{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-open-interpreter","slug":"open-interpreter","name":"Open Interpreter","type":"repo","url":"https://github.com/openinterpreter/open-interpreter","page_url":"https://unfragile.ai/open-interpreter","categories":["cli-tools"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-open-interpreter__cap_0","uri":"capability://code.generation.editing.natural.language.to.code.execution.with.local.runtime","name":"natural-language-to-code-execution-with-local-runtime","description":"Interprets natural language instructions and automatically generates, executes, and iterates on code in a local Python/system runtime without cloud submission. Uses an agentic loop that parses LLM outputs, detects code blocks, executes them via subprocess/exec, captures stdout/stderr, and feeds results back to the LLM for refinement—enabling multi-turn code generation with real-time feedback and error correction.","intents":["Run data analysis scripts from plain English descriptions without writing code myself","Automate file operations, system tasks, and data transformations by describing what I want done","Debug failing code by having the agent iteratively fix errors based on execution output","Prototype complex workflows by describing steps and letting the agent handle implementation details"],"best_for":["Solo developers and data scientists wanting rapid prototyping without context-switching to an IDE","Non-technical users automating repetitive tasks via natural language","Teams building LLM-powered automation agents that need local execution guarantees"],"limitations":["Execution happens in a single local Python process—no built-in sandboxing or resource limits, creating security risks if running untrusted prompts","Code generation quality depends entirely on the underlying LLM; no static analysis or pre-execution validation of generated code","Long-running tasks block the agent loop; no async execution or background job management","Context window limitations mean complex multi-file projects may exceed token budgets mid-execution"],"requires":["Python 3.10+","API key for OpenAI (default), Anthropic, or local LLM endpoint","System shell access (bash/zsh on Unix, PowerShell on Windows)","Sufficient disk space and RAM for code execution"],"input_types":["natural language instructions (text)","code snippets for debugging","file paths and system commands"],"output_types":["executed code results (stdout/stderr)","generated Python/shell scripts","file artifacts (CSV, JSON, images, etc.)","structured execution logs"],"categories":["code-generation-editing","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_1","uri":"capability://code.generation.editing.multi.language.code.generation.and.execution","name":"multi-language-code-generation-and-execution","description":"Generates and executes code in multiple programming languages (Python, JavaScript, Bash, R, etc.) by detecting language from context or explicit directives, then routing execution to the appropriate runtime or shell. The agent maintains language-specific execution contexts and can chain commands across languages within a single workflow.","intents":["Write and run Python data analysis, then execute JavaScript for web scraping, then bash for file operations—all from one natural language prompt","Generate R statistical scripts alongside Python preprocessing in a single workflow","Execute system commands and scripts without context-switching between different tools"],"best_for":["Data scientists and engineers working across polyglot stacks","DevOps and infrastructure teams automating multi-language deployments","Researchers combining statistical analysis (R) with machine learning (Python) in one session"],"limitations":["Requires all target language runtimes installed locally; no automatic dependency resolution or environment setup","Cross-language data passing relies on file I/O or stdout parsing—no native inter-process communication","Language detection from natural language is heuristic-based and may fail for ambiguous prompts","No built-in package/library management; generated code may fail if dependencies aren't pre-installed"],"requires":["Python 3.10+","Installed runtimes for target languages (Node.js for JavaScript, R for R scripts, Bash shell, etc.)","LLM API key (OpenAI, Anthropic, or local endpoint)"],"input_types":["natural language instructions","code snippets in any supported language","explicit language directives (e.g., 'write Python code to...')"],"output_types":["executed code output in target language","generated source files (.py, .js, .sh, .R, etc.)","cross-language workflow results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_10","uri":"capability://text.generation.language.interactive.multi.turn.conversation.with.code.context","name":"interactive-multi-turn-conversation-with-code-context","description":"Maintains a multi-turn conversation where the user can ask follow-up questions, request modifications, or provide feedback on generated code. The agent preserves conversation history and execution context, allowing users to refine results iteratively. Each turn includes the prior conversation, execution results, and any errors, enabling the LLM to understand the full context for generating improved code.","intents":["Ask follow-up questions about generated code without re-explaining the original task","Request modifications or refinements to prior results","Debug code by describing what went wrong and asking for fixes"],"best_for":["Interactive exploratory analysis where iteration is expected","Users refining code through multiple rounds of feedback","Collaborative workflows where multiple users provide input"],"limitations":["Conversation history grows unbounded, eventually exceeding LLM context windows","No explicit conversation management—users must manually manage context or risk token exhaustion","Ambiguous follow-up questions may be misinterpreted without full prior context","No conversation persistence across sessions; history is lost on restart"],"requires":["LLM with sufficient context window (4K+ tokens recommended)","Session management to track conversation history","Execution state preservation across turns"],"input_types":["natural language instructions","follow-up questions and refinement requests","feedback on prior results"],"output_types":["refined code based on feedback","modified execution results","conversation history and context"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_11","uri":"capability://tool.use.integration.local.llm.support.with.multiple.provider.integration","name":"local-llm-support-with-multiple-provider-integration","description":"Supports multiple LLM backends including OpenAI, Anthropic, local models (via Ollama, LM Studio, vLLM), and other providers through a unified interface. Users can specify their preferred LLM provider via configuration or environment variables, enabling flexibility in model choice and enabling offline-first workflows with local models. The agent abstracts provider-specific API differences.","intents":["Run Open Interpreter with local LLMs for privacy and offline operation","Switch between different LLM providers without changing code","Use cost-effective local models instead of expensive cloud APIs"],"best_for":["Privacy-conscious users avoiding cloud LLM APIs","Teams with on-premise LLM infrastructure","Cost-sensitive users wanting to minimize API spending"],"limitations":["Local LLM quality and speed vary widely; smaller models may generate poor code","Requires significant local compute resources (GPU recommended); no automatic model selection based on hardware","Provider abstraction may hide important differences in API behavior or model capabilities","No automatic fallback if primary LLM provider is unavailable"],"requires":["LLM provider API key (OpenAI, Anthropic) OR local LLM setup (Ollama, LM Studio, vLLM)","For local models: GPU with sufficient VRAM (8GB+ recommended) or CPU with patience","Network access to provider API OR local network access to LLM server"],"input_types":["LLM provider configuration (API key, endpoint URL, model name)","natural language instructions"],"output_types":["generated code from selected LLM","execution results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_12","uri":"capability://automation.workflow.terminal.based.interactive.interface.with.streaming.output","name":"terminal-based-interactive-interface-with-streaming-output","description":"Provides a command-line interface (REPL-like) where users type natural language instructions and receive streaming output of generated code and execution results. The interface displays code blocks, execution logs, and results in real-time, with syntax highlighting and formatted output. Users can interrupt execution, view history, and interact with the agent directly from the terminal.","intents":["Use Open Interpreter as an interactive terminal tool without GUI overhead","Stream code generation and execution results in real-time","Maintain a command history and session transcript"],"best_for":["Terminal-native developers and DevOps engineers","Headless/remote server environments without GUI support","Users preferring keyboard-driven workflows"],"limitations":["Terminal rendering limitations for complex visualizations or large outputs","Streaming output may be hard to parse programmatically; no structured output format","No built-in session recording or transcript export","Terminal width/height constraints may truncate output"],"requires":["Terminal emulator with ANSI color support","Python 3.10+","LLM API key or local LLM endpoint"],"input_types":["natural language instructions (typed into terminal)","keyboard commands (interrupt, history navigation)"],"output_types":["streaming code generation output","execution results with syntax highlighting","terminal logs and history"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_2","uri":"capability://code.generation.editing.iterative.error.correction.with.execution.feedback","name":"iterative-error-correction-with-execution-feedback","description":"Implements a feedback loop where execution errors (stderr, exceptions, timeouts) are captured and automatically fed back to the LLM as context for the next generation attempt. The agent parses error messages, identifies root causes, and regenerates code with corrections—repeating until success or max iterations reached. This enables self-healing code generation without manual intervention.","intents":["Fix broken code automatically by letting the agent see error messages and retry","Handle edge cases and missing dependencies by iteratively refining generated code","Reduce manual debugging time by automating the error-correction cycle"],"best_for":["Rapid prototyping scenarios where iteration speed matters more than first-pass correctness","Non-technical users who can't debug code themselves","Exploratory data analysis where trial-and-error is expected"],"limitations":["Infinite loops possible if the LLM generates the same error repeatedly; max iteration limits are crude safeguards","Error messages may be ambiguous or misleading, causing the LLM to misdiagnose root causes","No semantic understanding of errors—purely pattern-matching on stderr output","Retry logic increases total execution time and API costs proportionally to iteration count"],"requires":["LLM with sufficient context window to include error traces","Execution environment that captures and returns stderr/stdout","Configurable max iteration count (default typically 5-10)"],"input_types":["natural language task description","execution error messages (stderr)","exception tracebacks"],"output_types":["corrected code","execution results after successful retry","iteration history/logs"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_3","uri":"capability://automation.workflow.file.system.and.artifact.manipulation","name":"file-system-and-artifact-manipulation","description":"Generates and executes code that reads, writes, creates, and modifies files in the user's local filesystem. The agent can create new files, edit existing ones, generate artifacts (CSV, JSON, images, PDFs), and manage directory structures—all through generated code that runs with the user's file permissions. Artifacts are persisted to disk and accessible after execution.","intents":["Generate a CSV report from data analysis and save it to disk","Create configuration files, scripts, or documentation automatically","Batch process files (rename, convert, reorganize) without manual scripting","Generate visualizations or plots and save them as image files"],"best_for":["Data engineers and analysts generating reports and datasets","DevOps teams creating configuration files and deployment scripts","Content creators automating file generation and batch processing"],"limitations":["No built-in access control—generated code runs with full user permissions, creating data loss risks if prompts are malicious","File I/O errors (permissions, disk full, path issues) may not be gracefully handled by generated code","Large file operations can block the agent loop; no streaming or chunked I/O support","No version control or rollback—overwritten files are lost unless the user has backups"],"requires":["Write permissions to target directories","Sufficient disk space for generated artifacts","File system accessible from the Python runtime"],"input_types":["natural language descriptions of desired files/artifacts","data to be written (arrays, dataframes, text)","file paths and formats (CSV, JSON, PNG, PDF, etc.)"],"output_types":["created/modified files on disk","generated artifacts (reports, images, configs)","file operation logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_4","uri":"capability://automation.workflow.system.command.execution.and.shell.integration","name":"system-command-execution-and-shell-integration","description":"Executes arbitrary shell commands (bash, PowerShell, zsh) generated by the LLM, capturing stdout/stderr and feeding results back into the agentic loop. Enables system-level automation like package installation, process management, network operations, and OS-specific tasks. The agent can chain shell commands and parse their output for conditional logic.","intents":["Install dependencies and set up environments automatically","Run system diagnostics and collect hardware/software information","Automate deployment tasks (git operations, Docker commands, service restarts)","Perform network operations (curl, wget, SSH) from natural language descriptions"],"best_for":["DevOps and infrastructure engineers automating system administration","Developers setting up development environments without manual CLI work","System administrators automating routine maintenance tasks"],"limitations":["Executing arbitrary shell commands is a critical security risk—no sandboxing or command whitelisting by default","Shell syntax varies across OS (bash vs PowerShell); generated commands may fail on different platforms","Long-running commands block the agent loop; no background execution or timeout handling","Output parsing is fragile—commands with unstructured output may confuse the LLM's error detection"],"requires":["System shell access (bash/zsh on Unix, PowerShell on Windows)","Appropriate permissions for target operations (sudo access, network access, etc.)","LLM capable of generating syntactically correct shell commands"],"input_types":["natural language task descriptions","shell command snippets for debugging","system state queries"],"output_types":["command execution results (stdout/stderr)","system state information","generated shell scripts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_5","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.awareness","name":"context-aware-code-completion-with-codebase-awareness","description":"Generates code that is contextually aware of the user's current working directory, file structure, and previously executed code within the session. The agent maintains execution state and can reference variables, functions, and imports from prior steps, enabling multi-step workflows where later code builds on earlier results. This is implemented via persistent Python namespaces or session state tracking.","intents":["Load data in one step, then analyze it in subsequent steps without re-loading","Define helper functions once and reuse them across multiple generated code blocks","Build complex workflows incrementally where each step depends on prior results"],"best_for":["Data scientists building exploratory analysis workflows with multiple steps","Developers prototyping complex systems incrementally","Teams building reproducible analysis pipelines"],"limitations":["Session state is not persisted across restarts—long-running sessions may accumulate memory overhead","Variable naming conflicts and namespace pollution can occur if the LLM generates conflicting variable names","No explicit dependency tracking—if earlier code is modified, dependent code may break silently","Debugging multi-step workflows is harder because errors may originate from prior steps"],"requires":["Python runtime with persistent namespace (e.g., IPython kernel or exec() with shared globals)","Session management to track execution state across multiple code blocks"],"input_types":["natural language descriptions of analysis steps","references to prior results or variables","incremental code additions"],"output_types":["code that builds on prior execution state","results that depend on multi-step workflows","session state snapshots"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_6","uri":"capability://image.visual.image.generation.and.visualization.support","name":"image-generation-and-visualization-support","description":"Generates Python code (using matplotlib, seaborn, Plotly, PIL, etc.) that creates visualizations and images, then executes that code to produce image artifacts. The agent can interpret natural language descriptions of charts, plots, and graphics, generate the appropriate visualization code, and save outputs as PNG, SVG, or other formats. Results are displayed or saved to disk.","intents":["Create charts and plots from data by describing what visualization I want","Generate diagrams, infographics, or custom graphics without learning visualization libraries","Batch generate multiple visualizations from a dataset"],"best_for":["Data analysts and business intelligence teams creating reports","Researchers visualizing experimental results","Non-technical users generating graphics from data"],"limitations":["Visualization quality depends on LLM's knowledge of library APIs; complex custom visualizations may fail","Image generation is limited to code-based libraries (matplotlib, Plotly)—no generative AI image creation","Large datasets may produce slow or memory-intensive visualizations","Styling and aesthetic choices are limited to library capabilities; fine-grained design control requires manual code editing"],"requires":["Python visualization libraries installed (matplotlib, seaborn, Plotly, PIL, etc.)","Display backend for rendering (X11, Quartz, or headless backend for servers)","Sufficient memory for large datasets"],"input_types":["natural language descriptions of desired visualizations","data (arrays, dataframes, CSV files)","chart type specifications (bar, line, scatter, heatmap, etc.)"],"output_types":["generated visualization code (Python)","image files (PNG, SVG, PDF)","interactive plots (HTML for Plotly)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_7","uri":"capability://data.processing.analysis.natural.language.data.analysis.and.transformation","name":"natural-language-data-analysis-and-transformation","description":"Interprets natural language queries about data (CSV, JSON, dataframes) and generates Python code using pandas, NumPy, or SQL to perform analysis, filtering, aggregation, and transformation. The agent can load data, explore its structure, apply transformations, and return results—all from plain English descriptions without requiring SQL or pandas syntax knowledge.","intents":["Analyze a CSV file by asking questions in English ('show me sales by region', 'find outliers in this dataset')","Transform and clean data without writing pandas code","Perform statistical analysis and generate summary statistics"],"best_for":["Non-technical business analysts and domain experts","Data scientists rapidly exploring new datasets","Teams automating data pipeline tasks"],"limitations":["Complex analytical queries may require domain-specific knowledge that the LLM lacks","Data type inference is heuristic-based; mistyped columns may cause code generation failures","Large datasets (>1GB) may cause memory issues or slow execution","Generated code may be inefficient; no query optimization or indexing strategies"],"requires":["Python with pandas, NumPy, or SQL libraries installed","Data files in supported formats (CSV, JSON, Parquet, Excel, etc.)","Sufficient memory for in-memory data processing"],"input_types":["natural language data analysis queries","data files (CSV, JSON, Parquet, Excel)","dataframe references from prior execution"],"output_types":["analysis results (summary statistics, filtered data)","transformed datasets","generated pandas/SQL code","visualizations of analysis results"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_8","uri":"capability://data.processing.analysis.web.scraping.and.http.request.automation","name":"web-scraping-and-http-request-automation","description":"Generates Python code using requests, BeautifulSoup, Selenium, or similar libraries to fetch web content, parse HTML/JSON, and extract data. The agent can interpret natural language descriptions of scraping tasks and generate appropriate code for GET/POST requests, form submission, JavaScript rendering, and data extraction—then execute the code to retrieve results.","intents":["Scrape data from a website by describing what information I need","Automate API interactions and data collection from web services","Extract structured data from HTML pages without writing scraping code"],"best_for":["Data engineers and analysts collecting web data","Researchers gathering datasets from public sources","Business intelligence teams automating data ingestion"],"limitations":["JavaScript-heavy websites require Selenium or Playwright, which are slow and resource-intensive","No built-in handling of authentication, rate limiting, or robots.txt compliance","HTML structure changes break scraping code; no adaptive parsing","Legal and ethical risks—no warnings about terms of service or copyright violations"],"requires":["Python with requests, BeautifulSoup, or Selenium installed","Network access to target websites","Browser driver (Chromedriver, Geckodriver) if JavaScript rendering is needed"],"input_types":["natural language scraping task descriptions","URLs or API endpoints","HTML/JSON samples for structure inference"],"output_types":["scraped data (JSON, CSV, structured text)","generated scraping code (Python)","parsed HTML/JSON results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-open-interpreter__cap_9","uri":"capability://code.generation.editing.dependency.aware.code.generation.with.import.management","name":"dependency-aware-code-generation-with-import-management","description":"Generates code that includes appropriate import statements and handles missing dependencies by detecting ImportError exceptions and suggesting installation commands. The agent can infer required libraries from the task description and generate code that attempts to import them, with fallback logic for missing packages. This enables code generation that works across different environments without pre-installed dependencies.","intents":["Generate code that automatically installs missing libraries when executed","Use specialized libraries (scikit-learn, TensorFlow, etc.) without pre-installing them","Create portable code that works across different Python environments"],"best_for":["Users with limited Python environment setup knowledge","Rapid prototyping scenarios where dependency management overhead is undesirable","Shared environments where package installation may be restricted"],"limitations":["Automatic pip install during code execution is a security risk and may fail in restricted environments","No version pinning or compatibility checking; installed versions may conflict with existing packages","Large libraries (TensorFlow, PyTorch) take time to install, blocking the agent loop","No handling of system-level dependencies (CUDA, C libraries) that some packages require"],"requires":["pip or conda available in the Python environment","Network access to PyPI or package repositories","Write permissions to site-packages directory"],"input_types":["natural language task descriptions","library names or capabilities (e.g., 'machine learning', 'image processing')"],"output_types":["generated code with import statements","installation logs","executed code results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","API key for OpenAI (default), Anthropic, or local LLM endpoint","System shell access (bash/zsh on Unix, PowerShell on Windows)","Sufficient disk space and RAM for code execution","Installed runtimes for target languages (Node.js for JavaScript, R for R scripts, Bash shell, etc.)","LLM API key (OpenAI, Anthropic, or local endpoint)","LLM with sufficient context window (4K+ tokens recommended)","Session management to track conversation history","Execution state preservation across turns","LLM provider API key (OpenAI, Anthropic) OR local LLM setup (Ollama, LM Studio, vLLM)"],"failure_modes":["Execution happens in a single local Python process—no built-in sandboxing or resource limits, creating security risks if running untrusted prompts","Code generation quality depends entirely on the underlying LLM; no static analysis or pre-execution validation of generated code","Long-running tasks block the agent loop; no async execution or background job management","Context window limitations mean complex multi-file projects may exceed token budgets mid-execution","Requires all target language runtimes installed locally; no automatic dependency resolution or environment setup","Cross-language data passing relies on file I/O or stdout parsing—no native inter-process communication","Language detection from natural language is heuristic-based and may fail for ambiguous prompts","No built-in package/library management; generated code may fail if dependencies aren't pre-installed","Conversation history grows unbounded, eventually exceeding LLM context windows","No explicit conversation management—users must manually manage context or risk token exhaustion","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=open-interpreter","compare_url":"https://unfragile.ai/compare?artifact=open-interpreter"}},"signature":"hEzGqUZ990FLI+upEiv+X7xMDuhfQU3Pm1xGZ/+STL/mWTpa8fH+/QsrAdZhbQoRflttXe5n8d9MXh2j5/fzCg==","signedAt":"2026-06-21T09:01:09.575Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open-interpreter","artifact":"https://unfragile.ai/open-interpreter","verify":"https://unfragile.ai/api/v1/verify?slug=open-interpreter","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"}}