Agentic Workflows That Run in Production
Build multi-agent systems with drag-and-drop. Full execution tracing, built-in evaluation, and error handling that works on Day Two. Not a toy. Not a demo.
50+ Nodes. 9 Categories. Production-Tested.
Agents, control flow, data transforms, triggers, evaluation. Drag, drop, deploy.
Autonomous AI Agents
ReAct agents, function-calling agents, RAG agents, multi-agent supervisors. Agents that reason, plan, use tools, and run without hand-holding.
Sophisticated Control Flow
Conditional logic, loops, parallel execution, sub-graph maps, hierarchical workflows. Real orchestration for real business processes. Not just linear chains.
Data Transformations
JSON transformers, text parsers, data mappers, aggregators, custom Python/JS transforms. Shape data to fit your workflow. Not the other way around.
Source & Destination Nodes
Pull from databases, APIs, cloud storage, SaaS apps. Push results wherever your business needs them. Data flows in, decisions flow out.
Memory & Context
Semantic memory, conversation history, vector stores, persistent state. Agents that remember context across executions. Not stateless request handlers.
Event-Driven Triggers
Webhooks, schedules, email triggers, message queues, custom events. Workflows fire when the business event happens. No polling. No cron job babysitting.
See Every Decision Your Agent Makes
Hierarchical span tracing shows every LLM call, tool execution, and agent decision. Precise timing and full data. When something fails, you know exactly where and why.
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Hierarchical Span Tracing
Parent-child spans show reasoning steps, tool calls, and nested operations. Visual waterfall charts make debugging fast.
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Real-Time Execution Monitoring
Watch workflows execute live. Identify bottlenecks as they happen. Debug in real-time, not after the fact.
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Metrics Collection
Token usage, latency, success rates, custom metrics. Aggregate across traces. Numbers, not guesses.
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Execution Comparison
Compare executions side-by-side. See what changed between runs. Optimize with data, not intuition.
Catch Bad Outputs Before Users Do
LLM-as-Judge, RAG metrics, hallucination detection, pairwise comparison. Evaluation nodes built into the workflow. Quality gates that run automatically, every execution.
LLM as Judge
Configurable multi-criteria evaluation with chain-of-thought reasoning.
Relevance Grader
Evaluate if retrieved documents are relevant to queries.
Faithfulness Checker
Detect hallucinations by verifying context grounding.
RAG Metrics
Context precision, recall, faithfulness, and relevancy.
Answer Quality
Multi-dimensional quality scoring with weighted dimensions.
Pairwise Comparator
A/B test responses with position-bias mitigation.
Build Custom Nodes. Reuse Everywhere.
Write your logic in Python. Package it as a node. Deploy across every workflow your team builds.
Python-Powered Nodes
Full BaseNode API access: caching, state management, metrics logging, child span tracing, connected node communication. Real extensibility, not a plugin sandbox.
MCP Server Integration
Wrap MCP servers as workflow nodes. Give agents access to any tool through the standardized MCP interface. 100+ servers ready to use.
Marketplace Ready
Package nodes for your team or publish to the marketplace. Versioning, documentation, dependency management. Build once, ship everywhere.
Connects to Everything. No Glue Code.
Workflows talk directly to your AI infrastructure. Native integration, not API wrappers.
AI Gateway
Access 87+ self-hosted models and all major providers through unified APIs with built-in guardrails.
Data Sources
Connect to PostgreSQL, MongoDB, S3, APIs, and more. Pull data directly into your workflows.
Add-ons
Managed Redis, PostgreSQL, MongoDB, and Qdrant. Spin up infrastructure in seconds.
ML Models
Deploy custom ML models and use them as workflow nodes. Full MLOps integration.
Built for Day Two
The demo is easy. Keeping it running is the hard part. We built for that.
Drag-and-Drop Meets Real Code
Business users drag and drop. Engineers write custom nodes. Both ship on the same platform. That is People + Process + Platform.
Failures Are Expected. Handled.
Automatic retries, failover, dead-letter queues, graceful degradation. Workflows recover without waking anyone up.
Every Decision. Logged.
Full audit trails of AI decisions and data access. When compliance asks what the agent did, you have the answer.
Real-Time Voice and Conversation Workflows
Build voice agents, phone bots, and live conversation systems. Persistent sessions that process audio and text continuously, not one-shot batch jobs.
Voice Processing Pipeline
VAD, streaming speech-to-text, turn detection, streaming LLM, streaming text-to-speech. Full audio pipeline with configurable endpointing, interruption handling, and filler audio for zero-latency responses.
Telephony and Outbound Campaigns
Inbound and outbound calling via Twilio integration. Answering machine detection, cold and warm call transfers, and batch-driven campaign orchestration for high-volume outreach.
Streaming Control Flow
Streaming conditionals, switches, and merges for real-time routing. Route conversations based on detected emotion, caller intent, or tool call results without breaking the audio stream.
Live Testing in the Builder
Test streaming workflows directly in the builder with your microphone or text input. Watch frames flow through nodes in real time. Debug latency and conversation quality before deploying.
Session Analytics and Recordings
Latency percentiles, per-node breakdown, sentiment analysis across sessions. Full recording playback with synchronized transcripts. Disposition extraction for structured call outcomes.
Batch and Streaming Together
Launch outbound calling campaigns from batch workflows. Fire async batch jobs mid-conversation without blocking audio. Session Management API for external system integration.
AI Workflows That Compound. Not Expire.
Our FDEs embed with your team to build workflows that work on Day Two and Day Two Hundred.