{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-forevervm","slug":"forevervm","name":"ForeverVM","type":"mcp","url":"https://github.com/jamsocket/forevervm/tree/main/javascript/mcp-server","page_url":"https://unfragile.ai/forevervm","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-forevervm__cap_0","uri":"capability://automation.workflow.persistent.stateful.python.execution.with.memory.swapping","name":"persistent-stateful-python-execution-with-memory-swapping","description":"Creates and manages long-lived Python execution environments (machines) that maintain state across multiple instruction invocations, with automatic memory-to-disk swapping for idle machines. Machines are created with optional memory limits and tags, execute Python code sequentially, and automatically transition between active (in-memory) and idle (disk-swapped) states based on usage patterns. The system preserves all local variables, imports, and execution context between calls without requiring explicit serialization.","intents":["I want to run Python code that maintains state across multiple separate API calls without losing variables or imports","I need to manage multiple isolated Python environments that can be paused and resumed without memory overhead","I want to organize and filter persistent Python machines using metadata tags for multi-tenant or multi-project scenarios"],"best_for":["AI agents and LLM applications that need persistent execution contexts across tool calls","Multi-step data processing pipelines where intermediate state must survive between invocations","SaaS platforms managing isolated Python sandboxes for multiple users or projects"],"limitations":["Machines execute instructions sequentially — no built-in parallelization within a single machine","Swapping latency adds overhead when transitioning idle machines back to active state (exact timing not specified in architecture)","No built-in distributed execution — machines are single-process Python interpreters"],"requires":["ForeverVM service running (backend)","SDK for your language: @forevervm/sdk (JavaScript), forevervm (Python), or forevervm-sdk (Rust)","Network connectivity to ForeverVM service endpoint"],"input_types":["Python code strings (statements and expressions)","Machine configuration (memory limits, tags)"],"output_types":["Execution results (stdout, stderr, return values)","Machine state (id, tags, creation timestamp)","Instruction execution metadata"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_1","uri":"capability://tool.use.integration.multi.language.sdk.with.unified.api.surface","name":"multi-language-sdk-with-unified-api-surface","description":"Provides consistent client libraries in JavaScript, Python, and Rust that abstract the ForeverVM service API, exposing identical methods for machine creation, instruction execution, and machine management across all three languages. Each SDK implements the same core classes (ForeverVM client, Repl connection) and follows language-idiomatic patterns while maintaining API parity, enabling polyglot teams to use ForeverVM without language-specific learning curves.","intents":["I want to use ForeverVM from JavaScript, Python, or Rust without learning different APIs for each language","I need to integrate ForeverVM into a polyglot system where different services use different languages","I want consistent behavior and error handling across language implementations"],"best_for":["Teams using multiple programming languages who want unified tooling","Polyglot microservices architectures integrating ForeverVM","Organizations standardizing on ForeverVM across JavaScript, Python, and Rust codebases"],"limitations":["Only three languages supported — no Go, Java, or other language SDKs","Language-specific features may diverge slightly despite API parity (e.g., async/await patterns differ between JavaScript and Python)","SDK maintenance burden increases with each new language addition"],"requires":["Node.js 14+ (for JavaScript SDK @forevervm/sdk)","Python 3.8+ (for Python SDK forevervm)","Rust 1.56+ (for Rust SDK forevervm-sdk)","ForeverVM service endpoint accessible from client"],"input_types":["SDK initialization parameters (service URL, credentials)","Machine creation options (tags, memory limits)","Python code strings for instruction execution"],"output_types":["SDK client instances (ForeverVM, Repl)","Machine objects with metadata","Execution results and status"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_2","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.platforms","name":"mcp-server-integration-for-ai-platforms","description":"Exposes ForeverVM machines as tools through the Model Context Protocol (MCP), enabling AI platforms and LLM agents to discover, create, and execute Python code on persistent machines via a standardized tool-calling interface. The MCP server translates LLM function calls into ForeverVM machine operations, handling schema validation, result formatting, and error propagation back to the AI system.","intents":["I want my LLM agent to execute Python code with persistent state across multiple tool invocations","I need to expose ForeverVM as a standard MCP tool that any MCP-compatible AI platform can use","I want to give Claude, ChatGPT, or other LLMs the ability to create and manage Python execution environments"],"best_for":["AI agents and LLM applications using Claude, ChatGPT, or other MCP-compatible models","Teams building AI-powered data analysis or code execution features","Developers integrating ForeverVM into agentic AI workflows"],"limitations":["MCP server adds a translation layer between LLM tool calls and ForeverVM operations, introducing latency","LLM context window constraints may limit the amount of machine state visible to the AI","Tool schema complexity may require careful prompt engineering to guide LLM usage"],"requires":["MCP-compatible AI platform (Claude, ChatGPT with MCP support, or custom MCP client)","ForeverVM service running","MCP server running (JavaScript implementation available)","Network connectivity between MCP server and ForeverVM service"],"input_types":["LLM function calls with tool parameters","Machine creation requests (tags, memory)","Python code strings for execution"],"output_types":["Tool schemas (JSON) describing available operations","Execution results formatted for LLM consumption","Machine metadata and status"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_3","uri":"capability://data.processing.analysis.machine.tagging.and.filtering.system","name":"machine-tagging-and-filtering-system","description":"Enables organizing and querying machines using arbitrary key-value tags assigned at creation time, with filtering capabilities to retrieve machines matching specific tag criteria. Tags are stored as metadata on each machine and can be used to organize machines by project, user, environment, or any custom dimension without modifying the machine itself.","intents":["I want to organize machines by project, user, or environment and retrieve them by those dimensions","I need to filter machines for cleanup or monitoring based on custom metadata","I want to implement multi-tenant isolation where each tenant's machines are tagged and retrievable separately"],"best_for":["Multi-tenant SaaS platforms using ForeverVM","Teams managing large numbers of machines across multiple projects","Applications implementing machine lifecycle management and cleanup"],"limitations":["Tag filtering is basic key-value matching — no complex query language or full-text search","No tag-based access control — tags are organizational metadata only, not security boundaries","Tag updates require machine recreation (tags are immutable after creation)"],"requires":["ForeverVM SDK for your language","Machine creation with tags parameter"],"input_types":["Tag key-value pairs (strings)","Filter criteria (tag keys and values)"],"output_types":["Filtered list of machine objects","Machine metadata including tags"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_4","uri":"capability://automation.workflow.configurable.memory.limits.per.machine","name":"configurable-memory-limits-per-machine","description":"Allows specifying memory constraints when creating machines, enabling control over resource allocation and cost. Memory limits are enforced at the machine level, preventing runaway processes from consuming unlimited system resources and enabling predictable resource planning for multi-machine deployments.","intents":["I want to limit memory usage per machine to control costs and prevent resource exhaustion","I need to allocate different memory budgets to different machines based on workload requirements","I want to ensure one machine's memory usage doesn't impact other machines' performance"],"best_for":["Cost-conscious deployments managing many machines","Multi-tenant systems with per-tenant resource quotas","Applications with heterogeneous workloads requiring different memory allocations"],"limitations":["Memory limits are static at machine creation — cannot be adjusted after creation","No built-in memory monitoring or alerting when approaching limits","Exact memory enforcement mechanism not specified in architecture (soft vs hard limits unclear)"],"requires":["ForeverVM SDK","Memory limit value in appropriate units (exact units not specified in documentation)"],"input_types":["Memory limit value (numeric)"],"output_types":["Machine object with memory configuration","Execution errors if memory limit exceeded"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_5","uri":"capability://code.generation.editing.sequential.instruction.execution.with.result.streaming","name":"sequential-instruction-execution-with-result-streaming","description":"Executes Python statements and expressions sequentially on a machine, streaming results (stdout, stderr, return values) back to the client as they become available. Instructions are processed one at a time in FIFO order, with each instruction's execution isolated from others while sharing the machine's persistent state. Output streaming enables real-time feedback without waiting for full execution completion.","intents":["I want to execute Python code on a machine and see output in real-time as it runs","I need to execute multiple Python statements sequentially while maintaining shared state between them","I want to capture both stdout/stderr and return values from Python code execution"],"best_for":["Interactive REPL-like applications using ForeverVM","Real-time data processing pipelines with streaming output","AI agents executing multi-step Python workflows with intermediate feedback"],"limitations":["Sequential execution only — no parallel instruction processing within a single machine","Instructions are processed in order without priority queuing or cancellation support","Streaming implementation details not specified (buffering, chunk size, latency characteristics)"],"requires":["ForeverVM SDK with Repl class","Python code as string (statements or expressions)","Active machine to execute on"],"input_types":["Python code strings (statements and expressions)","Instruction metadata (optional)"],"output_types":["Stdout/stderr streams","Return values (serialized)","Execution status and metadata"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_6","uri":"capability://data.processing.analysis.machine.listing.and.discovery.with.metadata","name":"machine-listing-and-discovery-with-metadata","description":"Provides methods to enumerate all machines or filter machines by tags, returning machine objects with full metadata (id, creation timestamp, tags, memory configuration, current state). Machine discovery enables inventory management, monitoring, and lifecycle operations across multiple machines without requiring external state tracking.","intents":["I want to list all machines I've created to monitor their status and resource usage","I need to find machines matching specific criteria (tags, creation time) for cleanup or reuse","I want to implement machine lifecycle management (creation, reuse, deletion) based on discovery"],"best_for":["Applications managing large numbers of machines","Multi-tenant systems requiring per-tenant machine inventory","Monitoring and observability systems tracking machine lifecycle"],"limitations":["No pagination or cursor-based iteration for large machine lists (scalability unclear)","Filtering is tag-based only — no filtering by creation time, memory usage, or other metadata","No real-time machine state updates — listing returns point-in-time snapshot"],"requires":["ForeverVM SDK","Optional filter criteria (tags)"],"input_types":["Filter parameters (tag keys/values)"],"output_types":["List of machine objects with metadata","Machine id, tags, creation timestamp, memory configuration"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_7","uri":"capability://automation.workflow.cli.tools.for.machine.management","name":"cli-tools-for-machine-management","description":"Provides command-line interfaces in JavaScript, Python, and Rust for creating, listing, executing code on, and managing ForeverVM machines without requiring SDK integration. CLI tools expose the same core operations as SDKs (create, execute, list, delete) with shell-friendly output formats and argument parsing, enabling shell scripts and automation workflows.","intents":["I want to manage ForeverVM machines from the command line without writing code","I need to integrate ForeverVM into shell scripts and CI/CD pipelines","I want to quickly test ForeverVM functionality without building an application"],"best_for":["DevOps engineers and system administrators managing ForeverVM","Shell script and CI/CD pipeline automation","Quick prototyping and testing without SDK integration"],"limitations":["CLI output formats may not be optimized for programmatic parsing (exact format not specified)","No built-in machine state monitoring or watch mode","CLI feature parity with SDKs not guaranteed (some SDK features may lack CLI equivalents)"],"requires":["CLI tool installed for your language (JavaScript, Python, or Rust)","ForeverVM service running and accessible","Shell environment (bash, zsh, etc.)"],"input_types":["Command-line arguments and flags","Python code strings (for execution commands)","Machine identifiers and tags"],"output_types":["Text output (stdout/stderr)","JSON or structured output (format varies by command)","Exit codes indicating success/failure"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_8","uri":"capability://tool.use.integration.client.server.architecture.with.remote.execution","name":"client-server-architecture-with-remote-execution","description":"Implements a client-server model where SDK clients communicate with a remote ForeverVM service over the network, with the service managing all machine lifecycle, state persistence, and Python code execution. Clients send instructions and receive results via API calls, enabling decoupling of client applications from execution infrastructure and enabling multi-client access to shared machines.","intents":["I want to run Python code on a remote server without installing Python locally","I need multiple clients to access and share the same persistent Python machines","I want to scale machine management independently from client applications"],"best_for":["Distributed systems where execution infrastructure is centralized","Multi-client scenarios requiring shared machine access","Cloud-native applications separating compute from application logic"],"limitations":["Network latency adds overhead to every machine operation (exact latency not specified)","Requires ForeverVM service to be running and accessible — no embedded/local execution mode","Network failures can interrupt machine access (no built-in retry or failover logic specified)"],"requires":["ForeverVM service running on accessible network endpoint","SDK for your language","Network connectivity from client to service","Service authentication/authorization (mechanism not specified in architecture)"],"input_types":["API requests (machine operations, code execution)","Service configuration (endpoint URL, credentials)"],"output_types":["API responses (machine metadata, execution results)","Streaming results (stdout/stderr)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-forevervm__cap_9","uri":"capability://automation.workflow.monorepo.structure.with.language.specific.implementations","name":"monorepo-structure-with-language-specific-implementations","description":"Organizes ForeverVM as a monorepo containing parallel implementations in JavaScript, Python, and Rust, each with SDK, CLI, and supporting tools. The monorepo structure enables code sharing of common patterns and documentation while maintaining language-specific idioms and build systems (npm, pip, cargo). This architecture supports consistent versioning and coordinated releases across all language implementations.","intents":["I want to use ForeverVM from multiple languages with consistent APIs and behavior","I need to contribute to ForeverVM across multiple language implementations","I want to ensure all language implementations stay in sync with new features"],"best_for":["Teams maintaining SDKs across multiple languages","Organizations standardizing on ForeverVM across polyglot codebases","Contributors working on ForeverVM core functionality"],"limitations":["Monorepo complexity increases maintenance burden and CI/CD complexity","Language-specific build systems (npm, pip, cargo) must be coordinated","Code sharing between languages is limited (mostly patterns and documentation, not actual code)"],"requires":["Node.js 14+ (for JavaScript development)","Python 3.8+ (for Python development)","Rust 1.56+ (for Rust development)","Git for monorepo management"],"input_types":["Source code in JavaScript, Python, and Rust","Build configuration files (package.json, pyproject.toml, Cargo.toml)"],"output_types":["Published packages (@forevervm/sdk, forevervm, forevervm-sdk)","CLI tools for each language","Documentation and examples"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["ForeverVM service running (backend)","SDK for your language: @forevervm/sdk (JavaScript), forevervm (Python), or forevervm-sdk (Rust)","Network connectivity to ForeverVM service endpoint","Node.js 14+ (for JavaScript SDK @forevervm/sdk)","Python 3.8+ (for Python SDK forevervm)","Rust 1.56+ (for Rust SDK forevervm-sdk)","ForeverVM service endpoint accessible from client","MCP-compatible AI platform (Claude, ChatGPT with MCP support, or custom MCP client)","ForeverVM service running","MCP server running (JavaScript implementation available)"],"failure_modes":["Machines execute instructions sequentially — no built-in parallelization within a single machine","Swapping latency adds overhead when transitioning idle machines back to active state (exact timing not specified in architecture)","No built-in distributed execution — machines are single-process Python interpreters","Only three languages supported — no Go, Java, or other language SDKs","Language-specific features may diverge slightly despite API parity (e.g., async/await patterns differ between JavaScript and Python)","SDK maintenance burden increases with each new language addition","MCP server adds a translation layer between LLM tool calls and ForeverVM operations, introducing latency","LLM context window constraints may limit the amount of machine state visible to the AI","Tool schema complexity may require careful prompt engineering to guide LLM usage","Tag filtering is basic key-value matching — no complex query language or full-text search","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"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":"2026-05-24T12:16:20.440Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=forevervm","compare_url":"https://unfragile.ai/compare?artifact=forevervm"}},"signature":"VLGVc4j3EA8EDAHOE1nPi7fY27GpWImyG8EZAjXewFLhDElYL+w0/hHOnSwzVxrX8LFL+j6sf4Rm6nmZkhXqCg==","signedAt":"2026-06-15T22:24:09.232Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/forevervm","artifact":"https://unfragile.ai/forevervm","verify":"https://unfragile.ai/api/v1/verify?slug=forevervm","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"}}