{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-gptme--gptme","slug":"gptme--gptme","name":"gptme","type":"agent","url":"https://gptme.org/docs/","page_url":"https://unfragile.ai/gptme--gptme","categories":["ai-agents"],"tags":["agent","agents","ai-agents","ai-assistant","anthropic","chatbot","chatgpt","cli","code-generation","llamacpp","llm","llm-agent","llm-apps","openai","openrouter","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-gptme--gptme__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.unified.message.interface","name":"multi-provider llm integration with unified message interface","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, OpenRouter, local Ollama/llama.cpp) behind a unified provider architecture that normalizes message formats, handles token counting, and manages model-specific capabilities. Uses a provider registry pattern with pluggable backends that transform provider-specific APIs into a common interface, enabling seamless model switching without changing agent logic.","intents":["Switch between different LLM providers without rewriting agent code","Use local models via Ollama while maintaining compatibility with cloud APIs","Manage different model capabilities and token limits transparently","Route requests to different providers based on cost or latency requirements"],"best_for":["developers building multi-model AI agents","teams wanting provider independence","cost-conscious builders mixing local and cloud models"],"limitations":["Provider-specific features (vision, function calling) may not be uniformly available across all backends","Message transformation adds ~50-100ms latency per request","Token counting accuracy varies by provider implementation"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, OpenRouter) OR local Ollama instance","Network connectivity for cloud providers"],"input_types":["text prompts","structured messages with role/content"],"output_types":["text responses","structured message objects with metadata"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_1","uri":"capability://tool.use.integration.tool.based.agent.action.execution.with.schema.driven.function.calling","name":"tool-based agent action execution with schema-driven function calling","description":"Implements a tool system where LLMs invoke capabilities through a schema-based registry that maps tool names to executable functions. Each tool is a Python class inheriting from a base Tool interface with defined input schemas, execution logic, and output formatting. The agent parses LLM responses for tool invocations, validates against schemas, executes the tool, and feeds results back into the conversation loop.","intents":["Enable LLMs to execute shell commands, read/write files, and browse the web through structured tool calls","Validate tool inputs against schemas before execution to prevent malformed requests","Chain multiple tool calls together in a single agent loop","Create custom tools by extending the base Tool class"],"best_for":["developers building autonomous agents with concrete actions","teams needing sandboxed tool execution with input validation","builders extending gptme with domain-specific tools"],"limitations":["Tool execution is synchronous — long-running tools block the agent loop","No built-in timeout mechanism for runaway tool executions","Tool output must fit within LLM context window","Custom tools require Python knowledge and understanding of the Tool base class"],"requires":["Python 3.9+","Tool-compatible LLM (supports function calling or structured output)","Appropriate system permissions for tool execution (shell, file I/O, network)"],"input_types":["structured tool invocation objects","JSON schemas"],"output_types":["tool execution results","structured output with status/data/error fields"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_10","uri":"capability://tool.use.integration.multi.interface.agent.deployment.with.cli.rest.api.and.ncurses.ui","name":"multi-interface agent deployment with cli, rest api, and ncurses ui","description":"Provides three separate entry points for agent interaction: a CLI interface (gptme) for terminal use, a REST API server (gptme-server) for programmatic access, and an ncurses UI (gptme-nc) for interactive terminal UI. All interfaces share the same underlying agent logic and tool system, enabling deployment flexibility. The REST API exposes endpoints for chat, tool execution, and conversation management.","intents":["Run agents from the terminal with simple command-line interface","Integrate agents into applications via REST API","Provide interactive terminal UI for agent interaction","Deploy agents as services accessible over HTTP"],"best_for":["developers building agent applications with multiple interfaces","teams deploying agents as services","builders integrating agents into existing applications"],"limitations":["REST API adds network latency compared to direct CLI execution","ncurses UI is limited to terminal environments — no GUI support","All interfaces share the same underlying agent — no interface-specific optimizations","REST API requires authentication/authorization implementation by users"],"requires":["Python 3.9+","For REST API: Flask or similar web framework","For ncurses: terminal with ncurses support"],"input_types":["CLI arguments","HTTP requests with JSON","terminal input"],"output_types":["terminal output","JSON responses","terminal UI rendering"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_11","uri":"capability://memory.knowledge.conversation.persistence.and.context.management.with.message.history","name":"conversation persistence and context management with message history","description":"Manages conversation state through a message history system that stores all agent-user interactions with metadata (role, timestamp, tool calls). Conversations are persisted to disk (JSON or database) and can be resumed, enabling long-running agents that maintain context across sessions. The system handles message serialization, context window management, and conversation loading/saving.","intents":["Resume conversations after agent restarts without losing context","Maintain conversation history for auditing and debugging","Manage context window by selectively including/excluding historical messages","Export conversations for analysis or sharing"],"best_for":["agents that need to maintain state across sessions","teams building audit trails for agent actions","developers debugging agent behavior"],"limitations":["Full conversation history can exceed context window — requires selective inclusion","Persistence adds I/O overhead on every message","No built-in encryption — sensitive data is stored in plaintext","Large conversations consume significant disk space"],"requires":["Python 3.9+","File system write permissions","Sufficient disk space for conversation history"],"input_types":["messages with role/content","conversation metadata"],"output_types":["persisted conversation files","loaded message history"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_12","uri":"capability://planning.reasoning.dynamic.prompt.generation.with.configuration.driven.system.prompts","name":"dynamic prompt generation with configuration-driven system prompts","description":"Generates system prompts dynamically based on agent configuration, available tools, and context. The prompt generation system constructs detailed instructions that describe the agent's role, available tools with their schemas, and execution constraints. Prompts are customizable through configuration files and can be optimized using DSPy for improved agent performance.","intents":["Automatically generate system prompts that describe available tools to the LLM","Customize agent behavior through configuration without code changes","Optimize prompts for better agent performance using DSPy","Maintain consistency between tool definitions and LLM instructions"],"best_for":["developers building configurable agents","teams optimizing agent prompts for specific tasks","builders creating agent templates"],"limitations":["Dynamic prompt generation adds latency on agent startup","Prompt optimization with DSPy requires labeled training data","Large tool sets result in very long system prompts that consume context","Configuration-driven prompts may be less precise than hand-crafted prompts"],"requires":["Python 3.9+","Configuration files (YAML/JSON)","DSPy library for optimization (optional)"],"input_types":["configuration objects","tool definitions"],"output_types":["system prompt strings","optimized prompts"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_13","uri":"capability://planning.reasoning.evaluation.framework.for.agent.performance.measurement","name":"evaluation framework for agent performance measurement","description":"Provides an evaluation framework (gptme-eval) that measures agent performance on benchmark tasks using metrics like success rate, token efficiency, and execution time. The framework supports custom evaluation datasets, metric definitions, and comparison across different models and configurations. Results are aggregated and reported with statistical analysis.","intents":["Measure agent performance on standard benchmarks","Compare different LLM models and configurations","Optimize agent prompts and tool selection based on metrics","Track performance improvements over time"],"best_for":["teams optimizing agent performance","developers comparing LLM models for agent use","builders evaluating prompt changes"],"limitations":["Evaluation requires labeled benchmark datasets — expensive to create","Metrics are task-specific — benchmarks don't transfer across domains","Evaluation is slow due to multiple LLM calls per task","Statistical significance requires many evaluation runs"],"requires":["Python 3.9+","Benchmark dataset with expected outputs","LLM API access for evaluation runs"],"input_types":["benchmark tasks","expected outputs","agent configurations"],"output_types":["performance metrics","statistical analysis","comparison reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_14","uri":"capability://automation.workflow.configuration.hierarchy.with.environment.variable.and.file.based.overrides","name":"configuration hierarchy with environment variable and file-based overrides","description":"Implements a multi-level configuration system where settings can be defined in configuration files (YAML/JSON), environment variables, and command-line arguments, with a clear precedence hierarchy. Configuration is loaded at startup and merged across levels, enabling flexible deployment from development to production without code changes.","intents":["Configure agent behavior through files without code changes","Override configuration with environment variables for deployment","Manage different configurations for different environments (dev, prod)","Share configuration templates across teams"],"best_for":["teams deploying agents across multiple environments","developers managing complex agent configurations","builders creating agent templates"],"limitations":["Configuration precedence can be confusing — hard to debug which value is active","No built-in validation — invalid configurations fail at runtime","Large configuration files are hard to maintain","No built-in secret management — sensitive data must be handled separately"],"requires":["Python 3.9+","Configuration files (YAML/JSON) or environment variables"],"input_types":["configuration files","environment variables","CLI arguments"],"output_types":["merged configuration objects"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_2","uri":"capability://automation.workflow.persistent.shell.execution.with.command.history.and.safety.checks","name":"persistent shell execution with command history and safety checks","description":"Provides a shell tool that executes bash commands in a persistent environment, maintaining working directory state and command history across multiple invocations. Implements safety checks including command whitelisting/blacklisting, output truncation for large results, and error capture with exit codes. Uses subprocess with shell=True but applies filtering rules before execution.","intents":["Execute bash commands from the agent and capture output for analysis","Maintain shell state (working directory, environment variables) across multiple commands","Prevent dangerous commands (rm -rf /, sudo) from executing","Handle command failures gracefully and return error information to the agent"],"best_for":["developers building agents that automate system tasks","teams needing safe shell access from LLM agents","builders prototyping automation workflows"],"limitations":["Output is truncated to prevent context window overflow — large outputs are incomplete","No interactive shell support — commands must be non-interactive","Safety checks are allowlist/blocklist based and can be bypassed with creative command chaining","Working directory state is process-local and lost on agent restart"],"requires":["Unix-like OS (Linux, macOS) or Windows with bash compatibility","Python 3.9+","Appropriate file system and command permissions"],"input_types":["bash command strings"],"output_types":["stdout/stderr text","exit codes","structured execution results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_3","uri":"capability://code.generation.editing.python.repl.with.persistent.environment.and.output.capture","name":"python repl with persistent environment and output capture","description":"Implements an IPython-based Python execution tool that maintains a persistent Python environment across multiple code executions, enabling stateful computation and variable retention. Code is executed in an isolated namespace with output capture, error handling, and support for imports and library usage. Results are formatted and returned to the agent with execution metadata.","intents":["Execute Python code snippets from the agent for data analysis, computation, or testing","Maintain Python state (variables, imports, function definitions) across multiple code blocks","Capture and return both stdout and stderr from code execution","Support data visualization and file generation from Python code"],"best_for":["agents performing data analysis or scientific computing","developers building code-generation agents that need to validate output","teams automating Python-based workflows"],"limitations":["Execution is synchronous — long-running code blocks the agent","No timeout mechanism — infinite loops will hang the agent","Output is captured and truncated, potentially losing large results","IPython dependency adds overhead compared to raw Python execution"],"requires":["Python 3.9+","IPython library installed","Sufficient memory for persistent namespace"],"input_types":["Python code strings"],"output_types":["execution results","stdout/stderr","variable state"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_4","uri":"capability://code.generation.editing.file.manipulation.with.git.style.patching.and.atomic.writes","name":"file manipulation with git-style patching and atomic writes","description":"Provides three complementary file tools: save (create new files), patch (apply unified diff format changes), and append (add content to existing files). The patch tool parses git-style diffs and applies them incrementally, enabling surgical edits without rewriting entire files. All operations include error handling and validation to prevent data loss.","intents":["Create new files with generated content","Apply incremental changes to existing files using diff format","Append content to files without overwriting existing data","Validate file operations before committing changes"],"best_for":["code generation agents that need to edit existing files","developers building multi-file code generation workflows","teams automating file-based configuration management"],"limitations":["Patch tool requires properly formatted unified diffs — malformed diffs fail silently","No atomic multi-file transactions — partial failures leave inconsistent state","No built-in version control integration — changes aren't automatically committed","Large file operations may exceed context window when returning results"],"requires":["Python 3.9+","Write permissions on target directories","Proper diff format for patch operations"],"input_types":["file paths","file content strings","unified diff format"],"output_types":["success/failure status","file paths","operation metadata"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_5","uri":"capability://search.retrieval.web.automation.and.content.extraction.via.playwright","name":"web automation and content extraction via playwright","description":"Integrates Playwright for browser automation, enabling the agent to navigate websites, interact with elements, extract content, and capture screenshots. The browser tool manages a persistent browser session with support for JavaScript execution, form filling, and dynamic content loading. Results are returned as text extracts or screenshots for agent analysis.","intents":["Navigate to URLs and extract page content for analysis","Fill forms and submit data through web interfaces","Interact with JavaScript-heavy websites that require browser automation","Capture screenshots of web pages for visual analysis"],"best_for":["agents that need to interact with web applications","developers building web scraping workflows","teams automating web-based processes"],"limitations":["Playwright requires browser installation (Chromium, Firefox, or WebKit) — adds ~500MB+ disk space","Page loading and JavaScript execution add latency (2-5 seconds per page)","Dynamic content extraction is fragile — page structure changes break selectors","No built-in anti-bot detection handling — may fail on protected sites"],"requires":["Python 3.9+","Playwright library installed","Browser binaries (Chromium/Firefox/WebKit) installed via playwright install","Network connectivity"],"input_types":["URLs","CSS selectors","interaction commands"],"output_types":["extracted text","screenshots","structured page data"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_6","uri":"capability://image.visual.vision.based.image.analysis.and.screenshot.capture","name":"vision-based image analysis and screenshot capture","description":"Provides vision capabilities through a vision tool that analyzes images using multimodal LLM models (Claude Vision, GPT-4V), and a screenshot tool that captures desktop/window screenshots. Images are encoded as base64 and sent to the LLM with natural language queries, returning structured analysis. Screenshot tool integrates with the system to capture current state for agent awareness.","intents":["Analyze screenshots or images to understand visual state of applications","Extract text from images (OCR-like functionality via LLM)","Describe visual layouts and UI elements for automation","Capture current desktop state for agent decision-making"],"best_for":["agents automating GUI-based workflows","developers building visual testing agents","teams needing screenshot-based monitoring"],"limitations":["Requires multimodal LLM support — not all models support vision","Base64 encoding adds ~33% overhead to image data","Vision analysis latency is high (2-5 seconds per image)","Screenshot capture is OS-specific and may fail in headless environments"],"requires":["Python 3.9+","Multimodal LLM with vision support (Claude, GPT-4V, etc.)","Display server for screenshot capture (X11, Wayland, or Windows)"],"input_types":["image files","image paths","screenshot regions"],"output_types":["text descriptions","extracted information","image metadata"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_7","uri":"capability://planning.reasoning.hierarchical.task.decomposition.with.subagent.spawning","name":"hierarchical task decomposition with subagent spawning","description":"Implements a subagent tool that enables agents to spawn child agents for specific subtasks, creating a hierarchical execution model. Child agents inherit the parent's configuration and tools but operate independently with their own conversation history. Results are aggregated and returned to the parent agent, enabling complex multi-step workflows with task isolation.","intents":["Break complex tasks into subtasks and delegate to specialized subagents","Parallelize independent subtasks by spawning multiple subagents","Isolate task contexts to prevent state pollution between subtasks","Aggregate results from multiple subagents for final processing"],"best_for":["developers building complex multi-step automation workflows","teams needing task isolation and parallel execution","builders creating hierarchical agent systems"],"limitations":["Subagent spawning adds overhead — each subagent has its own LLM calls and context","No built-in parallelization — subagents execute sequentially unless manually parallelized","Context is not shared between subagents — each maintains separate conversation history","Debugging hierarchical execution is complex due to nested agent loops"],"requires":["Python 3.9+","LLM API access for each subagent","Sufficient memory for multiple agent instances"],"input_types":["task descriptions","context data"],"output_types":["subagent results","aggregated output"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_8","uri":"capability://automation.workflow.long.running.process.management.via.tmux.integration","name":"long-running process management via tmux integration","description":"Provides a tmux tool that manages long-running background processes through tmux sessions, enabling the agent to start processes, monitor their status, and retrieve output without blocking. Processes run in isolated tmux sessions that persist across agent invocations, allowing the agent to check status and collect results asynchronously.","intents":["Start long-running processes (servers, builds, tests) without blocking the agent","Monitor background process status and collect output","Manage multiple concurrent processes through tmux sessions","Retrieve process results after completion"],"best_for":["agents managing development workflows with long-running tasks","teams building CI/CD automation agents","developers needing background process management"],"limitations":["Requires tmux installation and configuration","Process output is captured asynchronously — timing issues may lose early output","No built-in process timeout — long-running processes may consume resources indefinitely","tmux sessions persist across agent restarts, requiring manual cleanup"],"requires":["Python 3.9+","tmux installed and configured","Unix-like OS (Linux, macOS)"],"input_types":["command strings","session names"],"output_types":["process status","output text","exit codes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gptme--gptme__cap_9","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.document.indexing.and.semantic.search","name":"retrieval-augmented generation with document indexing and semantic search","description":"Implements a RAG tool that indexes documents (code files, markdown, PDFs) into a vector database using embeddings, enabling semantic search over large codebases or knowledge bases. The tool supports querying with natural language, returning relevant documents ranked by semantic similarity. Uses embeddings from the configured LLM provider or local models.","intents":["Search large codebases for relevant files and functions by semantic meaning","Query documentation and knowledge bases with natural language","Provide context to the agent about relevant code or documentation","Build agents that understand and reason about large projects"],"best_for":["agents working with large codebases or knowledge bases","developers building code understanding agents","teams automating documentation-based workflows"],"limitations":["Indexing large codebases is slow and memory-intensive","Semantic search quality depends on embedding model quality","No built-in persistence — index is rebuilt on each agent restart unless saved","Embedding costs scale with codebase size"],"requires":["Python 3.9+","Embedding model access (OpenAI, local, etc.)","Vector database library (faiss, chromadb, etc.)","Sufficient memory for embeddings"],"input_types":["file paths","natural language queries"],"output_types":["ranked document results","semantic similarity scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, OpenRouter) OR local Ollama instance","Network connectivity for cloud providers","Tool-compatible LLM (supports function calling or structured output)","Appropriate system permissions for tool execution (shell, file I/O, network)","For REST API: Flask or similar web framework","For ncurses: terminal with ncurses support","File system write permissions","Sufficient disk space for conversation history","Configuration files (YAML/JSON)"],"failure_modes":["Provider-specific features (vision, function calling) may not be uniformly available across all backends","Message transformation adds ~50-100ms latency per request","Token counting accuracy varies by provider implementation","Tool execution is synchronous — long-running tools block the agent loop","No built-in timeout mechanism for runaway tool executions","Tool output must fit within LLM context window","Custom tools require Python knowledge and understanding of the Tool base class","REST API adds network latency compared to direct CLI execution","ncurses UI is limited to terminal environments — no GUI support","All interfaces share the same underlying agent — no interface-specific optimizations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5697895801539048,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-05-03T13:17:46Z"},"community":{"stars":4287,"forks":384,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gptme--gptme","compare_url":"https://unfragile.ai/compare?artifact=gptme--gptme"}},"signature":"nyup6NMxfI1dv/RxIp5HvK5ZJ3zpbUwdqnpiKIhaL1RN7HyDTWWLZ+evyJXZ4dfsuLOrD/pU5RhBn1CpFFyGBQ==","signedAt":"2026-06-20T09:37:26.271Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gptme--gptme","artifact":"https://unfragile.ai/gptme--gptme","verify":"https://unfragile.ai/api/v1/verify?slug=gptme--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"}}