chore: update author info and add SDK quickstart docs

.gitignore

@@ -221,3 +221,6 @@ __marimo__/
 .opencode/
 .agents/
 .tmp/
+
+# Release guide (local-only)
+Release_Guide.md

docs/sdk/quickstart.md

@@ -0,0 +1,67 @@
+# Quickstart
+
+!!! note "Prerequisites"
+    - Python 3.11+
+    - Install the package: `pip install openagent-eval`
+    - A compatible LLM provider (e.g., OpenAI) and any required credentials are available as environment variables.
+
+The following example shows the smallest possible workflow: create a configuration, run the evaluation engine on a tiny in‑memory dataset, and print the report summary.
+
+```python
+"""Minimal OpenAgent Eval example.
+Runs an async engine with a single question and prints the summary.
+"""
+
+import asyncio
+from pathlib import Path
+
+from openagent_eval.config.models import Config, LLMConfig, RetrieverConfig, MetricsConfig, DatasetConfig
+from openagent_eval.core.engine import Engine
+
+# 1️⃣  Build a configuration
+config = Config(
+    dataset=DatasetConfig(path="data/sample.json", limit=1),
+    llm=LLMConfig(provider="openai", model="gpt-4o-mini", temperature=0.0),
+    retriever=RetrieverConfig(provider="mock"),  # simple mock retriever
+    metrics=MetricsConfig(
+        retrieval=[],
+        generation=["faithfulness"],
+        performance=[],
+        cost=[],
+    ),
+)
+
+# 2️⃣  Create the engine
+engine = Engine(config)
+
+# 3️⃣  Define a tiny dataset inline (overrides the path above)
+#    The engine expects a list of dicts matching `DatasetItemModel`.
+dataset = [
+    {
+        "question": "What is the capital of France?",
+        "ground_truth": "Paris",
+        "metadata": {},
+    }
+]
+
+# 4️⃣  Run the evaluation (async)
+async def main():
+    report = await engine.run(dataset)
+    # The report contains a handy `summary` dict – print the most useful bits.
+    print("✅ Evaluation complete")
+    print("Total items:", report.summary["total_items"])
+    print("Successful:", report.summary["successful_evaluations"])
+    print("Metrics summary:", report.summary.get("metrics_summary", {}))
+    # Optionally persist the report using ReportManager (see next docs page).
+
+asyncio.run(main())
+```
+
+**What you just saw**
+- `Config` ties together dataset, LLM, retriever, and metrics.
+- `Engine` orchestrates provider construction, pipeline execution, and report generation.
+- The example uses the built‑in `mock` retriever so no external service is needed.
+
+Next, dive deeper into the mental model that powers this SDK.
+
+[Next: Concepts →](concepts.md)

openagent_eval/__init__.py

@@ -1,4 +1,4 @@
 """OpenAgent Eval - Open-source CLI framework for evaluating RAG systems and AI Agents."""
 
 __version__ = "0.4.5"
-__author__ = "OpenAgent Eval Contributors"
+__author__ = "OpenAgentHQ"

pyproject.toml

@@ -6,7 +6,10 @@ readme = "README.md"
 license = {text = "Apache-2.0"}
 requires-python = ">=3.11"
 authors = [
-    {name = "OpenAgent Eval Contributors"}
+    {name = "Himanshu Kumar"}
+]
+Maintainers = [
+    {name = "OpenAgentHQ Team", email = "openagenthq@gmail.com"}
 ]
 keywords = ["rag", "evaluation", "ai", "llm", "metrics", "cli", "nli", "corpus-health", "diagnosis", "synthetic-data"]
 classifiers = [