail summary table
+- For failures: expected vs actual output
+- Suggestions for fixing or changing evaluators
+
+### Results Format
+
+```json
+{
+  "evaluationSetResults": [{
+    "evaluationRunResults": [
+      {
+        "evaluationId": "test-1",
+        "evaluatorId": "ExactMatchEvaluator",
+        "result": { "score": 1.0 },
+        "errorMessage": null
+      }
+    ]
+  }]
+}
+```
+
+- Score 1.0 = PASS
+- Score < 1.0 = FAIL (show expected vs actual)
+- errorMessage present = ERROR (show message)
+
+---
+
+## Evaluator Reference
+
+### Deterministic Evaluators
+
+**ExactMatchEvaluator** - Exact output matching
+```json
+"ExactMatchEvaluator": {
+  "expectedOutput": { "result": "exact value" }
+}
+```
+
+**ContainsEvaluator** - Output contains substring
+```json
+"ContainsEvaluator": {
+  "searchText": "must contain this"
+}
+```
+
+**JsonSimilarityEvaluator** - JSON comparison with tolerance
+```json
+"JsonSimilarityEvaluator": {
+  "expectedOutput": { "value": 10.0 }
+}
+```
+
+### LLM-Based Evaluators
+
+**LLMJudgeOutputEvaluator** - Semantic correctness
+```json
+"LLMJudgeOutputEvaluator": {
+  "expectedOutput": { "summary": "Expected semantic meaning" }
+}
+```
+
+**LLMJudgeTrajectoryEvaluator** - Validate agent reasoning
+```json
+"LLMJudgeTrajectoryEvaluator": {
+  "expectedAgentBehavior": "The agent should first fetch data, then process it"
+}
+```
+
+---
+
+## Common Issues
+
+### "No evaluations found"
+- Check `evaluations/eval-sets/` directory exists
+- Verify JSON file is valid
+
+### Evaluator not found
+- Each evaluator needs a JSON config file in `evaluations/evaluators/`
+- Config file must have correct `evaluatorTypeId` (see templates above)
+- Config file must have `name` field at root level
+- LLM evaluators need `model` in `evaluatorConfig`
+
+### Evaluator skipped
+- Ensure evaluator is listed in root `evaluatorRefs` array
+- Check evaluator config file exists in `evaluations/evaluators/`
+
+### Schema mismatch
+- Run `uv run uipath init --no-agents-md-override` to refresh schemas
+- Check `entry-points.json` matches your Input/Output models