{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gptscript","slug":"gptscript","name":"GPTScript","type":"framework","url":"https://github.com/gptscript-ai/gptscript","page_url":"https://unfragile.ai/gptscript","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gptscript__cap_0","uri":"capability://planning.reasoning.natural.language.program.parsing.and.execution","name":"natural language program parsing and execution","description":"Parses .gpt files written in natural language syntax into executable programs, using a custom loader (pkg/loader/loader.go) that resolves program dependencies, tool references, and nested scripts. The Engine component orchestrates execution by interpreting natural language instructions as LLM prompts and tool invocations, enabling developers to write multi-step workflows without explicit control flow syntax.","intents":["Write automation scripts using plain English instead of traditional programming languages","Define multi-step LLM workflows with natural language instructions that execute sequentially","Create reusable program templates that can be parameterized and composed together"],"best_for":["Non-technical users building LLM automation workflows","Developers prototyping AI agents without boilerplate code","Teams migrating from shell scripts to LLM-powered automation"],"limitations":["No explicit error handling syntax — relies on LLM interpretation of failure states","Program semantics depend on LLM model capability — same script may behave differently across models","Limited debugging visibility into LLM reasoning — execution traces are opaque"],"requires":["Go runtime (GPTScript is written in Go)","Valid .gpt file with proper syntax","API credentials for at least one LLM provider (OpenAI, Anthropic, or custom)"],"input_types":["text (natural language instructions)","file paths (.gpt program files)","structured parameters (JSON/YAML)"],"output_types":["text (LLM responses)","structured data (JSON from tool outputs)","file artifacts (created by tools)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.registry.with.dynamic.model.selection","name":"multi-provider llm registry with dynamic model selection","description":"Implements a pluggable LLM provider system (pkg/llm/registry.go) that abstracts multiple LLM backends (OpenAI, Anthropic, custom remote APIs) behind a unified interface. The Registry component selects the appropriate provider based on requested model names, allowing programs to specify models declaratively without code changes. Supports both direct API integration (OpenAI client in pkg/openai/client.go) and remote provider delegation (pkg/remote/remote.go) for custom LLM services.","intents":["Switch between different LLM providers (OpenAI, Anthropic, local models) without rewriting scripts","Use multiple models within a single workflow by specifying model names in tool definitions","Integrate custom or self-hosted LLM services via remote API endpoints"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Enterprises with custom LLM deployments needing integration","Developers building model-agnostic automation frameworks"],"limitations":["Provider-specific features (e.g., vision, function calling) require manual capability detection","No automatic fallback if primary provider is unavailable — requires explicit retry logic","Latency varies significantly across providers — no built-in load balancing or latency optimization"],"requires":["API keys for selected providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, or custom endpoint URL)","Network access to provider endpoints","Model name matching provider's catalog (e.g., 'gpt-4', 'claude-3-opus')"],"input_types":["model name string","API credentials (environment variables or config)","LLM request parameters (temperature, max_tokens, etc.)"],"output_types":["LLM completions (text)","structured tool calls (JSON)","token usage metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_10","uri":"capability://tool.use.integration.sdk.server.for.programmatic.api.access","name":"sdk server for programmatic api access","description":"Exposes GPTScript functionality through an HTTP API server (pkg/server/server.go) that enables programmatic access from other applications. The SDK Server provides REST endpoints for program execution, chat sessions, model listing, and tool discovery. Supports both synchronous and asynchronous execution modes with webhook callbacks for long-running operations.","intents":["Integrate GPTScript into larger applications via HTTP API","Build web applications that execute .gpt programs on demand","Enable multi-user access to shared GPTScript instances"],"best_for":["Developers building web applications with LLM capabilities","Teams deploying GPTScript as a shared service","Organizations integrating GPTScript with existing backend systems"],"limitations":["HTTP overhead adds latency compared to direct CLI execution","No built-in authentication or authorization — requires external security layer","Concurrent request handling depends on server configuration — no built-in rate limiting","Webhook callbacks are not guaranteed — no retry logic or delivery guarantees"],"requires":["HTTP server infrastructure (port binding, network access)","Client libraries or HTTP tools to call the API","Proper firewall/network configuration for security"],"input_types":["HTTP requests (JSON body)","program file paths or inline program definitions","execution parameters"],"output_types":["HTTP responses (JSON)","execution results and logs","webhook callbacks (for async execution)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_11","uri":"capability://memory.knowledge.model.and.tool.discovery.with.capability.introspection","name":"model and tool discovery with capability introspection","description":"Provides introspection APIs (pkg/gptscript/gptscript.go ListModels, ListTools methods) that enumerate available LLM models and tools, enabling dynamic discovery of capabilities. The system queries LLM providers for available models and introspects tool definitions to expose their schemas and capabilities. Supports filtering and searching across available options.","intents":["Discover available LLM models at runtime without hardcoding model names","Enumerate available tools and their input/output schemas","Build dynamic UIs that adapt to available capabilities"],"best_for":["Developers building dynamic LLM applications","Teams managing multiple LLM providers with varying capabilities","Non-technical users exploring available tools and models"],"limitations":["Model discovery depends on provider API — not all providers expose model lists","Tool discovery is static — requires program reload to detect new tools","No capability matrix — difficult to determine which models support which features","Filtering is basic — no advanced search or sorting"],"requires":["Active LLM provider connections","API access to provider model listings","Loaded program definitions for tool discovery"],"input_types":["filter criteria (optional)","provider name (optional)"],"output_types":["model list (with metadata)","tool definitions (with schemas)","capability information"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_12","uri":"capability://automation.workflow.execution.monitoring.and.structured.logging.with.display.formatting","name":"execution monitoring and structured logging with display formatting","description":"Implements a monitoring system (pkg/monitor/display.go) that captures execution events, tool calls, and LLM interactions with structured logging and formatted display. The system tracks execution state, logs tool invocations with inputs/outputs, and provides real-time progress updates. Supports multiple output formats (text, JSON, structured logs) and configurable verbosity levels.","intents":["Monitor long-running workflows and see what tools are being called","Debug LLM behavior by inspecting prompts, completions, and tool calls","Export execution traces for auditing and compliance"],"best_for":["Developers debugging complex LLM workflows","Teams requiring execution auditing and compliance logging","Operations engineers monitoring production LLM systems"],"limitations":["Logging overhead can impact performance for high-frequency operations","Structured logs are not persisted by default — require external log aggregation","Verbosity levels are coarse — no fine-grained control over what gets logged","No built-in log rotation or retention policies"],"requires":["Logging infrastructure (stdout/stderr or external log sink)","Appropriate filesystem permissions for log files (if using file output)"],"input_types":["execution events (internal)","verbosity level configuration"],"output_types":["formatted logs (text/JSON)","execution traces","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_2","uri":"capability://tool.use.integration.schema.based.tool.calling.with.automatic.function.binding","name":"schema-based tool calling with automatic function binding","description":"Enables LLMs to invoke external tools through a schema-based function registry that automatically binds tool definitions to LLM function-calling APIs. Tools are defined declaratively in .gpt files with input/output schemas, and the Engine translates these into provider-native function calling formats (OpenAI functions, Anthropic tools, etc.). Supports built-in tools (file I/O, HTTP, shell commands) and custom tools via OpenAPI integration.","intents":["Allow LLMs to execute shell commands, read/write files, and make HTTP requests within workflows","Define custom tools with typed inputs/outputs that LLMs can discover and invoke autonomously","Integrate external APIs and services by exposing them as tools the LLM can call"],"best_for":["Developers building autonomous LLM agents that need system access","Teams integrating LLMs with existing APIs and command-line tools","Non-technical users defining tool capabilities without writing code"],"limitations":["Tool execution is synchronous — no built-in support for concurrent tool calls or async operations","Schema validation is basic — complex nested types or conditional schemas not fully supported","Tool output must be serializable to text/JSON — binary data requires base64 encoding"],"requires":["Tool definitions in .gpt file with input/output schemas","For shell tools: shell access (bash/sh on Unix, cmd on Windows)","For HTTP tools: network access to target endpoints","For file tools: filesystem permissions for read/write operations"],"input_types":["tool schema (JSON/YAML in .gpt files)","tool parameters (typed inputs matching schema)","OpenAPI specifications (for API integration)"],"output_types":["tool execution results (text/JSON)","structured tool call logs","error messages with context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_3","uri":"capability://tool.use.integration.built.in.system.tool.execution.shell.file.i.o.http","name":"built-in system tool execution (shell, file i/o, http)","description":"Provides a set of pre-integrated system tools (pkg/builtin/builtin.go) that LLMs can invoke directly: shell command execution, file read/write operations, and HTTP requests. These tools are automatically available in all programs without explicit definition, with sandboxing and permission controls. The Engine handles tool invocation, output capture, and error handling transparently.","intents":["Execute shell commands and scripts from within LLM workflows","Read and write files as part of multi-step automation","Make HTTP requests to APIs without defining custom tool wrappers"],"best_for":["Developers building system automation and DevOps workflows","Teams integrating LLMs with existing CLI tools and scripts","Rapid prototyping of LLM agents that need system access"],"limitations":["Shell execution inherits all security risks of shell access — no sandboxing beyond OS-level permissions","File operations are limited to paths accessible by the GPTScript process — no cross-user isolation","HTTP tool has no built-in rate limiting or retry logic — caller must implement backoff","Command output is captured as text — binary output requires base64 encoding"],"requires":["Shell access (bash/sh on Unix, cmd/PowerShell on Windows)","Filesystem permissions for target directories","Network access for HTTP tool","Appropriate API keys/credentials for external services"],"input_types":["shell command strings","file paths and content","HTTP method, URL, headers, body"],"output_types":["shell command stdout/stderr","file content (text/binary as base64)","HTTP response body and headers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_4","uri":"capability://tool.use.integration.openapi.specification.integration.for.api.tool.generation","name":"openapi specification integration for api tool generation","description":"Automatically generates tool definitions from OpenAPI/Swagger specifications, enabling LLMs to discover and invoke API endpoints without manual tool definition. The system parses OpenAPI specs, extracts endpoint schemas, and creates callable tools with proper input validation and response handling. Supports both local spec files and remote spec URLs.","intents":["Expose REST APIs to LLMs by providing OpenAPI specifications","Automatically generate tool definitions from existing API documentation","Enable LLMs to discover available API operations and parameters"],"best_for":["Teams with existing REST APIs wanting LLM integration","Developers building API-driven automation workflows","Organizations standardizing on OpenAPI for API documentation"],"limitations":["OpenAPI spec quality directly impacts tool usability — incomplete or inaccurate specs cause LLM errors","Complex request bodies (nested objects, polymorphism) may not translate cleanly to tool schemas","Authentication is not automatically handled — requires manual credential injection","No built-in response transformation — LLM receives raw API responses"],"requires":["Valid OpenAPI 3.0+ specification (JSON or YAML)","Network access to API endpoints (if spec references live services)","API credentials/keys for authenticated endpoints"],"input_types":["OpenAPI specification (file path or URL)","API endpoint parameters (derived from spec)"],"output_types":["Generated tool definitions (internal schema)","API responses (JSON/XML/text)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_5","uri":"capability://memory.knowledge.completion.caching.with.llm.aware.deduplication","name":"completion caching with llm-aware deduplication","description":"Implements a completion cache (pkg/gptscript/gptscript.go) that stores LLM responses and reuses them for identical inputs, reducing API costs and latency. The cache is keyed by prompt content, model, and parameters, with support for cache invalidation and manual clearing. Integrates with provider-native caching where available (e.g., OpenAI's prompt caching) and falls back to local caching.","intents":["Reduce LLM API costs by caching responses to repeated prompts","Speed up development iteration by avoiding redundant LLM calls","Enable deterministic behavior for testing and debugging"],"best_for":["Developers iterating on prompts and workflows","Cost-sensitive applications with repeated queries","Teams building reproducible LLM pipelines"],"limitations":["Cache is local to the GPTScript process — no distributed caching across instances","Cache invalidation is manual — no automatic expiration or staleness detection","Cache key is based on exact prompt match — minor prompt variations bypass cache","No cache statistics or observability — difficult to measure cache hit rates"],"requires":["Filesystem access for cache storage (if using persistent cache)","Sufficient disk space for cached responses"],"input_types":["LLM prompts (text)","model name and parameters"],"output_types":["cached LLM completions (text)","cache metadata (hit/miss status)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_6","uri":"capability://text.generation.language.interactive.chat.sessions.with.stateful.context.management","name":"interactive chat sessions with stateful context management","description":"Provides a chat interface (pkg/gptscript/gptscript.go Chat method) that maintains conversation history and context across multiple turns, enabling interactive LLM interactions within programs. The system manages message history, preserves tool execution context, and allows users to provide feedback or corrections. Supports both programmatic chat (via SDK) and CLI-based interactive mode.","intents":["Build interactive LLM applications with multi-turn conversations","Maintain context across multiple user inputs within a single session","Enable users to correct or refine LLM outputs iteratively"],"best_for":["Developers building conversational AI applications","Teams creating interactive debugging or analysis tools","Non-technical users needing iterative LLM assistance"],"limitations":["Context window is limited by LLM model — long conversations may exceed token limits","No automatic context summarization — developers must manage history manually","Chat state is not persisted — sessions are lost on process exit","No built-in multi-user isolation — all users share the same context"],"requires":["Active LLM provider connection","User input mechanism (CLI stdin or SDK callback)","Sufficient context window in selected model"],"input_types":["user messages (text)","tool execution results (from previous turns)"],"output_types":["LLM responses (text)","tool invocations (if LLM decides to call tools)","conversation history (structured)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_7","uri":"capability://automation.workflow.program.composition.and.dependency.resolution","name":"program composition and dependency resolution","description":"Enables programs to reference and compose other .gpt programs as tools, with automatic dependency resolution and parameter passing. The Program Loader (pkg/loader/loader.go) parses program references, resolves nested dependencies, and validates parameter compatibility. Supports both local file references and remote program URLs, enabling modular workflow construction.","intents":["Reuse common workflow patterns by composing multiple .gpt programs","Build complex multi-step workflows from simpler, testable components","Share program libraries across teams without code duplication"],"best_for":["Teams building large-scale automation frameworks","Developers creating reusable workflow libraries","Organizations standardizing on GPTScript for automation"],"limitations":["Circular dependencies are not detected — can cause infinite loops","Parameter passing is positional — no named parameters or defaults","No version management — program updates may break dependent workflows","Remote program loading has no caching — each execution fetches fresh copies"],"requires":["Valid .gpt program files for composition","Filesystem access for local program references","Network access for remote program URLs","Matching parameter signatures between composed programs"],"input_types":["program file paths or URLs","parameters to pass to composed programs"],"output_types":["composed program execution results","resolved dependency graph"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_8","uri":"capability://safety.moderation.credential.and.secret.management.with.environment.variable.injection","name":"credential and secret management with environment variable injection","description":"Manages API keys and credentials through environment variables and a credential store (pkg/cli/gptscript.go credential management), with support for secure storage and automatic injection into tool contexts. The system supports credential prompting (interactive input), environment variable loading, and credential caching. Credentials are scoped to specific tools and providers.","intents":["Securely manage API keys and credentials without hardcoding them in scripts","Prompt users for credentials interactively when needed","Automatically inject credentials into tool execution contexts"],"best_for":["Teams deploying GPTScript in production environments","Developers building multi-user applications","Organizations with credential management policies"],"limitations":["Credentials are stored in environment variables — no encryption at rest","No built-in credential rotation or expiration — manual updates required","Interactive prompting is not suitable for automated/headless execution","Credential scoping is basic — all tools can access all credentials"],"requires":["Environment variable support in the execution environment","User input mechanism for interactive credential prompting","Appropriate filesystem permissions for credential storage"],"input_types":["credential names and types","user input (for interactive prompting)"],"output_types":["injected environment variables","credential validation status"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__cap_9","uri":"capability://automation.workflow.cli.based.program.execution.with.streaming.output.and.progress.monitoring","name":"cli-based program execution with streaming output and progress monitoring","description":"Provides a command-line interface (pkg/cli/gptscript.go) that executes .gpt programs with real-time output streaming, progress monitoring, and structured logging. The CLI handles argument parsing, input/output redirection, and display formatting. Supports both synchronous execution (blocking until completion) and asynchronous execution (returning immediately with status tracking).","intents":["Execute .gpt programs from the command line with real-time feedback","Monitor long-running workflows and see intermediate results","Integrate GPTScript into shell scripts and CI/CD pipelines"],"best_for":["DevOps engineers automating infrastructure tasks","Developers building CLI tools powered by LLMs","Teams integrating GPTScript into existing shell workflows"],"limitations":["Output streaming adds latency — not suitable for latency-sensitive applications","Progress monitoring is basic — no detailed step-by-step breakdown","Error handling is limited to exit codes — no structured error reporting","CLI arguments are positional — no support for named arguments or flags"],"requires":["Terminal/console access for interactive output","Appropriate shell environment (bash, sh, cmd, PowerShell)","File access for program files and input/output redirection"],"input_types":["program file path","command-line arguments","stdin (for piped input)"],"output_types":["stdout (program output)","stderr (errors and logs)","exit code (success/failure status)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptscript__headline","uri":"capability://tool.use.integration.natural.language.programming.framework.for.llms","name":"natural language programming framework for llms","description":"GPTScript is a framework that allows developers to write scripts in natural language, enabling seamless execution by large language models (LLMs) with built-in tool calling and multi-step workflows.","intents":["best natural language programming framework","natural language programming for LLM integration","top frameworks for writing scripts with LLMs","natural language scripting tools for developers","how to use LLMs for programming tasks","best tools for integrating LLMs with command-line applications"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Go runtime (GPTScript is written in Go)","Valid .gpt file with proper syntax","API credentials for at least one LLM provider (OpenAI, Anthropic, or custom)","API keys for selected providers (OPENAI_API_KEY, ANTHROPIC_API_KEY, or custom endpoint URL)","Network access to provider endpoints","Model name matching provider's catalog (e.g., 'gpt-4', 'claude-3-opus')","HTTP server infrastructure (port binding, network access)","Client libraries or HTTP tools to call the API","Proper firewall/network configuration for security","Active LLM provider connections"],"failure_modes":["No explicit error handling syntax — relies on LLM interpretation of failure states","Program semantics depend on LLM model capability — same script may behave differently across models","Limited debugging visibility into LLM reasoning — execution traces are opaque","Provider-specific features (e.g., vision, function calling) require manual capability detection","No automatic fallback if primary provider is unavailable — requires explicit retry logic","Latency varies significantly across providers — no built-in load balancing or latency optimization","HTTP overhead adds latency compared to direct CLI execution","No built-in authentication or authorization — requires external security layer","Concurrent request handling depends on server configuration — no built-in rate limiting","Webhook callbacks are not guaranteed — no retry logic or delivery guarantees","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=gptscript","compare_url":"https://unfragile.ai/compare?artifact=gptscript"}},"signature":"rgUE30Nps5LIA8W7ioCdvgzNx+v377Z3BlttXXALgLv/0L1oNbgNGGB7YBlMMolJ8hXqw54bPqEUgvZOgQkzBw==","signedAt":"2026-06-21T12:50:48.623Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gptscript","artifact":"https://unfragile.ai/gptscript","verify":"https://unfragile.ai/api/v1/verify?slug=gptscript","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"}}