update readme

Author: xzrderek

README.md

@@ -1,108 +1,34 @@
-# Eval Protocol (EP)
+# Eval Protocol
 
 [![PyPI - Version](https://img.shields.io/pypi/v/eval-protocol)](https://pypi.org/project/eval-protocol/)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/eval-protocol/python-sdk)
 
-**The open-source framework to help you write evals for RL.**
+**Eval Protocol (EP) is an open solution for doing reinforcement learning fine-tuning on existing agents — across any language, container, or framework.**
 
-## 🚀 Features
+![Eval Protocol overview](./docs/intro.png)
 
-- **Pytest authoring**: `@evaluation_test` decorator to configure evaluations
-- **Robust rollouts**: Handles flaky LLM APIs and parallel execution
-- **Integrations**: Works with Langfuse, LangSmith, Braintrust, Responses API
-- **Agent support**: LangGraph and Pydantic AI
-- **MCP RL envs**: Build reinforcement learning environments with MCP
-- **Built-in benchmarks**: AIME, tau-bench
-- **LLM judge**: Stack-rank models using pairwise Arena-Hard-Auto
-- **Local UI**: Pivot/table views for real-time analysis
+Most teams already have complex agents running in production — often across remote services with heavy dependencies, Docker containers, or TypeScript backends deployed on Vercel. When they try to train or fine-tune these agents with reinforcement learning, connecting them to a trainer quickly becomes painful.
 
-## ⚡ Quickstart (no labels needed)
+Eval Protocol makes this possible in two ways:
 
-Install with your tracing platform extras and set API keys:
+1. **Expose your agent through a simple API**
+   Wrap your existing agent (Python, TypeScript, Docker, etc.) in a simple HTTP service using EP’s rollout interface. EP handles the rollout orchestration, metadata passing, and trace storage automatically.
+2. **Connect with any trainer**
+   Once your agent speaks the EP standard, it can be fine-tuned or evaluated with any supported trainer — Fireworks RFT, TRL, Unsloth, or your own — with no environment rewrites.
 
-```bash
-pip install 'eval-protocol[langfuse]'
+The result: RL that works out-of-the-box for existing production agents.
 
-# Model API keys (set what you need)
-export OPENAI_API_KEY=...
-export FIREWORKS_API_KEY=...
-export GEMINI_API_KEY=...
+## Who This Is For
 
-# Platform keys
-export LANGFUSE_PUBLIC_KEY=...
-export LANGFUSE_SECRET_KEY=...
-export LANGFUSE_HOST=https://your-deployment.com  # optional
-```
+- **Applied AI teams** adding RL to existing production agents.
+- **Research engineers** experimenting with fine-tuning complex, multi-turn or tool-using agents.
+- **MLOps teams** building reproducible, language-agnostic rollout pipelines.
 
-Minimal evaluation using the built-in AHA judge:
+## Quickstart
 
-```python
-from datetime import datetime
-import pytest
+- See the Quickstart repository: [eval-protocol/quickstart](https://github.com/eval-protocol/quickstart/tree/main)
 
-from eval_protocol import (
-    evaluation_test,
-    aha_judge,
-    EvaluationRow,
-    SingleTurnRolloutProcessor,
-    DynamicDataLoader,
-    create_langfuse_adapter,
-)
-
-
-def langfuse_data_generator() -> list[EvaluationRow]:
-    adapter = create_langfuse_adapter()
-    return adapter.get_evaluation_rows(
-        to_timestamp=datetime.utcnow(),
-        limit=20,
-        sample_size=5,
-    )
-
-
-@pytest.mark.parametrize(
-    "completion_params",
-    [
-        {"model": "openai/gpt-4.1"},
-        {"model": "fireworks_ai/accounts/fireworks/models/gpt-oss-120b"},
-    ],
-)
-@evaluation_test(
-    data_loaders=DynamicDataLoader(generators=[langfuse_data_generator]),
-    rollout_processor=SingleTurnRolloutProcessor(),
-)
-async def test_llm_judge(row: EvaluationRow) -> EvaluationRow:
-    return await aha_judge(row)
-```
-
-Run it:
-
-```bash
-pytest -q -s
-```
-
-The pytest output includes local links for a leaderboard and row-level traces (pivot/table) at `http://localhost:8000`.
-
-## Installation
-
-This library requires Python >= 3.10.
-
-### pip
-
-```bash
-pip install eval-protocol
-```
-
-### uv (recommended)
-
-```bash
-# Install uv (if needed)
-curl -LsSf https://astral.sh/uv/install.sh | sh
-
-# Add to your project
-uv add eval-protocol
-```
-
-## 📚 Resources
+## Resources
 
 - **[Documentation](https://evalprotocol.io)** – Guides and API reference
 - **[Discord](https://discord.com/channels/1137072072808472616/1400975572405850155)** – Community