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Documentation Index

Fetch the complete documentation index at: https://docs.triform.ai/llms.txt

Use this file to discover all available pages before exploring further.

This page outlines features we’re actively building or planning. Timelines are estimates and may change.

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In Progress

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Monitoring & Observability

Target: Q1 2026 Core observability infrastructure for understanding system behavior:
  • Logs — Structured runtime messages with contextual fields (Node, Project, Execution)
  • Traces — Step-by-step paths with timing across Nodes
  • Metrics — Quantitative indicators (counts, durations, error rates)
How you’ll use it:
  • Inspect Executions to see logs and trace waterfalls
  • Drill from an erroring Node into its Action context
  • Compare metrics across Deploys to validate improvements
Current focus:
  • Consistent event schemas across Actions/Flows
  • Lightweight log ingestion with contextual fields
  • Trace propagation through Agent tool calls
tip: Log with intent — Emit concise, structured logs that capture what happened and why.

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Planned

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Chat Client

Target: Q2 2026 A customizable, embeddable chat widget connecting end-users directly to your Triform Agents. Key features:
  • Single-line JavaScript integration
  • Customizable styling and branding
  • Rich media support (images, code, forms)
  • Built-in rate limiting and content filtering
  • Mobile SDKs (iOS, Android, React Native, Flutter)
Use cases: Customer support bots, product assistants, internal tools, e-commerce

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Alerts

Target: Q2 2026 Proactive notifications when critical conditions occur. Capabilities:
  • Define rules on logs, metrics, or evaluation scores
  • Send to email, chat, and webhooks
  • Group, dedupe, and throttle noisy signals
  • Link alerts to remediation runbooks
Example rules:
  • Error rate exceeds N% over M minutes
  • Evaluation score falls below threshold for k runs
  • Latency p95 exceeds T ms after deploy

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Persistent Storage

Target: Q3 2026 Managed storage for state that persists across executions. Use cases:
  • Session state for multi-turn conversations
  • Caching expensive computations
  • Storing user preferences
  • Accumulating data across runs

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Evaluations

Target: Q3 2026 Automated quality assessment for AI outputs. Capabilities:
  • Define evaluation criteria and scoring rubrics
  • Run evaluations on historical executions
  • Track quality trends over time
  • Set thresholds that trigger alerts

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Community Library

Target: Q4 2026 Shared repository of reusable components. Features:
  • Browse and search community contributions
  • One-click import into your Projects
  • Version management and updates
  • Rating and review system

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Advanced Logging & Metrics

Target: Q4 2026 Extended observability capabilities. Logging:
  • Structured logging with log levels
  • Advanced search and filtering
  • Log streaming (real-time tail)
  • External integrations (Datadog, Splunk, CloudWatch)
Metrics:
  • Custom metric definitions (counters, gauges, histograms)
  • Custom dashboards with drag-and-drop widgets
  • Metric-based alerting
  • External export (Prometheus, Graphite)

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Traces (Advanced)

Target: Q4 2026 Enhanced execution tracing and analysis. Capabilities:
  • Distributed tracing with span details
  • LLM-specific tracing (prompts, tool calls, token usage)
  • Cost attribution per span
  • OpenTelemetry export (Jaeger, Zipkin)
  • Flamegraph visualization

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Schedules (Enhanced)

Target: 2027 Advanced scheduling capabilities. Features:
  • Complex cron expressions
  • Timezone-aware scheduling
  • Schedule dependencies
  • Pause and resume schedules
  • Schedule versioning

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