{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-antonosika--gpt-engineer","slug":"antonosika--gpt-engineer","name":"gpt-engineer","type":"cli","url":"https://github.com/AntonOsika/gpt-engineer","page_url":"https://unfragile.ai/antonosika--gpt-engineer","categories":["app-builders"],"tags":["ai","autonomous-agent","code-generation","codebase-generation","codegen","coding-assistant","gpt-4","gpt-engineer","openai","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-antonosika--gpt-engineer__cap_0","uri":"capability://code.generation.editing.natural.language.to.code.generation.with.multi.step.llm.orchestration","name":"natural-language-to-code generation with multi-step llm orchestration","description":"Converts natural language specifications into executable code by orchestrating multiple LLM calls through a CliAgent that coordinates between AI interface, memory system, and execution environment. The agent implements a structured workflow that breaks down code generation into discrete steps (analysis, planning, implementation), with each step managed through the AI component's message formatting and token tracking. The system maintains conversation context across steps via DiskMemory, enabling iterative refinement based on execution feedback.","intents":["I want to describe what software I need in plain English and have it automatically generated","I need to rapidly prototype a full codebase from a specification without writing boilerplate","I want the AI to understand my requirements and generate production-ready code in one interaction"],"best_for":["solo developers prototyping MVPs quickly","teams experimenting with AI-assisted development workflows","developers wanting to offload boilerplate generation to AI"],"limitations":["Generated code quality depends heavily on specification clarity; vague requirements produce suboptimal output","No built-in code review or security scanning — generated code requires manual validation before production use","LLM context window limits project complexity; very large codebases may exceed token limits across multi-step workflow","Requires external LLM API (OpenAI, Anthropic, Azure) — no local-only generation without model provider"],"requires":["Python 3.9+","API key for OpenAI, Anthropic, Azure OpenAI, or compatible LLM provider","Natural language specification of software requirements","Disk space for generated code and memory artifacts"],"input_types":["natural language text (specification/prompt)","existing codebase (for improvement workflows)"],"output_types":["generated source code files (multiple languages)","execution logs and error messages","memory artifacts tracking generation history"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_1","uri":"capability://code.generation.editing.codebase.aware.code.improvement.with.context.aware.llm.prompting","name":"codebase-aware code improvement with context-aware llm prompting","description":"Analyzes existing codebases and applies targeted improvements by feeding the full code context into LLM prompts through the AI interface, which handles message formatting and token management. The system uses FilesDict abstraction to load and track all project files, then constructs prompts that include relevant code snippets alongside improvement instructions. The CliAgent orchestrates the improvement workflow, executing generated changes through DiskExecutionEnv and validating results against the original codebase.","intents":["I want to refactor an existing codebase to improve code quality or performance","I need to add features to an existing project while maintaining code consistency","I want the AI to understand my entire codebase context and suggest improvements"],"best_for":["teams maintaining legacy codebases wanting AI-assisted refactoring","developers seeking to modernize code patterns across a project","projects where understanding full context is critical for safe improvements"],"limitations":["Large codebases (>100K LOC) may exceed LLM context windows, requiring manual file selection","No built-in diff generation or merge conflict resolution — improvements must be manually reviewed and integrated","Improvement quality depends on code clarity and documentation; poorly documented code produces generic suggestions","No rollback mechanism if generated improvements break functionality — requires version control discipline"],"requires":["Python 3.9+","Existing codebase with readable file structure","API key for LLM provider","Clear improvement instructions or goals"],"input_types":["existing source code files","improvement instructions (natural language)","optional file selection filters"],"output_types":["improved source code files","execution results showing impact of changes","memory artifacts tracking improvement history"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_10","uri":"capability://text.generation.language.documentation.generation.and.code.commenting.from.specifications","name":"documentation generation and code commenting from specifications","description":"Generates documentation and code comments from natural language specifications and generated code through the documentation system, which uses LLM calls to produce human-readable documentation. The system can generate README files, API documentation, inline code comments, and architecture documentation based on the specification and generated code. Documentation is persisted alongside generated code artifacts.","intents":["I want documentation automatically generated for the code the AI creates","I need README files and API docs without manually writing them","I want inline code comments explaining the generated code logic"],"best_for":["teams wanting to maintain documentation alongside generated code","projects where documentation is critical for maintainability","rapid prototyping where manual documentation is impractical"],"limitations":["Generated documentation quality depends on code clarity and specification detail","Documentation is generated once; updates to code are not automatically reflected in docs","No support for specialized documentation formats (Sphinx, Doxygen, etc.)","Documentation generation adds latency and API cost to the generation workflow"],"requires":["Python 3.9+","LLM API credentials","Clear code and specifications for documentation generation"],"input_types":["generated source code","original specification/prompt"],"output_types":["README files","API documentation","inline code comments","architecture documentation"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.api.interface","name":"multi-provider llm abstraction with unified api interface","description":"Abstracts communication with diverse LLM providers (OpenAI, Anthropic, Azure OpenAI, open-source models) through a unified AI component interface that handles API calls, token tracking, and message formatting. The system normalizes provider-specific APIs into a common interface, managing authentication, request/response transformation, and error handling transparently. Token counting is integrated to track usage across multi-step workflows and prevent context window overflow.","intents":["I want to switch between different LLM providers without rewriting my code generation logic","I need to track token usage across a multi-step generation workflow to manage costs","I want to use open-source models alongside commercial APIs in the same system"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","developers building LLM-agnostic code generation systems","organizations with multi-cloud or hybrid LLM strategies"],"limitations":["Provider-specific features (vision, function calling, streaming) may not be uniformly supported across all backends","Token counting accuracy varies by provider; some models lack native token counters requiring estimation","API rate limits and quota management are provider-specific and not abstracted — requires per-provider configuration","Response format normalization adds latency (~50-100ms per call) for format translation"],"requires":["Python 3.9+","API credentials for at least one supported LLM provider","Network connectivity to LLM provider endpoints"],"input_types":["provider configuration (API key, model name, endpoint)","LLM prompts and messages (text)"],"output_types":["normalized LLM responses (text)","token usage metrics","provider-agnostic error messages"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_3","uri":"capability://memory.knowledge.persistent.memory.and.execution.history.tracking.via.disk.based.storage","name":"persistent memory and execution history tracking via disk-based storage","description":"Maintains conversation history, generated code artifacts, and execution results through DiskMemory abstraction that persists all workflow state to disk. The system stores intermediate outputs from each generation step, enabling users to inspect the reasoning process and resume interrupted workflows. FilesDict provides a file-system abstraction for managing generated code, while execution logs capture stdout, stderr, and return codes from running generated code.","intents":["I want to see the full history of how my code was generated, including intermediate steps","I need to resume a code generation workflow that was interrupted","I want to inspect what the AI generated at each step before final output"],"best_for":["developers debugging AI-generated code by inspecting generation steps","teams auditing AI code generation for compliance or security review","workflows requiring reproducibility and full traceability of generated artifacts"],"limitations":["Disk storage grows linearly with number of generation steps; large projects may consume significant disk space","No built-in cleanup or archival mechanism — old memory artifacts must be manually managed","Memory is local to execution environment; no distributed memory or cloud sync for team collaboration","No encryption of stored artifacts — sensitive code or prompts are stored in plaintext on disk"],"requires":["Python 3.9+","Writable disk space for memory artifacts","Local file system access (no remote/network storage support)"],"input_types":["generation workflow outputs (code, logs, metadata)"],"output_types":["persisted memory artifacts (JSON, code files)","execution logs and results","workflow history and intermediate outputs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_4","uri":"capability://automation.workflow.controlled.code.execution.environment.with.sandboxed.output.capture","name":"controlled code execution environment with sandboxed output capture","description":"Executes generated code in an isolated DiskExecutionEnv that captures stdout, stderr, and return codes without exposing the host system to arbitrary code execution risks. The execution environment provides a controlled context for validating generated code functionality, with output captured for feedback to the LLM in improvement loops. The system supports multiple programming languages through language-specific execution handlers.","intents":["I want to run generated code and see if it works before deploying it","I need to validate that generated code produces expected output","I want the AI to see execution results and improve code based on failures"],"best_for":["development workflows where code validation is critical before deployment","iterative code generation where execution feedback drives improvements","teams wanting to safely test AI-generated code before manual review"],"limitations":["Execution environment is not fully sandboxed — generated code can access host filesystem and network","No timeout enforcement; infinite loops or hanging code will block the workflow indefinitely","No resource limits (CPU, memory) — resource-intensive generated code can consume host resources","Language support is limited to configured execution handlers; unsupported languages fail silently","Output capture is text-only; binary outputs or GUI applications are not supported"],"requires":["Python 3.9+","Runtime environments for target languages (Python, Node.js, etc.)","Writable disk space for execution artifacts"],"input_types":["generated source code files","execution configuration (language, entry point, arguments)"],"output_types":["stdout and stderr text","return/exit codes","execution duration and resource usage","error messages and stack traces"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_5","uri":"capability://automation.workflow.cli.driven.workflow.orchestration.with.interactive.agent.coordination","name":"cli-driven workflow orchestration with interactive agent coordination","description":"Provides a command-line interface (gpte/ge/gpt-engineer commands) that orchestrates the entire code generation workflow through CliAgent, which coordinates between user input, LLM calls, file management, and execution. The CLI parses user specifications and configuration, invokes the appropriate agent workflow (generation or improvement), and manages the interaction loop. The agent system implements two primary workflows: generation (creating new code from prompts) and improvement (enhancing existing code).","intents":["I want a simple CLI command to generate code from a natural language specification","I need to configure which LLM provider and model to use for code generation","I want to improve an existing codebase through a CLI workflow"],"best_for":["developers preferring CLI tools over GUI interfaces","CI/CD pipelines integrating AI code generation as a workflow step","teams automating code generation as part of development infrastructure"],"limitations":["CLI interface is synchronous — long-running generation workflows block the terminal","No interactive prompting or real-time feedback during generation — users must wait for completion","Configuration is file-based or environment variables; no interactive setup wizard","Error messages are text-only; no structured error codes for programmatic error handling"],"requires":["Python 3.9+","CLI environment (bash, zsh, PowerShell, etc.)","LLM API credentials configured as environment variables or config files"],"input_types":["CLI arguments and flags","configuration files (YAML/JSON)","environment variables"],"output_types":["generated code files","CLI output logs","exit codes"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_6","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.execution.handlers","name":"multi-language code generation with language-specific execution handlers","description":"Generates code in multiple programming languages (Python, JavaScript, TypeScript, Go, Rust, etc.) through language-specific execution handlers configured in supported_languages. The system detects target language from specifications or explicit configuration, then routes generated code to appropriate execution environment. Each language handler encapsulates language-specific syntax, build requirements, and execution commands.","intents":["I want to generate code in a specific programming language based on my project needs","I need to generate a full-stack project with multiple languages (backend + frontend)","I want the AI to understand language-specific idioms and best practices"],"best_for":["polyglot teams working across multiple programming languages","full-stack projects requiring coordinated generation across backend and frontend","organizations standardizing on specific languages but needing flexibility"],"limitations":["Language support is limited to configured handlers; unsupported languages require custom handler implementation","Code quality varies by language; some languages have better LLM training data than others","No cross-language type checking or interface validation — generated code in different languages may have incompatible contracts","Build and runtime requirements are language-specific; execution environment must have all required runtimes installed"],"requires":["Python 3.9+","Runtime environments for target languages (Python 3.9+, Node.js 16+, Go 1.18+, etc.)","Language-specific build tools (pip, npm, cargo, etc.)"],"input_types":["natural language specification","target language specification (explicit or inferred)"],"output_types":["source code in target language","language-specific artifacts (compiled binaries, packages, etc.)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_7","uri":"capability://memory.knowledge.file.selection.and.project.structure.analysis.for.context.management","name":"file selection and project structure analysis for context management","description":"Analyzes project structure and selectively loads relevant files into LLM context through file selection mechanisms that filter large codebases to fit within token limits. The system uses FilesDict abstraction to manage file loading, with optional file selection filters that identify the most relevant files for a given task. This enables the AI to work with large projects by focusing on relevant code sections rather than loading entire codebases.","intents":["I have a large codebase but only want the AI to focus on specific modules or files","I need the AI to understand project structure and dependencies without exceeding token limits","I want to exclude generated files, tests, or dependencies from AI context"],"best_for":["large projects (>10K LOC) where full codebase context exceeds LLM limits","teams wanting to focus AI attention on specific project areas","monorepos with multiple independent components"],"limitations":["File selection heuristics may miss relevant files, leading to incomplete context","No automatic dependency resolution — AI may not understand cross-file dependencies if files aren't selected","Selection is static at workflow start; dynamic file selection based on LLM reasoning is not supported","No built-in understanding of project structure (monorepo, microservices, etc.) — requires manual configuration"],"requires":["Python 3.9+","Project with readable file structure","Optional: file selection configuration (patterns, filters)"],"input_types":["project directory structure","file selection filters (glob patterns, file types)"],"output_types":["filtered file list","selected file contents","project structure metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_8","uri":"capability://planning.reasoning.preprompt.customization.and.workflow.step.extensibility","name":"preprompt customization and workflow step extensibility","description":"Enables customization of LLM prompts through PrepromptHolder system and extensible workflow steps via custom_steps module, allowing users to inject domain-specific instructions and modify generation behavior. The system maintains a library of preprompts (system prompts, role definitions, task-specific instructions) that can be overridden or extended. Custom steps can be implemented to insert additional processing, validation, or LLM calls into the generation workflow.","intents":["I want to customize the AI's behavior with domain-specific instructions or coding standards","I need to add validation or processing steps to the generation workflow","I want to enforce specific code patterns or architectural decisions"],"best_for":["teams with specific coding standards or architectural patterns to enforce","organizations wanting to customize AI behavior without forking the codebase","advanced users building custom workflows on top of gpt-engineer"],"limitations":["Preprompt customization requires understanding LLM prompt engineering; poorly written prompts degrade output quality","Custom steps require Python coding; non-technical users cannot extend workflows","No validation of custom steps; broken steps can crash the entire workflow","Custom steps are not versioned or tracked; changes to steps are not recorded in memory artifacts"],"requires":["Python 3.9+","Understanding of LLM prompt engineering (for preprompts)","Python coding skills (for custom steps)"],"input_types":["custom preprompt text","custom step Python code"],"output_types":["modified LLM prompts","custom step outputs (varies by implementation)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-antonosika--gpt-engineer__cap_9","uri":"capability://data.processing.analysis.benchmarking.and.performance.measurement.system","name":"benchmarking and performance measurement system","description":"Provides built-in benchmarking infrastructure to measure code generation quality, speed, and cost across different configurations and models. The system captures metrics including token usage, generation time, execution results, and code quality indicators, enabling empirical comparison of different LLM providers, models, and workflow configurations. Benchmarking results are persisted for historical analysis and trend tracking.","intents":["I want to compare code generation quality across different LLM models","I need to measure the cost and speed of code generation for different configurations","I want to track how code generation quality improves over time"],"best_for":["teams evaluating LLM providers for code generation","organizations optimizing code generation workflows for cost/quality tradeoffs","researchers studying AI code generation performance"],"limitations":["Benchmarking requires running multiple generation workflows, consuming significant API costs","Code quality metrics are heuristic-based; no ground truth for comparing generated code quality","Benchmarks are specific to test cases; results may not generalize to other projects","No built-in statistical analysis; users must manually analyze benchmark results"],"requires":["Python 3.9+","Multiple LLM API credentials for comparative benchmarking","Test cases or specifications for benchmarking"],"input_types":["benchmark test cases","model/provider configurations to compare"],"output_types":["benchmark results (JSON/CSV)","performance metrics (tokens, time, cost)","code quality indicators"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for OpenAI, Anthropic, Azure OpenAI, or compatible LLM provider","Natural language specification of software requirements","Disk space for generated code and memory artifacts","Existing codebase with readable file structure","API key for LLM provider","Clear improvement instructions or goals","LLM API credentials","Clear code and specifications for documentation generation","API credentials for at least one supported LLM provider"],"failure_modes":["Generated code quality depends heavily on specification clarity; vague requirements produce suboptimal output","No built-in code review or security scanning — generated code requires manual validation before production use","LLM context window limits project complexity; very large codebases may exceed token limits across multi-step workflow","Requires external LLM API (OpenAI, Anthropic, Azure) — no local-only generation without model provider","Large codebases (>100K LOC) may exceed LLM context windows, requiring manual file selection","No built-in diff generation or merge conflict resolution — improvements must be manually reviewed and integrated","Improvement quality depends on code clarity and documentation; poorly documented code produces generic suggestions","No rollback mechanism if generated improvements break functionality — requires version control discipline","Generated documentation quality depends on code clarity and specification detail","Documentation is generated once; updates to code are not automatically reflected in docs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.846483827910896,"quality":0.32,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:09.057Z","last_commit":"2025-05-14T10:15:10Z"},"community":{"stars":55227,"forks":7326,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=antonosika--gpt-engineer","compare_url":"https://unfragile.ai/compare?artifact=antonosika--gpt-engineer"}},"signature":"7yXzWJU0Gk8IFDRKpkgUAm0m8uKIMjw85kqgLzskhb/QY7QQt667tvhRv7f4MIptAE/YM5R464pwKoAIT4RsAA==","signedAt":"2026-06-20T06:20:06.915Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/antonosika--gpt-engineer","artifact":"https://unfragile.ai/antonosika--gpt-engineer","verify":"https://unfragile.ai/api/v1/verify?slug=antonosika--gpt-engineer","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"}}