{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-devgpt","slug":"devgpt","name":"DevGPT","type":"agent","url":"https://github.com/jina-ai/dev-gpt","page_url":"https://unfragile.ai/devgpt","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-devgpt__cap_0","uri":"capability://code.generation.editing.natural.language.to.microservice.code.generation.with.iterative.refinement","name":"natural-language-to-microservice code generation with iterative refinement","description":"Transforms unstructured natural language requirements into complete, deployable microservice code through a multi-turn refinement loop. The system uses a Project Manager agent (powered by GPT) to iteratively enhance the user's description by asking clarifying questions, identifying required APIs, and creating test scenarios before passing refined specifications to the code generation phase. The Generator component then produces microservice.py, test_microservice.py, and requirements.txt files based on the refined specification, using GPT-4 or GPT-3.5-turbo with prompt engineering templates.","intents":["I want to describe a microservice idea in plain English and get working code without writing it myself","I need to clarify my requirements through an interactive conversation before code generation starts","I want the system to identify missing dependencies and API integrations automatically"],"best_for":["solo developers prototyping microservice ideas rapidly","non-technical founders who can articulate requirements but not code","teams wanting to automate boilerplate microservice scaffolding"],"limitations":["Experimental tool with known limitations in complex domain logic generation","GPT model quality directly impacts code quality — no guarantee of production-ready output","Requires manual review and testing of generated code before deployment","Limited to microservice patterns; not suitable for complex distributed systems or specialized algorithms"],"requires":["OpenAI API key (GPT-3.5-turbo or GPT-4 access)","Python 3.8+","Docker installed for containerization","Jina AI Cloud account for deployment (optional)"],"input_types":["natural language description (string)","optional user feedback during refinement loop"],"output_types":["Python microservice code","Unit test file","requirements.txt with dependencies","Dockerfile for containerization"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_1","uri":"capability://tool.use.integration.gpt.session.management.with.cost.tracking.and.response.parsing","name":"gpt session management with cost tracking and response parsing","description":"Manages stateful interactions with OpenAI's GPT models through a GPTSession abstraction that handles authentication, prompt engineering, response parsing, and cost tracking across multiple API calls. The system maintains session state to enable multi-turn conversations, parses structured responses from GPT (extracting code blocks, JSON, and plain text), and tracks token usage and API costs in real-time. Response parsing includes extraction of code snippets from markdown blocks, structured data from JSON responses, and error detection for malformed outputs.","intents":["I need to make multiple sequential GPT calls while maintaining conversation context","I want to track how much my microservice generation is costing in API fees","I need to reliably extract code and structured data from GPT responses"],"best_for":["developers building AI-powered code generation systems","teams managing OpenAI API costs across multiple projects","systems requiring reliable parsing of LLM-generated code"],"limitations":["Tightly coupled to OpenAI API; no multi-provider support (Anthropic, Ollama, etc.)","Cost tracking is approximate and depends on OpenAI's token counting accuracy","Response parsing is regex-based and may fail on edge cases (nested code blocks, unusual formatting)","No built-in retry logic or rate-limiting — relies on caller to handle API errors"],"requires":["OpenAI API key with GPT-3.5-turbo or GPT-4 access","Python 3.8+","requests library or equivalent HTTP client"],"input_types":["system prompt (string)","user message (string)","optional conversation history (list of dicts)"],"output_types":["parsed response (string, code, or JSON)","cost metadata (tokens, USD)","raw API response"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_10","uri":"capability://data.processing.analysis.structured.code.extraction.and.markdown.parsing.from.llm.responses","name":"structured code extraction and markdown parsing from llm responses","description":"Parses GPT responses to reliably extract code blocks, JSON structures, and plain text using regex-based and AST-based parsing techniques. The system handles multiple code block formats (markdown triple-backticks with language tags, indented code blocks, inline code), extracts code from mixed-content responses (e.g., explanations followed by code), and validates extracted code for syntax errors. Extracted code is then written to files or passed to subsequent generation steps.","intents":["I need to reliably extract Python code from GPT responses","I want to handle responses that mix explanations with code blocks","I need to validate extracted code before executing it"],"best_for":["systems that depend on parsing LLM-generated code","code generation pipelines that need robust error handling","teams building on top of GPT with custom parsing requirements"],"limitations":["Regex-based parsing is fragile and may fail on edge cases (nested code blocks, unusual formatting)","No support for non-Python code extraction (JavaScript, Go, etc.) without custom parsers","Syntax validation is basic; does not catch semantic errors or type issues","Performance degrades on very large responses (>100KB)"],"requires":["GPT response (string)","Python 3.8+ with ast and re modules"],"input_types":["GPT response text (string)","expected code language (optional)"],"output_types":["extracted code (string)","parsing metadata (code block count, language tags)","validation results (syntax errors, warnings)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_11","uri":"capability://planning.reasoning.user.feedback.collection.and.iterative.specification.improvement","name":"user feedback collection and iterative specification improvement","description":"Collects explicit user feedback during the PM refinement phase and uses it to iteratively improve the microservice specification. Users can approve or reject the PM's clarifying questions, provide additional context, or request specification changes. The system incorporates this feedback into the next iteration of the specification, creating a feedback loop that converges toward a detailed, user-approved specification before code generation begins.","intents":["I want to provide feedback on the PM's questions and refine the specification","I need to ensure my requirements are accurately captured before code generation","I want to iterate on the specification until I'm satisfied"],"best_for":["users who want to actively participate in requirement refinement","teams where specification approval is a formal gate before development","projects where requirement clarity is critical to success"],"limitations":["Feedback loop adds latency to the generation process","No structured feedback format; users must provide free-form text","System cannot distinguish between clarifications and scope changes","Feedback incorporation is heuristic-based; may not always improve specification quality"],"requires":["User engagement and willingness to provide feedback","OpenAI API key for feedback analysis and specification updates"],"input_types":["user feedback (string)","approval/rejection of PM questions (boolean)","additional context or requirements (string)"],"output_types":["updated specification","next set of clarifying questions","feedback incorporation summary"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_2","uri":"capability://code.generation.editing.automated.microservice.testing.and.self.healing.with.error.recovery","name":"automated microservice testing and self-healing with error recovery","description":"Generates unit tests alongside microservice code and implements a self-healing loop that detects test failures, analyzes error messages, and automatically regenerates code to fix issues. The system runs generated test_microservice.py files, captures assertion errors and exceptions, feeds error context back to GPT with the original code, and iteratively refines the implementation until tests pass. This creates a feedback loop where the AI developer learns from test failures and improves code quality without human intervention.","intents":["I want generated code to be automatically tested and fixed if it doesn't work","I need the system to learn from test failures and improve code iteratively","I want to reduce manual debugging of generated microservices"],"best_for":["teams automating microservice scaffolding with quality gates","developers who want generated code to be testable by default","systems where code generation errors should be caught before deployment"],"limitations":["Self-healing is limited to simple bugs; complex logic errors may require multiple iterations or fail to converge","Test generation quality depends on GPT's ability to infer correct test cases from requirements","No support for integration tests or external service mocking — only unit tests","Infinite loop risk if GPT cannot fix a fundamental design flaw; requires iteration limits"],"requires":["Python 3.8+ with pytest or unittest","OpenAI API key for error analysis and code regeneration","Generated microservice code with test file"],"input_types":["generated Python code","test file (test_microservice.py)","test execution output (stdout/stderr)"],"output_types":["fixed Python code","test execution results (pass/fail)","error analysis and fix rationale"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_3","uri":"capability://automation.workflow.docker.containerization.and.local.execution.with.streamlit.playground","name":"docker containerization and local execution with streamlit playground","description":"Automatically packages generated microservices into Docker containers with a Dockerfile, requirements.txt, and entrypoint configuration. The Runner component executes the containerized microservice locally using Docker, and simultaneously generates a Streamlit-based web playground that provides an interactive UI for testing the microservice endpoints without manual curl commands or code. The playground is automatically generated based on the microservice's function signatures and input/output types.","intents":["I want to run my generated microservice locally in an isolated environment","I need a quick web UI to test my microservice without writing client code","I want to verify the microservice works before deploying to the cloud"],"best_for":["developers testing microservices locally before cloud deployment","non-technical stakeholders who need to interact with generated services","teams wanting to reduce setup friction for local development"],"limitations":["Requires Docker to be installed and running on the local machine","Streamlit playground generation is basic and may not handle complex input types well","No support for stateful services or persistent storage in local execution","Performance is limited by local Docker resources; not suitable for load testing"],"requires":["Docker 20.10+ installed and running","Python 3.8+","Streamlit library (installed via requirements.txt)","Generated microservice code with Dockerfile"],"input_types":["generated microservice code","requirements.txt with dependencies","Dockerfile"],"output_types":["running Docker container","Streamlit web UI (localhost:8501)","container logs and execution output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_4","uri":"capability://automation.workflow.jina.cloud.deployment.with.automated.packaging.and.configuration","name":"jina cloud deployment with automated packaging and configuration","description":"Deploys generated and tested microservices to Jina AI Cloud through an automated Deployer component that handles authentication, image building, registry pushing, and cloud configuration. The system packages the Docker container, authenticates with Jina Cloud using API credentials, pushes the image to Jina's registry, and creates a cloud deployment with appropriate resource allocation and environment variables. The deployment process is abstracted behind a single CLI command, hiding the complexity of cloud infrastructure setup.","intents":["I want to deploy my generated microservice to the cloud with one command","I need to avoid manual Docker registry and cloud configuration steps","I want my microservice to be accessible via a public URL immediately after generation"],"best_for":["developers wanting frictionless microservice deployment","teams using Jina AI Cloud as their primary deployment platform","rapid prototyping scenarios where deployment speed matters"],"limitations":["Tightly coupled to Jina AI Cloud; no support for AWS, GCP, Azure, or other cloud providers","Requires Jina Cloud account and valid API credentials","No support for multi-region deployment or advanced cloud configuration","Deployment monitoring and scaling are limited to Jina Cloud's native capabilities"],"requires":["Jina AI Cloud account with active subscription","Jina Cloud API key and authentication credentials","Docker installed locally for image building","Generated microservice code with Dockerfile"],"input_types":["Docker image (built locally)","Jina Cloud API credentials","deployment configuration (resource limits, environment variables)"],"output_types":["deployed microservice URL","cloud deployment status","service endpoint details"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_5","uri":"capability://automation.workflow.cli.driven.workflow.orchestration.with.command.composition","name":"cli-driven workflow orchestration with command composition","description":"Provides a command-line interface that orchestrates the entire microservice lifecycle through discrete commands: `generate` (create microservice from description), `run` (execute locally with Streamlit UI), `deploy` (push to Jina Cloud), and `configure` (set API keys). The CLI chains these commands together in a workflow, managing state between steps and providing progress feedback. Each command is independently callable but designed to work sequentially, allowing users to generate once and deploy multiple times, or run locally before deploying.","intents":["I want a simple command-line interface to manage the entire microservice lifecycle","I need to generate, test locally, and deploy without switching between tools","I want to configure API keys once and reuse them across multiple projects"],"best_for":["developers comfortable with CLI tools","CI/CD pipelines that need to automate microservice generation and deployment","teams wanting a standardized workflow for microservice creation"],"limitations":["CLI-only interface; no GUI or web dashboard for non-technical users","Command composition is sequential; no parallel execution or conditional branching","Error messages may be cryptic if underlying GPT or Docker calls fail","No built-in undo/rollback functionality for failed deployments"],"requires":["Python 3.8+ with Click or argparse library","OpenAI API key configured via `configure` command","Docker installed for `run` and `deploy` commands","Jina Cloud credentials for `deploy` command"],"input_types":["CLI arguments (command name, description, flags)","configuration file (API keys, cloud credentials)"],"output_types":["generated microservice files","local execution logs","deployment status and URLs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_6","uri":"capability://planning.reasoning.requirement.refinement.through.interactive.multi.turn.pm.agent","name":"requirement refinement through interactive multi-turn pm agent","description":"The Project Manager agent engages users in an interactive conversation to refine vague or incomplete microservice requirements before code generation begins. The PM uses GPT to generate clarifying questions, identify missing API dependencies, create test scenarios, and build a detailed specification document. This multi-turn loop continues until the PM determines the specification is sufficiently detailed, at which point the refined description is passed to the code generation phase. The PM can ask follow-up questions based on user responses and iteratively improve the specification.","intents":["I have a rough idea for a microservice but need help clarifying the requirements","I want the system to identify what APIs and dependencies I'll need","I need test scenarios defined before code generation starts"],"best_for":["non-technical users who struggle to articulate detailed requirements","teams wanting to reduce back-and-forth with developers before coding","projects where requirement clarity directly impacts code quality"],"limitations":["PM agent quality depends on GPT's ability to ask relevant clarifying questions","Users may find the interactive loop tedious if they already have clear requirements","No support for domain-specific requirement templates or industry standards","PM decisions are not explainable — users see questions but not the reasoning behind them"],"requires":["OpenAI API key for GPT access","User willingness to engage in multi-turn conversation","Initial microservice description (even if vague)"],"input_types":["initial microservice description (string)","user responses to PM questions (strings)"],"output_types":["refined specification document","identified API dependencies","test scenarios","clarifying questions (during conversation)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_7","uri":"capability://text.generation.language.template.based.prompt.engineering.with.role.specific.instructions","name":"template-based prompt engineering with role-specific instructions","description":"Uses predefined prompt templates for different roles (Product Manager, Developer, DevOps) to guide GPT behavior consistently across multiple generation tasks. Each role has specific instructions, output formats, and constraints embedded in the prompt template. Templates are stored separately from code logic, allowing easy customization without modifying the core system. The system injects user context, previous conversation history, and task-specific parameters into templates before sending to GPT, enabling consistent and repeatable code generation patterns.","intents":["I want consistent code generation behavior across multiple microservices","I need to customize how the PM, Developer, and DevOps agents behave","I want to reuse proven prompt patterns without rewriting them each time"],"best_for":["teams running multiple microservice generation tasks","organizations wanting to enforce coding standards through prompts","developers who want to experiment with different prompt strategies"],"limitations":["Prompt engineering is an art; templates may not work well for all domains or use cases","Changes to templates require retraining or re-testing to verify impact","No built-in A/B testing framework to compare template effectiveness","Templates are GPT-specific; switching models may require template rewrites"],"requires":["Template files (YAML, JSON, or Python strings)","Understanding of prompt engineering best practices","OpenAI API key for testing template changes"],"input_types":["template definition (string with placeholders)","context variables (user description, previous responses, etc.)"],"output_types":["instantiated prompt (ready for GPT)","GPT response (code, specification, or analysis)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_8","uri":"capability://code.generation.editing.external.api.integration.detection.and.dependency.management","name":"external api integration detection and dependency management","description":"Analyzes refined microservice specifications to identify required external APIs (e.g., payment processors, weather services, authentication providers) and automatically includes them in the generated code and requirements.txt. The system uses GPT to parse the specification and extract API dependencies, then generates appropriate import statements, configuration placeholders, and API client initialization code. Dependencies are tracked and included in the requirements.txt file with pinned versions, ensuring reproducible builds.","intents":["I want the system to automatically detect what external APIs my microservice needs","I need API client libraries to be included in requirements.txt automatically","I want configuration placeholders for API keys without hardcoding them"],"best_for":["microservices that integrate with multiple external services","teams wanting to reduce manual dependency management","rapid prototyping where API integration setup is a bottleneck"],"limitations":["API detection is heuristic-based; may miss obscure or custom APIs","No support for APIs that require complex authentication flows (OAuth2, mTLS)","Generated API client code may not match the actual API's latest version","No validation that detected APIs are actually available or accessible"],"requires":["Refined microservice specification with API mentions","OpenAI API key for dependency extraction","Internet access to verify API availability (optional)"],"input_types":["microservice specification (string)","API detection rules (heuristics or patterns)"],"output_types":["list of detected API dependencies","requirements.txt entries with versions","generated API client initialization code"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devgpt__cap_9","uri":"capability://data.processing.analysis.cost.aware.code.generation.with.token.usage.tracking","name":"cost-aware code generation with token usage tracking","description":"Tracks OpenAI API token usage and cost across all generation steps (PM refinement, code generation, testing, error recovery) and provides real-time cost feedback to users. The system calculates costs based on model pricing (GPT-3.5-turbo vs GPT-4), token counts, and number of API calls, and can optionally enforce cost limits or warn users when costs exceed thresholds. Cost data is logged and can be analyzed to optimize prompt efficiency or choose cheaper models.","intents":["I want to know how much each microservice generation costs in API fees","I need to optimize my prompts to reduce token usage and costs","I want to set budget limits to prevent runaway API costs"],"best_for":["teams managing OpenAI API budgets across multiple projects","cost-conscious developers building on GPT","organizations wanting to track AI infrastructure spending"],"limitations":["Cost tracking is approximate and depends on OpenAI's token counting accuracy","No support for other LLM providers (Anthropic, Ollama) with different pricing models","Cost limits are enforced at the session level; no fine-grained per-request limits","Historical cost data is not persisted; requires external logging for long-term analysis"],"requires":["OpenAI API key with billing enabled","GPTSession with cost tracking enabled","Logging infrastructure for cost data (optional)"],"input_types":["API calls to GPT models","token counts (from OpenAI API responses)"],"output_types":["cost per API call (USD)","total cost per generation task","cost breakdown by model and step"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key (GPT-3.5-turbo or GPT-4 access)","Python 3.8+","Docker installed for containerization","Jina AI Cloud account for deployment (optional)","OpenAI API key with GPT-3.5-turbo or GPT-4 access","requests library or equivalent HTTP client","GPT response (string)","Python 3.8+ with ast and re modules","User engagement and willingness to provide feedback","OpenAI API key for feedback analysis and specification updates"],"failure_modes":["Experimental tool with known limitations in complex domain logic generation","GPT model quality directly impacts code quality — no guarantee of production-ready output","Requires manual review and testing of generated code before deployment","Limited to microservice patterns; not suitable for complex distributed systems or specialized algorithms","Tightly coupled to OpenAI API; no multi-provider support (Anthropic, Ollama, etc.)","Cost tracking is approximate and depends on OpenAI's token counting accuracy","Response parsing is regex-based and may fail on edge cases (nested code blocks, unusual formatting)","No built-in retry logic or rate-limiting — relies on caller to handle API errors","Regex-based parsing is fragile and may fail on edge cases (nested code blocks, unusual formatting)","No support for non-Python code extraction (JavaScript, Go, etc.) without custom parsers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.038Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=devgpt","compare_url":"https://unfragile.ai/compare?artifact=devgpt"}},"signature":"QV23UAsLIIGNcsv372tUXoPYo7EavA2gaQnf8mOEDqNhXPv+X+fkW/cFStp/jSof6rPhG25+w/xr28gGkM91AQ==","signedAt":"2026-07-08T02:05:41.322Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/devgpt","artifact":"https://unfragile.ai/devgpt","verify":"https://unfragile.ai/api/v1/verify?slug=devgpt","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"}}