01
Graph-First Builder
Design the agent as a workflow graph — typed nodes, conditional edges, versioned drafts, one-click publish.
VISUAL AGENT INFRASTRUCTURE
Build agents
like hardware.
AgentRow is a production platform for AI agents: wire the workflow as a graph, connect tools and knowledge, and watch every run execute — node by node, with nothing hidden.
GRAPH-FIRST BUILDERSEMANTIC TOOL RETRIEVALPGVECTOR RAGSCOPED MEMORYFULL RUN TRACES
AGENT 61 / RUN 1352GRAPH V3
INPUTSESSION 497
MEMORY READ94MS
KNOWLEDGE6 CHUNKS
LLM3115 TOK
TOOLBOX11 TOOLS
FINALIZESTREAM
POWER
The run, end to end
Scroll. The graph executes.
This is the actual shape of an AgentRow run — the same graph you build in the editor, the same order the runtime walks it.
AGENT GRAPH — V3EXECUTING
01/06
Input
A message arrives from chat, API, or an app event. Session, attachments, and app context enter the graph.
Control room
One surface for build, test, and trace
Chat with the agent while you edit it. The workflow, tools, knowledge, memory, and logs live beside the conversation.
Platform
Everything you need to ship reliable agents
02
Semantic Tool Retrieval
The toolbox picks the right tools per message with vector search plus lexical reranking — no prompt stuffing.
03
Tool & API Integrations
Give agents access to APIs, databases, webhooks, internal services, and custom actions.
04
Knowledge & RAG
Ground responses with document knowledge bases, pgvector retrieval, and score-aware reranking.
05
Scoped Memory
Workspace, app, agent, and user memory scopes with explicit read and write nodes in the graph.
06
Runtime Observability
Inspect traces, tool calls, logs, tokens, latency, errors, and step-by-step execution.
07
Usage Analytics
Track model usage, cost, latency, tool usage, and performance across users and apps.
08
Production Controls
Add approvals, guardrails, secrets, rate limits, and deployment-ready execution rules.
Observability
Every run, on the record
A clear execution timeline for every run: model calls, tool inputs and outputs, retrieval scores, trims, errors, latency, and token cost. No more guessing why an agent behaved a certain way.
Node-by-node waterfall with timings
Tool selection scores and drop reasons
Context trims and token budgets
Errors, retries, and cost per run
RUN #1352 — NODE EXECUTIONSCOMPLETED
INPUT<1MS
MEMORY_READ94MS
KNOWLEDGE_RETRIEVALTRIMMED135MS
LLM809MS3115 TOK
TOOLBOX233MS
LLM3.14S4204 TOK
ROUTER<1MS
CONTEXT_BUILDER<1MS
FINALIZE<1MS
MEMORY_WRITESKIPPED
DURATION
5.85S
TOKENS
8,327
STEPS
11
TOOL CALLS
1
Use cases
Agent apps for teams that need real work done
01UNIT
AI customer support
Answer from your knowledge base, look up orders with tools, and escalate to a human when the router says so.
02UNIT
Internal operations assistant
Wire internal APIs into the toolbox and let the agent handle lookups, updates, and reports — with an audit trail.
03UNIT
Sales proposal assistant
Ground drafts in your catalog and past proposals, keep tone in instructions, and review every generation.
04UNIT
E-commerce product assistant
Search products semantically, check stock through tools, and remember the customer across sessions.
05UNIT
Developer debugging assistant
Feed it logs and docs, give it read-only tools, and trace exactly which context produced each answer.
06UNIT
Workflow automation agent
Chain tools with conditional routes and approval gates so long-running work stays observable and safe.
Integrations
Your models, your tools, your infrastructure
Bring your own LLM keys or run local models. Retrieval runs on Postgres with pgvector — including AgentRow-managed embedding models for tools and knowledge.
OpenAI
Anthropic
Google
Ollama · Local
Postgres + pgvector
Managed Embeddings
HTTP APIs
Webhooks
LangGraph Runtime
EARLY ACCESS
Ready to rack up your first agent?
Start with a free workspace, wire your first graph, and scale when your agents do. Join the waitlist for early access.