{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-iniwap--aiforge","slug":"iniwap--aiforge","name":"AIForge","type":"agent","url":"https://github.com/iniwap/AIForge","page_url":"https://unfragile.ai/iniwap--aiforge","categories":["ai-agents"],"tags":["agent","agent-zero","agent0","agentic-ai","ai","ai-agents","ai-tool","ai-tools","aipy","aipyapp","aiwritex","crewai","deepseek","iflow","iflow-cli","manus-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-iniwap--aiforge__cap_0","uri":"capability://code.generation.editing.natural.language.to.executable.python.code.generation","name":"natural-language-to-executable-python-code-generation","description":"Transforms natural language task descriptions into executable Python code through LLM generation, implementing a 'Code is Agent' philosophy where generated code directly manipulates the execution environment. The system uses multi-turn LLM interactions with configurable providers (OpenAI, DeepSeek, OpenRouter, Ollama) to synthesize task-appropriate code that runs in an isolated Python sandbox with pre-installed common libraries, enabling self-correction through iterative feedback loops when execution fails.","intents":["I want to describe a task in plain English and have AI generate and run the Python code to accomplish it","I need to automate a workflow by converting natural language specifications into executable scripts","I want to leverage LLM reasoning to solve programming problems without writing code myself"],"best_for":["non-technical users automating data analysis and processing tasks","developers prototyping solutions quickly without manual coding","teams building AI-driven automation workflows with minimal engineering overhead"],"limitations":["Generated code quality depends on LLM model capability and prompt engineering; complex multi-step tasks may require iterative refinement","Execution sandbox isolation adds latency (~500ms-2s per execution cycle) compared to direct Python execution","No built-in type safety or static analysis of generated code before execution; runtime errors require feedback loops","Limited to Python ecosystem; cannot generate code for other languages or cross-language integrations"],"requires":["Python 3.8+","API key for at least one LLM provider (OpenAI, DeepSeek, OpenRouter) OR local Ollama instance","Network connectivity for cloud-based LLM providers (optional if using Ollama locally)"],"input_types":["natural language task description (string)","optional context/constraints (string)","optional file paths or data references (string)"],"output_types":["executable Python code (string)","execution results (structured data, text, or files)","error messages with feedback for retry"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.lazy.loading","name":"multi-provider-llm-abstraction-with-lazy-loading","description":"Provides a unified interface (AIForgeLLMManager) for seamless switching between multiple LLM providers including OpenAI, DeepSeek, OpenRouter, and local Ollama deployments. Implements lazy-loading to instantiate provider clients only when needed, reducing memory overhead and startup time. Each provider is abstracted behind a common interface, allowing runtime provider selection and fallback strategies without code changes.","intents":["I want to switch between different LLM providers (OpenAI, DeepSeek, local Ollama) without rewriting code","I need cost optimization by routing tasks to cheaper providers while maintaining quality","I want to run AIForge offline using local Ollama while keeping cloud providers as fallback options"],"best_for":["teams managing multiple LLM provider accounts and seeking cost optimization","developers building privacy-conscious applications using local models","organizations with hybrid cloud/on-premise infrastructure requirements"],"limitations":["Provider API compatibility varies; some advanced features (streaming, function calling) may not be uniformly supported across all providers","Lazy-loading adds ~50-200ms overhead on first provider initialization per session","No built-in load balancing or automatic failover between providers; requires explicit configuration","Rate limiting and quota management must be handled per-provider; no unified rate limiting layer"],"requires":["Python 3.8+","API keys for cloud providers (OpenAI, DeepSeek, OpenRouter) OR Ollama running locally on port 11434","Network connectivity for cloud providers OR Docker/local Ollama setup for offline operation"],"input_types":["provider name (string: 'openai', 'deepseek', 'openrouter', 'ollama')","model identifier (string: 'gpt-4', 'deepseek-chat', etc.)","optional API key or endpoint URL (string)"],"output_types":["LLM client instance (provider-specific)","LLM response (text/structured)","provider metadata (capabilities, rate limits)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_10","uri":"capability://automation.workflow.execution.state.persistence.across.multiple.code.runs","name":"execution-state-persistence-across-multiple-code-runs","description":"Maintains execution state (variables, imported modules, defined functions) across multiple code generation and execution cycles within a single session, allowing subsequent generated code to reference and build upon results from previous executions. The system preserves the Python interpreter state between runs, enabling multi-step workflows where each step depends on outputs from previous steps without requiring explicit state passing or serialization.","intents":["I want to run multiple related tasks sequentially where each task uses results from the previous task","I need to build up complex analyses step-by-step without redefining variables or reimporting libraries","I want the system to remember data and functions from earlier executions"],"best_for":["multi-step data analysis workflows where each step builds on previous results","interactive exploratory data analysis sessions","complex automation workflows with interdependent tasks"],"limitations":["State persistence is in-memory only; application restart clears all state","Large state objects (DataFrames, arrays) consume memory; no automatic garbage collection or state pruning","State conflicts may occur if multiple users/sessions share the same execution environment","Debugging state-dependent code is difficult; errors may depend on execution history rather than current code","No explicit state management interface; users cannot inspect, modify, or clear state without code"],"requires":["Python 3.8+","Single-threaded execution model (no concurrent execution in same state)","Sufficient memory for accumulated state objects"],"input_types":["task description (string)","optional state variable references (list of variable names to use from previous executions)"],"output_types":["task result (any Python object)","updated execution state (variables, functions, imports)","state summary (list of available variables and their types)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_11","uri":"capability://automation.workflow.detailed.execution.logging.and.debugging.information","name":"detailed-execution-logging-and-debugging-information","description":"Captures comprehensive execution logs including LLM prompts, generated code, execution output, error tracebacks, and timing information, storing them in structured format for debugging and auditing. The system provides detailed visibility into each step of the task execution pipeline, enabling developers to understand why code was generated a certain way and why execution succeeded or failed, with optional log export for external analysis.","intents":["I want to understand why the LLM generated specific code for my task","I need detailed error information to debug why code execution failed","I want to audit all tasks executed and the code generated for compliance or debugging purposes"],"best_for":["developers debugging AIForge behavior and code generation quality","teams with compliance requirements for task auditing and traceability","organizations analyzing LLM behavior and code generation patterns"],"limitations":["Detailed logging adds ~50-100ms overhead per execution for log serialization","Large execution logs consume disk space; long-running applications may require log rotation","Sensitive information (API keys, user data) may be captured in logs; requires careful log handling","Log format is application-specific; external tools require custom parsers for analysis","No built-in log analysis or visualization tools; users must write custom scripts for log exploration"],"requires":["Python 3.8+","Writable filesystem for log storage","Optional: log analysis tools (grep, jq, pandas) for external analysis"],"input_types":["log level configuration (DEBUG, INFO, WARNING, ERROR)","optional log output destination (file path, stdout, external service)","optional log format specification (JSON, plain text, structured)"],"output_types":["execution log (structured or plain text)","LLM prompt and response (for code generation transparency)","execution traceback (for error debugging)","timing information (LLM latency, execution duration, total time)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_2","uri":"capability://memory.knowledge.three.tier.intelligent.code.caching.with.semantic.analysis","name":"three-tier-intelligent-code-caching-with-semantic-analysis","description":"Implements a hierarchical caching system with three tiers: (1) AiForgeCodeCache—basic SQLite-backed storage with metadata indexing, (2) EnhancedAiForgeCodeCache—semantic analysis and success rate tracking to prioritize high-confidence cached solutions, (3) TemplateBasedCodeCache—pattern matching with parameter extraction for reusable code templates. The system prioritizes execution of previously successful code modules over LLM generation, significantly reducing API calls and latency by matching incoming tasks against cached solutions before invoking the LLM.","intents":["I want to avoid redundant LLM API calls by reusing code that successfully solved similar tasks before","I need to improve response time by serving cached solutions for common task patterns","I want to track which cached code solutions have the highest success rates and prefer those"],"best_for":["applications with repetitive or similar task patterns (data analysis, report generation, ETL workflows)","cost-sensitive deployments where reducing LLM API calls directly impacts operational expenses","teams building internal automation platforms with evolving task libraries"],"limitations":["Cache hit rates depend on task similarity; highly novel or unique tasks will miss cache and incur full LLM latency","Semantic analysis in EnhancedCache adds ~100-300ms overhead per cache lookup for similarity scoring","Template-based matching requires manual template definition; no automatic template extraction from successful executions","Cache invalidation strategy not explicitly documented; stale cached code may persist if underlying libraries or APIs change","SQLite backend limits concurrent access; high-throughput scenarios may experience contention"],"requires":["Python 3.8+","SQLite3 (included in Python standard library)","Optional: semantic similarity library (e.g., sentence-transformers) for EnhancedCache tier","Writable filesystem for cache database storage"],"input_types":["task description (string)","optional task metadata (dict with tags, category, parameters)","optional code template definitions (string with parameter placeholders)"],"output_types":["cached code solution (string) if hit","cache metadata (success rate, execution count, timestamp)","cache miss signal triggering LLM generation"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_3","uri":"capability://automation.workflow.isolated.python.execution.environment.with.error.recovery","name":"isolated-python-execution-environment-with-error-recovery","description":"Provides AIForgeRunner—a sandboxed Python execution environment that runs generated code with pre-installed common libraries (numpy, pandas, requests, etc.), real-time result feedback, detailed logging, and configurable error retry mechanisms. The environment maintains state persistence across multiple executions within a session, tracks execution errors, and supports automatic retry with up to N configurable rounds, allowing the LLM to receive feedback and self-correct failed code generation attempts.","intents":["I want to safely execute untrusted or AI-generated code without risking the host system","I need detailed execution logs and error messages to debug why generated code failed","I want the system to automatically retry failed code generation with feedback from execution errors"],"best_for":["applications executing user-submitted or AI-generated code with safety requirements","data analysis and processing workflows where execution errors are expected and recovery is needed","teams building multi-turn agent systems where code refinement through error feedback is critical"],"limitations":["Sandbox isolation adds ~500ms-2s latency per execution compared to direct Python execution","Pre-installed library set is fixed; dynamic package installation during execution is not supported","No resource limits (CPU, memory, execution time) enforced; runaway code can consume host resources","State persistence is in-memory only; no persistence across application restarts","Error messages from sandbox may be less detailed than native Python tracebacks due to execution wrapper overhead"],"requires":["Python 3.8+","Common data science libraries (numpy, pandas, matplotlib, requests, etc.) pre-installed in environment","Sufficient disk space for execution logs and temporary files"],"input_types":["Python code string (generated or user-provided)","optional input variables/context (dict)","optional retry configuration (max_retries: int, timeout: float)"],"output_types":["execution result (any Python object)","execution logs (string with stdout/stderr)","error traceback (string) if execution fails","retry feedback (error message passed to LLM for code refinement)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_4","uri":"capability://planning.reasoning.task.driven.workflow.orchestration.with.iterative.refinement","name":"task-driven-workflow-orchestration-with-iterative-refinement","description":"Orchestrates end-to-end task execution through AIForgeCore, which coordinates natural language input → LLM code generation → sandbox execution → error feedback → iterative refinement cycles. The system manages task state, tracks execution history, and implements a feedback loop where execution errors are analyzed and passed back to the LLM to generate corrected code, enabling complex multi-step workflows to complete autonomously without manual intervention.","intents":["I want to submit a complex task in natural language and have the system automatically handle code generation, execution, and error recovery","I need to automate workflows that may fail initially but can self-correct through iterative refinement","I want visibility into task execution history and the reasoning behind generated code"],"best_for":["non-technical users automating complex data analysis and processing workflows","teams building autonomous AI agents for business process automation","applications requiring hands-off task execution with minimal human oversight"],"limitations":["Iterative refinement cycles add latency; complex tasks may require 5-10 LLM calls before success, increasing API costs","No explicit task decomposition; complex multi-step workflows are handled as single monolithic tasks, limiting parallelization","Execution history is in-memory only; no persistent audit trail across application restarts","No built-in task prioritization or queuing; concurrent task submissions may compete for LLM resources","Feedback loop relies on LLM's ability to interpret error messages; cryptic errors may not lead to successful refinement"],"requires":["Python 3.8+","Configured LLM provider with API key or local Ollama instance","AIForgeCore instance initialized with provider configuration"],"input_types":["task description (string)","optional task context/constraints (string)","optional input data (files, variables, URLs)"],"output_types":["task result (any Python object or file)","execution summary (task status, iterations, final code)","execution logs (detailed trace of all generation and execution steps)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_5","uri":"capability://automation.workflow.configuration.management.with.multiple.initialization.modes","name":"configuration-management-with-multiple-initialization-modes","description":"Provides AIForgeConfig system supporting four initialization modes: (1) Quick Start—direct API key initialization, (2) Provider-Specific—explicit provider and model selection, (3) Configuration File—TOML-based declarative configuration, (4) Configuration Wizard—interactive setup assistant. The system abstracts provider credentials, model selection, cache settings, and execution parameters into a unified configuration object, enabling flexible deployment across different environments (local development, Docker, cloud platforms) without code changes.","intents":["I want to configure AIForge through environment variables or config files without modifying code","I need to deploy AIForge across multiple environments (dev, staging, production) with different LLM providers","I want an interactive setup wizard to configure AIForge without reading documentation"],"best_for":["DevOps teams managing AIForge deployments across multiple environments","non-technical users setting up AIForge for the first time","organizations with strict configuration management and audit requirements"],"limitations":["TOML configuration files must be manually maintained; no automatic configuration generation from runtime state","Configuration wizard is CLI-only; no GUI configuration tool for non-technical users","No built-in configuration validation; invalid settings may only be detected at runtime","Configuration changes require application restart; no hot-reload capability","Secrets management relies on environment variables; no built-in integration with secret vaults (AWS Secrets Manager, HashiCorp Vault)"],"requires":["Python 3.8+","TOML library (included in Python 3.11+, requires toml package for Python 3.8-3.10)","Optional: environment variables for API keys and provider configuration"],"input_types":["API key (string)","provider name (string: 'openai', 'deepseek', 'openrouter', 'ollama')","model identifier (string)","TOML configuration file (file path)","interactive wizard responses (user input)"],"output_types":["AIForgeConfig object (in-memory configuration)","configuration validation report (success/error messages)","generated TOML configuration file (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_6","uri":"capability://automation.workflow.command.line.interface.for.task.submission","name":"command-line-interface-for-task-submission","description":"Provides a CLI interface enabling users to submit tasks directly from the terminal, specifying task descriptions, LLM provider/model, cache preferences, and execution parameters through command-line arguments or interactive prompts. The CLI integrates with AIForgeCore to execute tasks and display results, logs, and execution history in a terminal-friendly format, enabling scripting and automation of AIForge workflows.","intents":["I want to run AIForge tasks from the command line without writing Python code","I need to integrate AIForge into shell scripts and CI/CD pipelines","I want to quickly test AIForge functionality without setting up a full application"],"best_for":["developers and DevOps engineers automating workflows through shell scripts","CI/CD pipeline integration for automated data processing and code generation tasks","power users preferring terminal-based interfaces over GUI applications"],"limitations":["CLI output formatting is text-based; complex results (large DataFrames, visualizations) are difficult to display in terminal","No interactive result exploration; users cannot drill down into execution results or modify parameters mid-execution","Long-running tasks block the terminal; no background execution or job queuing","Error messages are printed to stdout/stderr; no structured error reporting for programmatic parsing"],"requires":["Python 3.8+","AIForge package installed with core dependencies","Configured LLM provider (API key or local Ollama)","Terminal/shell environment"],"input_types":["task description (command-line argument or stdin)","provider name (--provider flag)","model identifier (--model flag)","cache settings (--use-cache, --cache-tier flags)","execution parameters (--timeout, --max-retries flags)"],"output_types":["task result (printed to stdout)","execution logs (printed to stderr or log file)","exit code (0 for success, non-zero for failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_7","uri":"capability://automation.workflow.graphical.user.interface.with.textual.terminal.ui","name":"graphical-user-interface-with-textual-terminal-ui","description":"Provides a terminal-based GUI using the Textual framework, enabling interactive task submission, real-time execution monitoring, result visualization, and configuration management through a mouse/keyboard-driven interface. The GUI displays execution logs, code generation steps, and results in organized panels, allowing non-technical users to interact with AIForge without command-line knowledge while maintaining full feature access.","intents":["I want a user-friendly interface to submit tasks and monitor execution without using the command line","I need to see real-time execution logs and code generation steps as they happen","I want to configure AIForge settings through a GUI without editing config files"],"best_for":["non-technical users and business analysts using AIForge for data analysis","teams preferring visual interfaces over command-line tools","developers debugging AIForge behavior through interactive execution monitoring"],"limitations":["Terminal UI is limited to text-based rendering; cannot display rich visualizations (charts, images) inline","Textual framework requires terminal emulator with specific capabilities; some legacy terminals may not render correctly","GUI responsiveness depends on terminal performance; large execution logs may cause UI lag","No persistent session management; closing the terminal loses execution history","Mouse support varies by terminal emulator; keyboard-only navigation may be required on some systems"],"requires":["Python 3.8+","AIForge package installed with gui optional dependencies (textual)","Terminal emulator with 256-color support and mouse support (optional)","Configured LLM provider"],"input_types":["task description (text input field)","provider selection (dropdown menu)","model selection (dropdown menu)","cache preferences (checkbox/toggle)","execution parameters (text input fields)"],"output_types":["task result (displayed in result panel)","execution logs (displayed in log panel with real-time updates)","generated code (displayed in code panel with syntax highlighting)","execution summary (status, iteration count, timing)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_8","uri":"capability://automation.workflow.web.interface.with.fastapi.backend","name":"web-interface-with-fastapi-backend","description":"Provides a web-based interface built on FastAPI and Uvicorn, exposing AIForge functionality through REST API endpoints and an optional web dashboard. The backend handles task submission, execution management, result retrieval, and configuration through HTTP endpoints, enabling remote access, integration with web applications, and deployment as a microservice. The web interface supports real-time execution updates through WebSocket or polling, allowing users to monitor task progress from any browser.","intents":["I want to access AIForge from a web browser without installing desktop applications","I need to integrate AIForge into existing web applications through REST APIs","I want to deploy AIForge as a cloud service accessible to multiple users"],"best_for":["teams building web applications that need AI-powered task automation","organizations deploying AIForge as a shared service across multiple users","developers integrating AIForge into existing web infrastructure"],"limitations":["Web interface adds network latency (~50-200ms per request) compared to local execution","No built-in authentication or authorization; requires external security layer (reverse proxy, API gateway) for production use","WebSocket support for real-time updates requires additional infrastructure (load balancer, session management)","Concurrent user requests may overwhelm single-instance deployment; horizontal scaling requires distributed state management","Web dashboard is optional and may not be included in all deployments; API-only deployments require custom client implementation"],"requires":["Python 3.8+","AIForge package installed with web optional dependencies (fastapi, uvicorn)","Network connectivity for client-server communication","Optional: reverse proxy (nginx, Apache) for production deployment","Optional: load balancer for horizontal scaling"],"input_types":["HTTP POST request with task description (JSON body)","provider and model selection (JSON body)","cache preferences (JSON body)","execution parameters (JSON body)"],"output_types":["HTTP response with task ID (JSON)","task status endpoint returning execution progress (JSON)","result endpoint returning task output (JSON or file download)","WebSocket stream with real-time execution logs (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-iniwap--aiforge__cap_9","uri":"capability://code.generation.editing.dynamic.library.availability.detection.and.code.adaptation","name":"dynamic-library-availability-detection-and-code-adaptation","description":"Automatically detects which libraries are available in the execution environment and adapts generated code to use available alternatives when preferred libraries are missing. The system maintains a library compatibility matrix and instructs the LLM to generate code using available libraries (e.g., using matplotlib instead of plotly if plotly is unavailable), ensuring generated code executes successfully without requiring manual library installation.","intents":["I want generated code to work even if some libraries are missing from the environment","I need the system to automatically suggest alternative libraries when preferred ones aren't available","I want to avoid execution failures due to missing dependencies"],"best_for":["environments with restricted library installation (sandboxed, containerized, cloud functions)","teams managing multiple execution environments with different library sets","applications requiring high reliability and minimal execution failures"],"limitations":["Library detection adds ~100-200ms overhead per execution to scan available packages","Alternative library suggestions may produce lower-quality results (e.g., matplotlib visualizations vs plotly interactivity)","No automatic library installation; users must pre-install alternatives or accept reduced functionality","Library compatibility matrix must be manually maintained; new libraries or versions require updates","Some code patterns are library-specific and cannot be easily adapted (e.g., TensorFlow-specific neural network code)"],"requires":["Python 3.8+","importlib or pkg_resources for library detection","Pre-installed alternative libraries for common use cases"],"input_types":["task description (string)","optional preferred libraries list (list of strings)","optional library constraints (dict with library names and version requirements)"],"output_types":["adapted code using available libraries (string)","library substitution report (mapping of preferred to used libraries)","compatibility warnings (if adapted code may have reduced functionality)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one LLM provider (OpenAI, DeepSeek, OpenRouter) OR local Ollama instance","Network connectivity for cloud-based LLM providers (optional if using Ollama locally)","API keys for cloud providers (OpenAI, DeepSeek, OpenRouter) OR Ollama running locally on port 11434","Network connectivity for cloud providers OR Docker/local Ollama setup for offline operation","Single-threaded execution model (no concurrent execution in same state)","Sufficient memory for accumulated state objects","Writable filesystem for log storage","Optional: log analysis tools (grep, jq, pandas) for external analysis","SQLite3 (included in Python standard library)"],"failure_modes":["Generated code quality depends on LLM model capability and prompt engineering; complex multi-step tasks may require iterative refinement","Execution sandbox isolation adds latency (~500ms-2s per execution cycle) compared to direct Python execution","No built-in type safety or static analysis of generated code before execution; runtime errors require feedback loops","Limited to Python ecosystem; cannot generate code for other languages or cross-language integrations","Provider API compatibility varies; some advanced features (streaming, function calling) may not be uniformly supported across all providers","Lazy-loading adds ~50-200ms overhead on first provider initialization per session","No built-in load balancing or automatic failover between providers; requires explicit configuration","Rate limiting and quota management must be handled per-provider; no unified rate limiting layer","State persistence is in-memory only; application restart clears all state","Large state objects (DataFrames, arrays) consume memory; no automatic garbage collection or state pruning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.17029143189704288,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:13.678Z","last_commit":"2025-09-10T01:11:08Z"},"community":{"stars":54,"forks":24,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=iniwap--aiforge","compare_url":"https://unfragile.ai/compare?artifact=iniwap--aiforge"}},"signature":"1zkns0wBosiL+x3x9VyJaFnNT21fck+/4F7Gu6PPNrlpWvsvfaujMHq7+uQW+OnfcQnfZ7Mr5m1+oqF8FAx0Cg==","signedAt":"2026-06-22T02:46:54.931Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/iniwap--aiforge","artifact":"https://unfragile.ai/iniwap--aiforge","verify":"https://unfragile.ai/api/v1/verify?slug=iniwap--aiforge","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"}}