microsoft/JARVIS

Features

• Task Planning Systems - Decomposes complex requests into actionable steps and coordinates expert model calls through an LLM-based planning system.
• LLM Application Orchestration - Coordinates large language models to plan complex requests and synthesize responses from multiple expert models.
• Automation Capability Benchmarks - Evaluates the capability of large language models to automate complex tasks using standardized benchmarking datasets.
• LLM Orchestrators - Uses a large language model to plan complex requests and coordinate expert models for final response synthesis.
• Local Model Management - Deploys and manages sets of expert models on local hardware across various scales and footprints.
• Domain-Expert Routing - Routes specific sub-tasks to specialized machine learning models based on their domain expertise and performance metrics.
• Multi-Model Workflow Coordinators - Coordinates large language models to sequence expert model calls and synthesize results into a final response.
• Automation Success Metrics - Implements a benchmark suite to measure model success in automating complex machine learning tasks using standardized datasets.
• Local Model Deployment - Manages the local deployment of flexible model instances that scale according to available hardware resources.
• LLM Evaluation - Evaluates the ability of large language models to automate complex tasks using standardized datasets and metrics.
• AI Workflow Serving - Creates web endpoints to trigger AI workflows and retrieve intermediate results from model selection stages.
• Workflow API Endpoints - Provides web endpoints that expose AI pipelines as programmable services for external triggering and result retrieval.
• Response Synthesis Pipelines - Combines intermediate outputs from various expert models into a single coherent final response through a refinement process.
• RESTful Workflow APIs - Wraps AI planning and execution pipelines as RESTful web endpoints for external programmatic triggering.
• Agent Tool Learning - Enhances agent capabilities with concise tool instructions.
• In Context Learning - Solving complex AI tasks by coordinating multiple foundation models.
• LLM Frameworks - System connecting language models with the broader machine learning community.
• Perception Models - Solving AI tasks by coordinating multiple foundation models.
• Reasoning And Planning - Connects language models to external AI tools for task solving.
• Reasoning Frameworks - Solving complex AI tasks by coordinating multiple models.
• Tool Optimization - Enhancing agents with concise tool instructions.

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