{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agentbench","slug":"agentbench","name":"AgentBench","type":"benchmark","url":"https://github.com/THUDM/AgentBench","page_url":"https://unfragile.ai/agentbench","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agentbench__cap_0","uri":"capability://planning.reasoning.multi.environment.agent.evaluation.with.standardized.task.interface","name":"multi-environment agent evaluation with standardized task interface","description":"Evaluates LLM agents across 8 heterogeneous task environments (OS, DB, KG, DCG, LTP, HH, WS, WB) through a unified Task interface that abstracts environment-specific implementations. Each task environment implements standard methods for sample retrieval, execution, and metric calculation, enabling systematic comparison of agent performance across fundamentally different domains without requiring agents to understand environment-specific APIs.","intents":["Compare how well different LLMs perform as autonomous agents across diverse real-world scenarios","Measure agent capabilities in web browsing, code execution, database queries, and game playing with consistent metrics","Benchmark proprietary vs open-source LLMs on standardized agent tasks to identify capability gaps"],"best_for":["LLM researchers evaluating agent architectures across multiple domains","Teams building production agents who need comparative performance data","Organizations assessing whether to adopt proprietary vs open-source LLMs for agent applications"],"limitations":["Web Shopping and Web Browsing environments require 15GB and 1GB disk space respectively, limiting local evaluation","Startup times vary significantly (5s to 3min) across environments, making full benchmark runs time-intensive","Metrics are environment-specific with no unified scoring mechanism across all 8 tasks, complicating cross-domain comparison"],"requires":["Python 3.7+","API keys for target LLM providers (OpenAI, Anthropic, or compatible endpoints)","Linux OS for native OS interaction environment","15GB+ disk space for Web Shopping environment"],"input_types":["task configuration (JSON/YAML)","agent implementation (Python class)","LLM API credentials"],"output_types":["structured metrics (JSON)","performance scores per environment","execution traces and conversation logs"],"categories":["planning-reasoning","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_1","uri":"capability://memory.knowledge.session.based.agent.task.interaction.management","name":"session-based agent-task interaction management","description":"Manages bidirectional communication between agents and task environments through a Session abstraction that handles message exchange, conversation history tracking, and state management across multi-turn interactions. The Session interface standardizes how agents send actions and receive observations, enabling any agent implementation (LLM-based, rule-based, or hybrid) to interact with any task environment without environment-specific integration code.","intents":["Enable agents to maintain conversation context across multiple turns of interaction with a task","Track and replay agent-task interactions for debugging and analysis","Abstract away environment-specific communication protocols so agents work across all 8 task types"],"best_for":["Developers implementing custom agents who need standardized task interaction","Researchers analyzing agent behavior through conversation logs and interaction traces","Teams building agent frameworks that need to support multiple task environments"],"limitations":["Session state is ephemeral by default with no built-in persistence mechanism for long-running agents","No automatic conversation compression or summarization for long interaction histories","Message format is environment-agnostic but lacks built-in validation or schema enforcement"],"requires":["Agent implementation conforming to Agent interface","Task environment implementing Task interface","Python 3.7+"],"input_types":["agent action (string/structured)","task observation (string/structured)"],"output_types":["conversation history (list of message tuples)","session state (dict)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_10","uri":"capability://planning.reasoning.web.browsing.environment.with.real.world.website.navigation","name":"web browsing environment with real-world website navigation","description":"Provides a Web Browsing environment (based on Mind2Web) that enables agents to navigate real websites and complete web-based tasks through simulated browser interactions. Agents can search, click links, fill forms, and extract information from web pages. The environment includes rendering of actual web pages and tracking of agent navigation paths. This environment tests agent capabilities in web understanding, navigation planning, and information extraction from complex web interfaces.","intents":["Evaluate agent ability to navigate complex real-world websites and complete web tasks","Test agent web understanding and information extraction capabilities","Measure agent performance on realistic web navigation and task completion"],"best_for":["Researchers evaluating agent web navigation and understanding capabilities","Teams assessing agents for web automation and information extraction","Organizations benchmarking agents on realistic web interaction scenarios"],"limitations":["Requires 1GB disk space for web page data","Startup time is ~5 minutes per evaluation","Web page rendering and interaction simulation may not capture all real-world web complexity","Success metrics depend on task-specific information extraction and may be difficult to evaluate automatically"],"requires":["1GB+ disk space","Python 3.7+","~5 minutes startup time per evaluation"],"input_types":["web task description (natural language)","target website or search query"],"output_types":["task success (information found or task completed)","interaction trace (navigation path, clicks, form submissions)","extracted information"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_11","uri":"capability://planning.reasoning.operating.system.command.execution.environment.with.linux.shell.interaction","name":"operating system command execution environment with linux shell interaction","description":"Provides an Operating System environment where agents interact with a Linux shell to execute commands, navigate file systems, and complete system administration tasks. Agents generate bash commands that are executed in a sandboxed Linux environment, with output returned as observations. The environment enforces resource limits and safety constraints to prevent harmful operations. This environment tests agent capabilities in command-line reasoning, file system navigation, and system administration.","intents":["Evaluate agent ability to use command-line interfaces and complete system administration tasks","Test agent understanding of file systems, permissions, and shell commands","Measure agent performance on realistic OS interaction scenarios"],"best_for":["Researchers evaluating agent command-line and system administration capabilities","Teams assessing agents for DevOps and system automation tasks","Organizations benchmarking agents on realistic OS interaction scenarios"],"limitations":["Requires < 500MB disk space but needs Linux environment","Startup time is ~5 seconds","Sandboxing may not capture all real-world OS complexity","Safety constraints may prevent agents from completing certain legitimate tasks"],"requires":["Linux OS or Linux container","Python 3.7+","Bash shell"],"input_types":["system task description (natural language)","initial file system state"],"output_types":["task success (command executed successfully)","command output (stdout/stderr)","file system state changes"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_12","uri":"capability://data.processing.analysis.database.query.environment.with.sql.execution.and.knowledge.graph.reasoning","name":"database query environment with sql execution and knowledge graph reasoning","description":"Provides Database and Knowledge Graph environments where agents execute SQL queries or SPARQL queries against structured data. The DB environment includes a relational database with schema information; agents must formulate correct SQL queries to retrieve information. The KG environment includes a knowledge graph; agents must reason over relationships and formulate queries. Both environments test agent capabilities in structured data understanding, query formulation, and logical reasoning.","intents":["Evaluate agent ability to formulate and execute SQL queries against relational databases","Test agent reasoning over knowledge graphs and structured relationships","Measure agent performance on information retrieval from structured data"],"best_for":["Researchers evaluating agent database and knowledge graph reasoning capabilities","Teams assessing agents for data analytics and information retrieval tasks","Organizations benchmarking agents on structured data interaction"],"limitations":["Requires < 500MB disk space for database and knowledge graph data","Startup time is ~20 seconds for DB and ~5 seconds for KG","Query correctness evaluation is strict; agents must formulate syntactically correct queries","Schema understanding is critical; agents must understand database structure to formulate correct queries"],"requires":["Python 3.7+","Database system (SQLite, PostgreSQL, etc.) or knowledge graph store"],"input_types":["information retrieval task (natural language)","database schema or knowledge graph structure"],"output_types":["task success (correct query results retrieved)","query formulation (SQL or SPARQL)","query execution results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_13","uri":"capability://planning.reasoning.household.task.environment.with.alfworld.based.home.automation.simulation","name":"household task environment with alfworld-based home automation simulation","description":"Provides a Household environment (based on ALFWorld) where agents complete household tasks in a simulated home environment. Tasks include finding objects, manipulating items, and completing household chores. The environment includes a 3D home simulation with object locations, agent actions (move, pick up, put down), and task success criteria. This environment tests agent capabilities in spatial reasoning, object tracking, and sequential task planning in realistic household scenarios.","intents":["Evaluate agent ability to complete household tasks in simulated home environments","Test agent spatial reasoning and object tracking capabilities","Measure agent performance on sequential task planning and execution"],"best_for":["Researchers evaluating agent spatial reasoning and household task capabilities","Teams assessing agents for home automation and robotics applications","Organizations benchmarking agents on sequential task planning"],"limitations":["Requires < 500MB disk space but needs 3D simulation engine","Startup time is ~10 seconds","Simulated environment may not capture all real-world household complexity","Task success depends on precise object locations and agent actions"],"requires":["Python 3.7+","3D simulation engine (ALFWorld)"],"input_types":["household task description (natural language)","initial home state (object locations, agent position)"],"output_types":["task success (household task completed)","action sequence (move, pick up, put down)","final home state"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_14","uri":"capability://planning.reasoning.lateral.thinking.puzzle.environment.with.constraint.based.problem.solving","name":"lateral thinking puzzle environment with constraint-based problem solving","description":"Provides a Lateral Thinking Puzzles environment where agents solve puzzles that require non-obvious reasoning and constraint satisfaction. Puzzles present a scenario and agents must ask yes/no questions to determine the solution. The environment tracks questions asked, answers provided, and whether agents arrive at correct solutions. This environment tests agent capabilities in hypothesis formation, information seeking, and constraint-based reasoning.","intents":["Evaluate agent ability to solve lateral thinking puzzles through hypothesis and questioning","Test agent reasoning and constraint satisfaction capabilities","Measure agent performance on problems requiring non-obvious solutions"],"best_for":["Researchers evaluating agent reasoning and hypothesis formation capabilities","Teams assessing agents for problem-solving and constraint satisfaction","Organizations benchmarking agents on creative reasoning tasks"],"limitations":["Requires < 500MB disk space for puzzle data","Startup time is ~5 seconds","Puzzle solutions are subjective; some puzzles may have multiple valid solutions","Success metrics depend on agent reaching correct solution within question limit"],"requires":["Python 3.7+"],"input_types":["puzzle scenario (natural language)","question (yes/no question)"],"output_types":["task success (correct solution found)","answer (yes/no)","question count","solution explanation"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_15","uri":"capability://planning.reasoning.digital.card.game.environment.with.strategic.gameplay.and.decision.making","name":"digital card game environment with strategic gameplay and decision-making","description":"Provides a Digital Card Game environment where agents play strategic card games requiring decision-making, resource management, and opponent modeling. The environment includes game rules, card mechanics, and win conditions. Agents must make strategic decisions about card play, resource allocation, and opponent prediction. This environment tests agent capabilities in strategic reasoning, game-theoretic thinking, and decision-making under uncertainty.","intents":["Evaluate agent ability to play strategic card games with complex rules and decision trees","Test agent strategic reasoning and opponent modeling capabilities","Measure agent performance on games requiring resource management and planning"],"best_for":["Researchers evaluating agent strategic reasoning and game-theoretic capabilities","Teams assessing agents for game-playing and decision-making tasks","Organizations benchmarking agents on strategic reasoning scenarios"],"limitations":["Requires < 500MB disk space for game data","Startup time is ~5 seconds","Game complexity may limit agent performance if rules are not well understood","Success metrics depend on game outcomes which may be stochastic"],"requires":["Python 3.7+"],"input_types":["game state (hand, board, resources)","available actions (playable cards, moves)"],"output_types":["game outcome (win/loss/draw)","action sequence (cards played, moves made)","game statistics (turns, resources used)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_2","uri":"capability://automation.workflow.distributed.task.execution.with.worker.pool.and.task.assignment","name":"distributed task execution with worker pool and task assignment","description":"Orchestrates parallel evaluation of agents across task samples using a Task Controller that manages a pool of Task Workers and a Task Assigner for load distribution. The framework spawns worker processes to execute agent-task interactions in parallel, with the Task Assigner distributing samples across workers and the Task Controller aggregating results and computing final metrics. This architecture enables efficient benchmarking of multiple agents or multiple samples without sequential bottlenecks.","intents":["Run agent evaluations across multiple task samples in parallel to reduce total benchmark time","Evaluate multiple agents concurrently against the same task environment","Scale benchmark execution across multi-core systems without manual parallelization code"],"best_for":["Researchers benchmarking multiple LLM agents who need results within hours rather than days","Teams with multi-core infrastructure who want to maximize hardware utilization","Organizations running continuous evaluation pipelines that need efficient resource usage"],"limitations":["Worker pool size must be manually configured; no automatic scaling based on system resources","Inter-process communication overhead can exceed gains for very fast tasks (< 1s execution time)","No built-in fault tolerance or retry logic for failed worker processes","Task assignment is round-robin; no intelligent load balancing for heterogeneous task durations"],"requires":["Multi-core CPU (2+ cores recommended)","Python 3.7+ with multiprocessing support","Sufficient memory for multiple concurrent agent instances"],"input_types":["task configuration","agent configuration","assignment configuration (worker count, sample distribution)"],"output_types":["aggregated metrics across all samples","per-sample execution logs","worker utilization statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_3","uri":"capability://data.processing.analysis.environment.specific.metric.calculation.and.performance.scoring","name":"environment-specific metric calculation and performance scoring","description":"Computes task-specific evaluation metrics for each of the 8 environments through environment-specific metric implementations that understand domain semantics (e.g., success rate for OS tasks, SQL correctness for DB tasks, game score for DCG). The Task interface includes a metrics() method that each environment implements to calculate performance scores from agent interaction traces, enabling meaningful evaluation of agent behavior within each domain's context.","intents":["Measure agent success rates and performance quality within each task domain using domain-appropriate metrics","Compare agent performance across environments using standardized scoring within each domain","Identify which task types agents struggle with through environment-specific performance breakdowns"],"best_for":["Researchers analyzing agent capability profiles across different task types","Teams identifying which agent architectures excel in specific domains (web vs database vs game)","Organizations making agent technology adoption decisions based on domain-specific performance"],"limitations":["Metrics are environment-specific with no unified scoring mechanism; cannot directly compare OS task performance to Web Shopping performance","Some environments (e.g., LTP puzzles) may have subjective success criteria that require manual evaluation","Metric implementations are task-specific and not easily transferable to custom environments"],"requires":["Completed agent-task interaction traces","Environment-specific metric implementation","Ground truth or oracle for success determination (varies by environment)"],"input_types":["interaction trace (sequence of agent actions and task observations)","task sample metadata"],"output_types":["scalar metric (success rate, score, accuracy)","detailed breakdown (per-sample results, error analysis)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_4","uri":"capability://tool.use.integration.extensible.task.environment.framework.with.custom.task.implementation","name":"extensible task environment framework with custom task implementation","description":"Provides an extension mechanism for adding custom task environments to the benchmark through documented Task and Agent interfaces that developers implement in Python. The framework includes extension guides (Extension_en.md, Extension_cn.md) that specify how to subclass Task base class, implement required methods (get_indices, execute, metrics), and integrate custom environments into the evaluation pipeline. This enables researchers to add domain-specific tasks beyond the 8 built-in environments.","intents":["Add custom task environments specific to your domain (e.g., medical diagnosis, financial analysis) to AgentBench","Implement domain-specific agents that interact with custom tasks using the standard Session interface","Extend AgentBench to evaluate agents on proprietary or specialized task types"],"best_for":["Researchers creating domain-specific agent benchmarks (e.g., biomedical, finance, robotics)","Teams evaluating agents on proprietary tasks without open-sourcing task implementations","Organizations building custom agent evaluation pipelines on top of AgentBench infrastructure"],"limitations":["Extension documentation is limited; requires reading source code to understand all extension points","Custom tasks must implement full Task interface including metrics() method; no partial implementations","No built-in validation or testing framework for custom task implementations","Custom tasks must handle their own resource management and cleanup"],"requires":["Python 3.7+","Understanding of Task interface contract","Domain-specific task environment or simulator","Metric implementation for success evaluation"],"input_types":["Task subclass implementation (Python)","Agent implementation (Python)","Task samples/data"],"output_types":["custom task environment integrated into AgentBench","evaluation metrics for custom task"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_5","uri":"capability://planning.reasoning.llm.agent.implementation.with.multi.provider.api.support","name":"llm agent implementation with multi-provider api support","description":"Provides pre-built LLM agent implementations that interact with task environments through standardized Agent interface, supporting multiple LLM providers (OpenAI, Anthropic, and compatible endpoints). Agents implement decision-making logic that processes task observations and generates actions, with the framework handling API calls, token management, and response parsing. Agents can be configured with different LLM models and parameters without code changes.","intents":["Evaluate different LLM models (GPT-4, Claude, open-source) as agents on the same benchmark tasks","Compare agent performance across different LLM providers without reimplementing agent logic","Configure agent behavior (temperature, max tokens, system prompts) through configuration files"],"best_for":["Researchers comparing LLM capabilities as agents across providers","Teams evaluating whether to use proprietary vs open-source LLMs for agent applications","Organizations running comparative benchmarks across multiple LLM models"],"limitations":["Agent implementations are relatively simple; no advanced reasoning patterns (chain-of-thought, tree-search) built-in","No built-in token counting or cost estimation for multi-turn interactions","API rate limiting and quota management must be handled externally","Agent configuration is limited to model selection and basic parameters; no fine-tuning support"],"requires":["API keys for target LLM providers (OpenAI, Anthropic, or compatible)","Network connectivity to LLM provider endpoints","Python 3.7+"],"input_types":["agent configuration (model, temperature, max_tokens)","task observation (string)"],"output_types":["agent action (string)","API response metadata (tokens used, latency)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_6","uri":"capability://planning.reasoning.naive.rule.based.agent.implementations.for.baseline.comparison","name":"naive rule-based agent implementations for baseline comparison","description":"Provides simple rule-based and heuristic agent implementations that serve as baselines for comparing LLM agent performance. These agents use pattern matching, keyword detection, or simple decision trees to generate actions without calling LLM APIs, enabling researchers to establish performance floors and understand how much value LLMs add over simple baselines. Naive agents implement the same Agent interface as LLM agents.","intents":["Establish baseline performance for each task environment to contextualize LLM agent results","Understand how much improvement LLMs provide over simple heuristic approaches","Debug task environments by running naive agents to verify task correctness"],"best_for":["Researchers establishing performance baselines for new benchmarks","Teams validating that task environments are solvable and metrics are meaningful","Organizations understanding the value proposition of LLM agents vs simple heuristics"],"limitations":["Naive agents are task-specific; cannot be reused across different environments","Performance on complex tasks (web browsing, game playing) is typically very poor, limiting baseline utility","No learning or adaptation; naive agents use fixed strategies regardless of task difficulty"],"requires":["Python 3.7+","Task environment implementation"],"input_types":["task observation (string)"],"output_types":["agent action (string)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_7","uri":"capability://automation.workflow.configuration.driven.task.and.agent.setup.with.yaml.json","name":"configuration-driven task and agent setup with yaml/json","description":"Enables declarative configuration of tasks, agents, and evaluation assignments through YAML/JSON configuration files rather than code. Configuration specifies task type, agent model, hyperparameters, sample selection, and worker allocation without requiring code changes. The framework parses configurations and instantiates appropriate task and agent implementations, enabling non-developers to run benchmarks and researchers to version control experimental setups.","intents":["Run benchmark experiments without writing Python code by specifying configuration files","Version control experimental setups (which agents, which tasks, which hyperparameters) as configuration","Enable non-technical stakeholders to run benchmarks by modifying configuration files"],"best_for":["Teams running multiple benchmark experiments with different configurations","Researchers versioning control experimental setups for reproducibility","Organizations enabling non-developers to run benchmarks"],"limitations":["Configuration is limited to built-in task and agent types; custom implementations require code","Complex experimental designs (e.g., conditional logic, dynamic parameter sweeps) require code","Configuration validation is minimal; invalid configurations may fail at runtime rather than during parsing"],"requires":["YAML or JSON configuration file","Python 3.7+"],"input_types":["task configuration (YAML/JSON)","agent configuration (YAML/JSON)","assignment configuration (YAML/JSON)"],"output_types":["instantiated Task objects","instantiated Agent objects","evaluation results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_8","uri":"capability://planning.reasoning.avalon.game.environment.with.strategic.gameplay.evaluation","name":"avalon game environment with strategic gameplay evaluation","description":"Implements a complete Avalon card game environment (a social deduction game) with game engine, rule enforcement, and multi-agent gameplay. Agents play as knights or spies in a 5-player game requiring deception, negotiation, and strategic reasoning. The environment tracks game state, enforces rules, and computes win/loss metrics. This environment tests agent capabilities in social reasoning, deception detection, and strategic planning beyond simple task completion.","intents":["Evaluate agent capabilities in social deduction games requiring negotiation and strategic reasoning","Test whether agents can engage in deception and detect deception from other agents","Measure agent performance in multi-agent competitive scenarios"],"best_for":["Researchers studying agent social reasoning and game-theoretic capabilities","Teams evaluating agents in competitive multi-agent scenarios","Organizations assessing agent capabilities beyond single-agent task completion"],"limitations":["Avalon game requires 5 players; cannot evaluate single agents in isolation","Game outcomes depend on other agents' strategies, making agent performance evaluation complex","No built-in support for human players; all players must be AI agents","Game state space is large, potentially requiring many iterations for meaningful statistics"],"requires":["Python 3.7+","5 agent implementations (can be same agent or different agents)"],"input_types":["game state (current round, votes, proposals)","agent role (knight or spy)"],"output_types":["agent action (vote, proposal, discussion)","game outcome (win/loss)","game statistics (rounds played, votes cast)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__cap_9","uri":"capability://planning.reasoning.web.shopping.environment.with.e.commerce.task.simulation","name":"web shopping environment with e-commerce task simulation","description":"Provides a Web Shopping environment (based on WebShop) that simulates e-commerce interactions where agents must search for products, navigate product pages, add items to cart, and complete purchases. The environment includes a simulated product catalog, search functionality, and checkout flow. Agents interact through natural language commands that are translated to shopping actions. This environment tests agent capabilities in information seeking, decision-making, and task completion in realistic web scenarios.","intents":["Evaluate agent ability to navigate e-commerce websites and complete shopping tasks","Test agent information-seeking strategies in product search and comparison","Measure agent performance on realistic web interaction tasks"],"best_for":["Researchers evaluating agent web navigation and information-seeking capabilities","Teams assessing agents for e-commerce automation tasks","Organizations benchmarking agents on realistic web interaction scenarios"],"limitations":["Requires 15GB disk space for product catalog and environment data","Startup time is ~3 minutes, making rapid iteration slow","Simulated e-commerce environment may not capture all real-world web complexity","Success metrics are task-specific (purchase completion) and may not generalize to other shopping scenarios"],"requires":["15GB+ disk space","Python 3.7+","~3 minutes startup time per evaluation"],"input_types":["shopping task description (natural language)","product constraints (price, features, etc.)"],"output_types":["task success (purchase completed or not)","interaction trace (search queries, page navigation, purchase details)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentbench__headline","uri":"capability://testing.quality.benchmark.framework.for.evaluating.llm.agents","name":"benchmark framework for evaluating llm agents","description":"AgentBench is a comprehensive benchmark framework designed specifically to evaluate Large Language Models (LLMs) as autonomous agents across diverse environments such as web browsing, game playing, and database queries.","intents":["best LLM agent benchmark","LLM evaluation framework for diverse environments","how to benchmark LLMs as agents","top tools for evaluating AI agents","LLM performance testing in real-world 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disk space respectively, limiting local evaluation","Startup times vary significantly (5s to 3min) across environments, making full benchmark runs time-intensive","Metrics are environment-specific with no unified scoring mechanism across all 8 tasks, complicating cross-domain comparison","Session state is ephemeral by default with no built-in persistence mechanism for long-running agents","No automatic conversation compression or summarization for long interaction histories","Message format is environment-agnostic but lacks built-in validation or schema enforcement","Requires 1GB disk space for web page data","Startup time is ~5 minutes per evaluation","Web page rendering and interaction simulation may not capture all real-world web complexity","Success metrics depend on task-specific information extraction and may be difficult to evaluate automatically","builder identity is not verified yet","no observed match outcomes 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