Does VVM support error handling?

VVM provides two ways to handle errors: semantic pattern matching for expected error states and try/except blocks for caught exceptions during runtime.

What makes VVM different from standard Python or JavaScript?

VVM includes native syntax for AI-specific operations, such as semantic predicates for natural language branching and specialized agent definitions with built-in retry and constraint logic.

Can VVM handle multiple agents at once?

Yes, VVM supports explicit parallelism through the pmap function, allowing you to run multiple agent calls or tasks concurrently.

How do I execute a .vvm file in Claude Code?

You can execute VVM programs by using the /vvm-run command, which triggers the VVM skill to interpret and run the logic.

What is VVM (Vibe Virtual Machine)?

VVM is a specialized programming language designed for agentic workflows where the LLM itself serves as the runtime environment, executing logic and coordinating agents.

Vibe Virtual Machine (VVM)

Name: Vibe Virtual Machine (VVM)
Author: karanchawla

bykaranchawla

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Data Science & ML

Orchestrates complex multi-agent workflows and AI-powered programs using a specialized language where the LLM acts as the runtime.

About

VVM (Vibe Virtual Machine) is a domain-specific language designed for building sophisticated agentic programs. It allows developers to define specialized agents, utilize semantic predicates for intent-based branching, and manage complex control flows directly through the LLM. By providing a structured syntax for pattern matching on AI outputs, iterative refinement loops, and parallel execution, VVM transforms Claude into a high-level engine for executing reliable, multi-step AI tasks with built-in quality constraints and error handling.

Key Features

Iterative refinement loops and quality constraints to ensure high-fidelity agent outputs.
Agentic DSL syntax for defining and calling specialized AI agents with unique prompts and skills.
17 GitHub stars
Dual-channel error handling via pattern matching and traditional try/except/finally blocks.
Built-in support for explicit parallelism using pmap for efficient multi-agent orchestration.
Semantic predicates for decision-making based on natural language meaning and intent.

Use Cases

Building multi-agent research pipelines that involve data collection, analysis, and synthesis.
Creating self-correcting code generation workflows with iterative refinement and validation.
Automating complex decision trees where branching depends on semantic evaluation of content.

About

Key Features

Iterative refinement loops and quality constraints to ensure high-fidelity agent outputs.
Agentic DSL syntax for defining and calling specialized AI agents with unique prompts and skills.
17 GitHub stars
Dual-channel error handling via pattern matching and traditional try/except/finally blocks.
Built-in support for explicit parallelism using pmap for efficient multi-agent orchestration.
Semantic predicates for decision-making based on natural language meaning and intent.

Use Cases

Building multi-agent research pipelines that involve data collection, analysis, and synthesis.
Creating self-correcting code generation workflows with iterative refinement and validation.
Automating complex decision trees where branching depends on semantic evaluation of content.