{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-truffle-ai-dexto","slug":"mcp-truffle-ai-dexto","name":"dexto","type":"repo","url":"https://github.com/truffle-ai/dexto","page_url":"https://unfragile.ai/mcp-truffle-ai-dexto","categories":["ai-agents"],"tags":["ai","ai-agent","ai-agents","ai-tools","contributions-welcome","function-calling","llm","mcp","mcp-client","modelcontextprotocol","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-truffle-ai-dexto__cap_0","uri":"capability://automation.workflow.configuration.driven.agent.instantiation.with.yaml.based.system.prompts","name":"configuration-driven agent instantiation with yaml-based system prompts","description":"Dexto enables agents to be defined entirely through YAML configuration files without requiring code changes, leveraging a configuration enrichment system that merges agent-specific settings with global preferences and LLM provider registries. The system parses agent configuration files, resolves system prompts, and initializes the DextoAgent runtime with pre-configured behavior, tool bindings, and LLM parameters. This approach decouples agent definition from deployment, allowing non-technical users to modify agent behavior through configuration alone.","intents":["Define and deploy multiple agent variants without code changes","Configure system prompts, tool access, and LLM parameters declaratively","Enable rapid iteration on agent behavior through configuration updates","Share agent configurations across teams without requiring code review"],"best_for":["teams building multiple agent variants with shared infrastructure","organizations wanting configuration-as-code for agent governance","non-technical stakeholders managing agent behavior"],"limitations":["Complex conditional logic in prompts requires templating or external resolution","No built-in version control for configuration drift across deployments","YAML schema validation is basic — no runtime type checking for custom fields"],"requires":["YAML configuration files in agent registry directory","Valid LLM provider configuration in global preferences","API keys for configured LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["YAML configuration files","system prompt text","LLM provider settings"],"output_types":["initialized DextoAgent instance","resolved configuration object with merged preferences"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.runtime.switching.with.token.cost.tracking","name":"multi-provider llm runtime switching with token cost tracking","description":"Dexto implements a provider-agnostic LLM service layer that abstracts OpenAI, Anthropic, and other providers through a unified interface, enabling agents to switch models at runtime without code changes. The system tracks token consumption per request, aggregates costs across sessions, and supports custom model configurations with fallback chains. The LLM service resolves API keys from environment variables or Dexto API key provisioning, handles provider-specific request formatting (function calling schemas, reasoning effort parameters), and maintains a cost ledger for billing and analytics.","intents":["Switch between GPT-4, Claude, and other models without redeploying agents","Track token usage and costs per agent session for billing","Configure custom models with specific parameter overrides","Implement fallback chains when primary model is unavailable"],"best_for":["teams managing costs across multiple LLM providers","applications requiring model flexibility for A/B testing","organizations with provider-specific contracts or quotas"],"limitations":["Token counting is approximate for some providers — actual billing may differ","No built-in cost optimization (e.g., routing to cheaper models based on task complexity)","Custom model configuration requires manual schema mapping for function calling","Reasoning effort parameters only supported on Claude models, not OpenAI"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Environment variables or Dexto API key provisioning configured","Valid model identifier in LLM configuration (e.g., 'gpt-4-turbo', 'claude-3-opus')"],"input_types":["model identifier string","LLM configuration object","API key credentials"],"output_types":["LLM completion response","token usage metadata","cost tracking record"],"categories":["tool-use-integration","cost-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_10","uri":"capability://image.visual.multimodal.input.support.with.image.processing.and.vision.capabilities","name":"multimodal input support with image processing and vision capabilities","description":"Dexto supports multimodal inputs including text, images, and other media types, enabling agents to process visual information and generate responses based on image analysis. The system handles image encoding (base64, URLs), passes images to vision-capable LLM providers (GPT-4 Vision, Claude 3 with vision), and integrates image processing into the message pipeline. Agents can receive images as input, analyze them using LLM vision capabilities, and reference image content in subsequent messages.","intents":["Analyze images and screenshots with vision-capable LLMs","Process documents and PDFs through OCR and vision","Build agents that understand visual content","Support mixed text and image conversations"],"best_for":["applications requiring document analysis or OCR","agents processing screenshots or visual content","systems building multimodal AI experiences"],"limitations":["Image support depends on LLM provider — not all models support vision","Large images may exceed token limits — no automatic resizing","Image encoding is manual — no built-in format conversion","Vision capabilities vary by provider — no abstraction layer"],"requires":["Vision-capable LLM model (GPT-4 Vision, Claude 3, etc.)","Image in supported format (JPEG, PNG, GIF, WebP)","API key for vision-capable provider"],"input_types":["image file path","image URL","base64-encoded image","text with image references"],"output_types":["image analysis text","structured extraction from image","mixed text and image response"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_11","uri":"capability://automation.workflow.opentelemetry.integration.with.distributed.tracing.and.observability","name":"opentelemetry integration with distributed tracing and observability","description":"Dexto implements OpenTelemetry integration for distributed tracing and observability, emitting traces for agent execution, tool calls, and LLM requests. The system exports traces to OpenTelemetry-compatible backends (Jaeger, Datadog, etc.), enabling visualization of agent execution flow, performance bottlenecks, and error propagation across distributed systems. Traces include structured metadata about agent state, tool execution, token usage, and latency, providing deep visibility into agent behavior.","intents":["Trace agent execution across distributed systems","Identify performance bottlenecks in agent workflows","Monitor LLM request latency and token usage","Debug multi-agent systems with distributed tracing"],"best_for":["teams running agents in production with observability requirements","distributed systems requiring end-to-end tracing","applications needing performance monitoring and debugging"],"limitations":["OpenTelemetry export adds latency — requires async batching","Trace sampling must be configured — no automatic intelligent sampling","Trace storage requires external backend — no built-in persistence","Trace correlation across services requires manual context propagation"],"requires":["OpenTelemetry SDK configured","Trace exporter (Jaeger, Datadog, etc.) running and accessible","Proper environment variables for exporter configuration"],"input_types":["trace context","span attributes","event metadata"],"output_types":["trace spans","trace events","exported telemetry data"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_12","uri":"capability://planning.reasoning.reasoning.effort.configuration.with.advanced.llm.features","name":"reasoning effort configuration with advanced llm features","description":"Dexto supports advanced LLM features like reasoning effort parameters (available on Claude models) that enable agents to request extended thinking or higher reasoning levels for complex problems. The system exposes reasoning effort configuration through agent settings, passes parameters to compatible LLM providers, and tracks additional costs associated with extended reasoning. Agents can dynamically adjust reasoning effort based on task complexity, enabling cost-effective use of advanced reasoning capabilities.","intents":["Enable extended thinking for complex reasoning tasks","Configure reasoning effort levels per agent or per request","Track costs of advanced reasoning features","Dynamically adjust reasoning based on task complexity"],"best_for":["agents solving complex reasoning problems","applications requiring high-quality analysis","systems optimizing for reasoning quality vs cost"],"limitations":["Reasoning effort only supported on Claude models — not OpenAI","Extended reasoning increases latency significantly","Cost tracking for reasoning effort is approximate","No automatic reasoning level selection based on task"],"requires":["Claude 3 or later model with reasoning support","Reasoning effort parameter in agent configuration","API key for Anthropic"],"input_types":["reasoning effort level (low, medium, high)","task description"],"output_types":["reasoning output","final response","reasoning cost metadata"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_13","uri":"capability://safety.moderation.tool.confirmation.and.approval.workflow.with.user.interaction","name":"tool confirmation and approval workflow with user interaction","description":"Dexto implements a tool confirmation system where sensitive or high-risk tool operations require explicit user approval before execution. When an agent attempts to call a tool marked as requiring confirmation, the system pauses execution, emits a confirmation request event, and waits for user approval through the UI, CLI, or API. The approval workflow integrates with the message processing pipeline, allowing agents to continue execution after approval or handle rejection gracefully.","intents":["Require user approval for sensitive tool operations","Implement compliance workflows for regulated operations","Prevent accidental or malicious tool execution","Audit all tool operations that require approval"],"best_for":["applications with compliance or security requirements","systems where tool execution has real-world consequences","organizations requiring audit trails for sensitive operations"],"limitations":["Approval workflow is synchronous — blocks agent execution","No timeout for approval requests — requires manual intervention","Approval decisions are not persisted — no audit log by default","No role-based approval (e.g., different users for different tools)"],"requires":["Tool marked as requiring confirmation in configuration","User interface or API endpoint for approval submission","Approval handler in agent runtime"],"input_types":["tool invocation request","approval decision (approve/reject)","user identity"],"output_types":["approval request event","approval decision response","tool execution result or rejection"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_2","uri":"capability://automation.workflow.event.driven.agent.runtime.with.message.processing.pipeline","name":"event-driven agent runtime with message processing pipeline","description":"Dexto's DextoAgent runtime implements an event-driven architecture where agent execution flows through a message processing pipeline that handles LLM calls, tool invocations, and state transitions. The system emits typed events (agent-started, tool-called, message-received, error-occurred) that can be subscribed to for real-time monitoring, logging, and mid-loop injection. Messages flow through a queue system that supports insertion of new messages during execution, enabling dynamic prompt injection and error recovery without restarting the agent.","intents":["Monitor agent execution in real-time with typed event subscriptions","Inject new messages or prompts mid-execution for error recovery","Log and audit all agent actions and decisions for compliance","Build real-time dashboards showing agent state and tool execution"],"best_for":["teams building observability and monitoring systems for agents","applications requiring mid-execution intervention or error recovery","organizations with compliance requirements for agent action auditing"],"limitations":["Event subscription is in-process only — no built-in pub/sub for distributed systems","Message queue is in-memory — no persistence across agent restarts","Mid-loop injection can cause context bloat if overused without compaction","Event ordering guarantees only within a single agent instance"],"requires":["TypeScript SDK or client library to subscribe to events","Event handler functions with proper async/await handling","Message queue implementation (built-in to DextoAgent)"],"input_types":["event type identifier","event payload object","message object for queue injection"],"output_types":["typed event object","event handler callback results","modified message queue state"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_3","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.with.tool.discovery.and.execution","name":"model context protocol (mcp) server integration with tool discovery and execution","description":"Dexto implements native MCP server support, allowing agents to discover and execute tools from external MCP servers through a standardized protocol. The system maintains a tool registry that maps MCP tool definitions to executable functions, handles tool invocation with schema validation, and supports tool confirmation workflows where sensitive operations require user approval before execution. Tools are discovered dynamically from MCP servers, cached in the tool registry, and executed within the agent's message processing pipeline with full error handling and result streaming.","intents":["Integrate external tools from MCP servers without custom code","Discover available tools dynamically from MCP server definitions","Require user approval for sensitive tool operations","Execute tools with schema validation and error recovery"],"best_for":["teams building extensible agent platforms with plugin ecosystems","organizations using MCP-compatible tools (Claude, Cursor, etc.)","applications requiring tool approval workflows for compliance"],"limitations":["Tool discovery is one-time at agent startup — no hot-reloading of MCP servers","Tool confirmation workflow is synchronous — blocks agent execution until approval","No built-in tool result caching — repeated tool calls execute fresh each time","MCP server connection failures are not automatically retried"],"requires":["MCP server running and accessible (local or remote)","MCP server configuration in agent config file","Tool confirmation handler if approval workflow is enabled"],"input_types":["MCP server definition","tool schema object","tool invocation parameters"],"output_types":["discovered tool definitions","tool execution result","approval request/response"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_4","uri":"capability://memory.knowledge.stateful.session.management.with.conversation.history.and.context.compaction","name":"stateful session management with conversation history and context compaction","description":"Dexto manages agent sessions as stateful containers that persist conversation history, maintain message context, and implement automatic context compaction to prevent token overflow. Sessions track message history with search capabilities, support message operations (edit, delete, search), and implement a compaction strategy that summarizes old messages or removes low-relevance context when token limits approach. The session lifecycle includes initialization, active conversation, and cleanup phases, with persistent storage backends (file-based or custom) for recovery across restarts.","intents":["Maintain multi-turn conversations with full history across sessions","Search and retrieve specific messages from conversation history","Automatically compress context when approaching token limits","Recover agent state after crashes or restarts"],"best_for":["long-running agents with multi-turn conversations","applications requiring conversation history search and audit","systems managing context for cost-sensitive LLM interactions"],"limitations":["Context compaction is lossy — summarization may drop important details","Message search is linear scan — no indexing for large histories","Session persistence requires external storage configuration — no default","Compaction strategy is fixed — no pluggable compression algorithms"],"requires":["Storage backend configuration (file system or custom)","Session ID for tracking conversation state","Token limit configuration for compaction triggers"],"input_types":["message object","search query string","session configuration"],"output_types":["session state object","message history array","search results"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_5","uri":"capability://planning.reasoning.multi.agent.orchestration.with.sub.agent.delegation.and.parallel.execution","name":"multi-agent orchestration with sub-agent delegation and parallel execution","description":"Dexto supports multi-agent systems where parent agents can spawn and delegate tasks to sub-agents, with support for parallel execution of independent sub-agents and result aggregation. The system implements an agent registry that tracks available agents, handles agent instantiation with configuration inheritance, and manages inter-agent communication through a standardized protocol. Sub-agents execute in parallel when possible, with results collected and returned to the parent agent for further processing, enabling hierarchical task decomposition and specialization.","intents":["Decompose complex tasks across specialized sub-agents","Execute independent sub-agents in parallel for performance","Inherit configuration from parent agents to sub-agents","Aggregate results from multiple sub-agents for final decision-making"],"best_for":["teams building hierarchical agent systems for complex workflows","applications requiring task specialization across multiple agents","systems needing parallel execution for performance"],"limitations":["Sub-agent communication is synchronous — no async result streaming","No built-in load balancing across sub-agent instances","Configuration inheritance is shallow — deep merging not supported","Parallel execution is limited by available system resources"],"requires":["Agent registry with sub-agent definitions","Parent agent configuration with sub-agent references","Shared LLM provider configuration for sub-agents"],"input_types":["sub-agent identifier","task input for delegation","configuration overrides"],"output_types":["sub-agent execution results","aggregated result object","execution metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_6","uri":"capability://tool.use.integration.rest.api.server.with.server.sent.events.sse.streaming.and.a2a.protocol","name":"rest api server with server-sent events (sse) streaming and a2a protocol","description":"Dexto exposes agents through a REST API server that supports both request/response and streaming modes via Server-Sent Events (SSE), enabling real-time agent execution monitoring from web clients. The API implements standard REST endpoints for session management, message submission, and history retrieval, while SSE streaming provides real-time event delivery for agent state changes and tool execution. The A2A (Agent-to-Agent) protocol enables agents to communicate with other agents through HTTP, supporting distributed multi-agent systems and cross-service orchestration.","intents":["Expose agents as HTTP APIs for web and mobile clients","Stream agent execution events in real-time to web UIs","Enable inter-agent communication through HTTP","Build distributed multi-agent systems across services"],"best_for":["teams building web UIs for agent interaction","applications requiring real-time agent monitoring","distributed systems with agents across multiple services"],"limitations":["SSE streaming is one-directional — client cannot send mid-stream messages","A2A protocol requires agents to be HTTP-accessible — no NAT traversal","REST API has no built-in rate limiting — requires external gateway","Session state is in-memory — no distributed session store by default"],"requires":["Node.js 18+ for API server","HTTP client for consuming REST API","SSE-compatible client library for streaming"],"input_types":["HTTP request with JSON body","session ID","message text"],"output_types":["JSON response","SSE event stream","agent execution results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_7","uri":"capability://automation.workflow.web.ui.with.real.time.state.management.and.component.architecture","name":"web ui with real-time state management and component architecture","description":"Dexto includes a web UI built with modern component architecture and real-time state management that connects to the API server via SSE for live agent execution updates. The UI implements reactive components that update in real-time as agent state changes, tool execution progresses, and messages arrive. State management integrates with the SSE stream to keep UI in sync with backend agent execution, providing a dashboard view of agent status, conversation history, and tool invocation results.","intents":["Provide interactive web interface for agent interaction","Monitor agent execution in real-time with live updates","Display conversation history and tool execution results","Enable user approval workflows for sensitive tool operations"],"best_for":["teams building user-facing agent applications","applications requiring real-time monitoring dashboards","systems with tool approval workflows"],"limitations":["UI state is client-side only — no server-side state persistence","Real-time updates depend on SSE connection stability","No built-in authentication — requires external auth layer","Component library is custom — no integration with popular UI frameworks"],"requires":["Modern web browser with SSE support","API server running and accessible","WebSocket or SSE connection to API server"],"input_types":["user input text","tool approval decisions","session selection"],"output_types":["rendered HTML/CSS","real-time state updates","API requests"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_8","uri":"capability://automation.workflow.cli.interface.with.interactive.mode.and.command.based.agent.execution","name":"cli interface with interactive mode and command-based agent execution","description":"Dexto provides a command-line interface that supports both interactive mode for real-time agent conversation and command-based execution for scripting and automation. The CLI implements a setup wizard for initial configuration, command parsing for agent invocation, and formatted output for results and errors. Interactive mode maintains session state across multiple turns, supports readline for command history, and provides real-time feedback on agent execution including tool calls and token usage.","intents":["Run agents from command line for scripting and automation","Interactively chat with agents in terminal","Configure agents and LLM providers through setup wizard","View agent execution details including tool calls and costs"],"best_for":["developers testing agents locally","teams automating agent execution in CI/CD pipelines","users preferring terminal-based interfaces"],"limitations":["Interactive mode is single-threaded — cannot handle concurrent requests","Output formatting is text-based — no rich formatting for complex results","Session persistence requires manual configuration","No built-in command history across CLI restarts"],"requires":["Node.js 18+ with npm or yarn","Terminal with ANSI color support for interactive mode","API keys configured in environment or config file"],"input_types":["command-line arguments","user input text in interactive mode","configuration files"],"output_types":["formatted text output","JSON results","error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-truffle-ai-dexto__cap_9","uri":"capability://automation.workflow.background.task.execution.with.job.scheduling.and.parallel.processing","name":"background task execution with job scheduling and parallel processing","description":"Dexto supports background task execution where agents can spawn long-running jobs that execute asynchronously without blocking the main agent loop. The system implements a job scheduler that tracks task status, supports job cancellation and retry logic, and enables parallel execution of independent tasks. Background tasks integrate with the event system to emit status updates, allowing monitoring of job progress through event subscriptions or polling.","intents":["Execute long-running tasks without blocking agent interaction","Schedule recurring or delayed agent tasks","Monitor background job status and progress","Implement retry logic for failed tasks"],"best_for":["agents performing time-consuming operations (file processing, API calls)","systems requiring asynchronous task execution","applications needing job scheduling capabilities"],"limitations":["Job state is in-memory — no persistence across restarts","No distributed job queue — limited to single process","Retry logic is basic — no exponential backoff or jitter","Job cancellation is not graceful — may leave partial state"],"requires":["Job definition with task function","Event subscription for status monitoring","Proper error handling in task functions"],"input_types":["task function","task parameters","schedule configuration"],"output_types":["job ID","job status object","task result"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["YAML configuration files in agent registry directory","Valid LLM provider configuration in global preferences","API keys for configured LLM providers (OpenAI, Anthropic, etc.)","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Environment variables or Dexto API key provisioning configured","Valid model identifier in LLM configuration (e.g., 'gpt-4-turbo', 'claude-3-opus')","Vision-capable LLM model (GPT-4 Vision, Claude 3, etc.)","Image in supported format (JPEG, PNG, GIF, WebP)","API key for vision-capable provider","OpenTelemetry SDK configured"],"failure_modes":["Complex conditional logic in prompts requires templating or external resolution","No built-in version control for configuration drift across deployments","YAML schema validation is basic — no runtime type checking for custom fields","Token counting is approximate for some providers — actual billing may differ","No built-in cost optimization (e.g., routing to cheaper models based on task complexity)","Custom model configuration requires manual schema mapping for function calling","Reasoning effort parameters only supported on Claude models, not OpenAI","Image support depends on LLM provider — not all models support vision","Large images may exceed token limits — no automatic resizing","Image encoding is manual — no built-in format conversion","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3768718570484929,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-04-29T18:34:55Z"},"community":{"stars":613,"forks":70,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-truffle-ai-dexto","compare_url":"https://unfragile.ai/compare?artifact=mcp-truffle-ai-dexto"}},"signature":"MEEoe4pWYK/t4jenUb+/alRFEIaDDSB3tngXLLEMvMBkdAB1Pj9grI5Cbxi8xqjQQvfPjHuDIDRG4+tE1Q9oBA==","signedAt":"2026-06-20T11:52:00.656Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-truffle-ai-dexto","artifact":"https://unfragile.ai/mcp-truffle-ai-dexto","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-truffle-ai-dexto","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"}}