{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-shroominic--codeinterpreter-api","slug":"shroominic--codeinterpreter-api","name":"codeinterpreter-api","type":"repo","url":"https://discord.gg/Vaq25XJvvW","page_url":"https://unfragile.ai/shroominic--codeinterpreter-api","categories":["code-review-security"],"tags":["chatgpt","chatgpt-code-generation","code-interpreter","codeinterpreter","langchain","llm-agent"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-shroominic--codeinterpreter-api__cap_0","uri":"capability://code.generation.editing.natural.language.to.python.code.generation.with.llm.routing","name":"natural-language-to-python-code-generation-with-llm-routing","description":"Translates natural language requests into executable Python code by routing prompts through configurable LLM providers (OpenAI, Azure OpenAI, Anthropic) via LangChain abstraction layer. The system maintains conversation memory across interactions, allowing the LLM to reference prior code execution results and refine generated code iteratively based on runtime feedback. Implementation uses LangChain's agent framework to chain LLM calls with code execution feedback loops.","intents":["I want to describe a data analysis task in plain English and have it automatically converted to working Python code","I need the LLM to learn from code execution errors and regenerate corrected code in the next iteration","I want to switch between different LLM providers without changing my application code"],"best_for":["developers building conversational data analysis tools","teams integrating ChatGPT Code Interpreter functionality into existing applications","non-technical users who want to perform analysis via natural language"],"limitations":["LLM quality and code correctness depend entirely on model capability — no static analysis or validation of generated code before execution","Conversation memory is session-scoped; no persistent cross-session learning or fine-tuning","Requires valid API credentials for at least one LLM provider; no offline code generation mode"],"requires":["Python 3.8+","API key for OpenAI, Azure OpenAI, or Anthropic","LangChain library (included in dependencies)","Network connectivity for LLM API calls"],"input_types":["natural language text","file references (CSV, JSON, images, etc.)"],"output_types":["Python code (as string)","execution results","generated files"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_1","uri":"capability://code.generation.editing.sandboxed.python.code.execution.with.package.auto.installation","name":"sandboxed-python-code-execution-with-package-auto-installation","description":"Executes arbitrary Python code in an isolated CodeBox environment (local or remote API) with automatic dependency resolution and installation. The system intercepts import statements, detects missing packages, and installs them via pip before execution continues. Output (stdout, stderr, generated files) is captured and returned to the caller. Supports both synchronous and asynchronous execution patterns.","intents":["I want to run user-generated or LLM-generated Python code safely without it affecting my host system","I need automatic package installation so users don't have to manually manage dependencies","I want to capture code output and generated files for display or further processing"],"best_for":["SaaS platforms offering code execution as a service","data analysis applications requiring isolated execution environments","educational platforms teaching Python programming"],"limitations":["Sandboxed environment has resource constraints (CPU, memory, disk) — long-running or memory-intensive computations may timeout or fail","Network access from sandbox may be restricted depending on deployment configuration","No built-in timeout enforcement at the API level — relies on CodeBox implementation for execution limits","File system isolation means code cannot access host system files unless explicitly mounted"],"requires":["Python 3.8+","CodeBox (local Docker-based or remote CodeBox API endpoint)","Docker daemon running (for local CodeBox)","API credentials for remote CodeBox deployment (if not using local)"],"input_types":["Python code (as string)","input files (CSV, JSON, images, etc.)"],"output_types":["stdout/stderr text","generated files","structured execution results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_10","uri":"capability://data.processing.analysis.internet.access.from.sandboxed.code.execution","name":"internet-access-from-sandboxed-code-execution","description":"Allows executed code to access external internet resources (APIs, web scraping, downloading files) from within the sandboxed environment. Network access is configured at the CodeBox level and can be restricted or allowed based on deployment requirements. Code can make HTTP requests, download datasets, and interact with external services.","intents":["I want code to fetch data from APIs or web sources as part of analysis","I need to download datasets from the internet for processing","I want to integrate with external services (weather APIs, stock data, etc.) in generated code"],"best_for":["data analysis workflows requiring external data sources","web scraping applications","integration with third-party APIs"],"limitations":["Network access may be restricted in some deployment environments for security reasons","External API rate limits and timeouts can cause code execution to fail","No built-in caching of external data — repeated requests hit the network each time","Security risk if untrusted code is executed — malicious code could exfiltrate data or perform attacks"],"requires":["Python 3.8+","CodeBox configured with internet access","Network connectivity from CodeBox to external services"],"input_types":["Python code with network requests","API endpoints, URLs"],"output_types":["downloaded data","API responses","analysis results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_2","uri":"capability://data.processing.analysis.file.upload.download.with.session.scoped.storage","name":"file-upload-download-with-session-scoped-storage","description":"Manages input and output files within a session-scoped temporary storage system. Users upload files (CSV, images, documents, etc.) which are stored in a session directory, made available to executed code, and can be downloaded after processing. The File class provides a high-level abstraction for file operations. Session cleanup removes all temporary files when the session ends. Supports both synchronous and asynchronous file operations.","intents":["I want to upload a CSV file, have Python code analyze it, and download the results","I need generated files (plots, reports, converted formats) to be available for download after code execution","I want automatic cleanup of temporary files when the session ends"],"best_for":["data analysis platforms with file upload/download workflows","document processing applications","file conversion tools"],"limitations":["Files are session-scoped and deleted when session ends — no persistent storage across sessions without external integration","File size limits depend on CodeBox configuration and available disk space","No built-in file versioning or history tracking","Concurrent file operations may have race conditions if not properly synchronized"],"requires":["Python 3.8+","CodeBox with writable temporary directory","Sufficient disk space for input and output files"],"input_types":["CSV, JSON, Excel, images, PDFs, text files, binary files"],"output_types":["CSV, JSON, images, PDFs, text files, generated plots"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_3","uri":"capability://memory.knowledge.multi.turn.conversation.with.execution.context.memory","name":"multi-turn-conversation-with-execution-context-memory","description":"Maintains conversation history and execution context across multiple turns within a single CodeInterpreterSession. Each turn includes the user prompt, generated code, execution output, and any files produced. The LLM can reference prior execution results when generating new code, enabling iterative refinement and multi-step workflows. Context is stored in memory and passed to the LLM on each turn via LangChain's message history mechanism.","intents":["I want to ask follow-up questions that build on previous code execution results","I need the LLM to remember what data was loaded and analyzed in prior turns","I want to iteratively refine analysis by asking the LLM to modify or extend previous code"],"best_for":["interactive data analysis workflows","exploratory coding sessions","educational tutoring systems"],"limitations":["Conversation memory is in-memory only — lost when session ends unless explicitly persisted","No automatic context pruning — very long conversations may exceed LLM token limits","Memory is not shared across sessions — each new session starts fresh","No built-in mechanism to save/load conversation history"],"requires":["Python 3.8+","CodeInterpreterSession instance","Sufficient memory for conversation history (typically not a constraint for reasonable session lengths)"],"input_types":["natural language text","follow-up questions"],"output_types":["conversation history","execution results with context"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_4","uri":"capability://automation.workflow.flexible.deployment.with.local.and.remote.codebox.backends","name":"flexible-deployment-with-local-and-remote-codebox-backends","description":"Abstracts code execution backend through a configurable CodeBox integration layer that supports both local Docker-based execution and remote CodeBox API endpoints. Developers can switch between local development (full control, no external dependencies) and production deployment (scalable, managed infrastructure) by changing configuration. The system handles authentication, request routing, and result marshaling transparently.","intents":["I want to develop locally with a self-hosted CodeBox but deploy to a managed CodeBox API in production","I need to scale code execution across multiple machines without changing my application code","I want to avoid managing Docker infrastructure while still having isolated code execution"],"best_for":["teams transitioning from development to production","SaaS platforms requiring scalable code execution","organizations with infrastructure constraints"],"limitations":["Remote CodeBox API requires valid credentials and network connectivity","Latency for remote execution is higher than local (network round-trip overhead)","Debugging remote execution failures is harder than local execution","Cost implications of remote API usage must be considered for high-volume deployments"],"requires":["Python 3.8+","Docker daemon (for local CodeBox) OR CodeBox API credentials (for remote)","Network connectivity to CodeBox API (if using remote)"],"input_types":["configuration parameters","Python code"],"output_types":["execution results","generated files"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_5","uri":"capability://data.processing.analysis.data.analysis.and.visualization.with.common.python.libraries","name":"data-analysis-and-visualization-with-common-python-libraries","description":"Enables data analysis workflows by automatically installing and providing access to popular Python libraries (pandas, numpy, matplotlib, seaborn, plotly, etc.) within the execution sandbox. The LLM can generate code that loads datasets, performs statistical analysis, creates visualizations, and exports results. The system handles library installation transparently when code imports these packages.","intents":["I want to upload a CSV and ask the LLM to analyze it and create visualizations","I need statistical summaries, correlations, and insights extracted from datasets","I want to generate publication-quality plots without writing matplotlib code manually"],"best_for":["business intelligence and analytics platforms","data science education tools","research data analysis workflows"],"limitations":["Large dataset analysis may be constrained by sandbox memory limits","Visualization output is limited to static formats (PNG, SVG) unless interactive libraries like Plotly are used","No real-time streaming analysis — all operations are batch-based","Statistical analysis is limited to what the installed libraries support; advanced statistical packages may not be available"],"requires":["Python 3.8+","CodeBox with sufficient disk space for library installation","Input data in supported formats (CSV, JSON, Excel, Parquet, etc.)"],"input_types":["CSV, JSON, Excel, Parquet files","natural language analysis requests"],"output_types":["statistical summaries","plots (PNG, SVG, HTML)","processed datasets (CSV, JSON)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_6","uri":"capability://automation.workflow.async.api.support.for.high.throughput.services","name":"async-api-support-for-high-throughput-services","description":"Provides both synchronous and asynchronous APIs for code execution, allowing integration into async Python frameworks (FastAPI, aiohttp, etc.). Async operations enable non-blocking execution, allowing a single application instance to handle multiple concurrent code execution requests without thread overhead. The async interface mirrors the synchronous API, making it easy to switch between them.","intents":["I want to build a FastAPI service that handles multiple concurrent code execution requests","I need non-blocking code execution so my web server doesn't freeze while waiting for results","I want to scale horizontally by handling more concurrent requests per instance"],"best_for":["web services and APIs requiring high concurrency","serverless functions with time constraints","real-time collaborative coding platforms"],"limitations":["Async API requires Python 3.8+ with async/await support","Debugging async code is more complex than synchronous code","Not all CodeBox backends may support concurrent execution — local Docker may have resource constraints","Error handling in async contexts requires careful exception management"],"requires":["Python 3.8+","async-compatible CodeBox backend","async web framework (FastAPI, aiohttp, etc.) for practical use"],"input_types":["Python code","natural language prompts"],"output_types":["execution results","generated files"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_7","uri":"capability://tool.use.integration.custom.tool.registration.and.function.calling","name":"custom-tool-registration-and-function-calling","description":"Allows developers to register custom Python functions as tools that the LLM can call during code generation. Tools are exposed to the LLM via a schema-based registry, enabling the agent to invoke custom logic (API calls, database queries, external services) as part of generated code execution. Tool definitions include name, description, and parameter schema for LLM understanding.","intents":["I want the LLM to be able to call my custom functions or APIs as part of the generated code","I need to extend the LLM's capabilities with domain-specific tools without modifying the core system","I want to control what external services the LLM can access by registering approved tools"],"best_for":["domain-specific applications requiring custom logic integration","platforms with proprietary APIs that need LLM access","systems requiring fine-grained control over LLM capabilities"],"limitations":["Tool registration requires manual setup — no automatic discovery of available functions","Tool schemas must be manually defined; no automatic schema generation from function signatures","LLM may not reliably call tools if descriptions are ambiguous or incomplete","No built-in tool versioning or deprecation mechanism"],"requires":["Python 3.8+","CodeInterpreterSession instance","Custom Python functions to register as tools"],"input_types":["Python function definitions","tool schema definitions"],"output_types":["tool call results","execution output"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_8","uri":"capability://automation.workflow.frontend.integration.with.streamlit.and.chainlit","name":"frontend-integration-with-streamlit-and-chainlit","description":"Provides pre-built frontend integrations for Streamlit and Chainlit, enabling rapid deployment of conversational code execution interfaces without building custom UI. These integrations handle session management, file upload/download, conversation display, and code execution triggering. Developers can deploy a fully functional web interface by writing minimal configuration code.","intents":["I want to quickly deploy a web interface for code execution without building custom UI","I need a chat-like interface where users can ask questions and see code execution results","I want to support file uploads and downloads in a user-friendly way"],"best_for":["rapid prototyping of code execution applications","non-technical teams building data analysis interfaces","internal tools and dashboards"],"limitations":["Streamlit and Chainlit have their own limitations and styling constraints","Customization beyond provided integrations requires forking or extending the framework","Performance may be limited for high-traffic applications (Streamlit reruns entire script on interaction)","Deployment requires Streamlit Cloud or self-hosted infrastructure"],"requires":["Python 3.8+","Streamlit or Chainlit library","CodeInterpreterSession instance"],"input_types":["configuration parameters","custom CSS/styling (limited)"],"output_types":["web interface","rendered chat UI"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-shroominic--codeinterpreter-api__cap_9","uri":"capability://code.generation.editing.error.handling.and.execution.feedback.loops","name":"error-handling-and-execution-feedback-loops","description":"Captures code execution errors (syntax errors, runtime exceptions, import failures) and returns them to the LLM as part of the conversation context. The LLM can then analyze the error, understand what went wrong, and generate corrected code in the next turn. Error messages include stack traces and contextual information. This enables iterative debugging without user intervention.","intents":["I want the LLM to automatically fix code errors by learning from execution failures","I need detailed error messages that help the LLM understand what went wrong","I want to avoid manual debugging by having the LLM iterate on failed code"],"best_for":["interactive code generation systems","educational platforms teaching programming","automated data analysis workflows"],"limitations":["LLM may not always correctly interpret error messages or generate correct fixes","Some errors (e.g., infinite loops, resource exhaustion) may not be recoverable without user intervention","Error feedback loops can create infinite retry cycles if the LLM cannot fix the underlying issue","Timeout errors may not provide detailed context about what caused the timeout"],"requires":["Python 3.8+","CodeBox with error capture capability","LLM with sufficient reasoning capability to interpret errors"],"input_types":["Python code","error messages"],"output_types":["error details","corrected code","execution results"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for OpenAI, Azure OpenAI, or Anthropic","LangChain library (included in dependencies)","Network connectivity for LLM API calls","CodeBox (local Docker-based or remote CodeBox API endpoint)","Docker daemon running (for local CodeBox)","API credentials for remote CodeBox deployment (if not using local)","CodeBox configured with internet access","Network connectivity from CodeBox to external services","CodeBox with writable temporary directory"],"failure_modes":["LLM quality and code correctness depend entirely on model capability — no static analysis or validation of generated code before execution","Conversation memory is session-scoped; no persistent cross-session learning or fine-tuning","Requires valid API credentials for at least one LLM provider; no offline code generation mode","Sandboxed environment has resource constraints (CPU, memory, disk) — long-running or memory-intensive computations may timeout or fail","Network access from sandbox may be restricted depending on deployment configuration","No built-in timeout enforcement at the API level — relies on CodeBox implementation for execution limits","File system isolation means code cannot access host system files unless explicitly mounted","Network access may be restricted in some deployment environments for security reasons","External API rate limits and timeouts can cause code execution to fail","No built-in caching of external data — repeated requests hit the network each time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.562778669426619,"quality":0.32,"ecosystem":0.5800000000000001,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2024-11-07T03:28:33Z"},"community":{"stars":3850,"forks":391,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=shroominic--codeinterpreter-api","compare_url":"https://unfragile.ai/compare?artifact=shroominic--codeinterpreter-api"}},"signature":"FoOehcz5vDsYjPw1zyCNUjmhhcI/XJS0dNwPdyVn/hX4jPyNKMELae5Kc7d75X6QZjouImjoboiAWi1w14CXDQ==","signedAt":"2026-06-20T11:01:07.908Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/shroominic--codeinterpreter-api","artifact":"https://unfragile.ai/shroominic--codeinterpreter-api","verify":"https://unfragile.ai/api/v1/verify?slug=shroominic--codeinterpreter-api","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"}}