{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_instill","slug":"instill","name":"Instill","type":"product","url":"https://www.instill.tech","page_url":"https://unfragile.ai/instill","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_instill__cap_0","uri":"capability://automation.workflow.visual.pipeline.builder.for.ai.workflows","name":"visual pipeline builder for ai workflows","description":"Drag-and-drop interface that constructs directed acyclic graphs (DAGs) representing multi-step AI pipelines without code. Users connect nodes representing data sources, transformations, model invocations, and outputs; the platform compiles these visual definitions into executable workflow specifications that handle data flow, error propagation, and conditional branching between steps.","intents":["I want to chain together multiple AI models and data sources without writing integration boilerplate","I need to visualize how data flows through my AI pipeline before deployment","I want to quickly prototype a multi-step workflow and iterate on it without redeploying code"],"best_for":["Solo developers and small teams building proof-of-concept AI applications","Non-technical product managers prototyping AI workflows","Startups avoiding cloud function infrastructure complexity"],"limitations":["Visual abstractions may obscure complex conditional logic or error handling patterns that are easier to express in code","DAG-based model limits cyclic dependencies and real-time streaming workflows","No version control integration for pipeline definitions — changes are tracked in platform only, not in Git"],"requires":["Web browser with modern JavaScript support","API keys for target AI model providers (OpenAI, Hugging Face, etc.)","Instill account with workspace access"],"input_types":["visual node configuration","JSON/YAML pipeline definitions (if importing)"],"output_types":["executable workflow specification","pipeline execution logs and traces"],"categories":["automation-workflow","visual-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_1","uri":"capability://tool.use.integration.multi.provider.ai.model.orchestration","name":"multi-provider ai model orchestration","description":"Native integration layer that abstracts over heterogeneous AI model APIs (OpenAI, Anthropic, Hugging Face, local models) through a unified interface. The platform translates pipeline-level model invocation requests into provider-specific API calls, handling authentication, request/response transformation, rate limiting, and fallback logic across different model families without requiring custom adapter code.","intents":["I want to swap between OpenAI and Anthropic models in my pipeline without rewriting integration code","I need to invoke both cloud-hosted and self-hosted models in the same workflow","I want to implement model fallback logic if one provider is unavailable or rate-limited"],"best_for":["Teams evaluating multiple model providers and wanting to avoid vendor lock-in","Developers building cost-optimized pipelines that route requests to cheaper models when appropriate","Organizations running hybrid cloud/on-premise AI infrastructure"],"limitations":["Abstraction layer adds latency (~50-200ms per model invocation) due to request transformation and routing logic","Provider-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking) may not be fully exposed through the unified interface","No built-in cost optimization or intelligent routing based on model performance metrics — all routing is manual or rule-based"],"requires":["API keys for at least one supported model provider","Network connectivity to external model APIs or self-hosted model endpoints","Understanding of model-specific input constraints (token limits, supported formats)"],"input_types":["text prompts","structured model parameters (temperature, max_tokens, etc.)","multi-modal inputs (text + images for vision models)"],"output_types":["model completions/responses","structured outputs (JSON if model supports it)","token usage and cost metadata"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_10","uri":"capability://safety.moderation.api.key.and.credential.management.with.encryption","name":"api key and credential management with encryption","description":"Centralized credential storage system that securely manages API keys, database passwords, and authentication tokens used by pipeline connectors and model providers. Credentials are encrypted at rest, rotated automatically, and accessed by pipelines through secure references rather than hardcoded values. Supports multiple authentication methods (API keys, OAuth, basic auth, custom headers).","intents":["I want to store my OpenAI API key securely without hardcoding it in my pipeline","I need to rotate database credentials without updating all my pipelines","I want to share pipelines with team members without exposing sensitive credentials"],"best_for":["Teams managing multiple credentials across pipelines and environments","Organizations with security policies requiring encrypted credential storage","Developers sharing pipelines without exposing sensitive information"],"limitations":["Credential rotation is manual — no automatic rotation based on expiration policies","No audit logging of credential access — users cannot see who accessed which credentials and when","Credential sharing is all-or-nothing — no fine-grained access control per credential","No support for temporary/short-lived credentials (e.g., STS tokens) — only static credentials"],"requires":["Instill workspace with credential management permissions","Valid credentials for target services"],"input_types":["credential values (API keys, passwords, tokens)","credential metadata (name, type, associated service)"],"output_types":["secure credential references for use in pipelines","credential list with metadata"],"categories":["safety-moderation","security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_11","uri":"capability://automation.workflow.pipeline.templates.and.marketplace","name":"pipeline templates and marketplace","description":"Pre-built pipeline templates for common use cases (sentiment analysis, document classification, data enrichment) that users can clone and customize. The platform provides a template marketplace where community members can share templates, with versioning and dependency tracking. Templates include documentation, example inputs/outputs, and configuration guides.","intents":["I want to quickly start building a sentiment analysis pipeline without designing it from scratch","I want to see how other users have solved similar problems","I want to share my pipeline design with the community"],"best_for":["Developers new to Instill looking for examples and starting points","Teams building common AI workflows (classification, summarization, etc.)","Community members contributing reusable pipeline designs"],"limitations":["Template marketplace is small compared to competitors like Make or Zapier — limited template availability","No quality assurance or vetting of community templates — users must evaluate template reliability themselves","Templates may become outdated if they depend on deprecated model APIs or connectors","No template versioning or dependency management — breaking changes in dependencies can break templates"],"requires":["Instill account to access and clone templates","Understanding of template configuration and customization"],"input_types":["template selection","customization parameters"],"output_types":["cloned pipeline ready for customization","template documentation and examples"],"categories":["automation-workflow","community"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_12","uri":"capability://automation.workflow.real.time.pipeline.monitoring.and.alerting","name":"real-time pipeline monitoring and alerting","description":"Monitoring dashboard that tracks pipeline health metrics (success rate, average latency, error rate) and enables users to configure alerts based on thresholds or anomalies. The platform collects metrics from all pipeline executions, aggregates them by time window, and sends notifications via email or webhooks when conditions are met. Supports custom metrics from pipeline steps.","intents":["I want to be notified immediately if my production pipeline starts failing","I need to track how long my pipelines take to execute and identify performance regressions","I want to set up alerts if my model inference latency exceeds a threshold"],"best_for":["Teams running production AI pipelines requiring uptime monitoring","Developers optimizing pipeline performance and tracking metrics over time","Operations teams managing multiple pipelines and needing centralized alerting"],"limitations":["Alerting is basic — only supports simple threshold-based rules, not complex anomaly detection","No integration with external monitoring platforms (Datadog, PagerDuty) — alerts are Instill-only","Metrics retention is limited on free tier — historical data may be purged after 30 days","No support for custom metrics from arbitrary pipeline steps — only built-in metrics are tracked"],"requires":["Instill workspace with monitoring permissions","Configuration of alert thresholds and notification channels"],"input_types":["alert configuration (metric, threshold, notification channel)","custom metrics from pipeline steps"],"output_types":["monitoring dashboard with metrics visualization","alert notifications (email, webhook)","historical metrics data"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_2","uri":"capability://data.processing.analysis.data.source.connector.library.with.schema.inference","name":"data source connector library with schema inference","description":"Pre-built connectors for common data sources (databases, APIs, cloud storage, data warehouses) that automatically infer schema and handle authentication. When a user connects a data source, the platform introspects the source to discover available tables/fields, generates type information, and exposes this metadata to downstream pipeline steps for validation and transformation planning.","intents":["I want to pull data from my PostgreSQL database into an AI pipeline without writing SQL queries manually","I need to understand what fields are available in my data source before building transformations","I want to connect to multiple data sources (S3, Snowflake, REST APIs) in a single pipeline without managing separate credentials"],"best_for":["Data engineers building ETL pipelines that feed AI models","Analysts connecting business databases to AI workflows without SQL expertise","Teams managing multiple data sources across cloud and on-premise systems"],"limitations":["Schema inference works well for structured sources (databases, CSVs) but struggles with semi-structured data (nested JSON, unstructured text)","Connector library is limited compared to enterprise platforms like Talend or Informatica — missing connectors for niche data sources","No built-in data quality checks or validation rules — relies on downstream transformations to catch data anomalies"],"requires":["Valid credentials for target data source (connection string, API key, OAuth token)","Network access to data source (firewall rules, VPN if on-premise)","Data source must support introspection/metadata queries (not all APIs expose schema information)"],"input_types":["connection credentials","query parameters (table name, SQL filter, API endpoint)"],"output_types":["structured data (rows/records)","schema metadata (field names, types, constraints)","data samples for preview"],"categories":["data-processing-analysis","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_3","uri":"capability://automation.workflow.pipeline.execution.and.monitoring.with.step.level.tracing","name":"pipeline execution and monitoring with step-level tracing","description":"Runtime execution engine that processes pipeline DAGs step-by-step, capturing detailed execution traces including input/output data, latency, errors, and model invocation details at each node. The platform provides a web-based dashboard showing real-time execution status, historical run logs, and performance metrics that enable debugging and optimization without accessing logs directly.","intents":["I want to see exactly what data was passed between each step in my pipeline when it fails","I need to identify which pipeline step is causing latency bottlenecks","I want to replay a failed pipeline run with the same inputs to debug issues"],"best_for":["Developers debugging complex multi-step AI workflows in production","Teams monitoring pipeline reliability and performance without DevOps infrastructure","Data scientists iterating on pipeline logic and needing visibility into intermediate results"],"limitations":["Trace storage is limited on free tier — historical logs may be purged after 30 days","No built-in alerting or anomaly detection — users must manually check dashboard for failures","Trace granularity is at the step level; fine-grained profiling within a single model invocation requires external tools","No integration with external monitoring platforms (Datadog, New Relic) — metrics are siloed in Instill"],"requires":["Instill workspace with pipeline execution permissions","Network connectivity to Instill platform (cloud-based monitoring)","Sufficient trace storage quota on account tier"],"input_types":["pipeline execution requests","input data for pipeline"],"output_types":["execution trace with step-level details","performance metrics (latency, token usage)","error logs and stack traces","intermediate step outputs"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_4","uri":"capability://data.processing.analysis.data.transformation.and.preprocessing.nodes","name":"data transformation and preprocessing nodes","description":"Built-in transformation operators (filtering, mapping, aggregation, type conversion, text processing) that can be inserted into pipelines to clean and reshape data between source and model invocation. These nodes support both visual configuration (for simple transformations) and code-based custom logic (for complex operations), with type validation ensuring data contracts between pipeline steps.","intents":["I want to filter and reshape data from my database before sending it to an AI model","I need to normalize text input (lowercase, remove special characters) before model inference","I want to aggregate results from multiple data sources into a single input for my AI pipeline"],"best_for":["Data engineers building ETL-like preprocessing steps without writing separate transformation code","Developers avoiding custom Python/JavaScript code for simple data cleaning operations","Teams standardizing data preparation logic across multiple pipelines"],"limitations":["Visual transformation builder is limited to common operations — complex statistical transformations or domain-specific logic require custom code nodes","No support for distributed transformations — all processing happens sequentially on single execution instance","Limited performance optimization for large datasets — transformations are not automatically parallelized or cached"],"requires":["Input data must be structured (JSON, CSV, database rows) — unstructured data requires custom parsing","Understanding of data types and schema for validation to work correctly"],"input_types":["structured data (JSON objects, arrays, database rows)","transformation configuration (visual or code-based)"],"output_types":["transformed structured data","type validation errors if schema mismatch detected"],"categories":["data-processing-analysis","transformation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_5","uri":"capability://automation.workflow.conditional.branching.and.error.handling.in.pipelines","name":"conditional branching and error handling in pipelines","description":"Control flow operators that enable pipelines to branch based on data conditions (if/else logic) and handle errors gracefully through retry policies, fallback steps, and error-specific routing. The platform evaluates conditions at runtime and directs execution to different pipeline paths, with support for timeout handling and dead-letter queues for failed executions.","intents":["I want my pipeline to take different actions based on the output of a previous step","I need to retry a failed API call with exponential backoff before giving up","I want to route failed pipeline executions to a separate error-handling workflow"],"best_for":["Developers building resilient production pipelines that handle failures gracefully","Teams implementing complex business logic that requires conditional routing","Systems requiring audit trails of failed executions for compliance"],"limitations":["Conditional logic is limited to simple comparisons and boolean operations — complex decision trees require multiple nested branches","Retry policies are global per step; no fine-grained control over retry behavior per error type","No built-in circuit breaker pattern — repeated failures don't automatically disable failing steps","Error context is limited to error message and type; no structured error metadata for programmatic handling"],"requires":["Understanding of pipeline execution model and error propagation","Configuration of retry policies and timeout thresholds"],"input_types":["condition expressions (comparisons, boolean logic)","retry configuration (max attempts, backoff strategy)","error routing rules"],"output_types":["execution path taken based on condition evaluation","retry attempt logs","error routing decisions"],"categories":["automation-workflow","control-flow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_6","uri":"capability://automation.workflow.pipeline.versioning.and.deployment.management","name":"pipeline versioning and deployment management","description":"Version control system for pipeline definitions that tracks changes, enables rollback to previous versions, and manages deployment across environments (dev, staging, production). The platform stores pipeline versions in its database and provides a UI for comparing versions, promoting pipelines between environments, and scheduling deployments with approval workflows.","intents":["I want to roll back my pipeline to a previous version if a recent change breaks production","I need to test pipeline changes in a staging environment before deploying to production","I want to track who made changes to my pipeline and when for audit purposes"],"best_for":["Teams managing multiple pipeline versions across environments","Organizations requiring audit trails and change approval workflows","Developers iterating on pipelines and needing safe rollback capabilities"],"limitations":["Pipeline definitions are versioned in Instill only — no Git integration for version control alongside code","Deployment approval workflows are basic; no integration with external approval systems (Slack, email)","No blue-green deployment or canary release strategies — deployments are all-or-nothing","Version history is limited on free tier — older versions may be purged"],"requires":["Instill workspace with pipeline management permissions","Understanding of environment configuration and deployment targets"],"input_types":["pipeline definition changes","deployment configuration (target environment, approval requirements)"],"output_types":["version history with change metadata","deployment status and logs","rollback confirmations"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_7","uri":"capability://tool.use.integration.webhook.triggered.pipeline.execution","name":"webhook-triggered pipeline execution","description":"HTTP endpoint generation that allows external systems to trigger pipeline execution via POST requests with JSON payloads. The platform creates unique webhook URLs for each pipeline, validates incoming requests, maps request body fields to pipeline input parameters, and returns execution results or status asynchronously. Supports authentication via API keys and request signing for security.","intents":["I want to trigger my AI pipeline from a web application or third-party service via HTTP","I need to receive pipeline results back in my application after execution completes","I want to secure my webhook endpoint so only authorized services can trigger it"],"best_for":["Developers integrating Instill pipelines into web applications or microservices","Teams building event-driven architectures that trigger AI workflows from external systems","Applications needing on-demand AI inference without managing infrastructure"],"limitations":["Webhook execution is synchronous by default — long-running pipelines may timeout if they exceed platform timeout limits (typically 30-300 seconds)","No built-in request queuing or rate limiting — high-volume webhook traffic may be throttled or rejected","Response payload is limited to pipeline output only — no metadata about execution time or resource usage in response","Webhook URLs are not customizable — users must use platform-generated URLs"],"requires":["Network connectivity from triggering system to Instill platform","API key for webhook authentication","Understanding of JSON request/response format"],"input_types":["JSON request body","URL path parameters","HTTP headers (for authentication)"],"output_types":["JSON response with pipeline output","HTTP status codes (200 for success, 4xx/5xx for errors)","execution metadata (optional)"],"categories":["tool-use-integration","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_8","uri":"capability://automation.workflow.batch.processing.and.scheduled.pipeline.execution","name":"batch processing and scheduled pipeline execution","description":"Scheduling system that enables pipelines to run on fixed schedules (cron-like expressions) or process large datasets in batches. The platform queues batch jobs, distributes execution across available workers, and provides progress tracking and result aggregation. Supports both time-based triggers (e.g., daily at 2 AM) and data-driven triggers (e.g., when new files appear in S3).","intents":["I want to run my data processing pipeline every night to update my database with AI-generated insights","I need to process a large CSV file by splitting it into batches and running my pipeline on each batch","I want to trigger my pipeline automatically when new data arrives in my cloud storage"],"best_for":["Data teams running nightly or periodic AI processing jobs","Systems processing large datasets that need to be split into manageable chunks","Event-driven architectures that respond to data arrival or system events"],"limitations":["Batch processing is not truly distributed — large batches are processed sequentially on single worker, not parallelized across multiple machines","Scheduling is limited to cron-like expressions — no support for complex scheduling logic (e.g., 'run if previous job succeeded')","Batch result aggregation is basic — no built-in support for merging results from multiple batch runs into a single output","No support for incremental processing — each batch run processes all data, not just new/changed data"],"requires":["Cron expression syntax knowledge for time-based scheduling","Configuration of batch size and processing parameters","Sufficient execution quota for scheduled runs"],"input_types":["schedule configuration (cron expression or trigger condition)","batch parameters (batch size, data source)"],"output_types":["batch execution logs","aggregated results from all batches","progress tracking and completion status"],"categories":["automation-workflow","scheduling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_instill__cap_9","uri":"capability://code.generation.editing.custom.code.nodes.with.sandboxed.execution","name":"custom code nodes with sandboxed execution","description":"Ability to insert custom JavaScript or Python code into pipelines that executes in a sandboxed runtime environment. The platform provides access to pipeline context (previous step outputs, input parameters) through language-specific SDKs, handles dependency management, and isolates code execution to prevent security issues. Custom nodes are treated as first-class pipeline steps with input/output validation.","intents":["I want to implement complex business logic that can't be expressed with visual transformation nodes","I need to call a custom Python library or external API that isn't available as a pre-built connector","I want to implement custom validation or data enrichment logic in my pipeline"],"best_for":["Developers comfortable with code who need flexibility beyond visual builders","Teams with custom business logic that requires programming","Systems integrating specialized libraries or domain-specific algorithms"],"limitations":["Sandboxed execution adds latency (~100-500ms per custom node) due to isolation overhead","Dependency management is limited — users must declare dependencies upfront; dynamic imports may not work","Execution timeout is enforced (typically 30 seconds) — long-running computations will be killed","No direct file system access — custom code cannot read/write files outside the sandbox","Debugging is limited to console logs — no interactive debugger or breakpoints"],"requires":["JavaScript (Node.js 14+) or Python 3.8+ knowledge","Understanding of pipeline context and data flow","Declaration of external dependencies (npm packages or PyPI packages)"],"input_types":["JavaScript or Python code","dependency declarations","pipeline context (previous outputs, input parameters)"],"output_types":["custom code execution results","console logs for debugging","errors and exceptions"],"categories":["code-generation-editing","extensibility"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","API keys for target AI model providers (OpenAI, Hugging Face, etc.)","Instill account with workspace access","API keys for at least one supported model provider","Network connectivity to external model APIs or self-hosted model endpoints","Understanding of model-specific input constraints (token limits, supported formats)","Instill workspace with credential management permissions","Valid credentials for target services","Instill account to access and clone templates","Understanding of template configuration and customization"],"failure_modes":["Visual abstractions may obscure complex conditional logic or error handling patterns that are easier to express in code","DAG-based model limits cyclic dependencies and real-time streaming workflows","No version control integration for pipeline definitions — changes are tracked in platform only, not in Git","Abstraction layer adds latency (~50-200ms per model invocation) due to request transformation and routing logic","Provider-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking) may not be fully exposed through the unified interface","No built-in cost optimization or intelligent routing based on model performance metrics — all routing is manual or rule-based","Credential rotation is manual — no automatic rotation based on expiration policies","No audit logging of credential access — users cannot see who accessed which credentials and when","Credential sharing is all-or-nothing — no fine-grained access control per credential","No support for temporary/short-lived credentials (e.g., STS tokens) — only static credentials","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:31.445Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=instill","compare_url":"https://unfragile.ai/compare?artifact=instill"}},"signature":"lKZLVHNQGVsaR8MXH90hSTNBMWX12SGew0uorQZWJuL7LHln4ToTRA+3KGwSVkgLjlKy2uIHG2QzoDqD1bIADA==","signedAt":"2026-06-22T22:02:04.444Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/instill","artifact":"https://unfragile.ai/instill","verify":"https://unfragile.ai/api/v1/verify?slug=instill","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"}}