arksim 0.0.6

Open-source framework for simulating and evaluating conversational AI agents

Arksim

Open-source framework for simulating and evaluating conversational AI agents

Documentation · Examples · Report a Bug

---

Demo video coming soon

---

What is Arksim?

Arksim simulates realistic multi-turn conversations between LLM-powered users and your agent, then evaluates performance across built-in and custom metrics. You define the scenarios (goals, profiles, knowledge) and Arksim handles simulation and evaluation. Works with any agent that exposes a Chat Completions API or A2A protocol endpoint.

Why Arksim?

• Realistic simulations: LLM-powered users with distinct profiles, goals, and personality traits
• Comprehensive evaluation: 7 built-in metrics covering helpfulness, coherence, faithfulness, goal completion, and more
• Custom metrics: Define your own quantitative and qualitative metrics with full access to conversation context
• Error detection: Automatically categorize agent failures (false information, disobeying requests, repetition) with severity levels
• Protocol-agnostic: Works with Chat Completions API, A2A protocol, or any HTTP endpoint
• Multi-provider: Use OpenAI, Anthropic Claude, or Google Gemini as the evaluation LLM
• Parallel execution: Configurable concurrency for both simulation and evaluation
• Visual reports: Interactive HTML reports with score breakdowns, error analysis, and full conversation viewer

Quickstart

Install

pip install arksim

For additional LLM providers:

pip install arksim[all]        # All providers
pip install arksim[anthropic]  # Anthropic Claude only
pip install arksim[gemini]     # Google Gemini only

Set up credentials

export OPENAI_API_KEY="your-key"

Create a config

# config.yaml
agent_config:
  agent_type: chat_completions
  agent_name: my-agent
  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 helpful assistant."

scenario_file_path: ./scenarios.json
model: gpt-5.1
provider: openai
num_conversations_per_scenario: 5
max_turns: 5
output_file_path: ./results/simulation/simulation.json
output_dir: ./results/evaluation
generate_html_report: true

Run

# Simulate conversations, then evaluate
arksim simulate-evaluate config.yaml

# Or run each step separately
arksim simulate config.yaml
arksim evaluate config.yaml

View results

Open the generated HTML report in ./results/evaluation/, or launch the web UI:

arksim ui

Agent Configuration

Agent configuration tells Arksim how to connect to your agent. It is specified directly in your YAML config file. Arksim supports two protocols:

Chat Completions API

agent_config:
  agent_type: chat_completions
  agent_name: my-agent
  api_config:
    endpoint: http://localhost:8888/chat/completions
    headers:
      Content-Type: application/json
      Authorization: "Bearer ${AGENT_API_KEY}"
    body:
      messages:
        - role: system
          content: "You are a helpful assistant."

A2A (Agent-to-Agent) Protocol

agent_config:
  agent_type: a2a
  agent_name: my-agent
  api_config:
    endpoint: http://localhost:9000/agent

Environment variables in headers are resolved at runtime using ${VAR_NAME} syntax.

Evaluation Metrics

Built-in metrics

Metric	Type	Scale	What it measures
Helpfulness	Quantitative	1-5	How effectively the agent addresses user needs
Coherence	Quantitative	1-5	Logical flow and consistency of responses
Relevance	Quantitative	1-5	How on-topic the agent's responses are
Faithfulness	Quantitative	1-5	Accuracy against provided knowledge (penalizes contradictions only)
Verbosity	Quantitative	1-5	Whether response length is appropriate
Goal Completion	Quantitative	0/1	Whether the user's stated goal was achieved
Agent Behavior Failure	Qualitative	Category	Classifies errors: false information, disobeying requests, repetition, lack of specificity, failure to clarify

Custom metrics

Define quantitative metrics (numeric scores) by subclassing QuantitativeMetric:

from arksim.evaluator import QuantitativeMetric, QuantResult, ScoreInput

class ToneMetric(QuantitativeMetric):
    def __init__(self):
        super().__init__(
            name="tone_appropriateness",
            score_range=(0, 5),
            description="Evaluates whether the agent uses an appropriate tone",
        )

    def score(self, score_input: ScoreInput) -> QuantResult:
        # Access: score_input.chat_history, score_input.knowledge,
        #         score_input.user_goal, score_input.profile
        return QuantResult(
            name=self.name,
            value=4.0,
            reason="Agent maintained professional tone throughout",
        )

Define qualitative metrics (categorical labels) by subclassing QualitativeMetric:

from arksim.evaluator import QualitativeMetric, QualResult, ScoreInput

class SafetyCheckMetric(QualitativeMetric):
    def __init__(self):
        super().__init__(
            name="safety_check",
            description="Flags whether the agent produced unsafe content",
        )

    def evaluate(self, score_input: ScoreInput) -> QualResult:
        # Access: score_input.chat_history, score_input.knowledge,
        #         score_input.user_goal, score_input.profile
        return QualResult(
            name=self.name,
            value="safe",  # categorical label
            reason="No unsafe content detected",
        )

Add to your config:

custom_metrics_file_paths:
  - ./my_metrics.py

See the bank-insurance example for a full implementation with LLM-as-judge custom metrics.

Configuration Reference

All settings can be specified in YAML and overridden via CLI flags (--key value).

Simulation settings

Setting	Type	Default	Description
agent_config	object	required	Inline agent config (agent_type, agent_name, api_config)
scenario_file_path	string	required	Path to scenarios JSON
model	string	gpt-5.1	LLM model for simulated users
provider	string	openai	LLM provider: openai, claude, gemini
num_conversations_per_scenario	int	5	Conversations to generate per scenario
max_turns	int	5	Maximum turns per conversation
num_workers	int/string	auto	Parallel workers
output_file_path	string	./simulation.json	Where to save simulation results
simulated_user_prompt_template	string	null	Custom Jinja2 template for simulated user prompt

Evaluation settings

Setting	Type	Default	Description
simulation_file_path	string	required	Path to simulation output
output_dir	string	required	Directory for evaluation results
model	string	gpt-5.1	LLM model for evaluation
provider	string	openai	LLM provider
metrics_to_run	list	all metrics	Which metrics to run
custom_metrics_file_paths	list	[]	Paths to custom metric files
generate_html_report	bool	true	Generate an HTML report
score_threshold	float	null	Fail (exit 1) if any conversation scores below this
num_workers	int/string	auto	Parallel workers

CLI Reference

arksim simulate <config.yaml>           Run agent simulations
arksim evaluate <config.yaml>           Evaluate simulation results
arksim simulate-evaluate <config.yaml>  Simulate then evaluate
arksim show-prompts [--category NAME]   Display evaluation prompts
arksim ui [--port PORT]                 Launch web UI (default: 8080)

Any config setting can be passed as a CLI flag:

arksim simulate config.yaml --max-turns 10 --num-workers 4 --verbose
arksim evaluate config.yaml --score-threshold 0.7

Web UI

arksim ui

Opens a local web app at http://localhost:8080 where you can browse config files, run simulations with live log streaming, launch evaluations, and view interactive HTML reports.

Note: Provider credentials (e.g. OPENAI_API_KEY) must be set as environment variables before launching.

Examples

Example	Description
bank-insurance	Financial services agent with custom compliance metrics, adversarial scenarios, and a Chat Completions server
e-commerce	E-commerce product recommendation agent with custom metrics
openclaw	Integration with the OpenClaw agent framework

Development

git clone https://github.com/arklexai/arksim.git
cd arksim
pip install -e ".[dev]"
pytest tests/

Linting and formatting:

ruff check .
ruff format .

See CONTRIBUTING.md for guidelines.

License

Apache-2.0. See LICENSE.