Author: Subhadip Mitra
Description: Adaptive Reasoning Through Evaluation of Multi-agent Intelligent Systems

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# ARTEMIS Agents

Adaptive Reasoning Through Evaluation of Multi-agent Intelligent Systems

A production-ready framework for structured multi-agent debates with adaptive evaluation, causal reasoning, and built-in safety monitoring.

## What is ARTEMIS?

ARTEMIS is an open-source implementation of the Adaptive Reasoning and Evaluation Framework for Multi-agent Intelligent Systems — a framework designed to improve complex decision-making through structured debates between AI agents.

Unlike general-purpose multi-agent frameworks, ARTEMIS is purpose-built for debate-driven decision-making with:

- Hierarchical Argument Generation (H-L-DAG): Structured, context-aware argument synthesis at strategic, tactical, and operational levels
- Adaptive Evaluation with Causal Reasoning (L-AE-CR): Dynamic criteria weighting with causal analysis
- Jury Scoring Mechanism: Fair, multi-perspective evaluation of arguments
- Ethical Alignment: Built-in ethical considerations in both generation and evaluation
- Safety Monitoring: Real-time detection of sandbagging, deception, and manipulation

## Why ARTEMIS?

Feature | AutoGen | CrewAI | CAMEL | ARTEMIS
--- | --- | --- | --- | ---
Multi-agent debates | Basic | Basic | 2-3 agents | N agents
Structured argument generation | No | No | No | H-L-DAG
Causal reasoning | No | No | No | L-AE-CR
Adaptive evaluation | No | No | No | Dynamic weights
Ethical alignment | No | No | No | Built-in
Sandbagging detection | No | No | No | Metacognition
Reasoning model support | Limited | Limited | No | o1/R1 native
MCP server mode | No | No | No | Yes

## Quick Example

    from artemis import Debate, Agent

    # Create debate agents
    agents = [
        Agent(name="Proponent", role="Argues in favor", model="gpt-4o"),
        Agent(name="Opponent", role="Argues against", model="gpt-4o"),
    ]

    # Run the debate
    debate = Debate(
        topic="Should AI systems be given legal personhood?",
        agents=agents,
        rounds=3
    )

    result = await debate.run()

    print(f"Verdict: {result.verdict.decision}")
    print(f"Confidence: {result.verdict.confidence:.0%}")

## Key Features

### Structured Debates

ARTEMIS implements a rigorous debate structure with:

- Opening statements from each agent
- Multiple argumentation rounds with rebuttals
- Evidence-based reasoning with causal links
- Jury deliberation for fair verdicts

### Safety First

Built-in monitors detect problematic AI behavior:

- Sandbagging Detection: Identifies when agents deliberately underperform
- Deception Monitoring: Catches misleading arguments or manipulation
- Behavior Tracking: Monitors for unexpected behavioral drift
- Ethics Guard: Ensures debates stay within ethical bounds

### Framework Integrations

Use ARTEMIS with your existing tools:

- LangChain: As a structured tool
- LangGraph: As a workflow node
- CrewAI: As a crew tool
- MCP: As a universal server

## Research Foundation

ARTEMIS is based on peer-reviewed research:

> Adaptive Reasoning and Evaluation Framework for Multi-agent Intelligent Systems in Debate-driven Decision-making Mitra, S. (2025). Technical Disclosure Commons. Read the paper

## Benchmarks

We've run ARTEMIS against AutoGen, CrewAI, and CAMEL across 60 structured debates. See the benchmark results and analysis in the README.

## Get Started

Ready to dive in? Check out the Installation Guide or jump straight to the Quick Start.

## License

ARTEMIS is released under the Apache License 2.0. See LICENSE for details.