{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-dotagent","slug":"dotagent","name":"dotagent","type":"agent","url":"https://github.com/dot-agent/dotagent","page_url":"https://unfragile.ai/dotagent","categories":["ai-agents","deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-dotagent__cap_0","uri":"capability://automation.workflow.cross.platform.agent.deployment.with.unified.runtime","name":"cross-platform agent deployment with unified runtime","description":"Deploys agentic applications to heterogeneous compute environments (cloud VMs, local PCs, mobile devices) through a unified agent runtime abstraction layer that handles platform-specific bootstrapping, dependency resolution, and lifecycle management. The system abstracts away OS-level differences (Linux containers, Windows executables, iOS/Android runtimes) behind a common deployment interface, enabling write-once-deploy-anywhere agent workflows.","intents":["Deploy the same agent codebase to AWS Lambda, a local development machine, and an iOS app without rewriting platform-specific code","Manage agent lifecycle (startup, shutdown, resource cleanup) consistently across cloud and edge devices","Run agents on resource-constrained mobile devices with automatic model quantization and runtime optimization"],"best_for":["Teams building multi-device agent applications (cloud + edge + mobile)","Developers wanting to avoid platform-specific deployment boilerplate","Organizations deploying agents to heterogeneous infrastructure (on-prem + cloud hybrid)"],"limitations":["Platform-specific features (iOS push notifications, Android background services) require custom integration code","Mobile deployment requires native app wrapper; cannot run arbitrary Python directly on iOS","Cross-platform dependency conflicts may require environment-specific pinning"],"requires":["Python 3.8+ for core runtime","Docker for cloud deployments (optional but recommended)","Platform SDKs: Xcode for iOS, Android Studio for Android","Cloud provider CLI tools (AWS CLI, gcloud, etc.) for cloud targets"],"input_types":["agent code (Python modules)","configuration files (YAML/JSON)","dependency manifests (requirements.txt, pyproject.toml)"],"output_types":["deployed agent instances","runtime logs and telemetry","health status and metrics"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_1","uri":"capability://automation.workflow.agent.configuration.and.environment.management","name":"agent configuration and environment management","description":"Manages agent configuration, environment variables, secrets, and runtime parameters through a declarative configuration system that supports environment-specific overrides and secure credential injection. The system separates configuration from code, enabling the same agent binary to run in development, staging, and production with different behaviors without recompilation.","intents":["Define agent behavior (model selection, API endpoints, tool configurations) in YAML without hardcoding","Inject API keys and secrets securely at deployment time without storing them in version control","Override configuration per environment (dev uses local LLM, prod uses cloud API) without code changes"],"best_for":["Teams managing agents across multiple environments (dev/staging/prod)","Developers wanting to separate configuration from agent logic","Security-conscious teams needing centralized secret management"],"limitations":["No built-in encryption at rest; secrets must be managed by external systems (AWS Secrets Manager, HashiCorp Vault)","Configuration hot-reloading not supported; agent restart required for changes","Limited validation of configuration schema; type errors may only surface at runtime"],"requires":["YAML or JSON configuration files","Environment variable support in deployment target","Access to secret management system (optional but recommended)"],"input_types":["YAML/JSON configuration files","environment variables","command-line arguments"],"output_types":["resolved configuration object","validated runtime parameters"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_10","uri":"capability://tool.use.integration.agent.plugin.and.extension.system","name":"agent plugin and extension system","description":"Enables extending agent functionality through plugins and extensions without modifying core agent code. The system provides a plugin interface for adding custom tools, integrations, and behaviors, with automatic plugin discovery, loading, and lifecycle management. Plugins can be loaded from local filesystem, package repositories, or remote sources.","intents":["Add a custom tool to an agent by writing a plugin, without modifying the agent's core code","Create a marketplace of agent plugins that users can install and compose","Enable third-party developers to extend agent functionality without access to source code"],"best_for":["Teams building extensible agent platforms or agent marketplaces","Organizations wanting to allow third-party customization of agents","Developers building modular agent architectures"],"limitations":["Plugin isolation is not enforced; malicious plugins can access agent internals or crash the agent","Plugin versioning and dependency management can be complex; no automatic conflict resolution","Plugin performance is not isolated; slow plugins can impact overall agent performance"],"requires":["Plugin interface definition (abstract base class, protocol)","Plugin loader and discovery mechanism","Package repository or filesystem for plugin distribution","Plugin lifecycle hooks (init, shutdown, etc.)"],"input_types":["plugin code","plugin configuration","plugin metadata"],"output_types":["loaded plugins","plugin registry","plugin execution results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_2","uri":"capability://automation.workflow.agent.lifecycle.and.process.management","name":"agent lifecycle and process management","description":"Manages agent process lifecycle including startup, graceful shutdown, resource cleanup, and health monitoring across different deployment targets. Implements process supervision patterns (restart on failure, resource limits, signal handling) that adapt to the underlying platform (systemd on Linux, launchd on macOS, Windows Services on Windows, background tasks on mobile).","intents":["Automatically restart an agent if it crashes, with exponential backoff to prevent restart loops","Gracefully shutdown an agent, allowing in-flight requests to complete before terminating","Monitor agent health and resource usage (CPU, memory) and alert if thresholds are exceeded"],"best_for":["Production deployments requiring high availability and automatic recovery","Teams running agents as background services on servers or devices","Developers building self-healing agent infrastructure"],"limitations":["Restart policies are basic (fixed backoff, max retries); no advanced orchestration like Kubernetes","Health checks are agent-defined; no automatic detection of hung processes without explicit heartbeat","Resource limits are enforced at process level; no fine-grained per-task resource allocation"],"requires":["Process supervision capability on target platform (systemd, launchd, Windows Services, or custom supervisor)","Agent must implement health check endpoint or heartbeat mechanism","Proper signal handling in agent code (SIGTERM, SIGINT)"],"input_types":["agent process configuration","health check parameters","restart policy settings"],"output_types":["process status (running/stopped/failed)","health metrics and logs","restart history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_3","uri":"capability://automation.workflow.agent.packaging.and.distribution","name":"agent packaging and distribution","description":"Packages agent code, dependencies, and configuration into distributable artifacts (Docker images, Python wheels, mobile app bundles) that can be deployed to target platforms. The system handles dependency resolution, transitive dependency conflicts, and platform-specific binary compilation (e.g., native extensions for different CPU architectures).","intents":["Create a Docker image containing an agent and all dependencies, ready to push to a container registry","Package an agent as a Python wheel for distribution via PyPI or private package repositories","Build a mobile app bundle containing an agent runtime and agent code for iOS/Android distribution"],"best_for":["Teams distributing agents to multiple users or deployment targets","Organizations with strict dependency management and security scanning requirements","Developers building agent marketplaces or plugin ecosystems"],"limitations":["No automatic dependency vulnerability scanning; requires integration with external tools (Snyk, Dependabot)","Binary compatibility issues may arise when packaging for multiple CPU architectures (ARM, x86)","Mobile app packaging requires native build tools and signing certificates; cannot be fully automated without manual setup"],"requires":["Docker for container packaging (optional)","Python packaging tools (setuptools, wheel, build)","Platform SDKs for mobile (Xcode, Android Studio)","Dependency lock files (poetry.lock, requirements.lock) for reproducible builds"],"input_types":["agent source code","dependency manifests (requirements.txt, pyproject.toml)","configuration files","build scripts"],"output_types":["Docker images","Python wheels/sdists","mobile app bundles (IPA, APK)","executable binaries"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_4","uri":"capability://tool.use.integration.agent.communication.and.rpc.interface","name":"agent communication and rpc interface","description":"Exposes agent functionality through a standardized RPC interface (HTTP, gRPC, or message queue) that allows external systems to invoke agent actions, query state, and receive responses. The system handles serialization/deserialization of complex types, request routing, and response formatting across different transport protocols.","intents":["Call agent functions from a web application via HTTP REST endpoints","Invoke agent actions from other agents via gRPC for low-latency inter-agent communication","Queue agent tasks asynchronously via message brokers (RabbitMQ, Kafka) for decoupled processing"],"best_for":["Teams building agent APIs for external consumption","Developers creating multi-agent systems with inter-agent communication","Organizations needing asynchronous agent task processing"],"limitations":["No built-in authentication/authorization; requires integration with external auth systems (OAuth, JWT)","Request/response serialization overhead adds latency; not suitable for ultra-low-latency scenarios","Message queue support requires external broker infrastructure; no in-process queue"],"requires":["HTTP server framework (FastAPI, Flask) for HTTP interface","gRPC libraries for gRPC support (optional)","Message broker (RabbitMQ, Kafka) for async messaging (optional)","Network connectivity between client and agent"],"input_types":["HTTP requests (JSON payload)","gRPC messages (protobuf)","message queue messages"],"output_types":["HTTP responses (JSON)","gRPC responses (protobuf)","message queue acknowledgments"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_5","uri":"capability://memory.knowledge.agent.state.persistence.and.recovery","name":"agent state persistence and recovery","description":"Persists agent state (conversation history, task progress, internal variables) to durable storage and enables recovery from crashes or restarts without losing context. The system abstracts storage backends (local filesystem, cloud object storage, databases) and handles serialization of complex state objects.","intents":["Save agent conversation history to a database so the agent can resume conversations after a restart","Persist long-running task progress so that if the agent crashes mid-task, it can resume from the last checkpoint","Backup agent state to cloud storage for disaster recovery and multi-region failover"],"best_for":["Long-running agents that must survive restarts without losing context","Conversational agents that need to maintain conversation history across sessions","Mission-critical agents requiring disaster recovery capabilities"],"limitations":["No built-in consistency guarantees; concurrent writes may cause data corruption without external locking","State serialization can be slow for large objects; may impact agent responsiveness","Storage backend must be explicitly configured; no automatic selection based on deployment target"],"requires":["Durable storage backend (filesystem, S3, database)","Serialization library (pickle, JSON, protobuf)","Database client libraries if using database backend (optional)"],"input_types":["agent state objects","checkpoint markers","recovery signals"],"output_types":["persisted state snapshots","recovery metadata","backup artifacts"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_6","uri":"capability://automation.workflow.agent.monitoring.and.observability","name":"agent monitoring and observability","description":"Collects agent metrics, logs, and traces to enable monitoring, debugging, and performance analysis. The system integrates with standard observability platforms (Prometheus, Datadog, ELK) and provides built-in instrumentation for common agent operations (tool calls, LLM API calls, state changes).","intents":["Monitor agent CPU/memory usage and alert if resource consumption exceeds thresholds","Trace agent execution flow to debug why an agent made a particular decision","Analyze agent performance metrics (latency, error rates, tool call success rates) to identify bottlenecks"],"best_for":["Teams running production agents requiring visibility into behavior and performance","Developers debugging complex agent logic and decision-making","Organizations with compliance requirements for audit logging"],"limitations":["Instrumentation adds overhead; high-frequency logging can impact agent performance","Observability platform integration requires external service setup; no built-in local storage","Sensitive data in logs (API responses, user inputs) must be manually redacted"],"requires":["Observability platform (Prometheus, Datadog, ELK, etc.)","Logging library (Python logging, structlog)","Metrics collection library (prometheus_client, etc.)","Network connectivity to observability backend"],"input_types":["agent execution events","performance metrics","log messages","trace spans"],"output_types":["metrics (counters, gauges, histograms)","logs (structured or unstructured)","traces (execution flow)","dashboards and alerts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_7","uri":"capability://automation.workflow.agent.resource.management.and.scaling","name":"agent resource management and scaling","description":"Manages computational resources (CPU, memory, GPU) allocated to agents and enables horizontal scaling (running multiple agent instances) with load balancing. The system adapts resource allocation based on agent workload and supports auto-scaling policies (scale up under high load, scale down when idle).","intents":["Allocate GPU resources to an agent that uses vision models, ensuring other agents don't starve for GPU memory","Run multiple instances of an agent behind a load balancer to handle increased request volume","Automatically scale the number of agent instances based on queue depth or request latency"],"best_for":["High-traffic agent deployments requiring horizontal scaling","Resource-constrained environments (mobile, edge) where careful resource allocation is critical","Teams running multiple agents on shared infrastructure"],"limitations":["Auto-scaling policies are basic; no advanced orchestration like Kubernetes HPA","GPU resource allocation requires manual configuration; no automatic GPU detection or allocation","Scaling decisions are reactive (based on current metrics); no predictive scaling"],"requires":["Load balancer (HAProxy, nginx, cloud provider LB)","Resource monitoring (cgroup limits on Linux, resource limits on cloud platforms)","Metrics collection for scaling decisions (CPU, memory, queue depth)"],"input_types":["resource allocation policies","scaling thresholds","agent workload metrics"],"output_types":["resource allocation decisions","scaling actions (add/remove instances)","load distribution"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_8","uri":"capability://automation.workflow.agent.versioning.and.rollback","name":"agent versioning and rollback","description":"Manages multiple versions of agent code and configuration, enabling safe deployments with automatic rollback on failure. The system tracks version history, supports canary deployments (gradually rolling out new versions to a subset of traffic), and enables instant rollback to previous versions if issues are detected.","intents":["Deploy a new agent version to 10% of traffic first, then gradually increase to 100% if metrics look good","Automatically rollback to the previous agent version if error rate exceeds a threshold after deployment","Compare behavior between two agent versions to validate that a new version doesn't regress performance"],"best_for":["Teams deploying agent updates frequently and wanting to minimize risk","Organizations with strict SLAs requiring rapid rollback capability","Developers validating agent changes before full rollout"],"limitations":["Canary deployment requires traffic splitting logic; not all load balancers support this natively","Rollback is code/config only; cannot rollback data migrations or schema changes","Version comparison requires baseline metrics from previous version; no automatic regression detection"],"requires":["Version control system (Git)","Artifact registry for storing agent versions","Load balancer supporting traffic splitting (optional but recommended)","Metrics collection for rollback decisions"],"input_types":["agent code changes","configuration updates","deployment policies"],"output_types":["versioned artifacts","deployment status","rollback decisions"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dotagent__cap_9","uri":"capability://automation.workflow.agent.testing.and.validation.framework","name":"agent testing and validation framework","description":"Provides tools for testing agent behavior, validating outputs, and detecting regressions. The system supports unit tests (testing individual agent functions), integration tests (testing agent interaction with external tools), and end-to-end tests (testing complete agent workflows). Includes assertion helpers for validating agent outputs (e.g., checking that responses are valid JSON, contain expected fields).","intents":["Write unit tests that verify an agent function returns the expected output for given inputs","Create integration tests that verify an agent can successfully call external tools (APIs, databases)","Build end-to-end tests that simulate user interactions and verify the agent produces correct results"],"best_for":["Teams building production agents requiring high confidence in correctness","Developers iterating on agent logic and wanting fast feedback loops","Organizations with quality assurance requirements"],"limitations":["Testing non-deterministic agent behavior (LLM outputs) is inherently difficult; requires mocking or fixed seeds","Integration tests require external service access; cannot run in isolated environments without mocks","No built-in performance benchmarking; requires external tools for latency/throughput testing"],"requires":["Testing framework (pytest, unittest)","Mocking library for external dependencies (unittest.mock, pytest-mock)","Test data and fixtures","Access to external services for integration tests (or mocks)"],"input_types":["test cases","test fixtures","mock configurations"],"output_types":["test results (pass/fail)","coverage reports","performance metrics"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ for core runtime","Docker for cloud deployments (optional but recommended)","Platform SDKs: Xcode for iOS, Android Studio for Android","Cloud provider CLI tools (AWS CLI, gcloud, etc.) for cloud targets","YAML or JSON configuration files","Environment variable support in deployment target","Access to secret management system (optional but recommended)","Plugin interface definition (abstract base class, protocol)","Plugin loader and discovery mechanism","Package repository or filesystem for plugin distribution"],"failure_modes":["Platform-specific features (iOS push notifications, Android background services) require custom integration code","Mobile deployment requires native app wrapper; cannot run arbitrary Python directly on iOS","Cross-platform dependency conflicts may require environment-specific pinning","No built-in encryption at rest; secrets must be managed by external systems (AWS Secrets Manager, HashiCorp Vault)","Configuration hot-reloading not supported; agent restart required for changes","Limited validation of configuration schema; type errors may only surface at runtime","Plugin isolation is not enforced; malicious plugins can access agent internals or crash the agent","Plugin versioning and dependency management can be complex; no automatic conflict resolution","Plugin performance is not isolated; slow plugins can impact overall agent performance","Restart policies are basic (fixed backoff, max retries); no advanced orchestration like Kubernetes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.038Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dotagent","compare_url":"https://unfragile.ai/compare?artifact=dotagent"}},"signature":"JQMFjDj4s9ZTG7gVhyutPe5axGd3OtEwtGVJDMJQ9VbgfmL3cRqkPEOxVgGkHZH393M8/BmcITvxJmNb2ahLDA==","signedAt":"2026-06-19T20:08:42.717Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dotagent","artifact":"https://unfragile.ai/dotagent","verify":"https://unfragile.ai/api/v1/verify?slug=dotagent","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"}}