{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-kompas-ai","slug":"kompas-ai","name":"Kompas AI","type":"product","url":"https://kompas.ai/","page_url":"https://unfragile.ai/kompas-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-kompas-ai__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.model.selection.and.abstraction","name":"multi-provider llm model selection and abstraction","description":"Kompas AI provides a unified interface to select and swap between different LLM providers (OpenAI, Anthropic, local models, etc.) without rebuilding the agent logic. The platform abstracts provider-specific API differences through a standardized request/response schema, allowing developers to test multiple models against the same conversation context and compare outputs without code changes.","intents":["I want to test my agent with GPT-4, Claude, and Llama without rewriting integration code","I need to switch LLM providers mid-development based on cost or performance metrics","I want to compare model outputs on the same conversation to choose the best performer"],"best_for":["teams evaluating multiple LLM providers for production agents","developers prototyping agents and wanting flexibility to change models","cost-conscious builders wanting to compare pricing/performance tradeoffs"],"limitations":["Provider-specific features (vision, function calling schemas) may not be fully abstracted, requiring conditional logic","Latency differences between providers are not automatically optimized","Token counting and rate limiting vary by provider and must be managed separately"],"requires":["API keys for selected LLM providers (OpenAI, Anthropic, etc.)","Internet connectivity to reach provider APIs","Understanding of model-specific capabilities and limitations"],"input_types":["text prompts","conversation history","model configuration parameters"],"output_types":["text responses","structured metadata (tokens used, latency, provider)"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_1","uri":"capability://automation.workflow.conversational.agent.builder.with.visual.workflow.configuration","name":"conversational agent builder with visual workflow configuration","description":"Kompas AI offers a UI-driven agent builder that allows non-technical users to define agent behavior, conversation flows, and decision logic through visual components rather than code. The platform likely uses a node-based graph editor or form-based configuration to define agent instructions, system prompts, and conversation rules that are then compiled into executable agent logic.","intents":["I want to build a customer support agent without writing Python or JavaScript","I need to define conversation branches and conditional logic visually","I want to quickly iterate on agent behavior without deploying code"],"best_for":["non-technical product managers and business users building agents","rapid prototyping teams wanting fast iteration cycles","organizations with limited engineering resources"],"limitations":["Complex custom logic may require fallback to code or scripting","Visual workflows can become cluttered with many branches or conditions","Debugging visual configurations is harder than reading source code"],"requires":["Web browser with modern JavaScript support","Basic understanding of conversation design and agent behavior","Access to Kompas AI platform (account/login)"],"input_types":["visual node configurations","text instructions and prompts","conditional logic rules"],"output_types":["executable agent configuration","conversation flow diagrams","deployable agent artifacts"],"categories":["automation-workflow","agent-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_2","uri":"capability://memory.knowledge.agent.conversation.memory.and.context.management","name":"agent conversation memory and context management","description":"Kompas AI manages conversation history and context across multiple turns, maintaining state about user interactions, previous responses, and conversation context. The platform likely implements a context window management strategy that summarizes or truncates older messages to fit within LLM token limits while preserving semantic meaning through embeddings or abstractive summarization.","intents":["I want my agent to remember what the user said 10 messages ago","I need to keep conversation context within token limits without losing important information","I want to persist conversations across sessions and resume them later"],"best_for":["multi-turn conversational agents requiring long-term context","customer support bots needing to reference conversation history","agents handling complex workflows spanning many interactions"],"limitations":["Token limits still constrain maximum context length regardless of summarization","Summarization may lose nuanced details important for specific use cases","No explicit control over which messages are prioritized for retention"],"requires":["Conversation storage backend (likely provided by Kompas AI)","Session management and user identification","Sufficient token budget in LLM for context + new query"],"input_types":["user messages","agent responses","conversation metadata"],"output_types":["conversation history","summarized context","context window status"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_3","uri":"capability://tool.use.integration.tool.and.function.integration.with.schema.based.calling","name":"tool and function integration with schema-based calling","description":"Kompas AI enables agents to call external tools, APIs, and functions through a schema-based function calling mechanism. The platform likely maintains a registry of available tools with JSON schemas defining inputs/outputs, allowing the LLM to decide when and how to invoke them based on conversation context. Integration points may include REST APIs, webhooks, or native function bindings.","intents":["I want my agent to fetch data from my database when answering user questions","I need the agent to trigger actions like sending emails or creating tickets","I want to integrate my custom business logic into the agent's decision-making"],"best_for":["agents requiring access to external data sources or business systems","automation workflows that need to perform actions beyond conversation","teams integrating agents with existing API infrastructure"],"limitations":["Tool schemas must be precisely defined or LLM may misuse them","No built-in retry logic or error recovery for failed tool calls","Tool latency directly impacts agent response time"],"requires":["Tool endpoints accessible from Kompas AI platform (network connectivity)","JSON schema definitions for each tool","Authentication credentials for protected tools/APIs"],"input_types":["tool schema definitions","API endpoints","authentication tokens"],"output_types":["tool call results","structured data from external systems","execution status and errors"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_4","uri":"capability://automation.workflow.agent.deployment.and.hosting.with.conversation.endpoints","name":"agent deployment and hosting with conversation endpoints","description":"Kompas AI provides hosting and deployment infrastructure for agents, exposing them as conversation endpoints (likely REST APIs or WebSocket connections) that can be embedded in applications or accessed via chat interfaces. The platform handles scaling, request routing, and conversation session management without requiring developers to manage servers or containers.","intents":["I want to deploy my agent without managing infrastructure or servers","I need to expose my agent as an API that my web app can call","I want to embed a chat widget in my website that uses my agent"],"best_for":["teams without DevOps expertise wanting quick agent deployment","SaaS products needing to add conversational features","rapid prototyping and MVP validation"],"limitations":["Limited control over deployment regions or infrastructure specifics","Scaling behavior and rate limits are platform-determined","Vendor lock-in to Kompas AI hosting (no self-hosted option mentioned)"],"requires":["Kompas AI account with deployment permissions","Configured agent ready for deployment","Internet connectivity for agents to reach external services"],"input_types":["agent configuration","deployment settings"],"output_types":["API endpoint URLs","conversation session tokens","chat widget embed code"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_5","uri":"capability://planning.reasoning.agent.testing.and.conversation.simulation","name":"agent testing and conversation simulation","description":"Kompas AI includes built-in testing capabilities allowing developers to simulate conversations, test agent responses, and validate behavior before deployment. The platform likely provides conversation playback, test case management, and metrics collection to measure agent performance across different scenarios and LLM models.","intents":["I want to test my agent with predefined conversation scenarios","I need to compare how different LLM models respond to the same inputs","I want to measure agent accuracy and identify failure cases"],"best_for":["QA teams validating agent behavior before production","developers iterating on agent logic and comparing model outputs","teams measuring agent performance metrics"],"limitations":["Test coverage depends on manually created test cases","Simulated conversations may not capture real-world edge cases","No automated test generation from conversation logs"],"requires":["Configured agent with test scenarios","Access to testing interface in Kompas AI platform"],"input_types":["test conversation scripts","expected outputs","model configurations"],"output_types":["test results and pass/fail status","performance metrics","comparison reports"],"categories":["planning-reasoning","testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_6","uri":"capability://data.processing.analysis.agent.analytics.and.conversation.monitoring","name":"agent analytics and conversation monitoring","description":"Kompas AI collects and visualizes metrics about agent conversations including response quality, user satisfaction, common failure patterns, and usage statistics. The platform likely aggregates conversation logs, extracts insights through analysis, and provides dashboards for monitoring agent health and performance in production.","intents":["I want to see how many conversations my agent handles daily","I need to identify conversations where the agent failed or gave poor responses","I want to track user satisfaction and identify improvement areas"],"best_for":["product managers monitoring agent adoption and performance","teams optimizing agent behavior based on real usage patterns","organizations tracking agent ROI and business impact"],"limitations":["Analytics depend on conversation logging which may have privacy implications","Insights are retrospective and don't enable real-time intervention","Custom metrics require additional configuration or API access"],"requires":["Active conversations flowing through deployed agent","Access to analytics dashboard in Kompas AI platform","User consent for conversation logging (privacy/compliance)"],"input_types":["conversation logs","user feedback","agent responses"],"output_types":["usage dashboards","performance metrics","failure analysis reports"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-kompas-ai__cap_7","uri":"capability://text.generation.language.agent.customization.through.system.prompts.and.instructions","name":"agent customization through system prompts and instructions","description":"Kompas AI allows developers to customize agent behavior through system prompts, instructions, and personality definitions that shape how the LLM responds. The platform likely provides prompt templates, instruction builders, and preview capabilities to test how different prompts affect agent outputs before deployment.","intents":["I want to define my agent's personality and tone of voice","I need to add specific instructions about what the agent should and shouldn't do","I want to test different prompts to see which produces better responses"],"best_for":["teams fine-tuning agent behavior without model retraining","product teams defining brand voice and customer interaction style","developers experimenting with prompt engineering"],"limitations":["Prompt effectiveness varies significantly by LLM model","Complex instructions may conflict or confuse the model","No version control or rollback for prompt changes"],"requires":["Understanding of prompt engineering best practices","Access to agent configuration in Kompas AI platform"],"input_types":["text prompts","instruction definitions","personality parameters"],"output_types":["customized agent behavior","response previews"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["API keys for selected LLM providers (OpenAI, Anthropic, etc.)","Internet connectivity to reach provider APIs","Understanding of model-specific capabilities and limitations","Web browser with modern JavaScript support","Basic understanding of conversation design and agent behavior","Access to Kompas AI platform (account/login)","Conversation storage backend (likely provided by Kompas AI)","Session management and user identification","Sufficient token budget in LLM for context + new query","Tool endpoints accessible from Kompas AI platform (network connectivity)"],"failure_modes":["Provider-specific features (vision, function calling schemas) may not be fully abstracted, requiring conditional logic","Latency differences between providers are not automatically optimized","Token counting and rate limiting vary by provider and must be managed separately","Complex custom logic may require fallback to code or scripting","Visual workflows can become cluttered with many branches or conditions","Debugging visual configurations is harder than reading source code","Token limits still constrain maximum context length regardless of summarization","Summarization may lose nuanced details important for specific use cases","No explicit control over which messages are prioritized for retention","Tool schemas must be precisely defined or LLM may misuse them","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","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=kompas-ai","compare_url":"https://unfragile.ai/compare?artifact=kompas-ai"}},"signature":"s9U/6m5VWoMSmpQOH9ePYBZVrZW2fzAOPTJGp6jO9PinXYSAxDjRitSaGhH4OyyBof/eOJZ8/w9d3X2msqqbDA==","signedAt":"2026-06-20T20:01:13.234Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kompas-ai","artifact":"https://unfragile.ai/kompas-ai","verify":"https://unfragile.ai/api/v1/verify?slug=kompas-ai","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"}}