{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agent4rec","slug":"agent4rec","name":"Agent4Rec","type":"repo","url":"https://github.com/LehengTHU/Agent4Rec","page_url":"https://unfragile.ai/agent4rec","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agent4rec__cap_0","uri":"capability://planning.reasoning.llm.powered.generative.agent.simulation.with.persona.driven.behavior","name":"llm-powered generative agent simulation with persona-driven behavior","description":"Creates 1,000 autonomous agents initialized from MovieLens-1M user data, each embodying distinct social traits (conformity, activity, diversity preferences) and personalized movie preferences. Agents use LLM-based decision-making to generate realistic reactions to recommendations, retrieving contextual memories of past interactions and synthesizing responses that reflect individual behavioral patterns rather than deterministic algorithms.","intents":["Simulate authentic user behavior in recommendation systems without human subject trials","Test how recommendation algorithms perform against diverse, independent agent personas","Understand emergent dynamics when 1,000+ agents interact with the same recommender model","Generate synthetic user interaction logs for offline evaluation of recommendation systems"],"best_for":["Recommender system researchers evaluating algorithm performance at scale","Teams building recommendation engines who need synthetic user interaction data","Researchers studying social dynamics and conformity effects in recommendation systems"],"limitations":["LLM-based decision-making introduces non-deterministic behavior; results may vary across runs unless seeds are fixed","Simulation speed limited by LLM API latency; 1,000 agents with page-by-page interactions can require hours to complete","Agent personas derived from MovieLens-1M only; domain-specific to movies and may not generalize to other recommendation domains","Memory system stores full interaction history per agent; scales linearly with simulation length, requiring significant storage for long-running simulations"],"requires":["Python 3.8+","MovieLens-1M dataset (preprocessed user ratings and movie metadata)","LLM API access (OpenAI, Anthropic, or compatible provider with function-calling support)","Sufficient API quota for ~1,000 agents × interaction steps × LLM calls per step"],"input_types":["MovieLens-1M user-item interaction matrix (ratings)","Movie metadata (titles, genres, release dates)","Recommender model outputs (ranked item lists per user)"],"output_types":["Interaction logs (agent ID, timestamp, action, item, rating, reasoning)","Evaluation metrics (click-through rate, conversion, diversity, fairness)","Agent memory snapshots (interaction history per agent)"],"categories":["planning-reasoning","agent-simulation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_1","uri":"capability://memory.knowledge.memory.augmented.agent.decision.making.with.contextual.retrieval","name":"memory-augmented agent decision-making with contextual retrieval","description":"Each agent maintains a persistent memory system that stores past interactions (watched movies, ratings, evaluations, exits) and retrieves relevant memories when deciding how to respond to new recommendations. The memory system uses semantic or temporal retrieval to surface contextually relevant past experiences, which the LLM then incorporates into its reasoning to generate consistent, history-aware decisions rather than stateless responses.","intents":["Ensure agent decisions reflect their interaction history and learned preferences","Generate consistent agent behavior across multiple recommendation rounds","Simulate how users develop preferences and fatigue over time","Evaluate how recommendation algorithms exploit or adapt to user memory and learning"],"best_for":["Researchers studying long-horizon user behavior and preference evolution","Teams evaluating recommendation algorithms' ability to adapt to user feedback","Simulation scenarios requiring multi-session user interactions"],"limitations":["Memory retrieval adds latency (~50-200ms per decision) depending on memory size and retrieval method","No built-in memory compression; full interaction history stored per agent, leading to O(n) memory growth with simulation length","Retrieval strategy (semantic vs. temporal) not configurable in base implementation; may miss relevant memories for certain decision types","LLM context window limits how much memory can be included in a single decision prompt; long interaction histories may be truncated"],"requires":["Memory storage backend (in-memory dict, database, or vector store for semantic retrieval)","Interaction logging system to record all agent actions","Optional: embedding model for semantic memory retrieval (if using similarity-based lookup)"],"input_types":["Agent interaction history (list of past actions, ratings, evaluations)","Current recommendation context (items presented, agent state)"],"output_types":["Retrieved memory subset (relevant past interactions)","LLM decision with memory-informed reasoning"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_2","uri":"capability://tool.use.integration.multi.model.recommender.system.integration.and.orchestration","name":"multi-model recommender system integration and orchestration","description":"Provides a pluggable architecture for integrating multiple recommendation algorithms (Matrix Factorization, MultVAE, LightGCN, baseline models) into a unified simulation framework. The Arena component orchestrates the flow of user-item interactions through selected recommender models, collecting predictions and passing them to agents for evaluation. Models are loaded from configuration, trained or pre-trained, and called in a standardized way regardless of underlying implementation.","intents":["Compare multiple recommendation algorithms against the same set of simulated agents","Evaluate how different recommender models perform in realistic user interaction scenarios","Benchmark recommendation algorithms without requiring human user studies","Integrate custom recommender models into the simulation framework"],"best_for":["Recommender system researchers comparing algorithm performance","Teams evaluating multiple recommendation approaches before production deployment","Researchers studying how recommendation algorithms interact with user behavior"],"limitations":["Model integration requires implementing a standard interface; custom models need adapter code","Training/inference time varies significantly by model; simulation speed bottlenecked by slowest model","No built-in model caching; recommendations regenerated for each agent-model pair per simulation step","Models must fit in memory; large neural models (LightGCN on 1M users) may require distributed training or inference"],"requires":["Recommender model implementations (PyTorch, TensorFlow, or scikit-learn compatible)","Pre-trained model weights or training data","Configuration file specifying which models to load and their hyperparameters","MovieLens-1M user-item interaction matrix for model training/inference"],"input_types":["User ID, item ID (for generating recommendations)","Model configuration (model type, hyperparameters, checkpoint path)"],"output_types":["Ranked list of recommended items per user","Recommendation scores/probabilities (optional)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_3","uri":"capability://planning.reasoning.page.by.page.recommendation.interaction.simulation.with.multi.action.responses","name":"page-by-page recommendation interaction simulation with multi-action responses","description":"Simulates realistic user-recommendation interactions by presenting items in pages (multiple recommendations per round) and allowing agents to take diverse actions: watch, rate, evaluate, exit, or respond to interviews. Each action is generated by the LLM based on the agent's persona, memory, and the presented recommendations, creating a multi-step interaction loop that mirrors how users browse and interact with recommendation interfaces.","intents":["Simulate realistic browsing behavior where users see multiple recommendations at once","Evaluate how recommendation ranking affects user engagement (click-through, watch rates)","Test recommendation systems' ability to handle diverse user actions beyond binary like/dislike","Generate realistic interaction sequences for offline evaluation metrics"],"best_for":["Researchers studying recommendation interface design and user engagement","Teams evaluating ranking algorithms' impact on user behavior","Simulation scenarios requiring multi-action user responses"],"limitations":["Page-based interaction adds complexity; agents must decide which items to engage with from a set rather than responding to single items","LLM decision-making for each action introduces latency; simulating 1,000 agents × 10 pages × 5 items × 4 actions per page can require thousands of LLM calls","Action generation is non-deterministic; same agent may respond differently to identical recommendations across runs","No built-in modeling of user fatigue or attention decay across pages; agents treat all pages equally"],"requires":["LLM with function-calling or structured output support to generate discrete actions","Action schema definition (watch, rate, evaluate, exit, interview)","Recommendation ranking from recommender models","Agent persona and memory context"],"input_types":["Ranked list of recommended items (page of N items)","Agent persona and preferences","Agent interaction history (memory)"],"output_types":["Action sequence (list of {action, item_id, rating/evaluation} tuples)","Agent reasoning/explanation for actions","Updated agent state (memory, engagement level)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_4","uri":"capability://data.processing.analysis.persona.based.agent.initialization.from.real.user.data","name":"persona-based agent initialization from real user data","description":"Initializes 1,000 agents by extracting user personas from MovieLens-1M dataset, deriving each agent's movie preferences, social traits (conformity, activity level, diversity preferences), and demographic characteristics from real user rating patterns. The initialization process maps historical user behavior to agent attributes, enabling agents to exhibit preferences grounded in actual user data rather than synthetic or random distributions.","intents":["Create diverse, realistic agent personas based on real user behavior","Ensure simulated agents reflect the diversity of actual MovieLens users","Initialize agents with preferences that correlate with real user patterns","Enable reproducible simulations by seeding agents from fixed dataset"],"best_for":["Researchers wanting to ground agent behavior in real user data","Teams evaluating recommendation algorithms on realistic user distributions","Simulation scenarios requiring diverse agent personas"],"limitations":["Personas derived from MovieLens-1M only; may not represent modern user populations or non-movie domains","Persona extraction is lossy; complex user behavior compressed into discrete traits (conformity, activity, diversity)","Social traits (conformity, activity) inferred from rating patterns; inference method not fully specified in documentation","Agent diversity limited by MovieLens-1M user distribution; underrepresented user groups in dataset will be underrepresented in simulation"],"requires":["MovieLens-1M dataset (user ratings, movie metadata)","Data processing pipeline to extract personas from raw ratings","Trait inference model (statistical or heuristic-based)"],"input_types":["User-item rating matrix from MovieLens-1M","Movie metadata (genres, release dates)"],"output_types":["Agent persona objects (user_id, preferences, social_traits, demographics)","Preference vectors or embeddings per agent"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_5","uri":"capability://data.processing.analysis.evaluation.metrics.computation.and.causal.analysis.for.recommendation.performance","name":"evaluation metrics computation and causal analysis for recommendation performance","description":"Computes standard recommendation evaluation metrics (click-through rate, conversion, diversity, fairness) from agent interaction logs and performs causal analysis to understand how recommendation algorithm choices affect user behavior. The evaluation framework aggregates agent actions across the simulation, calculates metrics per model, and enables comparative analysis of how different recommenders influence agent engagement and satisfaction.","intents":["Measure recommendation algorithm performance using realistic user interaction data","Compare multiple recommenders on standard metrics (CTR, conversion, diversity, fairness)","Analyze causal effects of recommendation choices on user behavior","Identify which algorithm components drive user engagement or satisfaction"],"best_for":["Recommender system researchers evaluating algorithm performance","Teams comparing multiple recommendation approaches before deployment","Researchers studying causal effects in recommendation systems"],"limitations":["Metrics computed from simulated agent behavior, not real users; may not correlate with actual user metrics","Causal analysis limited to observational data from simulation; cannot establish true causality without controlled experiments","Metric definitions may not align with business objectives (e.g., CTR may not correlate with user satisfaction)","No built-in statistical significance testing; differences between models may be due to random variation"],"requires":["Agent interaction logs (actions, ratings, timestamps)","Ground truth user preferences or satisfaction labels (optional, for validation)","Metric definitions and aggregation logic"],"input_types":["Agent interaction logs (agent_id, action, item_id, rating, timestamp)","Recommender model identifiers"],"output_types":["Metric values per model (CTR, conversion, diversity, fairness)","Comparative analysis (model A vs. model B)","Causal analysis results (feature importance, effect sizes)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_6","uri":"capability://automation.workflow.configuration.driven.simulation.orchestration.and.experiment.management","name":"configuration-driven simulation orchestration and experiment management","description":"Provides a configuration-based system for defining and running recommendation simulation experiments, specifying which recommender models to evaluate, agent parameters, interaction settings, and evaluation metrics. The Arena component reads configuration files, initializes the simulation environment, orchestrates the interaction loop across all agents and models, and collects results in a structured format for analysis.","intents":["Define and run recommendation simulation experiments without code changes","Manage multiple simulation configurations and compare results","Reproduce simulations by version-controlling configuration files","Scale simulations to different numbers of agents or interaction rounds via configuration"],"best_for":["Researchers running multiple simulation experiments with different parameters","Teams managing recommendation evaluation pipelines","Practitioners wanting to evaluate algorithms without modifying code"],"limitations":["Configuration schema may not support all customization needs; complex experiments may require code changes","No built-in experiment tracking or versioning; results must be manually organized","Configuration validation is minimal; invalid settings may cause runtime errors mid-simulation","Simulation state not easily checkpointable; long-running simulations cannot be paused and resumed"],"requires":["Configuration file format (YAML, JSON, or Python dict)","Arena implementation that reads and applies configuration","All required data and models specified in configuration"],"input_types":["Configuration file (model names, agent count, interaction rounds, metrics)","Data paths (MovieLens-1M, model checkpoints)"],"output_types":["Simulation results (interaction logs, metrics, analysis)","Configuration snapshot (for reproducibility)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_7","uri":"capability://automation.workflow.advertisement.integration.and.sponsored.recommendation.evaluation","name":"advertisement integration and sponsored recommendation evaluation","description":"Integrates advertisement or sponsored items into the recommendation simulation, allowing evaluation of how agents respond to ads mixed with organic recommendations. The system can inject sponsored items into recommendation pages and measure agent engagement (clicks, watches, ratings) with ads versus organic items, enabling analysis of ad effectiveness and potential bias in recommendation algorithms.","intents":["Evaluate how recommendation algorithms handle sponsored content","Measure user engagement with ads versus organic recommendations","Analyze potential biases introduced by ad placement in recommendations","Test recommendation systems' ability to balance user satisfaction with ad revenue"],"best_for":["Researchers studying ad-aware recommendation systems","Teams evaluating monetization strategies in recommendation platforms","Practitioners analyzing fairness and bias in ad-inclusive recommendations"],"limitations":["Ad integration method not fully specified; unclear how ads are injected into recommendations","Agent behavior toward ads may not reflect real user behavior (e.g., ad blindness, skepticism)","No built-in modeling of ad relevance or user interest in ads; ads treated as regular items","Evaluation metrics for ads (engagement, conversion) may not align with business objectives (revenue, ROI)"],"requires":["Ad/sponsored item dataset with metadata","Mechanism for injecting ads into recommendation pages","Agent decision-making logic for ad engagement"],"input_types":["Sponsored item list with metadata","Ad placement strategy (position in page, frequency)","Recommendation page from recommender model"],"output_types":["Agent engagement with ads (clicks, watches, ratings)","Ad performance metrics (CTR, conversion, revenue)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agent4rec__cap_8","uri":"capability://automation.workflow.distributed.agent.simulation.with.parallel.interaction.processing","name":"distributed agent simulation with parallel interaction processing","description":"Supports parallel execution of agent interactions across multiple processes or machines, enabling simulation of 1,000+ agents at scale. The Arena component can distribute agent-model interactions across available compute resources, collecting results from parallel workers and aggregating them into final metrics. This architecture allows simulations to complete in reasonable time despite the computational cost of LLM-based decision-making per agent.","intents":["Scale simulations to 1,000+ agents without prohibitive runtime","Parallelize agent interactions to reduce total simulation time","Distribute computation across multiple machines for large-scale experiments","Enable interactive iteration on recommendation algorithms"],"best_for":["Researchers running large-scale recommendation simulations","Teams with access to multi-core or distributed compute infrastructure","Practitioners needing fast iteration on algorithm evaluation"],"limitations":["Parallelization adds complexity; requires careful synchronization of shared state (recommender models, data)","LLM API rate limits may bottleneck parallel execution; 1,000 agents × N interactions × M LLM calls per interaction can exceed API quotas","Memory overhead increases with parallelization; each worker maintains agent state and memory","Debugging distributed simulations is harder than single-process execution"],"requires":["Multi-core CPU or distributed compute cluster (e.g., Kubernetes, Ray, Spark)","Parallel execution framework (multiprocessing, Ray, or custom distributed system)","Shared access to recommender models and data (or replication across workers)"],"input_types":["Agent batch assignments (which agents to process on each worker)","Shared recommender models and data"],"output_types":["Aggregated interaction logs from all workers","Aggregated metrics across all agents"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","MovieLens-1M dataset (preprocessed user ratings and movie metadata)","LLM API access (OpenAI, Anthropic, or compatible provider with function-calling support)","Sufficient API quota for ~1,000 agents × interaction steps × LLM calls per step","Memory storage backend (in-memory dict, database, or vector store for semantic retrieval)","Interaction logging system to record all agent actions","Optional: embedding model for semantic memory retrieval (if using similarity-based lookup)","Recommender model implementations (PyTorch, TensorFlow, or scikit-learn compatible)","Pre-trained model weights or training data","Configuration file specifying which models to load and their hyperparameters"],"failure_modes":["LLM-based decision-making introduces non-deterministic behavior; results may vary across runs unless seeds are fixed","Simulation speed limited by LLM API latency; 1,000 agents with page-by-page interactions can require hours to complete","Agent personas derived from MovieLens-1M only; domain-specific to movies and may not generalize to other recommendation domains","Memory system stores full interaction history per agent; scales linearly with simulation length, requiring significant storage for long-running simulations","Memory retrieval adds latency (~50-200ms per decision) depending on memory size and retrieval method","No built-in memory compression; full interaction history stored per agent, leading to O(n) memory growth with simulation length","Retrieval strategy (semantic vs. temporal) not configurable in base implementation; may miss relevant memories for certain decision types","LLM context window limits how much memory can be included in a single decision prompt; long interaction histories may be truncated","Model integration requires implementing a standard interface; custom models need adapter code","Training/inference time varies significantly by model; simulation speed bottlenecked by slowest model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"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":"2026-06-17T09:51:02.370Z","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=agent4rec","compare_url":"https://unfragile.ai/compare?artifact=agent4rec"}},"signature":"e7Tf31HyM+lwq6Imrr/CEktyY4q/2sBpyFHegSOoOEc7fkrVt5pIFeds+H7zsWQrfjMd08c+Eg+CWpQ4BqU4CQ==","signedAt":"2026-06-20T03:38:37.685Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agent4rec","artifact":"https://unfragile.ai/agent4rec","verify":"https://unfragile.ai/api/v1/verify?slug=agent4rec","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"}}