{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-multiagent-debate","slug":"multiagent-debate","name":"Multiagent Debate","type":"repo","url":"https://github.com/composable-models/llm_multiagent_debate","page_url":"https://unfragile.ai/multiagent-debate","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-multiagent-debate__cap_0","uri":"capability://planning.reasoning.structured.multi.round.debate.orchestration.with.agent.role.assignment","name":"structured multi-round debate orchestration with agent role assignment","description":"Orchestrates multiple LLM agents through structured debate rounds where agents iteratively build on each other's responses to refine answers. The system implements a generation phase that progresses from independent reasoning to collaborative refinement, with agents assigned distinct perspectives or roles across configurable debate rounds. Each round captures agent interactions as structured state, enabling systematic evaluation of how collaborative reasoning improves factuality and reasoning accuracy compared to single-agent baselines.","intents":["I want to run multiple LLM agents in debate format to improve answer quality on reasoning tasks","I need to compare single-agent vs multi-agent debate performance on factuality metrics","I want to configure the number of agents and debate rounds for different task domains"],"best_for":["researchers validating multi-agent reasoning improvements","teams building fact-checking or reasoning verification systems","developers prototyping collaborative LLM workflows"],"limitations":["Debate rounds are sequential, not parallel — each round waits for all agents to respond, adding latency proportional to agent count × round count","No built-in agent specialization — all agents use the same base model, limiting perspective diversity","Requires external LLM API calls for each agent in each round, resulting in high token consumption and cost"],"requires":["Python 3.7+","OpenAI API key with access to gpt-3.5-turbo-0301 model","numpy 1.22.4+, pandas 1.5.3+, openai 0.27.6+"],"input_types":["task questions (text)","task-specific context (text, JSON)","ground truth data (JSON, CSV)"],"output_types":["structured debate transcripts (JSON)","final agent responses (text)","evaluation metrics (JSON, CSV)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_1","uri":"capability://automation.workflow.task.domain.specific.generation.pipeline.with.configurable.agent.count.and.debate.rounds","name":"task-domain-specific generation pipeline with configurable agent count and debate rounds","description":"Provides modular generation modules for four distinct reasoning domains (Math, GSM, MMLU, Biography) that each implement specialized debate logic while accepting configurable parameters for agent count and debate round count. The generation phase processes domain-specific inputs through task-adapted prompts, manages agent state across rounds, and produces structured output files with naming conventions encoding experimental parameters (e.g., output_agents_N_rounds_R.json). This architecture enables systematic experimentation across different agent configurations without modifying core debate logic.","intents":["I want to run the same debate experiment with different numbers of agents (e.g., 3 vs 5 agents) to measure scaling effects","I need to test how debate round count affects answer quality on math word problems","I want to generate debate outputs for multiple task domains using consistent parameter configurations"],"best_for":["researchers conducting ablation studies on agent count and round count","teams comparing debate effectiveness across different task types","developers building configurable multi-agent reasoning pipelines"],"limitations":["Task domains are hardcoded — adding new domains requires implementing new generation modules with custom debate logic","Parameter validation is minimal — invalid agent counts or round counts may produce unexpected behavior or API errors","Output file naming convention is implicit in code, making it difficult to track which parameters produced which results without documentation"],"requires":["Python 3.7+","OpenAI API key with gpt-3.5-turbo-0301 access","Task-specific dataset files (GSM dataset, MMLU dataset, biography articles, or generated math problems)","numpy, pandas, openai libraries as specified in requirements.txt"],"input_types":["task questions (text)","agent count parameter (integer)","debate round count parameter (integer)","task-specific context (JSON, CSV)"],"output_types":["debate transcripts with agent responses per round (JSON)","final answers from each agent (text)","parameterized output files (JSON with naming convention: output_agents_N_rounds_R.json)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_2","uri":"capability://data.processing.analysis.ground.truth.based.evaluation.framework.with.domain.specific.metrics","name":"ground-truth-based evaluation framework with domain-specific metrics","description":"Implements evaluation modules (eval_gsm.py, eval_mmlu.py, eval_conversation.py) that systematically compare generated debate responses against ground truth data to measure improvements in factuality and reasoning accuracy. Each evaluation module encodes domain-specific metrics (e.g., exact match for math, factual accuracy for biography, multiple-choice accuracy for MMLU) and produces structured evaluation results. The framework enables quantitative comparison between single-agent baselines and multi-agent debate outputs, with results aggregated across test sets for statistical analysis.","intents":["I want to measure whether multi-agent debate improves answer accuracy compared to single-agent responses","I need to evaluate debate outputs against ground truth data for multiple task domains","I want to generate accuracy metrics and comparison statistics for research papers or reports"],"best_for":["researchers validating multi-agent debate improvements with quantitative metrics","teams building evaluation pipelines for reasoning systems","developers comparing baseline vs debate performance on standardized benchmarks"],"limitations":["Evaluation metrics are hardcoded per task domain — custom metrics require modifying evaluation modules","No statistical significance testing built-in — results are raw accuracy numbers without confidence intervals or p-values","Ground truth data must be pre-formatted for each task domain; mismatched formats will cause evaluation failures"],"requires":["Python 3.7+","Generated debate outputs in expected JSON format","Ground truth datasets (GSM dataset, MMLU dataset, biography articles with facts)","pandas, numpy for data processing"],"input_types":["generated debate responses (JSON)","ground truth answers (JSON, CSV, text)","task domain identifier (string)"],"output_types":["accuracy metrics per sample (JSON)","aggregated accuracy statistics (JSON, CSV)","comparison reports (text, JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_3","uri":"capability://automation.workflow.multi.task.reasoning.benchmark.support.with.standardized.task.interfaces","name":"multi-task reasoning benchmark support with standardized task interfaces","description":"Provides implementations for four distinct reasoning task domains (Math, Grade School Math, MMLU, Biography) with standardized generation and evaluation interfaces that enable systematic comparison across task types. Each task domain is implemented as a modular pair of generation and evaluation modules that follow consistent architectural patterns while accommodating domain-specific requirements. The system processes inputs through standardized pipelines, generating structured outputs with consistent naming conventions, enabling researchers to run identical debate experiments across different reasoning domains and compare relative improvements.","intents":["I want to test multi-agent debate on multiple reasoning task types to validate generalizability","I need to compare debate effectiveness across math, reading comprehension, and factual reasoning tasks","I want to run the same agent count and round count configuration across all task domains for fair comparison"],"best_for":["researchers validating multi-agent debate across diverse reasoning domains","teams building general-purpose reasoning improvement systems","developers benchmarking multi-agent approaches on standardized datasets"],"limitations":["Only four task domains are supported — extending to new domains requires implementing new generation and evaluation modules","Task domains have different dataset requirements and formats, making it difficult to add new tasks without understanding each domain's specifics","No unified task interface abstraction — each task domain has custom code paths, limiting code reuse and maintainability"],"requires":["Python 3.7+","OpenAI API key with gpt-3.5-turbo-0301 access","Task-specific datasets: GSM dataset, MMLU dataset, biography articles, or generated math problems","numpy, pandas, openai libraries"],"input_types":["task domain identifier (string: 'math', 'gsm', 'mmlu', 'biography')","task questions or prompts (text)","task-specific context (JSON, CSV, text)"],"output_types":["debate transcripts (JSON)","evaluation metrics per task domain (JSON, CSV)","cross-domain comparison reports (JSON, text)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_4","uri":"capability://tool.use.integration.llm.api.abstraction.with.openai.gpt.3.5.turbo.integration","name":"llm api abstraction with openai gpt-3.5-turbo integration","description":"Provides abstraction layer for OpenAI API interactions, specifically integrating with the gpt-3.5-turbo-0301 model for all agent reasoning. The system manages API calls across multiple agents and debate rounds, handling request formatting, response parsing, and error handling. Integration points include agent prompt construction, response extraction from API outputs, and state management across sequential API calls. The abstraction enables swapping model versions or providers by modifying configuration, though current implementation is tightly coupled to OpenAI's API format.","intents":["I want to run multi-agent debate using OpenAI's GPT-3.5-turbo model without managing API calls directly","I need to handle API rate limits and failures gracefully across multiple agents and rounds","I want to track token usage and costs for multi-agent experiments"],"best_for":["researchers using OpenAI models for multi-agent experiments","teams building on top of OpenAI API without custom LLM infrastructure","developers prototyping multi-agent systems with managed LLM services"],"limitations":["Tightly coupled to OpenAI API format and gpt-3.5-turbo-0301 model — switching to other providers or models requires code changes","No built-in retry logic or exponential backoff for API failures — transient errors may cause experiment failures","Token usage tracking is not implemented — no visibility into costs or token consumption per agent/round","API key management relies on environment variables without validation — misconfiguration will fail silently at runtime"],"requires":["Python 3.7+","OpenAI API key set in environment (OPENAI_API_KEY)","openai library version 0.27.6","Network access to OpenAI API endpoints"],"input_types":["debate prompts (text)","agent instructions (text)","task context (text)"],"output_types":["LLM responses (text)","parsed agent answers (text)","API response metadata (JSON)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_5","uri":"capability://memory.knowledge.debate.round.state.management.with.agent.response.tracking","name":"debate round state management with agent response tracking","description":"Manages state across multiple debate rounds, tracking each agent's responses and building context for subsequent rounds. The system maintains agent response history, constructs prompts that reference previous round outputs, and ensures agents can build on each other's reasoning. State is stored in memory during execution and serialized to JSON output files for persistence and analysis. The architecture enables agents to see prior responses and refine their answers iteratively, implementing the core collaborative refinement mechanism of the debate approach.","intents":["I want agents to see previous round responses and build on them in subsequent rounds","I need to track which agent said what across all debate rounds for analysis","I want to persist debate state to files for later analysis and reproducibility"],"best_for":["researchers analyzing how agents refine answers across debate rounds","teams building iterative reasoning systems where context accumulates","developers debugging multi-round agent interactions"],"limitations":["State is stored in memory during execution — no checkpointing, so long experiments cannot be resumed if interrupted","No state validation — malformed responses or API errors can corrupt debate state without detection","Context window grows with each round — very long debates may exceed LLM context limits, causing later rounds to have incomplete history","No deduplication of agent responses — if multiple agents generate identical answers, all copies are stored, wasting space and context"],"requires":["Python 3.7+","In-memory storage for debate state (proportional to agent count × round count × response length)","File system write access for JSON output persistence"],"input_types":["agent responses (text)","round number (integer)","agent identifier (string or integer)"],"output_types":["debate state (in-memory data structure)","serialized debate transcripts (JSON)","agent response history (JSON)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_6","uri":"capability://automation.workflow.parameterized.experiment.configuration.with.output.naming.conventions","name":"parameterized experiment configuration with output naming conventions","description":"Enables systematic experimentation by accepting configurable parameters (agent count, debate round count) and encoding them into output file names using standardized conventions (e.g., output_agents_N_rounds_R.json). This approach enables researchers to run multiple experiments with different configurations and automatically organize results by parameters. The naming convention makes it easy to identify which configuration produced which results without requiring separate metadata files. Configuration is passed as command-line arguments or function parameters, with minimal validation.","intents":["I want to run experiments with different agent counts (3, 5, 7) and automatically organize results by configuration","I need to compare debate effectiveness across different round counts without manually tracking which output corresponds to which configuration","I want to batch-run multiple experiments with different parameters and analyze results systematically"],"best_for":["researchers conducting ablation studies on agent count and round count","teams running systematic experiments with multiple configurations","developers building experiment tracking systems for multi-agent research"],"limitations":["Parameter validation is minimal — invalid values (negative agent count, zero rounds) may produce unexpected behavior","Naming convention is implicit in code — no centralized configuration schema, making it difficult to document or validate parameters","No experiment metadata file — results are identified only by file name, with no timestamp, model version, or other context","No built-in experiment tracking or result aggregation — researchers must manually parse output files and organize results"],"requires":["Python 3.7+","Command-line argument parsing or function parameter passing","File system write access for output files"],"input_types":["agent count (integer)","debate round count (integer)","task domain (string)"],"output_types":["parameterized output file names (string)","experiment results (JSON files with parameter-encoded names)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_7","uri":"capability://data.processing.analysis.dataset.loading.and.preprocessing.for.heterogeneous.task.formats","name":"dataset loading and preprocessing for heterogeneous task formats","description":"Loads and preprocesses task-specific datasets in different formats (GSM dataset, MMLU dataset, biography articles in JSON, generated math problems) and normalizes them into consistent input formats for debate generation. Each task domain has custom preprocessing logic that extracts questions, context, and ground truth from domain-specific file formats. The preprocessing layer abstracts format differences, enabling the debate generation pipeline to work with consistent input structures despite underlying dataset heterogeneity.","intents":["I want to load GSM, MMLU, and biography datasets and run debate on them without writing custom parsing code","I need to extract questions and ground truth from different dataset formats and normalize them for debate","I want to sample subsets of datasets for faster experimentation without loading entire datasets"],"best_for":["researchers working with multiple standardized benchmarks","teams building data pipelines for multi-task reasoning systems","developers prototyping on public datasets without custom data engineering"],"limitations":["Dataset formats are hardcoded — adding new datasets requires implementing custom preprocessing logic","No data validation — malformed dataset files will cause silent failures or incorrect results","No sampling or stratification — loading large datasets may consume excessive memory; no built-in support for dataset subsets","No caching — datasets are reloaded from disk on each run, adding latency for large datasets"],"requires":["Python 3.7+","Task-specific dataset files in expected formats (GSM JSON, MMLU CSV, biography JSON)","pandas, numpy for data processing","File system read access to dataset locations"],"input_types":["dataset file paths (string)","task domain identifier (string)","optional: sample size (integer)"],"output_types":["normalized question-answer pairs (list of dicts)","ground truth labels (list of strings)","task-specific context (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_8","uri":"capability://text.generation.language.debate.prompt.engineering.with.agent.role.differentiation","name":"debate prompt engineering with agent role differentiation","description":"Constructs specialized debate prompts that guide agents through structured reasoning while optionally assigning distinct roles or perspectives. Prompts are engineered to encourage agents to build on previous responses, challenge assumptions, and refine answers through iterative rounds. The system encodes debate instructions, task context, and prior round responses into prompts that are sent to the LLM. Prompt engineering is task-specific, with different prompt templates for math, word problems, reading comprehension, and factual reasoning to optimize for domain-specific reasoning patterns.","intents":["I want to design prompts that encourage agents to engage in genuine debate rather than just repeating answers","I need to guide agents to build on each other's reasoning across multiple rounds","I want to optimize prompts for different task domains (math vs reading comprehension) to improve debate quality"],"best_for":["researchers optimizing debate prompts for reasoning quality","teams building prompt engineering systems for multi-agent reasoning","developers fine-tuning debate behavior through prompt iteration"],"limitations":["Prompts are hardcoded in generation modules — no centralized prompt management or versioning","No prompt testing or validation framework — changes to prompts require manual testing across all task domains","Prompt effectiveness is not measured — no metrics for evaluating whether prompts actually encourage debate vs. independent reasoning","Role differentiation is not implemented — all agents receive identical prompts, limiting perspective diversity"],"requires":["Python 3.7+","Understanding of task-specific reasoning patterns to design effective prompts","Access to generation modules to modify prompt templates"],"input_types":["task question (text)","task context (text)","prior round responses (text, optional)","agent role or perspective (string, optional)"],"output_types":["constructed debate prompt (text)","prompt with context and history (text)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-multiagent-debate__cap_9","uri":"capability://automation.workflow.research.paper.implementation.with.reproducible.experimental.methodology","name":"research paper implementation with reproducible experimental methodology","description":"Implements the methodology described in the paper 'Improving Factuality and Reasoning in Language Models through Multiagent Debate' with reproducible experimental setup, standardized datasets, and systematic evaluation. The system provides end-to-end pipelines for generating debate outputs and evaluating them against ground truth, enabling researchers to reproduce paper results and build on the methodology. The implementation includes task-specific configurations that match paper experiments, enabling direct comparison of results.","intents":["I want to reproduce the results from the multiagent debate paper","I need to validate the paper's claims about debate improving factuality and reasoning","I want to extend the paper's methodology to new task domains or models"],"best_for":["researchers reproducing published results","teams validating research claims before building on them","developers implementing paper methodologies in production systems"],"limitations":["Implementation is specific to gpt-3.5-turbo-0301 — results may differ with other models or model versions","Paper methodology may not be fully documented in code — some design decisions may require reading the paper","Reproducibility depends on exact API behavior — OpenAI model updates or API changes may affect results","No version control for paper implementation — changes to code may diverge from paper methodology without tracking"],"requires":["Python 3.7+","OpenAI API key with gpt-3.5-turbo-0301 access","Paper 'Improving Factuality and Reasoning in Language Models through Multiagent Debate'","Task-specific datasets used in paper experiments"],"input_types":["paper experiment configuration (implicit in code)","task datasets (as specified in paper)"],"output_types":["debate results matching paper format (JSON)","evaluation metrics matching paper metrics (JSON, CSV)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","OpenAI API key with access to gpt-3.5-turbo-0301 model","numpy 1.22.4+, pandas 1.5.3+, openai 0.27.6+","OpenAI API key with gpt-3.5-turbo-0301 access","Task-specific dataset files (GSM dataset, MMLU dataset, biography articles, or generated math problems)","numpy, pandas, openai libraries as specified in requirements.txt","Generated debate outputs in expected JSON format","Ground truth datasets (GSM dataset, MMLU dataset, biography articles with facts)","pandas, numpy for data processing","Task-specific datasets: GSM dataset, MMLU dataset, biography articles, or generated math problems"],"failure_modes":["Debate rounds are sequential, not parallel — each round waits for all agents to respond, adding latency proportional to agent count × round count","No built-in agent specialization — all agents use the same base model, limiting perspective diversity","Requires external LLM API calls for each agent in each round, resulting in high token consumption and cost","Task domains are hardcoded — adding new domains requires implementing new generation modules with custom debate logic","Parameter validation is minimal — invalid agent counts or round counts may produce unexpected behavior or API errors","Output file naming convention is implicit in code, making it difficult to track which parameters produced which results without documentation","Evaluation metrics are hardcoded per task domain — custom metrics require modifying evaluation modules","No statistical significance testing built-in — results are raw accuracy numbers without confidence intervals or p-values","Ground truth data must be pre-formatted for each task domain; mismatched formats will cause evaluation failures","Only four task domains are supported — extending to new domains requires implementing new generation and evaluation modules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=multiagent-debate","compare_url":"https://unfragile.ai/compare?artifact=multiagent-debate"}},"signature":"pjdo0IQySs00xqKcVfLGv5TVKCigpssyrH5QN7iQRmMsIFilHL7QNIZjV+VedxZyEzQpxumyNJpC4Uc/DBYwDg==","signedAt":"2026-06-20T18:15:34.006Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/multiagent-debate","artifact":"https://unfragile.ai/multiagent-debate","verify":"https://unfragile.ai/api/v1/verify?slug=multiagent-debate","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}