{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-genia","slug":"genia","name":"GeniA","type":"agent","url":"https://github.com/genia-dev/GeniA","page_url":"https://unfragile.ai/genia","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-genia__cap_0","uri":"capability://planning.reasoning.llm.driven.agentic.task.execution.with.function.calling.orchestration","name":"llm-driven agentic task execution with function-calling orchestration","description":"GeniA implements a central Agent System that processes user requests by leveraging OpenAI's function-calling API to dynamically select and invoke tools from a registry. The agent maintains conversation context, decomposes complex tasks into subtasks, and iteratively executes tool calls based on LLM reasoning, enabling autonomous completion of platform engineering workflows without explicit step-by-step user direction.","intents":["I want an AI agent to autonomously execute multi-step deployment tasks in production without manual intervention between steps","I need the agent to reason about which tools to use and in what order based on the current state","I want to give high-level instructions and have the agent figure out the implementation details"],"best_for":["DevOps and SRE teams automating repetitive platform engineering workflows","Engineering teams wanting to reduce context-switching and cross-team dependencies","Organizations with complex multi-tool environments seeking unified orchestration"],"limitations":["Depends on OpenAI API availability and rate limits — no built-in fallback to alternative LLM providers in core agent loop","Agent reasoning quality bounded by LLM capabilities — complex multi-step tasks may require explicit prompting or skill engineering","No native persistence of agent state across sessions — conversation history must be managed externally for long-running workflows"],"requires":["Python 3.x","OpenAI API key with function-calling support (GPT-3.5-turbo or GPT-4)","Network access to OpenAI endpoints"],"input_types":["natural language user requests","conversation history with context","tool definitions and schemas"],"output_types":["tool invocation decisions with parameters","task execution results","natural language responses to user"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_1","uri":"capability://tool.use.integration.declarative.tool.system.with.multi.backend.implementation.support","name":"declarative tool system with multi-backend implementation support","description":"GeniA provides a Tool System where tools are defined declaratively in YAML/JSON configuration files (functions.json, tools.yaml) and can be implemented as Python functions, HTTP endpoints, OpenAPI interfaces, or reusable Skills. The LLM Function Repository validates tool schemas, manages instantiation, and abstracts away implementation details, allowing engineers to add new capabilities without modifying core agent code.","intents":["I want to add a new tool to GeniA without touching the agent logic or redeploying","I need to wrap existing APIs, scripts, or services as tools the agent can invoke","I want to reuse tool definitions across multiple deployment environments"],"best_for":["Platform teams building extensible automation platforms","Organizations with heterogeneous tool ecosystems (Kubernetes, Terraform, custom scripts, SaaS APIs)","Teams wanting to democratize tool integration without requiring agent framework expertise"],"limitations":["Tool schema validation is static — no runtime type checking or parameter validation beyond JSON schema","Error handling and retry logic must be implemented per-tool — no built-in circuit breakers or exponential backoff","OpenAPI tool support requires well-formed specs — malformed or incomplete specs may cause agent confusion"],"requires":["YAML or JSON configuration files","Python 3.x for Python function tools","HTTP endpoint accessibility for URL-based tools","Valid OpenAPI 3.0 spec for OpenAPI tools"],"input_types":["YAML/JSON tool definitions","Python function signatures","OpenAPI 3.0 specifications","HTTP endpoint URLs"],"output_types":["Validated tool schema","Tool invocation results","Error messages and logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_10","uri":"capability://automation.workflow.error.handling.and.recovery.with.execution.rollback","name":"error handling and recovery with execution rollback","description":"GeniA implements error handling and recovery mechanisms that allow tasks to fail gracefully and, in some cases, rollback to previous states. The system can catch tool execution errors, log them with context, and either retry with different parameters, invoke alternative tools, or escalate to human operators. Skills can include explicit rollback steps for destructive operations.","intents":["I want the agent to retry a failed deployment with different parameters instead of giving up","I need the agent to rollback a deployment if subsequent steps fail","I want clear error messages and logs when the agent cannot complete a task"],"best_for":["Teams running destructive operations (deployments, infrastructure changes) that require rollback capability","Organizations wanting to minimize manual intervention when agent tasks fail","SRE teams automating incident response with recovery procedures"],"limitations":["Rollback is skill-specific — not all operations support automatic rollback","Error recovery is heuristic-based — the agent may not always choose the best recovery strategy","Rollback may not be idempotent — repeated rollbacks could cause unintended state changes"],"requires":["Tools that support idempotent operations or explicit rollback","Skills with defined rollback steps","Ability to detect and log errors from tool invocations"],"input_types":["Tool execution results","Error messages and exceptions","Rollback parameters"],"output_types":["Error logs with context","Retry attempts with modified parameters","Rollback confirmations"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_11","uri":"capability://memory.knowledge.documentation.and.knowledge.base.integration.for.tool.discovery","name":"documentation and knowledge base integration for tool discovery","description":"GeniA includes a documentation system that helps the agent discover and understand available tools and skills. The system maintains tool descriptions, usage examples, and parameter documentation that the agent can reference when deciding which tools to invoke. This enables the agent to make informed decisions about tool selection without requiring explicit user guidance.","intents":["I want the agent to understand what tools are available and when to use them","I need the agent to know the correct parameters for complex tools without asking me","I want to maintain a knowledge base of best practices that the agent can reference"],"best_for":["Teams with large tool ecosystems wanting to help the agent navigate options","Organizations documenting operational procedures and best practices","Teams building internal developer platforms with many tools and services"],"limitations":["Documentation is static — no automatic updates when tools change","Agent tool selection is still heuristic — good documentation doesn't guarantee correct tool choice","Documentation must be manually maintained — no automatic sync with tool implementations"],"requires":["Tool documentation in supported formats (Markdown, docstrings, OpenAPI specs)","Knowledge base storage (file system, database, or vector store)","Integration between documentation and tool registry"],"input_types":["Tool descriptions and examples","Parameter documentation","Best practice guides"],"output_types":["Tool discovery results","Usage examples","Parameter recommendations"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_2","uri":"capability://automation.workflow.reusable.skill.composition.and.chaining","name":"reusable skill composition and chaining","description":"GeniA implements a Skills System that encapsulates multi-step workflows as reusable, composable units that can be invoked by the agent or chained together. Skills are defined declaratively and can combine multiple tools, conditional logic, and error handling, enabling teams to build higher-order abstractions (e.g., 'deploy-with-rollback', 'incident-response') that the agent can invoke as atomic operations.","intents":["I want to define a complex multi-step workflow once and reuse it across different contexts","I need to create domain-specific abstractions that hide tool complexity from the agent","I want to version and test workflows independently before the agent uses them"],"best_for":["Teams with standardized operational procedures (runbooks, playbooks)","Organizations building internal developer platforms with common workflows","SRE and DevOps teams automating incident response and deployment procedures"],"limitations":["Skill composition is linear/sequential — no native support for parallel execution or complex branching logic","Skills are opaque to the agent — the agent cannot introspect or modify skill behavior at runtime","No built-in skill versioning or rollback — managing skill evolution requires external version control"],"requires":["Skill definition format (YAML or Python classes)","Access to underlying tools that skills compose","Python 3.x for skill execution"],"input_types":["Skill definitions","Tool outputs from previous steps","User parameters"],"output_types":["Skill execution results","Intermediate step outputs","Error logs and execution traces"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_3","uri":"capability://automation.workflow.multi.interface.user.interaction.streamlit.slack.terminal","name":"multi-interface user interaction (streamlit, slack, terminal)","description":"GeniA provides three distinct user interfaces — a Streamlit web application, Slack integration, and terminal CLI — all backed by the same core agent and tool systems. Each interface handles user input, displays agent responses, and manages conversation state independently, allowing teams to interact with the same automation platform through their preferred communication channel without duplicating agent logic.","intents":["I want to trigger platform engineering tasks from Slack without leaving my chat application","I need a web dashboard to monitor and manage agent executions","I want to use GeniA from the command line for local development and testing"],"best_for":["Distributed teams using Slack as primary communication platform","Organizations wanting to expose automation through multiple channels","DevOps teams needing both interactive (web/CLI) and asynchronous (Slack) execution modes"],"limitations":["Slack interface has latency constraints — long-running tasks (>30s) may timeout without explicit async handling","Streamlit web app is single-threaded — concurrent user sessions may experience performance degradation","Terminal interface lacks rich formatting for complex outputs — structured data (tables, logs) may be difficult to parse"],"requires":["Python 3.x","Streamlit 1.0+ for web interface","Slack Bot Token and signing secret for Slack integration","Terminal/shell access for CLI"],"input_types":["Natural language text (all interfaces)","File uploads (Streamlit)","Slack message threads and reactions"],"output_types":["Formatted text responses","Execution logs and traces","Structured data tables","Slack message threads with results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_4","uri":"capability://memory.knowledge.conversation.context.management.and.state.persistence","name":"conversation context management and state persistence","description":"GeniA implements a Conversation Management system that maintains user context, conversation history, and execution state across multiple interactions. The system tracks previous tool invocations, their results, and user feedback, enabling the agent to make informed decisions based on accumulated context rather than treating each request in isolation.","intents":["I want the agent to remember previous deployment attempts and avoid repeating failed steps","I need to provide feedback on agent decisions and have it adjust behavior accordingly","I want to review the full execution history of a complex multi-step task"],"best_for":["Teams running long-running or iterative automation workflows","Organizations requiring audit trails and execution accountability","DevOps teams debugging agent behavior and optimizing tool usage"],"limitations":["Context window is bounded by LLM token limits — very long conversations may require summarization or truncation","No built-in persistence layer — conversation state is in-memory unless explicitly saved to external storage","Context management is manual — no automatic pruning or summarization of old conversation history"],"requires":["Python 3.x","External storage (database, file system) for persistence","Sufficient memory for conversation history in active sessions"],"input_types":["User messages","Tool execution results","User feedback and corrections"],"output_types":["Conversation history","Execution traces","Context summaries"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_5","uri":"capability://tool.use.integration.production.environment.integration.and.credential.management","name":"production environment integration and credential management","description":"GeniA integrates with production systems by managing credentials, API keys, and authentication tokens securely, allowing tools to access external services (Kubernetes, cloud providers, monitoring systems, etc.) without exposing secrets in code or configuration. The system abstracts credential handling so tools can be defined generically while credentials are injected at runtime based on environment and user context.","intents":["I want the agent to access production Kubernetes clusters without hardcoding credentials","I need to grant different agents or users different levels of access to production systems","I want to audit which credentials were used for each tool invocation"],"best_for":["Enterprise teams with strict credential management policies","Organizations running GeniA in production with multiple environments (dev, staging, prod)","Teams requiring audit trails for compliance (SOC2, HIPAA, etc.)"],"limitations":["Credential injection is environment-specific — no built-in multi-cloud credential abstraction","No native support for credential rotation — external systems must manage key lifecycle","Credential scope is coarse-grained — cannot easily restrict tool access to specific resources within a system"],"requires":["Secure credential storage (environment variables, secrets manager, or vault)","Authentication tokens or API keys for target systems","Network access to production systems from GeniA deployment"],"input_types":["Credential identifiers","User context (identity, permissions)","Tool invocation requests"],"output_types":["Authenticated tool invocations","Audit logs with credential usage","Access control decisions"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_6","uri":"capability://automation.workflow.domain.specific.task.automation.for.platform.engineering.workflows","name":"domain-specific task automation for platform engineering workflows","description":"GeniA provides pre-built capabilities and domain knowledge for common platform engineering tasks across R&D, DevOps, FinOps, SecOps, and SRE domains. The system includes tools and skills tailored to these domains (deployments, log analysis, infrastructure provisioning, cost optimization, security assessment, incident response) and can reason about domain-specific constraints and best practices.","intents":["I want the agent to understand deployment best practices and avoid common mistakes","I need automated log analysis and troubleshooting for production incidents","I want the agent to identify cost optimization opportunities in my infrastructure"],"best_for":["Platform engineering and DevOps teams automating domain-specific workflows","SRE teams building incident response automation","FinOps teams optimizing cloud resource costs"],"limitations":["Domain knowledge is encoded in prompts and skills — no machine learning or adaptive learning from execution history","Domain coverage is limited to pre-built capabilities — novel or organization-specific workflows require custom skill development","Best practices are static — no automatic updates when industry standards or tool versions change"],"requires":["Domain-specific tools and APIs (Kubernetes, Terraform, cloud SDKs, monitoring systems)","Pre-built skills for target domain","Access to production systems and logs"],"input_types":["High-level domain requests (e.g., 'deploy this service', 'analyze this incident')","System state and logs","Configuration files"],"output_types":["Executed domain tasks","Analysis results and recommendations","Deployment confirmations"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_7","uri":"capability://automation.workflow.extensible.configuration.system.with.environment.specific.settings","name":"extensible configuration system with environment-specific settings","description":"GeniA uses a Configuration Layer (settings.toml, prompts, tool definitions) that allows teams to customize agent behavior, tool availability, and prompts without modifying core code. The system supports environment-specific configurations, enabling the same codebase to run in different contexts (dev, staging, prod) with different tool sets, prompts, and constraints.","intents":["I want to customize the agent's behavior and prompts for my organization's specific needs","I need to enable different tools in different environments (e.g., destructive tools only in staging)","I want to version control my agent configuration alongside my infrastructure code"],"best_for":["Teams deploying GeniA across multiple environments","Organizations wanting to customize agent behavior without forking the codebase","Teams using infrastructure-as-code and wanting configuration parity"],"limitations":["Configuration is static — no runtime reconfiguration without redeployment","No built-in configuration validation — invalid settings may cause runtime errors","Configuration precedence is implicit — environment variable overrides and defaults may be confusing"],"requires":["TOML or YAML configuration files","Environment variables for sensitive settings","File system access for configuration files"],"input_types":["Configuration files (TOML, YAML)","Environment variables","Prompt templates"],"output_types":["Loaded configuration","Validated settings","Applied prompts and tool definitions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_8","uri":"capability://automation.workflow.observability.and.execution.monitoring.with.metrics.and.logs","name":"observability and execution monitoring with metrics and logs","description":"GeniA provides built-in observability through metrics collection, structured logging, and execution tracing. The system logs tool invocations, results, errors, and agent decisions, enabling teams to monitor agent behavior, debug failures, and optimize performance. Metrics track execution time, success rates, and tool usage patterns.","intents":["I want to understand why the agent made a particular decision or failed a task","I need to monitor agent performance and identify bottlenecks","I want to audit which tools were invoked and what they did"],"best_for":["Teams running GeniA in production and requiring operational visibility","Organizations with compliance requirements for execution auditing","DevOps teams optimizing agent performance and tool usage"],"limitations":["Logging is local by default — no built-in integration with centralized logging systems (ELK, Datadog, etc.)","Metrics are basic — no advanced analytics or anomaly detection","Log volume can be high for verbose execution traces — may require log rotation or filtering"],"requires":["Python 3.x logging infrastructure","File system or external logging service for log storage","Metrics collection system (optional, for advanced monitoring)"],"input_types":["Tool invocations and results","Agent decisions and reasoning","Error messages and exceptions"],"output_types":["Structured logs","Execution traces","Metrics and performance data"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-genia__cap_9","uri":"capability://tool.use.integration.extensibility.framework.for.custom.tools.and.skills","name":"extensibility framework for custom tools and skills","description":"GeniA provides clear patterns and documentation for extending the platform with custom tools and skills. The framework allows developers to add new capabilities by implementing tool functions, defining tool schemas, or composing existing tools into skills, with minimal changes to core agent code. The system includes examples and templates for common extension patterns.","intents":["I want to add a tool for my custom internal service without modifying GeniA core code","I need to create a skill that combines multiple tools for a specific workflow","I want to integrate GeniA with a new external service or API"],"best_for":["Teams building internal developer platforms on top of GeniA","Organizations with custom tools and services needing agent integration","Developers extending GeniA for domain-specific use cases"],"limitations":["Extension patterns are documented but not enforced — developers must follow conventions manually","No built-in testing framework for custom tools — teams must write their own tests","Custom tools are not automatically validated — malformed tools may cause agent failures"],"requires":["Python 3.x","Understanding of GeniA's tool and skill architecture","Access to GeniA source code or plugin directory"],"input_types":["Custom tool implementations (Python functions)","Tool schema definitions (JSON/YAML)","Skill compositions"],"output_types":["Registered tools","Validated tool schemas","Executable skills"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.x","OpenAI API key with function-calling support (GPT-3.5-turbo or GPT-4)","Network access to OpenAI endpoints","YAML or JSON configuration files","Python 3.x for Python function tools","HTTP endpoint accessibility for URL-based tools","Valid OpenAPI 3.0 spec for OpenAPI tools","Tools that support idempotent operations or explicit rollback","Skills with defined rollback steps","Ability to detect and log errors from tool invocations"],"failure_modes":["Depends on OpenAI API availability and rate limits — no built-in fallback to alternative LLM providers in core agent loop","Agent reasoning quality bounded by LLM capabilities — complex multi-step tasks may require explicit prompting or skill engineering","No native persistence of agent state across sessions — conversation history must be managed externally for long-running workflows","Tool schema validation is static — no runtime type checking or parameter validation beyond JSON schema","Error handling and retry logic must be implemented per-tool — no built-in circuit breakers or exponential backoff","OpenAPI tool support requires well-formed specs — malformed or incomplete specs may cause agent confusion","Rollback is skill-specific — not all operations support automatic rollback","Error recovery is heuristic-based — the agent may not always choose the best recovery strategy","Rollback may not be idempotent — repeated rollbacks could cause unintended state changes","Documentation is static — no automatic updates when tools change","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:20.440Z","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=genia","compare_url":"https://unfragile.ai/compare?artifact=genia"}},"signature":"ec2ytLw9bkKsFy31bmzLtRa59a7JVrLoJzeoxjcykADBkqVW4mM/QLmbu1UTxX9Mbr7ebWRq+ETspPYRPEzCAQ==","signedAt":"2026-06-16T03:52:36.371Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/genia","artifact":"https://unfragile.ai/genia","verify":"https://unfragile.ai/api/v1/verify?slug=genia","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"}}