{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-jarvis","slug":"jarvis","name":"JARVIS","type":"framework","url":"https://github.com/microsoft/JARVIS","page_url":"https://unfragile.ai/jarvis","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-jarvis__cap_0","uri":"capability://planning.reasoning.llm.orchestrated.multi.model.task.execution","name":"llm-orchestrated multi-model task execution","description":"Uses an LLM controller to analyze user requests, decompose them into subtasks, select appropriate expert models from HuggingFace Hub based on model descriptions, execute those models sequentially or in parallel, and synthesize results into coherent responses. The LLM acts as a central planner and coordinator, maintaining context across all execution stages and making dynamic model selection decisions based on task requirements.","intents":["I need to solve a complex AI task that requires multiple specialized models working together","I want to leverage HuggingFace's model ecosystem without manually orchestrating each model","I need a system that can understand natural language requests and automatically select the right tools"],"best_for":["researchers exploring multi-model AI systems and AGI capabilities","developers building task automation systems that need flexible model composition","teams wanting to leverage HuggingFace Hub models without custom integration code"],"limitations":["LLM controller latency compounds with each stage (planning, selection, execution, synthesis) — typical end-to-end latency 5-30 seconds depending on model count","Model selection relies on HuggingFace model descriptions which may be incomplete or misleading, leading to suboptimal model choices","No built-in caching of model selection decisions — each request triggers full planning cycle even for identical task types","Requires LLM API access (OpenAI, Anthropic, etc.) or local LLM deployment; cannot work with inference-only models"],"requires":["Python 3.7+","API key for LLM provider (OpenAI, Anthropic) OR local LLM deployment (Llama, Mistral, etc.)","HuggingFace API token for model access","YAML configuration file for deployment mode and model registry"],"input_types":["natural language text (user requests)","structured task descriptions","multimodal inputs (text + image URLs for vision tasks)"],"output_types":["natural language text responses","structured task execution results","model predictions from selected expert models"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_1","uri":"capability://planning.reasoning.four.stage.task.workflow.with.intermediate.result.inspection","name":"four-stage task workflow with intermediate result inspection","description":"Implements a structured four-stage pipeline where Stage 1 (Task Planning) decomposes user requests into subtasks, Stage 2 (Model Selection) identifies appropriate HuggingFace models, Stage 3 (Task Execution) runs selected models and collects outputs, and Stage 4 (Response Generation) synthesizes results. Each stage produces inspectable intermediate outputs, enabling debugging and partial result retrieval without completing the full pipeline.","intents":["I want to see what subtasks the system identified before execution begins","I need to retrieve intermediate results (task plans or execution outputs) without waiting for final synthesis","I want to debug which models were selected and why for a given request"],"best_for":["researchers studying LLM reasoning and task decomposition patterns","developers building explainable AI systems that need to show reasoning steps","teams debugging model selection failures or unexpected task breakdowns"],"limitations":["Intermediate stages are sequential — cannot parallelize task planning and model selection, adding latency","No rollback mechanism — if Stage 3 execution fails, no automatic retry or alternative model selection","Response synthesis in Stage 4 is LLM-dependent and may hallucinate or misinterpret model outputs if they're in unexpected formats","Intermediate result storage is in-memory only — no persistence across requests unless explicitly saved"],"requires":["Python 3.7+","LLM API access or local deployment","HuggingFace API token"],"input_types":["natural language user requests"],"output_types":["task decomposition (Stage 1)","model selection list with descriptions (Stage 2)","model execution results (Stage 3)","synthesized natural language response (Stage 4)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_10","uri":"capability://text.generation.language.response.synthesis.from.multi.model.outputs","name":"response synthesis from multi-model outputs","description":"Synthesizes final natural language responses by aggregating outputs from multiple executed models. The synthesis stage uses the LLM controller to interpret model predictions, resolve conflicts between models, integrate results into a coherent narrative, and generate human-readable responses. Synthesis is context-aware, incorporating task decomposition and model selection reasoning from earlier stages.","intents":["I want to combine outputs from multiple models into a single coherent response","I need the system to handle cases where different models produce conflicting results","I want responses that explain which models were used and why"],"best_for":["developers building user-facing AI systems that orchestrate multiple models","teams needing explainable outputs that show model contributions","researchers studying how LLMs synthesize information from multiple sources"],"limitations":["Synthesis quality depends entirely on LLM capability — weak LLMs may hallucinate or misinterpret model outputs","No validation that synthesized responses are factually correct or consistent with model outputs","Conflict resolution between models is implicit in LLM reasoning; no explicit conflict detection or resolution strategy","Synthesis adds latency (additional LLM inference) and cost (additional API calls)","No built-in mechanism to preserve model-specific details or confidence scores in final response"],"requires":["Python 3.7+","LLM API access or local LLM deployment","Model execution results from Stage 3"],"input_types":["task description","task decomposition","model selections","model execution results"],"output_types":["natural language response","synthesized output with model attribution (optional)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_11","uri":"capability://automation.workflow.yaml.based.configuration.for.deployment.and.model.registry","name":"yaml-based configuration for deployment and model registry","description":"Uses YAML configuration files to specify deployment modes (local/remote/hybrid), local deployment scales (minimal/standard/full), model registry definitions, and inference parameters. Configuration is declarative and version-controllable, enabling reproducible deployments and easy switching between configurations without code changes. Supports environment variable substitution for sensitive credentials.","intents":["I want to switch between local and cloud deployment without changing code","I need to version-control my deployment configuration alongside my code","I want to manage different configurations for development, staging, and production"],"best_for":["DevOps teams managing JARVIS deployments across environments","developers wanting reproducible, version-controlled configurations","organizations with strict configuration management requirements"],"limitations":["YAML syntax errors are not caught until runtime; no schema validation at configuration load time","No built-in configuration merging or inheritance; complex deployments require duplication or manual composition","Environment variable substitution is basic and doesn't support complex transformations or defaults","Configuration changes require system restart; no hot-reload capability","No audit trail of configuration changes; manual version control is required"],"requires":["Python 3.7+","YAML parser (included in standard library)","Valid YAML syntax"],"input_types":["YAML configuration files"],"output_types":["parsed configuration objects","deployment specifications"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_2","uri":"capability://search.retrieval.huggingface.hub.model.discovery.and.dynamic.selection","name":"huggingface hub model discovery and dynamic selection","description":"Queries HuggingFace Hub's model registry to discover available models, retrieves their metadata (descriptions, tags, task types), and uses the LLM controller to match task requirements against model capabilities. Selection is performed by embedding task descriptions and model descriptions in semantic space or via LLM reasoning, enabling dynamic model discovery without hardcoded model lists.","intents":["I want to automatically find the best HuggingFace model for a given task without manually searching the Hub","I need the system to adapt to new models added to HuggingFace without code changes","I want to leverage the full breadth of HuggingFace's model ecosystem for task solving"],"best_for":["researchers exploring model discovery and selection mechanisms","developers building systems that need to stay current with new HuggingFace models","teams wanting to avoid hardcoding model lists and enable dynamic model composition"],"limitations":["HuggingFace Hub queries add 1-3 second latency per request (network I/O + model metadata retrieval)","Model descriptions on HuggingFace are often incomplete, inconsistent, or marketing-focused rather than technical, leading to poor selection accuracy","No built-in model quality filtering — may select poorly-maintained or deprecated models if their descriptions match task requirements","Model selection is non-deterministic when multiple models have similar descriptions, leading to inconsistent behavior across identical requests","Requires HuggingFace API token and internet connectivity; cannot work offline"],"requires":["HuggingFace API token (free tier available)","Internet connectivity to HuggingFace Hub","Python 3.7+"],"input_types":["task descriptions (natural language or structured)","task type identifiers"],"output_types":["ranked list of HuggingFace model identifiers","model metadata (descriptions, task types, download counts)","model selection reasoning (if LLM-based selection)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_3","uri":"capability://automation.workflow.flexible.deployment.mode.configuration.local.remote.hybrid","name":"flexible deployment mode configuration (local, remote, hybrid)","description":"Supports three deployment modes configurable via YAML: Local Mode executes all models on local hardware, HuggingFace Mode uses only remote HuggingFace inference endpoints, and Hybrid Mode mixes local and remote execution. Local deployments offer three scales (minimal, standard, full) with different RAM requirements (12GB, 16GB, 42GB) and model coverage, enabling resource-constrained deployments.","intents":["I want to run JARVIS entirely locally without cloud dependencies for privacy or latency reasons","I need to minimize infrastructure costs by using HuggingFace's free inference endpoints","I want to balance latency and cost by running frequently-used models locally and rare models remotely"],"best_for":["organizations with strict data privacy requirements or air-gapped environments","developers prototyping with limited hardware who want to start with HuggingFace remote inference","teams optimizing for cost-latency tradeoffs with heterogeneous hardware"],"limitations":["Local Mode requires significant GPU memory (12-42GB depending on scale) — minimal scale still requires >12GB VRAM","Local model loading adds 30-120 second startup time per deployment scale before first inference","Hybrid Mode requires manual configuration of which models run locally vs remotely; no automatic load balancing or failover","HuggingFace Mode has rate limits and inference endpoint availability issues; no built-in retry or fallback logic","Full local deployment (42GB+) is impractical for most consumer hardware; requires enterprise-grade GPUs"],"requires":["Python 3.7+","For Local Mode: GPU with 12GB+ VRAM (NVIDIA CUDA 11.0+ or AMD ROCm)","For HuggingFace Mode: HuggingFace API token and internet connectivity","For Hybrid Mode: Both GPU hardware and HuggingFace API token","YAML configuration file specifying deployment mode and scale"],"input_types":["YAML configuration with inference_mode and local_deployment parameters"],"output_types":["deployed model instances (local) or inference endpoint references (remote)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_4","uri":"capability://tool.use.integration.multi.interface.access.http.api.cli.web.ui","name":"multi-interface access (http api, cli, web ui)","description":"Exposes JARVIS functionality through three interfaces: Server API mode provides HTTP endpoints (/hugginggpt for full service, /tasks for Stage 1 results, /results for Stages 1-3 results), CLI mode offers text-based interaction, and Web UI provides browser-based access. All interfaces share the same underlying four-stage workflow, enabling different user personas to interact with the system.","intents":["I want to integrate JARVIS into my application via REST API calls","I want to test JARVIS interactively from the command line without writing code","I want to provide a user-friendly web interface for non-technical users"],"best_for":["developers building applications that need to call JARVIS as a service","researchers experimenting with JARVIS interactively","teams deploying JARVIS as a shared service with multiple user types"],"limitations":["HTTP API is stateless — no session management or request history; each request is independent","CLI mode is synchronous only — cannot submit long-running tasks and poll for results asynchronously","Web UI is basic and not production-hardened; no authentication, rate limiting, or request queuing","All interfaces share the same underlying LLM and model resources — no per-interface resource isolation or prioritization"],"requires":["Python 3.7+","For Server API: Flask or similar HTTP server (included)","For Web UI: Modern web browser with JavaScript support","LLM API access and HuggingFace token"],"input_types":["JSON request bodies (HTTP API)","text input (CLI)","form submissions (Web UI)"],"output_types":["JSON responses (HTTP API)","formatted text output (CLI)","HTML rendered responses (Web UI)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_5","uri":"capability://data.processing.analysis.taskbench.benchmark.for.task.automation.evaluation","name":"taskbench benchmark for task automation evaluation","description":"Provides a benchmark dataset and evaluation framework for measuring LLM performance on task automation and multi-model orchestration. TaskBench includes task instances with ground-truth model selections and expected outputs, enabling quantitative evaluation of JARVIS's task planning, model selection, and execution accuracy. The framework measures both task completion rate and quality of intermediate reasoning steps.","intents":["I want to measure how well my LLM controller performs at task decomposition and model selection","I need a standardized benchmark to compare different orchestration strategies","I want to evaluate whether my model selection matches expert-curated selections"],"best_for":["researchers developing and comparing LLM-based task orchestration systems","teams evaluating different LLM models for controller performance","organizations measuring improvement in task automation capabilities over time"],"limitations":["TaskBench is static — does not automatically update as HuggingFace Hub adds new models, making benchmark results stale","Ground-truth model selections may be suboptimal or outdated; no mechanism to update annotations as better models are released","Evaluation metrics are coarse (task completion rate) and don't capture partial success or near-miss model selections","No built-in support for evaluating cost-latency tradeoffs or deployment-specific performance (local vs remote)"],"requires":["Python 3.7+","TaskBench dataset (included in repository)","LLM API access for evaluation","HuggingFace token"],"input_types":["task instances from TaskBench dataset","LLM controller outputs (task plans, model selections)"],"output_types":["task completion rate metrics","model selection accuracy","execution success rate","detailed evaluation reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_6","uri":"capability://text.generation.language.easytool.instruction.generation.for.improved.tool.use","name":"easytool instruction generation for improved tool use","description":"Generates concise, structured tool instructions from model descriptions to improve LLM tool-calling accuracy. EasyTool formats HuggingFace model descriptions into standardized instruction templates that highlight key capabilities, input/output formats, and usage constraints, reducing ambiguity in model selection and improving LLM reasoning about which models to invoke.","intents":["I want to improve my LLM's accuracy at selecting the right models by providing clearer tool descriptions","I need to standardize how model capabilities are communicated to the LLM controller","I want to reduce hallucination and incorrect model selections caused by ambiguous model descriptions"],"best_for":["developers optimizing LLM controller performance for task orchestration","researchers studying how instruction clarity affects LLM tool-calling accuracy","teams deploying JARVIS and wanting to improve model selection quality"],"limitations":["Instruction generation is heuristic-based and may not capture all nuances of complex models","No feedback loop — instructions are static and don't improve based on model selection failures","Standardized templates may oversimplify models with multiple use cases or complex parameter interactions","Requires manual curation for domain-specific models where generic templates are insufficient"],"requires":["Python 3.7+","HuggingFace model descriptions (from Hub metadata)"],"input_types":["HuggingFace model metadata (name, description, task type, tags)"],"output_types":["structured tool instructions in standardized format","formatted descriptions suitable for LLM prompting"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_7","uri":"capability://data.processing.analysis.data.generation.pipeline.for.task.automation.datasets","name":"data generation pipeline for task automation datasets","description":"Generates synthetic task instances for training and evaluation by sampling from task templates, creating diverse task variations with corresponding ground-truth model selections. The pipeline produces structured datasets with task descriptions, expected subtask decompositions, selected models, and execution results, enabling creation of large-scale benchmarks without manual annotation.","intents":["I want to create a large dataset of task automation examples for training or evaluation","I need to generate diverse task variations to test robustness of my orchestration system","I want to avoid manual annotation overhead by synthetically generating ground-truth data"],"best_for":["researchers creating datasets for task automation research","teams building training data for LLM fine-tuning on task decomposition","organizations needing large-scale benchmarks without manual annotation costs"],"limitations":["Synthetic data may not reflect real-world task distribution or complexity; generated tasks may be simpler or more regular than actual user requests","Ground-truth model selections are generated algorithmically and may not match human expert selections","No validation that generated tasks are actually solvable with selected models; synthetic data may contain impossible task-model combinations","Diversity of generated tasks is limited by template coverage; rare or novel task types may be underrepresented"],"requires":["Python 3.7+","Task templates (included in repository)","HuggingFace model registry access"],"input_types":["task templates with variable slots","model registry metadata"],"output_types":["synthetic task instances","task decompositions","model selections","execution results","structured dataset files (JSON, CSV)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_8","uri":"capability://memory.knowledge.inference.process.with.context.management.across.stages","name":"inference process with context management across stages","description":"Manages context and state throughout the four-stage inference pipeline, maintaining task descriptions, intermediate results, and model outputs across stages. The inference engine passes context from task planning through model selection and execution to response generation, enabling the LLM to reason about relationships between subtasks and model outputs. Context is managed in-memory with optional serialization for debugging.","intents":["I want the LLM to maintain awareness of all subtasks and their relationships throughout execution","I need to debug inference by inspecting what context was available at each stage","I want to enable the LLM to make decisions based on intermediate results from earlier stages"],"best_for":["developers building complex multi-step task automation systems","researchers studying how context affects LLM reasoning in orchestration","teams debugging model selection or response generation failures"],"limitations":["In-memory context management limits scalability — large task decompositions with many subtasks consume significant memory","No automatic context pruning — irrelevant information from earlier stages is retained, potentially confusing the LLM","Context serialization for debugging is manual and not standardized; no built-in logging of context state at each stage","No context caching across requests — identical task decompositions are recomputed for each request"],"requires":["Python 3.7+","Sufficient RAM for in-memory context storage (varies with task complexity)"],"input_types":["user requests","intermediate stage outputs"],"output_types":["context objects passed between stages","serialized context for debugging"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jarvis__cap_9","uri":"capability://automation.workflow.model.execution.with.error.handling.and.result.collection","name":"model execution with error handling and result collection","description":"Executes selected HuggingFace models with standardized error handling, timeout management, and result collection. The execution engine invokes models via HuggingFace inference APIs or local deployments, captures outputs in a standardized format, handles failures gracefully (timeouts, OOM, API errors), and collects results for synthesis. Supports both synchronous execution and asynchronous batching.","intents":["I want to execute multiple models reliably without my system crashing on model failures","I need to handle long-running model inference with timeouts to prevent hanging","I want standardized result collection so the LLM can easily process model outputs"],"best_for":["developers building production systems that need robust model execution","teams deploying JARVIS at scale with diverse models and failure modes","organizations needing observability into model execution failures"],"limitations":["Error handling is basic — timeouts and OOM errors are caught but not retried; no fallback to alternative models","Result standardization assumes models return compatible output formats; complex or unusual model outputs may not be captured correctly","No built-in result validation — malformed or nonsensical model outputs are passed through without filtering","Execution is synchronous by default; asynchronous batching requires explicit configuration and adds complexity","No resource isolation — a single model consuming excessive memory can crash the entire system"],"requires":["Python 3.7+","HuggingFace API token (for remote execution) or GPU (for local execution)","Sufficient memory/compute for model inference"],"input_types":["model identifiers","model inputs (text, images, etc.)","execution parameters (timeout, batch size)"],"output_types":["model predictions","execution status (success, timeout, error)","execution metadata (latency, resource usage)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","API key for LLM provider (OpenAI, Anthropic) OR local LLM deployment (Llama, Mistral, etc.)","HuggingFace API token for model access","YAML configuration file for deployment mode and model registry","LLM API access or local deployment","HuggingFace API token","LLM API access or local LLM deployment","Model execution results from Stage 3","YAML parser (included in standard library)","Valid YAML syntax"],"failure_modes":["LLM controller latency compounds with each stage (planning, selection, execution, synthesis) — typical end-to-end latency 5-30 seconds depending on model count","Model selection relies on HuggingFace model descriptions which may be incomplete or misleading, leading to suboptimal model choices","No built-in caching of model selection decisions — each request triggers full planning cycle even for identical task types","Requires LLM API access (OpenAI, Anthropic, etc.) or local LLM deployment; cannot work with inference-only models","Intermediate stages are sequential — cannot parallelize task planning and model selection, adding latency","No rollback mechanism — if Stage 3 execution fails, no automatic retry or alternative model selection","Response synthesis in Stage 4 is LLM-dependent and may hallucinate or misinterpret model outputs if they're in unexpected formats","Intermediate result storage is in-memory only — no persistence across requests unless explicitly saved","Synthesis quality depends entirely on LLM capability — weak LLMs may hallucinate or misinterpret model outputs","No validation that synthesized responses are factually correct or consistent with model outputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","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=jarvis","compare_url":"https://unfragile.ai/compare?artifact=jarvis"}},"signature":"zijUuewNf7YGRkVHF0vM02gQ+MRuU6VMQK758OU2lBnpwuTfKLkIqP6zW5dk58ui5X9Ud9+XoEY48c/3Jtv9Bw==","signedAt":"2026-06-21T12:57:45.766Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jarvis","artifact":"https://unfragile.ai/jarvis","verify":"https://unfragile.ai/api/v1/verify?slug=jarvis","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"}}