{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"keywords-ai","slug":"keywords-ai","name":"Keywords AI","type":"platform","url":"https://keywordsai.co","page_url":"https://unfragile.ai/keywords-ai","categories":["deployment-infra"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"$49/mo"},"status":"active","verified":false},"capabilities":[{"id":"keywords-ai__cap_0","uri":"capability://tool.use.integration.unified.llm.gateway.with.provider.abstraction","name":"unified-llm-gateway-with-provider-abstraction","description":"Routes requests to 500+ external LLM models (OpenAI, Anthropic, etc.) through a single API endpoint, abstracting provider-specific request/response formats and handling protocol translation. Implements request caching, automatic retries with exponential backoff, and fallback routing to alternative models when primary provider fails, reducing integration complexity from managing N provider SDKs to a single gateway interface.","intents":["I want to switch between LLM providers without rewriting my application code","I need automatic failover when my primary LLM provider is rate-limited or down","I want to cache identical requests across different models to reduce API costs","I need to route requests to different models based on latency, cost, or availability without application-level logic"],"best_for":["teams building multi-provider LLM applications","developers wanting to reduce vendor lock-in to single LLM provider","production systems requiring high availability and automatic failover"],"limitations":["Gateway throughput capped by tier: Pro=412 req/min, Team=8,400 req/min, Enterprise=custom","Request caching only applies to identical inputs; no semantic caching or embedding-based deduplication","Fallback routing requires manual configuration; no intelligent routing based on model capability matching","Latency overhead from gateway layer not quantified in documentation"],"requires":["API key for at least one supported LLM provider (OpenAI, Anthropic, etc.)","Respan account (free tier available)","Network access to Respan gateway endpoints"],"input_types":["JSON request bodies (OpenAI Chat Completions format)","Custom metadata and tags for request tracking"],"output_types":["JSON responses (normalized across providers)","Streaming responses (SSE format)","Structured trace data with latency and cost metrics"],"categories":["tool-use-integration","api-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_1","uri":"capability://memory.knowledge.versioned.prompt.management.with.deployment","name":"versioned-prompt-management-with-deployment","description":"Stores, versions, and deploys prompts through a web IDE with git-like version control, enabling teams to track prompt changes, rollback to previous versions, and deploy new prompts to production through the gateway without code changes. Integrates with the unified gateway to serve deployed prompt versions at inference time, supporting A/B testing by routing traffic to different prompt versions.","intents":["I want to iterate on prompts without redeploying my application code","I need to track who changed what in my prompts and when, with rollback capability","I want to A/B test different prompt versions in production and measure their impact on quality metrics","I need to manage prompt versions across development, staging, and production environments"],"best_for":["teams with non-technical prompt engineers who need UI-based editing","organizations running frequent prompt experiments and iterations","teams wanting to decouple prompt changes from application deployment cycles"],"limitations":["Prompt versioning tied to Respan platform; no native git integration for version control","No collaborative real-time editing; concurrent edits not supported","A/B testing requires manual traffic split configuration; no automatic winner selection","Prompt deployment latency not documented; unclear if changes propagate instantly or with delay"],"requires":["Respan account with Team tier or higher for A/B testing features","Web browser access to Respan UI","At least one LLM provider API key configured"],"input_types":["Plain text prompts","Prompt templates with variable placeholders","System messages and user messages"],"output_types":["Versioned prompt artifacts","Deployment metadata (version ID, timestamp, deployed-by user)","A/B test traffic split configuration"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_10","uri":"capability://planning.reasoning.a.b.testing.framework.with.traffic.splitting","name":"a-b-testing-framework-with-traffic-splitting","description":"Enables A/B testing by deploying multiple prompt or model versions and routing traffic to each variant based on configurable split percentages (e.g., 50% to variant A, 50% to variant B). Automatically collects metrics for each variant (latency, cost, quality) and provides statistical comparison dashboards to determine which variant performs better. Supports gradual rollout (canary deployment) by starting with small traffic percentages and increasing based on performance.","intents":["I want to test a new prompt version on 10% of traffic before rolling it out to everyone","I need to compare quality metrics between two model versions to decide which to use","I want to run an A/B test and have Respan automatically tell me which variant is statistically better","I need to gradually increase traffic to a new variant as I gain confidence in its performance"],"best_for":["teams running frequent prompt/model experiments with quantitative success criteria","organizations wanting to reduce risk of deploying new variants by testing on subset of traffic","teams with statistical expertise to interpret A/B test results"],"limitations":["Statistical significance testing not documented; unclear if Respan calculates p-values or confidence intervals","Traffic splitting configuration UI not documented; unclear if percentages can be adjusted in real-time","Variant assignment strategy not specified; unclear if deterministic (same user always sees same variant) or random","A/B test duration and sample size recommendations not provided; teams must manually determine when test is conclusive"],"requires":["Respan account (Team tier minimum)","Multiple prompt or model versions deployed","Sufficient traffic volume to achieve statistical significance"],"input_types":["Variant configuration (prompt/model version, traffic percentage)","Quality metrics to compare (latency, cost, custom evaluation scores)"],"output_types":["Per-variant metrics dashboards","Statistical comparison reports","Variant assignment logs (for reproducibility)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_11","uri":"capability://safety.moderation.team.collaboration.with.role.based.access.control","name":"team-collaboration-with-role-based-access-control","description":"Supports multiple team members with role-based access control (RBAC), enabling organizations to grant different permissions to engineers, product managers, and finance teams. Tracks who made changes to prompts, deployments, and alert configurations with audit logs, and supports team-scoped dashboards and alerts. Integrates with Google SSO for authentication (Pro/Team tiers) with SAML support on Enterprise tier.","intents":["I want to give my product manager read-only access to dashboards without exposing API keys","I need to track who deployed which prompt version and when for compliance and debugging","I want to restrict cost alerts to the finance team and quality alerts to the ML team","I need to manage access for contractors or external teams without sharing credentials"],"best_for":["organizations with multiple teams (engineering, product, finance) needing different access levels","companies with compliance requirements (SOC 2, HIPAA) needing audit trails","teams using enterprise SSO (SAML) for identity management"],"limitations":["RBAC roles not enumerated; unclear what specific permissions are available (read, write, delete, etc.)","Google SSO only on Pro/Team tiers; SAML requires Enterprise tier, limiting mid-market adoption","Audit log retention not documented; unclear how long change history is retained","Team-scoped resource isolation not specified; unclear if teams can see each other's prompts/dashboards"],"requires":["Respan account (Pro tier minimum for RBAC, Enterprise for SAML)","Google account or SAML identity provider (Enterprise only)","Team member email addresses"],"input_types":["Team member email and role assignment","SAML configuration (Enterprise only)"],"output_types":["Audit logs with user, action, resource, and timestamp","Team-scoped dashboards and alerts","Access control policies"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_12","uri":"capability://automation.workflow.latency.optimization.with.request.caching","name":"latency-optimization-with-request-caching","description":"Caches identical LLM requests at the gateway level and returns cached responses without calling the LLM provider, reducing latency and cost for repeated queries. Supports cache invalidation strategies (TTL, manual) and provides cache hit/miss metrics on dashboards. Works transparently for requests routed through the Respan gateway without application-level changes.","intents":["I want to reduce latency for frequently-asked questions by caching responses","I need to reduce LLM API costs by avoiding duplicate requests for the same query","I want to see cache hit rates to understand how much latency/cost savings we're getting","I need to invalidate cached responses when my knowledge base or prompts change"],"best_for":["applications with high query repetition (FAQ bots, documentation assistants)","cost-sensitive workloads where reducing API calls is critical","teams wanting to improve latency without changing application code"],"limitations":["Cache key generation strategy not documented; unclear if based on exact match or semantic similarity","Cache invalidation options not fully specified; unclear what TTL options are available","Cache storage limits not documented; unclear if there are size limits or eviction policies","Cache effectiveness metrics not documented; unclear if cache hit rate is available per-model or per-prompt"],"requires":["Respan account (Pro tier minimum)","Requests routed through Respan gateway"],"input_types":["LLM requests (automatically cached)","Cache invalidation configuration (TTL, manual triggers)"],"output_types":["Cached responses (identical to non-cached responses)","Cache hit/miss metrics and dashboards"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_13","uri":"capability://automation.workflow.self.hosted.deployment.for.enterprise.data.residency","name":"self-hosted-deployment-for-enterprise-data-residency","description":"Offers self-hosted deployment option for Enterprise tier customers, allowing Keywords AI infrastructure to run on customer's own servers or cloud account. Enables data residency compliance (e.g., data must stay in EU for GDPR). Self-hosted deployment includes all Keywords AI features (gateway, tracing, evaluation, dashboards). Requires customer to manage infrastructure, updates, and security patches. Specific deployment options (Kubernetes, Docker, VMs) not documented.","intents":["I need to comply with data residency requirements (GDPR, HIPAA)","I want to keep all LLM traces and metrics on my own infrastructure","I need to integrate Keywords AI with my existing deployment infrastructure","I want to avoid vendor lock-in by running Keywords AI on my own servers"],"best_for":["Enterprise customers with data residency requirements","organizations with strict data governance policies","teams with existing infrastructure and DevOps expertise"],"limitations":["Self-hosted deployment available only on Enterprise tier (custom pricing)","Deployment options not documented — unclear if Kubernetes, Docker, or VMs are supported","Infrastructure requirements not specified — unclear CPU, memory, storage needs","Update and maintenance process not documented — unclear how patches are deployed","Support model for self-hosted not documented — unclear if SLA applies"],"requires":["Enterprise tier Keywords AI contract","Infrastructure to host Keywords AI (cloud account or on-premises servers)","DevOps expertise to manage deployment and updates","Network connectivity between application and self-hosted Keywords AI"],"input_types":["Infrastructure configuration (cloud provider, region, sizing)","LLM provider credentials for gateway"],"output_types":["Self-hosted Keywords AI deployment","API endpoint for gateway and tracing","Deployment documentation and support"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_14","uri":"capability://safety.moderation.saml.authentication.for.enterprise.access.control","name":"saml-authentication-for-enterprise-access-control","description":"Supports SAML 2.0 authentication for Enterprise tier customers, enabling integration with corporate identity providers (Okta, Azure AD, etc.). Allows centralized user management and access control through existing identity infrastructure. Supports role-based access control (RBAC) and single sign-on (SSO). SAML is available only on Enterprise tier; Pro/Team tiers use Google OAuth.","intents":["I want to integrate Keywords AI with our corporate identity provider (Okta, Azure AD)","I need to enforce single sign-on (SSO) for all Keywords AI users","I want to manage Keywords AI access through our existing identity infrastructure","I need to implement role-based access control for Keywords AI features"],"best_for":["Enterprise organizations with existing SAML identity providers","teams requiring centralized access control and SSO","organizations with strict authentication policies"],"limitations":["SAML authentication available only on Enterprise tier (custom pricing)","RBAC implementation not documented — unclear what roles are supported","SAML configuration process not documented — unclear setup complexity","No SCIM provisioning mentioned — user management may be manual","No multi-factor authentication (MFA) mentioned"],"requires":["Enterprise tier Keywords AI contract","SAML 2.0 identity provider (Okta, Azure AD, etc.)","SAML metadata exchange with Keywords AI"],"input_types":["SAML identity provider configuration","User roles and access policies"],"output_types":["SAML authentication tokens","Access control enforcement","Audit logs of authentication events"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_2","uri":"capability://data.processing.analysis.end.to.end.execution.tracing.with.rich.context","name":"end-to-end-execution-tracing-with-rich-context","description":"Captures complete execution traces from production LLM calls including request/response content, latency, token counts, cost, and custom metadata, storing traces in a searchable index with 7-30 day retention. Enables filtering and searching by content keywords, latency ranges, cost thresholds, quality tags, and custom properties, with trace replay functionality allowing developers to re-run requests through the playground for debugging.","intents":["I need to understand why a specific user's LLM request failed or produced poor output","I want to find all requests that exceeded my latency or cost budget in the last 24 hours","I need to debug a production issue by replaying a request with the exact same inputs and model version","I want to analyze patterns in failed requests to identify systematic prompt or model issues"],"best_for":["production LLM applications requiring post-incident debugging","teams analyzing LLM behavior patterns and failure modes","developers optimizing prompts based on real production data"],"limitations":["Trace retention limited by tier: Pro=7 days, Team=30 days; Enterprise=custom","Search/filter capabilities not fully documented; unclear if full-text search or keyword-only","Trace replay does not capture external state changes; replayed requests may produce different outputs if dependencies changed","No automatic trace sampling; all requests traced, which may impact performance at high throughput"],"requires":["Respan account (free tier available with 100k logs/month)","Integration with Respan gateway (automatic for requests routed through gateway)","Sufficient log quota; overage costs $8 per 100k logs"],"input_types":["LLM request/response pairs (captured automatically)","Custom metadata tags and properties (user-provided)"],"output_types":["Searchable trace index with latency, cost, and quality metrics","Trace replay data for debugging","Batch export in JSONL or CSV format"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_3","uri":"capability://data.processing.analysis.multi.judge.evaluation.framework.with.datasets","name":"multi-judge-evaluation-framework-with-datasets","description":"Evaluates LLM outputs using three judge types—code-based (custom Python functions), human review (manual annotation), and LLM-as-judge (using another LLM to score outputs)—against versioned evaluation datasets. Stores evaluation scores in a queryable database, enabling teams to track quality metrics over time, compare model/prompt versions, and identify regressions. Supports custom evaluation metrics and integrates with dashboards for visualization.","intents":["I want to measure whether my new prompt version produces better outputs than the old version using multiple evaluation criteria","I need to create a test dataset and run automated evaluations against it before deploying a new model","I want to have humans review a sample of outputs and track their scores alongside automated metrics","I need to track quality regressions over time as I iterate on prompts and models"],"best_for":["teams running frequent prompt/model experiments with quantitative quality gates","organizations with quality assurance workflows requiring human review","developers building evaluation pipelines for LLM applications"],"limitations":["LLM-as-judge evaluators limited by tier: Pro=2 evaluators, Team+=unlimited; each evaluation incurs additional cost ($1 per 1k scores)","Human review requires manual annotation; no built-in workflow for distributing reviews or managing annotator disagreement","Code-based evaluators require Python knowledge; no visual/low-code evaluation builder","Evaluation latency not documented; unclear if evaluations run synchronously or asynchronously"],"requires":["Respan account (Pro tier minimum)","Evaluation dataset in JSONL format with input/expected-output pairs","For LLM-as-judge: additional LLM provider API key","For code-based: Python 3.x runtime (execution environment not specified)"],"input_types":["Evaluation datasets (JSONL format with input/output pairs)","Python code for custom evaluation functions","LLM prompt templates for LLM-as-judge evaluators"],"output_types":["Evaluation scores (numeric or categorical)","Aggregated metrics (pass rate, average score, etc.)","Comparison reports between model/prompt versions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_4","uri":"capability://automation.workflow.real.time.alerting.with.production.signal.triggers","name":"real-time-alerting-with-production-signal-triggers","description":"Monitors production LLM metrics (latency, cost, quality, error rate) in real-time and triggers alerts via Slack, email, or SMS when thresholds are breached. Supports conditional alerting based on custom properties (e.g., alert only for requests from specific users or with specific tags) and can trigger automated workflows or webhooks in response to production signals, enabling teams to respond to issues without manual monitoring.","intents":["I want to be notified immediately if my LLM API latency exceeds 5 seconds or cost per request spikes","I need to trigger an automated action (e.g., switch to fallback model) when quality scores drop below a threshold","I want to alert only on issues affecting specific user segments or request types, not all traffic","I need to integrate production signals into my incident response workflow (e.g., create PagerDuty incident)"],"best_for":["production LLM applications with SLA requirements","teams running cost-sensitive LLM workloads needing budget alerts","organizations with on-call rotations requiring real-time notifications"],"limitations":["Alert trigger types not fully documented; unclear what metrics beyond latency/cost/quality are supported","Conditional alerting based on custom properties requires manual configuration; no rule builder UI documented","Webhook-triggered automations require external infrastructure; no built-in workflow automation engine","Alert fatigue risk if thresholds not tuned carefully; no intelligent alert deduplication or grouping mentioned"],"requires":["Respan account (Team tier minimum for advanced alerting)","Slack workspace or email/SMS configured for notifications","For webhook automations: external service with HTTP endpoint"],"input_types":["Alert threshold configuration (latency, cost, quality metrics)","Custom property filters (user ID, request tags, etc.)","Webhook URLs for automated responses"],"output_types":["Slack messages, emails, or SMS notifications","Webhook POST requests with alert context","Alert history and acknowledgment logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_5","uri":"capability://data.processing.analysis.customizable.observability.dashboards.with.80.graph.types","name":"customizable-observability-dashboards-with-80-graph-types","description":"Provides a visual dashboard builder with 80+ pre-built graph types for tracking quality, latency, cost, and behavior metrics across LLM requests. Supports custom properties and dimensions, enabling teams to slice metrics by model, prompt version, user segment, or any custom tag. Dashboards update in real-time as new requests are processed, and can be shared across teams for collaborative monitoring.","intents":["I want to visualize how latency and cost have trended over the last week across different models","I need to compare quality metrics between my A/B test variants in real-time","I want to create a dashboard showing LLM performance by user segment or geographic region","I need to share production metrics with non-technical stakeholders (product managers, executives)"],"best_for":["teams with diverse stakeholders (engineers, product, finance) needing different metric views","organizations running A/B tests and needing real-time performance comparison","teams wanting to avoid custom dashboard development (Grafana, Datadog, etc.)"],"limitations":["Graph types not enumerated; unclear which specific visualizations are available (time series, heatmaps, distributions, etc.)","Dashboard persistence and sharing model not documented; unclear if dashboards are team-scoped or organization-scoped","Custom properties support mentioned but query language/filtering syntax not specified","Real-time update latency not documented; unclear if dashboards refresh every second or every minute"],"requires":["Respan account (Pro tier minimum)","Production traces flowing through Respan gateway","Web browser for dashboard access"],"input_types":["Trace data with metrics (latency, cost, quality scores)","Custom properties and tags from requests","Dashboard configuration (selected metrics, time range, dimensions)"],"output_types":["Interactive visualizations (charts, graphs, tables)","Shareable dashboard URLs","Exported reports (format not specified)"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_6","uri":"capability://data.processing.analysis.batch.data.export.with.scheduled.webhooks","name":"batch-data-export-with-scheduled-webhooks","description":"Exports production traces and evaluation scores in batch format (JSONL, CSV) on-demand or on a schedule via webhooks, enabling teams to integrate Respan data into data warehouses, analytics platforms, or custom analysis pipelines. Supports conditional export (e.g., export only traces matching specific filters) and PII masking for compliance, with configurable retention policies to manage data storage costs.","intents":["I want to export all production traces to my data warehouse for long-term analysis and compliance","I need to set up a daily webhook that sends evaluation scores to my analytics platform","I want to export only traces from a specific user segment or time range for analysis","I need to mask sensitive data (user IDs, content) before exporting for compliance reasons"],"best_for":["organizations with data warehouses (Snowflake, BigQuery, Redshift) needing LLM data integration","teams running custom analytics or ML pipelines on production LLM data","companies with compliance requirements (HIPAA, GDPR) needing data masking and retention control"],"limitations":["PII masking and log omission only available on Enterprise tier; Pro/Team tiers cannot redact sensitive data","Webhook delivery guarantees not documented; unclear if retries or at-least-once delivery is supported","Conditional export filtering syntax not specified; unclear what query language is supported","Export latency not documented; unclear if batch exports are immediate or delayed"],"requires":["Respan account (Pro tier minimum for basic export, Enterprise for PII masking)","For webhooks: external HTTP endpoint with POST support","For data warehouse: compatible format (JSONL, CSV) and schema"],"input_types":["Export configuration (format, filters, schedule)","Webhook URL and authentication credentials","Retention policy settings"],"output_types":["JSONL files with trace data","CSV files with evaluation scores","Webhook POST requests with batch data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_7","uri":"capability://tool.use.integration.opentelemetry.standard.data.ingestion","name":"opentelemetry-standard-data-ingestion","description":"Accepts trace data in OpenTelemetry format, enabling teams to send LLM execution traces from their own instrumentation rather than routing all requests through Respan gateway. Integrates with OpenTelemetry collectors and exporters, allowing teams to use Respan as a backend for observability data collected from distributed systems. Supports custom span attributes and semantic conventions for LLM-specific metadata.","intents":["I want to send traces from my existing OpenTelemetry instrumentation to Respan without changing my application code","I need to correlate LLM traces with application traces from my distributed system","I want to use Respan's observability dashboards for data collected via OpenTelemetry, not just gateway requests","I need to integrate Respan with my existing observability stack (Jaeger, Datadog, etc.)"],"best_for":["teams already using OpenTelemetry for application observability","organizations with distributed systems needing end-to-end trace correlation","developers wanting to avoid vendor lock-in to Respan's proprietary tracing format"],"limitations":["OpenTelemetry integration details not documented; unclear which exporters/collectors are supported","LLM-specific semantic conventions not specified; unclear what span attributes Respan expects","Trace ingestion throughput limits not documented; unclear if OpenTelemetry traces count against gateway rate limits","Mapping between OpenTelemetry spans and Respan metrics (cost, quality) not explained"],"requires":["Respan account (Pro tier minimum)","OpenTelemetry SDK/instrumentation in application","OpenTelemetry exporter configured to send to Respan endpoint"],"input_types":["OpenTelemetry trace data (OTLP protocol)","Custom span attributes and tags","Semantic conventions for LLM spans"],"output_types":["Ingested traces in Respan observability system","Queryable trace index with custom attributes","Integrated dashboards and alerts"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_8","uri":"capability://data.processing.analysis.cost.tracking.and.budget.management.per.request","name":"cost-tracking-and-budget-management-per-request","description":"Tracks LLM API costs at request granularity (cost per token, per request, per model) by integrating with provider pricing data, aggregates costs by model/prompt/user/custom dimension, and enables budget alerts when spending exceeds thresholds. Provides cost breakdown dashboards showing which models, prompts, or user segments are driving expenses, enabling teams to optimize for cost without sacrificing quality.","intents":["I want to understand which models or prompts are most expensive and optimize them","I need to set a monthly budget and be alerted if we're on track to exceed it","I want to charge back LLM costs to different teams or customers based on their usage","I need to compare cost-per-quality-point across different model/prompt combinations"],"best_for":["cost-sensitive organizations running high-volume LLM applications","teams with multi-tenant systems needing cost allocation and chargeback","organizations optimizing LLM spend across multiple models and providers"],"limitations":["Cost calculation depends on provider pricing data; unclear how frequently pricing is updated or if custom pricing is supported","Cost tracking only for requests through Respan gateway; OpenTelemetry-ingested traces may not include cost data","Budget alerts are reactive (notify when exceeded) not proactive (forecast and warn before exceeding)","Cost breakdown by custom dimension requires manual tagging; no automatic cost allocation rules"],"requires":["Respan account (Pro tier minimum)","Requests routed through Respan gateway (for automatic cost tracking)","LLM provider API keys configured"],"input_types":["Request metadata (model, tokens, provider)","Custom tags for cost allocation (team, customer, project)","Budget threshold configuration"],"output_types":["Cost metrics per request, model, prompt, user, or custom dimension","Cost breakdown dashboards","Budget alerts and reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__cap_9","uri":"capability://automation.workflow.slack.integration.for.alerts.and.notifications","name":"slack-integration-for-alerts-and-notifications","description":"Sends real-time alerts and notifications to Slack channels when production thresholds are breached (latency, cost, quality, error rate), with rich formatting including metric values, affected requests, and recommended actions. Supports channel routing based on alert type or custom properties, enabling teams to direct different alerts to different channels (e.g., cost alerts to finance, quality alerts to ML team).","intents":["I want my on-call engineer to be notified immediately when latency spikes or errors increase","I need to send cost alerts to our finance team and quality alerts to our ML team in separate Slack channels","I want to include links to the affected traces in Slack alerts so I can debug without leaving Slack","I need to acknowledge alerts in Slack and have that reflected in Respan's alert history"],"best_for":["teams using Slack for incident communication and on-call management","organizations wanting to avoid context-switching between Respan and Slack for alerts","teams with multiple stakeholders (ML, finance, ops) needing different alert channels"],"limitations":["Alert formatting and customization options not documented; unclear if alerts can be templated","Slack channel routing logic not specified; unclear if based on alert type, custom properties, or both","Alert acknowledgment in Slack not documented; unclear if Slack reactions or commands are supported","Slack app permissions and setup process not documented"],"requires":["Respan account (Team tier minimum for advanced alerting)","Slack workspace with admin access to install apps","Slack channels configured for alert routing"],"input_types":["Alert configuration (threshold, channel, routing rules)","Slack workspace and channel IDs"],"output_types":["Slack messages with alert details and trace links","Alert acknowledgment metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keywords-ai__headline","uri":"capability://deployment.infra.unified.llm.devops.platform","name":"unified llm devops platform","description":"A comprehensive platform for managing and deploying large language models (LLMs) with features like API gateway, model routing, and real-time performance monitoring, tailored for DevOps teams.","intents":["best LLM DevOps platform","LLM deployment for real-time monitoring","API gateway for large language models","model routing solutions for AI","observability tools for LLMs"],"best_for":["DevOps teams","AI model developers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["API key for at least one supported LLM provider (OpenAI, Anthropic, etc.)","Respan account (free tier available)","Network access to Respan gateway endpoints","Respan account with Team tier or higher for A/B testing features","Web browser access to Respan UI","At least one LLM provider API key configured","Respan account (Team tier minimum)","Multiple prompt or model versions deployed","Sufficient traffic volume to achieve statistical significance","Respan account (Pro tier minimum for RBAC, Enterprise for SAML)"],"failure_modes":["Gateway throughput capped by tier: Pro=412 req/min, Team=8,400 req/min, Enterprise=custom","Request caching only applies to identical inputs; no semantic caching or embedding-based deduplication","Fallback routing requires manual configuration; no intelligent routing based on model capability matching","Latency overhead from gateway layer not quantified in documentation","Prompt versioning tied to Respan platform; no native git integration for version control","No collaborative real-time editing; concurrent edits not supported","A/B testing requires manual traffic split configuration; no automatic winner selection","Prompt deployment latency not documented; unclear if changes propagate instantly or with delay","Statistical significance testing not documented; unclear if Respan calculates p-values or confidence intervals","Traffic splitting configuration UI not documented; unclear if percentages can be adjusted in real-time","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"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=keywords-ai","compare_url":"https://unfragile.ai/compare?artifact=keywords-ai"}},"signature":"nREVOarZVI9jSkuGyWON6JN3dbjrZUo7Oe3B+KQQV1dN49ydX01HF3/8cJdFbb49MWHMXuw6EfIfagGnKNLuDQ==","signedAt":"2026-06-20T11:24:47.753Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/keywords-ai","artifact":"https://unfragile.ai/keywords-ai","verify":"https://unfragile.ai/api/v1/verify?slug=keywords-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"}}