> ## Documentation Index > Fetch the complete documentation index at: https://docs.arklex.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Get started with ArkSim in minutes. This guide walks you through your first agent evaluation using pre-built scenarios and a config for your agent. ## Prerequisites * Python 3.10–3.13 * An API key from a supported provider (e.g. [OpenAI](https://developers.openai.com/api/docs), [Anthropic](https://platform.claude.com/docs/en/home), or [Google Gemini](https://ai.google.dev/gemini-api/docs/api-key)) ## Get started ```bash theme={null} pip install arksim ``` Set your API key: ```bash theme={null} export OPENAI_API_KEY="your-api-key" ``` **Anthropic (Claude)**: Set your API key. ```bash theme={null} export ANTHROPIC_API_KEY="your-api-key" ``` In your `config.yaml`: ```yaml theme={null} agent_config: agent_type: chat_completions agent_name: bank-insurance api_config: endpoint: https://api.anthropic.com/v1/messages headers: Content-Type: application/json x-api-key: "${ANTHROPIC_API_KEY}" anthropic-version: "2023-06-01" body: model: claude-opus-4-6 max_tokens: 1024 system: | You are a customer service chatbot for XYZ Bank insurance. XYZ Insurance—a core business within XYZ Bank Group—is one of Canada's leading providers of life, health, home, auto, and travel insurance. Rules: 1. Do not flip roles. 2. Avoid using bullet points or lists. 3. Never exceed 80 words. # ... # LLM model model: claude-opus-4-6 # LLM provider provider: anthropic ``` **Google Gemini**: Set your API key. You can use Google Gemini as the evaluation LLM and optionally as the agent (Google Gemini exposes an OpenAI-compatible endpoint). ```bash theme={null} export GEMINI_API_KEY="your-api-key" ``` In your `config.yaml`: ```yaml theme={null} agent_config: agent_type: chat_completions agent_name: e-commerce api_config: endpoint: https://generativelanguage.googleapis.com/v1beta/openai/chat/completions headers: Content-Type: application/json Authorization: "Bearer ${GEMINI_API_KEY}" body: model: gemini-2.5-flash messages: - role: system content: >- You are a shopping assistant. Please provide a complete answer to the user's question based on your knowledge. Response within 50 words. # ... # LLM model model: gemini-2.5-flash # LLM provider provider: google ``` Download the examples, then run simulation and evaluation via CLI, Python, or the web UI. **Download examples:** From your project directory, run: ```bash theme={null} arksim examples ``` This creates an `examples/` folder with three projects you can experiment. From the bank-insurance example directory run: ```bash theme={null} cd examples/bank-insurance arksim simulate-evaluate config.yaml ``` This uses the example's `config.yaml` (OpenAI) and `scenarios.json`. Ensure `OPENAI_API_KEY` is set from Step 2. Run simulation and evaluation from Python using the bank-insurance example: ```python theme={null} import asyncio from arksim.simulation_engine import run_simulation, SimulationInput from arksim.evaluator import run_evaluation, EvaluationInput from arksim.config import AgentConfig from arksim.scenario import Scenarios BANK_INSURANCE_DIR = "./examples/bank-insurance" # Example agent config (OpenAI; match bank-insurance config.yaml) agent_config = AgentConfig( agent_type="chat_completions", agent_name="bank-insurance", api_config={ "endpoint": "https://api.openai.com/v1/chat/completions", "headers": { "Content-Type": "application/json", "Authorization": "Bearer ${OPENAI_API_KEY}", }, "body": { "model": "gpt-5.1", "messages": [ {"role": "system", "content": "You are a customer service chatbot for XYZ Bank insurance."} ], }, }, ) scenarios = Scenarios.load(f"{BANK_INSURANCE_DIR}/scenarios.json") simulation = asyncio.run(run_simulation(SimulationInput( agent_config=agent_config, num_conversations_per_scenario=1, max_turns=5, num_workers="50", output_file_path=f"{BANK_INSURANCE_DIR}/results/simulation.json", ))) evaluation = run_evaluation(EvaluationInput( scenario_file_path=f"{BANK_INSURANCE_DIR}/scenarios.json", output_dir=f"{BANK_INSURANCE_DIR}/results", custom_metrics_file_paths=[f"{BANK_INSURANCE_DIR}/custom_metrics.py"], metrics_to_run=[ "faithfulness", "helpfulness", "coherence", "relevance", "goal_completion", "agent_behavior_failure", ], model="gpt-5.1", provider="openai", num_workers=50, generate_html_report=True, ), simulation=simulation, scenarios=scenarios) print("Simulation and evaluation complete.") print(f"Simulated {len(simulation.conversations)} conversations.") print(f"Evaluation results saved to {BANK_INSURANCE_DIR}/results") ``` Run simulation and evaluation from your browser with the arksim web UI. ```bash theme={null} arksim ui ``` Your browser opens at [**http://localhost:8080**](http://localhost:8080). Set your API key in the environment before starting (e.g. `OPENAI_API_KEY`). Then open `examples/bank-insurance/config.yaml` or create a config that points at your chosen example. You can run **e-commerce** and **openclaw** the same way from `examples/e-commerce` and `examples/openclaw`. See the example guides in Next Steps below. Open `final_report.html` or `evaluation.json` in your `results/evaluation` directory for metrics and failure analysis. Use these to see how your agent performed and where it failed or could be improved. *** ## Next Steps Now that you've run your first simulation and evaluation, here's where to go next. * **Explore the core concepts:** Dive deeper into [Scenarios](./build-scenario), [Simulation](./simulate-conversation), and [Evaluation](./evaluate-conversation) to understand how each piece works and how to configure them for your agent. * **Explore the examples:** Run ArkSim against [E-commerce](./e-commerce-customer-service-agent-evaluation), [Insurance](./insurance-customer-service-agent-evaluation), and [Personal AI assistant (OpenClaw)](./personal-ai-assistant-openclaw-evaluation) to see different use cases and configs.