{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-voyager","slug":"voyager","name":"Voyager","type":"agent","url":"https://voyager.minedojo.org/","page_url":"https://unfragile.ai/voyager","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-voyager__cap_0","uri":"capability://planning.reasoning.lifelong.learning.through.autonomous.task.decomposition.and.skill.acquisition","name":"lifelong learning through autonomous task decomposition and skill acquisition","description":"Voyager uses an LLM backbone to autonomously decompose high-level Minecraft objectives into executable sub-tasks, then learns and caches successful skill implementations as reusable code modules. The system maintains a dynamic skill library that grows over time, allowing the agent to compose previously-learned skills to solve novel problems without retraining. This creates a cumulative learning loop where each solved task expands the agent's capability repertoire for future challenges.","intents":["Build an agent that improves its problem-solving abilities over time without manual intervention","Create a system where learned solutions automatically transfer to new, unseen tasks","Develop an autonomous agent that can tackle increasingly complex objectives by composing learned primitives"],"best_for":["Researchers studying continual learning and skill transfer in embodied AI","Teams building long-horizon autonomous agents that must adapt to novel environments","Developers prototyping lifelong learning systems where task complexity increases over time"],"limitations":["Skill library growth is unbounded — no automatic pruning or consolidation of redundant skills, leading to potential memory bloat","Transfer learning effectiveness depends heavily on semantic similarity between task domains; dissimilar tasks may not benefit from cached skills","Requires significant compute for LLM inference at each planning step; latency scales with skill library size during retrieval","No explicit mechanism for skill forgetting or deprecation — outdated or incorrect skills persist in the library"],"requires":["Minecraft Java Edition environment with API access","LLM API access (likely GPT-4 or equivalent for complex reasoning)","Python 3.8+ runtime","Sufficient GPU/compute for real-time LLM inference during agent execution"],"input_types":["natural language task descriptions","Minecraft world state observations (block types, entity positions, inventory)","previously-learned skill code modules"],"output_types":["executable Python/Minecraft command sequences","skill code modules (reusable functions)","structured task decomposition trees","success/failure telemetry and learning logs"],"categories":["planning-reasoning","automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_1","uri":"capability://planning.reasoning.llm.guided.hierarchical.task.planning.with.dynamic.subtask.generation","name":"llm-guided hierarchical task planning with dynamic subtask generation","description":"Voyager decomposes complex Minecraft objectives into hierarchical subtasks by prompting an LLM with the current world state, available skills, and task description. The LLM generates intermediate goals and execution strategies, which are then grounded into concrete action sequences. The planner dynamically adjusts the decomposition based on execution feedback, re-planning when subtasks fail or when the environment changes unexpectedly.","intents":["Automatically break down complex, open-ended goals into executable steps without manual scripting","Enable an agent to adapt its plan when subtasks fail or preconditions change","Generate task hierarchies that leverage previously-learned skills to solve novel problems"],"best_for":["Autonomous agents operating in partially-observable, dynamic environments","Systems requiring flexible, goal-driven planning without hardcoded decision trees","Research teams studying LLM-based planning and reasoning in embodied domains"],"limitations":["LLM planning quality degrades with world state complexity; token limits constrain context window for large environments","No formal verification of plan feasibility — generated subtasks may be impossible given current agent capabilities","Replanning overhead increases latency; frequent re-planning due to failures can cause exponential slowdown","Hallucination risk: LLM may generate subtasks that reference non-existent skills or impossible actions"],"requires":["LLM API with sufficient context window (8K+ tokens recommended)","Real-time world state observation system (Minecraft API or equivalent)","Skill library or action registry for grounding LLM outputs to executable primitives"],"input_types":["natural language task objectives","structured world state (entity positions, block types, agent inventory)","skill/action registry with descriptions"],"output_types":["hierarchical task trees (goal → subgoals → actions)","execution plans with estimated success probability","replanning triggers and alternative strategies"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_2","uri":"capability://memory.knowledge.skill.library.management.with.semantic.retrieval.and.code.generation","name":"skill library management with semantic retrieval and code generation","description":"Voyager maintains a searchable library of learned skills as executable code modules, indexed by semantic descriptions. When planning, the system retrieves relevant skills using embedding-based similarity search or LLM-guided retrieval, then composes them into execution plans. New skills are generated by the LLM, executed in the environment, and added to the library if successful. The library persists across episodes, enabling cumulative learning.","intents":["Store and retrieve learned behaviors as reusable code modules across multiple episodes","Automatically compose learned skills to solve novel tasks without retraining","Build a searchable knowledge base of successful strategies that grows over time"],"best_for":["Long-horizon agents that must accumulate and reuse learned behaviors","Teams building skill-based hierarchical RL systems with persistent memory","Researchers studying compositional generalization in embodied AI"],"limitations":["Skill retrieval quality depends on embedding quality and semantic descriptions; poorly-described skills may not be retrieved when relevant","No automatic skill consolidation or deduplication; similar skills may coexist, wasting storage and retrieval time","Skill composition assumes independence; no explicit handling of skill conflicts or ordering constraints","Skill code may become stale or incorrect as environment dynamics change; no validation mechanism"],"requires":["Embedding model for semantic similarity (likely sentence-transformers or OpenAI embeddings)","Persistent storage backend (database or file system) for skill code and metadata","Code execution sandbox for testing and validating generated skills","LLM for skill generation and semantic description"],"input_types":["task descriptions and world state","skill code modules (Python or Minecraft command syntax)","skill metadata (name, description, preconditions, postconditions)"],"output_types":["retrieved skill modules ranked by relevance","composed execution plans combining multiple skills","newly-generated skill code with validation results"],"categories":["memory-knowledge","code-generation-editing","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_3","uri":"capability://code.generation.editing.autonomous.code.generation.and.execution.with.environment.feedback","name":"autonomous code generation and execution with environment feedback","description":"Voyager generates executable code (Python or Minecraft commands) from LLM outputs, executes it in a sandboxed Minecraft environment, and captures execution results (success/failure, observations, errors). Feedback from execution is fed back into the LLM planning loop to refine strategies. This creates a tight feedback loop where code generation, execution, and learning are interleaved.","intents":["Generate and execute code autonomously without human intervention or manual testing","Use execution results to improve subsequent code generation and planning","Validate generated code in a safe, sandboxed environment before deploying to production"],"best_for":["Autonomous agents that must generate and execute code in real-time","Systems requiring tight feedback loops between planning and execution","Research on code generation with execution-based validation"],"limitations":["Code generation quality depends on LLM capability; complex logic may require multiple iterations to generate correctly","Execution latency adds overhead; each code generation → execution → feedback cycle incurs LLM inference cost","Sandbox isolation may not capture all real-world constraints; generated code may fail in production despite passing sandbox tests","Error handling is reactive; the system must observe failures and re-plan rather than predicting failures proactively"],"requires":["Minecraft Java Edition with API/scripting interface","Code execution sandbox (isolated Python interpreter or Minecraft command executor)","LLM API for code generation","Real-time environment observation and state tracking"],"input_types":["natural language task descriptions","world state observations","execution feedback (success/failure, error messages, observations)"],"output_types":["executable code (Python or Minecraft commands)","execution results and telemetry","error logs and failure analysis"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_4","uri":"capability://data.processing.analysis.multi.modal.world.state.representation.and.observation","name":"multi-modal world state representation and observation","description":"Voyager constructs a structured representation of the Minecraft world state including entity positions, block types, inventory contents, and agent status. This state is encoded into natural language descriptions and/or structured data that can be consumed by the LLM planner. The observation system continuously monitors the environment and updates state representations, enabling the agent to react to dynamic changes.","intents":["Represent complex, high-dimensional environment state in a format consumable by LLMs","Track dynamic changes in the environment and update the agent's world model in real-time","Enable the agent to ground abstract plans into concrete actions based on current observations"],"best_for":["Embodied AI agents operating in complex, partially-observable environments","Systems bridging low-level environment observations and high-level LLM reasoning","Research on grounding language models in embodied domains"],"limitations":["State representation is lossy; high-dimensional observations (e.g., pixel data) must be abstracted, losing fine-grained details","Observation latency may cause stale state; rapid environment changes may not be captured in time for planning","State representation design is domain-specific; generalizing to new environments requires re-engineering observation systems","Token budget constraints limit the amount of state information that can be included in LLM prompts"],"requires":["Minecraft API or equivalent for querying world state","State abstraction/encoding logic (rule-based or learned)","Real-time observation polling or event-driven state updates"],"input_types":["raw Minecraft world data (block grid, entity list, inventory)","agent status (position, health, held item)"],"output_types":["natural language descriptions of world state","structured state representations (JSON, graphs)","observation summaries for LLM consumption"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_5","uri":"capability://code.generation.editing.iterative.skill.refinement.through.execution.based.learning","name":"iterative skill refinement through execution-based learning","description":"When a generated skill fails or produces suboptimal results, Voyager uses execution feedback to iteratively refine the skill code. The LLM analyzes failure modes, generates improved versions of the skill, and re-executes in the environment. This process repeats until the skill succeeds or a maximum iteration limit is reached. Successful refined skills are added to the library for future reuse.","intents":["Improve skill quality through iterative refinement based on execution feedback","Debug and fix generated code without manual intervention","Build increasingly robust skills that handle edge cases and failures"],"best_for":["Autonomous agents that must improve their own code quality over time","Systems where manual debugging is infeasible or undesirable","Research on self-improving code generation and learning"],"limitations":["Refinement iterations add latency; each iteration requires LLM inference and environment execution","Refinement may converge to local optima; the LLM may not discover fundamentally better strategies","Iteration limits prevent infinite loops but may terminate before finding good solutions","Refinement quality depends on error message quality; poor error reporting limits the LLM's ability to diagnose failures"],"requires":["LLM API for code refinement","Execution environment with detailed error reporting","Skill validation metrics or success criteria"],"input_types":["skill code with execution results","error messages and failure analysis","execution telemetry (success rate, performance metrics)"],"output_types":["refined skill code","refinement history and iteration logs","validation results for refined skills"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_6","uri":"capability://planning.reasoning.long.horizon.objective.pursuit.with.intermediate.milestone.tracking","name":"long-horizon objective pursuit with intermediate milestone tracking","description":"Voyager can pursue complex, long-horizon objectives (e.g., 'build a house') by decomposing them into intermediate milestones and tracking progress toward each milestone. The system monitors whether milestones are achieved and adjusts the plan if progress stalls. This enables the agent to maintain focus on distant goals while handling short-term failures and replanning.","intents":["Enable agents to pursue complex, multi-step objectives that require sustained effort over many episodes","Track progress toward long-term goals and detect when the agent is stuck or off-track","Decompose distant goals into achievable intermediate milestones"],"best_for":["Agents operating in environments with sparse rewards or long-horizon objectives","Systems requiring goal-directed behavior over extended time horizons","Research on long-horizon planning and goal-directed learning"],"limitations":["Milestone decomposition quality depends on LLM reasoning; poorly-chosen milestones may not lead to the final goal","Progress tracking requires well-defined success criteria for each milestone; ambiguous milestones are hard to validate","Stalled progress detection may be slow; the agent may waste time pursuing infeasible milestones before detecting failure","Replanning overhead increases with goal complexity; very long-horizon objectives may require frequent replanning"],"requires":["LLM for milestone decomposition and progress assessment","Milestone validation system (automated or manual)","Progress tracking and monitoring infrastructure"],"input_types":["high-level objective descriptions","world state and agent status","milestone definitions and success criteria"],"output_types":["milestone decompositions","progress tracking logs","replanning triggers and alternative strategies"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-voyager__cap_7","uri":"capability://planning.reasoning.curriculum.based.task.progression.and.difficulty.scaling","name":"curriculum-based task progression and difficulty scaling","description":"Voyager can be configured to pursue tasks in a curriculum order, starting with simpler objectives and progressing to more complex ones. The system tracks success rates and adjusts task difficulty based on agent performance. Easier tasks help the agent build foundational skills that transfer to harder tasks, creating a natural learning progression.","intents":["Gradually increase task complexity as the agent's skill library grows","Use simpler tasks to bootstrap learning before tackling complex objectives","Automatically adjust curriculum difficulty based on agent performance"],"best_for":["Training agents in environments with natural task hierarchies","Systems where curriculum learning improves sample efficiency","Research on curriculum learning and progressive task difficulty"],"limitations":["Curriculum design is domain-specific; generalizing curricula to new environments requires manual design","Difficulty scaling heuristics may not align with actual task difficulty; the agent may struggle with seemingly-easy tasks","Curriculum progression may create skill gaps; skills learned in early tasks may not transfer to later tasks","No automatic curriculum generation; curricula must be manually specified or learned offline"],"requires":["Task set with defined difficulty levels or ordering","Performance tracking and success rate monitoring","Curriculum progression logic (manual or learned)"],"input_types":["task descriptions with difficulty metadata","agent performance metrics (success rate, time-to-completion)"],"output_types":["curriculum progression logs","difficulty adjustment recommendations","skill transfer analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Minecraft Java Edition environment with API access","LLM API access (likely GPT-4 or equivalent for complex reasoning)","Python 3.8+ runtime","Sufficient GPU/compute for real-time LLM inference during agent execution","LLM API with sufficient context window (8K+ tokens recommended)","Real-time world state observation system (Minecraft API or equivalent)","Skill library or action registry for grounding LLM outputs to executable primitives","Embedding model for semantic similarity (likely sentence-transformers or OpenAI embeddings)","Persistent storage backend (database or file system) for skill code and metadata","Code execution sandbox for testing and validating generated skills"],"failure_modes":["Skill library growth is unbounded — no automatic pruning or consolidation of redundant skills, leading to potential memory bloat","Transfer learning effectiveness depends heavily on semantic similarity between task domains; dissimilar tasks may not benefit from cached skills","Requires significant compute for LLM inference at each planning step; latency scales with skill library size during retrieval","No explicit mechanism for skill forgetting or deprecation — outdated or incorrect skills persist in the library","LLM planning quality degrades with world state complexity; token limits constrain context window for large environments","No formal verification of plan feasibility — generated subtasks may be impossible given current agent capabilities","Replanning overhead increases latency; frequent re-planning due to failures can cause exponential slowdown","Hallucination risk: LLM may generate subtasks that reference non-existent skills or impossible actions","Skill retrieval quality depends on embedding quality and semantic descriptions; poorly-described skills may not be retrieved when relevant","No automatic skill consolidation or deduplication; similar skills may coexist, wasting storage and retrieval time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","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=voyager","compare_url":"https://unfragile.ai/compare?artifact=voyager"}},"signature":"TKrgIdwe7CH93u4ORQgOE6pvhDDLo7x7exnyvcb1PJrO6TmfdC5dFI6EZKzrPLICrgTj22ajliaBACpNLP1DCQ==","signedAt":"2026-06-20T03:28:21.847Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/voyager","artifact":"https://unfragile.ai/voyager","verify":"https://unfragile.ai/api/v1/verify?slug=voyager","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"}}