{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gptme","slug":"gptme","name":"gptme","type":"agent","url":"https://github.com/ErikBjare/gptme","page_url":"https://unfragile.ai/gptme","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gptme__cap_0","uri":"capability://text.generation.language.multi.provider.llm.conversation.management.with.persistent.state","name":"multi-provider llm conversation management with persistent state","description":"Maintains stateful conversations across multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) with automatic provider selection and fallback logic. Implements conversation persistence to disk, allowing users to resume multi-turn interactions without losing context. Uses a provider abstraction layer that normalizes API differences across incompatible interfaces, enabling seamless switching between models mid-conversation.","intents":["Switch between different LLM providers without losing conversation history","Resume long-running conversations across terminal sessions","Compare responses from multiple models on the same prompt","Use local models (Ollama) as fallback when cloud APIs are unavailable"],"best_for":["Solo developers building multi-model AI workflows","Teams evaluating different LLM providers without vendor lock-in","Privacy-conscious users wanting local-first LLM execution"],"limitations":["Provider API rate limits apply independently — no built-in request batching or queuing","Context window management is manual — no automatic summarization when exceeding model limits","Conversation persistence is file-based — no distributed state management for multi-machine setups"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic) OR local Ollama instance","Disk space for conversation history storage"],"input_types":["text prompts","conversation history (JSON/serialized format)"],"output_types":["text responses","structured conversation logs","provider metadata"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_1","uri":"capability://code.generation.editing.self.correcting.code.execution.with.error.feedback.loops","name":"self-correcting code execution with error feedback loops","description":"Executes code (Python, shell, etc.) in an isolated environment and feeds execution errors back to the LLM for automatic correction. Implements a feedback loop where the model analyzes error messages, modifies code, and re-executes until success or max retries. Captures stdout, stderr, and exit codes to provide rich error context for the correction prompt.","intents":["Run Python scripts and automatically fix runtime errors without manual intervention","Execute shell commands and let the AI debug failed operations","Iteratively refine code until it produces correct output","Capture and analyze execution failures for learning"],"best_for":["Developers prototyping scripts and wanting automatic error recovery","Non-technical users running code without understanding error messages","Automation workflows requiring resilient code execution"],"limitations":["Infinite loops or long-running processes can exhaust retry budgets — max retries must be configured","Security: executing arbitrary code requires trust in LLM outputs — no sandboxing beyond process isolation","Error feedback can mislead the model if error messages are ambiguous or misleading","No built-in timeout enforcement — long-running corrections can block the terminal"],"requires":["Python 3.9+","Execution environment with shell access (bash, zsh, etc.)","Write permissions to temporary directories for script execution"],"input_types":["Python code strings","Shell commands","Code snippets with syntax errors"],"output_types":["execution results (stdout/stderr)","exit codes","corrected code","error analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_10","uri":"capability://tool.use.integration.provider.configuration.and.api.key.management","name":"provider configuration and api key management","description":"Manages API keys and provider configuration for multiple LLM services (OpenAI, Anthropic, Ollama, etc.). Implements secure credential storage (environment variables, config files) and provider selection logic. Supports fallback providers if the primary provider is unavailable or exhausted quota.","intents":["Configure multiple LLM providers without hardcoding API keys","Switch between providers based on availability or cost","Use local models (Ollama) as fallback when cloud APIs fail","Manage provider-specific settings (temperature, max tokens, etc.)"],"best_for":["Developers managing multiple LLM provider accounts","Teams wanting provider flexibility without vendor lock-in","Privacy-conscious users wanting local-first options"],"limitations":["API key storage is not encrypted — relies on OS-level security (environment variables, file permissions)","No built-in credential rotation — keys must be manually updated","Fallback logic is simple — no intelligent provider selection based on cost or latency","Provider-specific settings are not validated — misconfiguration can cause silent failures"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic) OR local Ollama instance","Configuration file or environment variables"],"input_types":["API keys","provider names","configuration parameters"],"output_types":["provider instances","configuration validation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_11","uri":"capability://memory.knowledge.conversation.persistence.and.serialization","name":"conversation persistence and serialization","description":"Saves and loads conversations to disk in a structured format (JSON, YAML, etc.), enabling conversation replay and sharing. Implements serialization of message history, metadata (timestamps, model used, tokens), and conversation state. Supports conversation listing and search by metadata.","intents":["Save conversations for later review or reference","Share conversations with teammates or for documentation","Replay conversations to understand decision-making process","Search conversation history by date, model, or content"],"best_for":["Developers documenting their problem-solving process","Teams sharing knowledge through conversation examples","Researchers analyzing LLM behavior across conversations"],"limitations":["Serialization format is fixed — no built-in conversion between formats","Search is basic — no full-text indexing or semantic search","No versioning — overwriting conversations loses history","Large conversations can be slow to load — no lazy loading or pagination"],"requires":["Python 3.9+","Disk space for conversation storage","File system access"],"input_types":["conversation objects","metadata"],"output_types":["serialized conversation files","conversation metadata","conversation lists"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_2","uri":"capability://tool.use.integration.file.system.manipulation.with.llm.driven.intent.interpretation","name":"file system manipulation with llm-driven intent interpretation","description":"Allows the LLM to read, write, create, and modify files on the user's filesystem through natural language commands. Implements a file operation abstraction that interprets high-level intents ('create a config file', 'append logs') into concrete filesystem operations. Maintains a working directory context and supports glob patterns for batch operations.","intents":["Create and edit configuration files based on natural language descriptions","Batch rename or reorganize files matching patterns","Generate boilerplate code files for new projects","Append logs or data to existing files"],"best_for":["Developers automating file generation and project scaffolding","Non-technical users managing file operations through natural language","Automation workflows requiring dynamic file creation"],"limitations":["No built-in version control — overwrites files without git history preservation","No permission model — LLM can modify any file the terminal user can access","Glob pattern matching is basic — complex file selection requires manual path specification","No atomic transactions — partial failures in batch operations leave inconsistent state"],"requires":["Write permissions to target directories","Python 3.9+","Filesystem access (local or mounted)"],"input_types":["natural language file operation requests","file paths","glob patterns","file content (text, code)"],"output_types":["created/modified files","file operation logs","status confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_3","uri":"capability://search.retrieval.web.browsing.and.content.retrieval.with.llm.summarization","name":"web browsing and content retrieval with llm summarization","description":"Fetches web pages, extracts content, and summarizes them using the LLM. Implements HTTP client integration with automatic content parsing (HTML to text), handling of redirects and authentication. The LLM can request specific URLs, and responses are automatically summarized or analyzed based on the original query intent.","intents":["Research topics by browsing multiple web pages and synthesizing information","Fetch API documentation and have the LLM explain relevant sections","Monitor web content changes by comparing fetched pages","Extract structured data from web pages (tables, lists, etc.)"],"best_for":["Developers researching APIs and documentation without leaving the terminal","Researchers gathering information from multiple sources","Automation workflows requiring web data ingestion"],"limitations":["No JavaScript execution — only static HTML content is retrieved, dynamic pages render as empty","No cookie/session management — authenticated content behind login walls is inaccessible","Rate limiting not enforced — rapid requests to the same domain may trigger blocks","Large pages can exceed LLM context windows — no automatic chunking or pagination"],"requires":["Internet connectivity","Python 3.9+","HTTP client library (requests or similar)"],"input_types":["URLs","natural language queries about web content","page content (HTML)"],"output_types":["summarized web content","extracted data","structured information"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_4","uri":"capability://image.visual.vision.based.image.analysis.and.ocr","name":"vision-based image analysis and ocr","description":"Processes images (PNG, JPEG, etc.) and sends them to vision-capable LLMs (GPT-4V, Claude Vision) for analysis. Supports OCR, object detection, scene understanding, and image-to-text conversion. Implements image encoding and multimodal prompt construction, allowing users to ask questions about image content in natural language.","intents":["Extract text from screenshots or scanned documents (OCR)","Analyze diagrams, charts, or visual content for information","Describe images in natural language for accessibility","Debug visual issues by analyzing screenshots"],"best_for":["Developers debugging UI issues by analyzing screenshots","Researchers extracting data from visual documents","Non-technical users describing image content"],"limitations":["Requires vision-capable LLM provider — not all models support image input","Image size limits vary by provider — large images may need resizing","No local vision models — depends on cloud APIs (OpenAI, Anthropic)","Accuracy varies by image quality and complexity — OCR fails on low-resolution or handwritten text"],"requires":["Vision-capable LLM API (OpenAI GPT-4V or Anthropic Claude Vision)","API key for vision provider","Image file (PNG, JPEG, etc.)"],"input_types":["image files (PNG, JPEG, GIF, WebP)","natural language queries about images","image URLs"],"output_types":["text descriptions","extracted text (OCR)","structured analysis","answers to image-based questions"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_5","uri":"capability://tool.use.integration.tool.use.orchestration.with.schema.based.function.calling","name":"tool-use orchestration with schema-based function calling","description":"Implements a function registry where tools (code execution, file operations, web browsing, etc.) are exposed to the LLM as callable functions with JSON schemas. The LLM decides when to invoke tools based on user intent, and results are fed back into the conversation. Supports both native provider function-calling APIs (OpenAI, Anthropic) and fallback prompt-based tool invocation for models without native support.","intents":["Let the LLM autonomously decide which tools to use for a task","Chain multiple tools together (e.g., fetch data, process it, save results)","Expose custom tools to the LLM without modifying core logic","Support tool use across different LLM providers with consistent semantics"],"best_for":["Developers building agentic workflows with tool composition","Teams extending gptme with custom tools","Automation requiring multi-step tool orchestration"],"limitations":["Tool invocation is sequential — no parallel tool execution","No built-in tool result caching — repeated tool calls with identical inputs re-execute","Schema validation is provider-dependent — some models ignore schema constraints","Fallback prompt-based tool calling is less reliable than native function-calling APIs"],"requires":["Python 3.9+","LLM provider with function-calling support (OpenAI, Anthropic) or fallback prompt-based invocation","Tool definitions with JSON schemas"],"input_types":["tool schemas (JSON)","user intents","tool execution results"],"output_types":["tool invocation decisions","tool execution results","conversation responses"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_6","uri":"capability://text.generation.language.streaming.response.rendering.with.real.time.token.output","name":"streaming response rendering with real-time token output","description":"Renders LLM responses to the terminal in real-time as tokens arrive, providing immediate visual feedback. Implements streaming protocol handling for different providers (OpenAI, Anthropic) and formats output with syntax highlighting for code blocks. Supports interactive interruption of long-running responses and graceful handling of stream errors.","intents":["See LLM responses appear in real-time without waiting for full completion","Interrupt long responses mid-generation","Syntax-highlight code blocks in terminal output","Monitor token generation for debugging or cost tracking"],"best_for":["Terminal-first developers wanting responsive feedback","Users on slow connections where streaming reduces perceived latency","Debugging workflows requiring real-time token inspection"],"limitations":["Streaming adds complexity to error handling — partial responses may be incomplete if stream fails","Terminal rendering performance degrades with very long responses (>10k tokens)","Syntax highlighting is limited to common languages — custom language support requires parser updates","Interruption is not graceful — stopping mid-stream may leave partial output in buffer"],"requires":["Terminal with ANSI color support","Python 3.9+","LLM provider with streaming API support"],"input_types":["streaming token events","response metadata"],"output_types":["rendered terminal output","syntax-highlighted code blocks","token count metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_7","uri":"capability://memory.knowledge.conversation.context.management.with.token.counting","name":"conversation context management with token counting","description":"Tracks conversation history and manages context windows by counting tokens and pruning old messages when approaching model limits. Implements provider-specific token counting (OpenAI tiktoken, Anthropic token counting API) to accurately estimate context usage. Supports configurable context window sizes and pruning strategies (oldest-first, least-relevant-first).","intents":["Maintain long conversations without hitting token limits","Understand how much context is being used in current conversation","Automatically trim old messages to stay within model limits","Estimate costs based on token usage"],"best_for":["Developers running long-lived conversations","Cost-conscious users wanting to track token usage","Teams managing shared conversation budgets"],"limitations":["Token counting is approximate — actual token usage may differ from estimates","Pruning strategies are simple — no semantic importance weighting","Context window limits are static — no dynamic adjustment based on response length","No built-in cost tracking — token counts must be manually converted to costs"],"requires":["Python 3.9+","Token counting library (tiktoken for OpenAI, provider API for others)","Knowledge of model token limits"],"input_types":["conversation messages","model identifiers"],"output_types":["token counts","pruned conversation history","context usage reports"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_8","uri":"capability://text.generation.language.multi.turn.conversation.with.message.role.management","name":"multi-turn conversation with message role management","description":"Maintains conversation state across multiple turns with proper role management (user, assistant, system). Implements message history serialization and deserialization, supporting different message formats across providers. Handles system prompts, user instructions, and assistant responses with proper sequencing and validation.","intents":["Have multi-turn conversations where context from previous messages is preserved","Set system prompts to guide the LLM's behavior across the conversation","Replay conversations from saved history","Validate message sequences to prevent malformed conversations"],"best_for":["Developers building conversational workflows","Users wanting persistent multi-turn interactions","Teams sharing conversation templates"],"limitations":["Message format is provider-specific — converting between OpenAI and Anthropic formats requires mapping","No built-in conversation branching — alternative paths require separate conversation files","System prompts are static — no dynamic prompt injection based on conversation state","Message validation is basic — no semantic checking for conversation coherence"],"requires":["Python 3.9+","Message history storage (file-based or database)"],"input_types":["user messages","system prompts","conversation history"],"output_types":["assistant responses","serialized conversation state","message sequences"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__cap_9","uri":"capability://automation.workflow.cli.argument.parsing.and.command.composition","name":"cli argument parsing and command composition","description":"Parses command-line arguments to construct user prompts and configure agent behavior. Supports both interactive mode (REPL-style) and single-command mode (one-shot execution). Implements argument validation and help text generation, allowing users to invoke the agent with natural language commands directly from the shell.","intents":["Run one-shot AI commands from the shell without entering interactive mode","Configure agent behavior (model, provider, temperature) via CLI flags","Compose complex prompts from multiple command-line arguments","Integrate gptme into shell scripts and automation pipelines"],"best_for":["Shell script developers integrating AI into automation","Users preferring one-shot commands over interactive sessions","Teams building CLI tools on top of gptme"],"limitations":["Complex prompts are awkward to express via CLI arguments — long prompts require stdin or files","No built-in shell completion — users must remember flag names","Argument parsing is basic — no support for complex nested structures","One-shot mode loses conversation context — each invocation is independent"],"requires":["Python 3.9+","Shell environment (bash, zsh, etc.)"],"input_types":["CLI arguments","stdin input","file paths"],"output_types":["parsed prompts","configuration objects","command output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gptme__headline","uri":"capability://ai.agents.personal.ai.terminal.assistant","name":"personal ai terminal assistant","description":"A personal AI assistant that operates directly in your terminal, enabling code execution, file manipulation, web browsing, and persistent conversations with self-correcting capabilities across multiple LLM providers.","intents":["best personal AI assistant","AI assistant for terminal use","terminal-based AI for coding","AI agent with web browsing capabilities","AI assistant for file manipulation"],"best_for":["developers","data scientists"],"limitations":["requires terminal access","may need LLM API keys"],"requires":["terminal environment"],"input_types":["text commands"],"output_types":["text responses","file outputs"],"categories":["ai-agents"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic) OR local Ollama instance","Disk space for conversation history storage","Execution environment with shell access (bash, zsh, etc.)","Write permissions to temporary directories for script execution","Configuration file or environment variables","Disk space for conversation storage","File system access","Write permissions to target directories","Filesystem access (local or mounted)"],"failure_modes":["Provider API rate limits apply independently — no built-in request batching or queuing","Context window management is manual — no automatic summarization when exceeding model limits","Conversation persistence is file-based — no distributed state management for multi-machine setups","Infinite loops or long-running processes can exhaust retry budgets — max retries must be configured","Security: executing arbitrary code requires trust in LLM outputs — no sandboxing beyond process isolation","Error feedback can mislead the model if error messages are ambiguous or misleading","No built-in timeout enforcement — long-running corrections can block the terminal","API key storage is not encrypted — relies on OS-level security (environment variables, file permissions)","No built-in credential rotation — keys must be manually updated","Fallback logic is simple — no intelligent provider selection based on cost or latency","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":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"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=gptme","compare_url":"https://unfragile.ai/compare?artifact=gptme"}},"signature":"xmDdD+lYAOuYkPKhKkzvUKrp4X3uR5CkQqHoVVpcmu4du40lrlPylhErrRvvLvi84zvGwR+ViLmJ9oXZt7wZBg==","signedAt":"2026-06-22T21:15:49.328Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gptme","artifact":"https://unfragile.ai/gptme","verify":"https://unfragile.ai/api/v1/verify?slug=gptme","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"}}