AIAI Weather Agent (With HTTP request)
AI agent that retrieves live weather data via API.
Turn ideas into specialized AI agents with MCP, API access, coding control, and three architectures: Tool-Centric, Sub-Agent Teams, and Action Selection. Connect business systems, check each step, and move from prototype to live automation without rebuilding from scratch.
Free plan available·No credit card·Deploy in 5 min
Most builders focus on one path: API-first tooling, basic no-code flows, or chat wrappers. Latenode combines workflow automation, coding, APIs, MCP, and deployment controls so teams can use three production architectures: Tool-Centric, Sub-Agent Teams, and Action Selection.
Start with drag-and-drop, then drop into JavaScript, Python, HTTP calls, or parameter-level control when your agent logic gets complex. Build from scratch, adapt a starter pattern, or refine pre-built flows without giving up developer flexibility.
Connect models, tools and data, and verified connections in one place. Leverage OpenAI, Gemini, retrieval, and one tool server to create flows for real-world work.
Policy rules, checks, permissions, and preview tools help you verify agent behavior before rollout. Inspect traces faster, keep monitoring in one place, and maintain enterprise-grade reliability as usage grows.
Use the same visual builder to support three agent patterns. Pick the architecture that fits the task, then reuse the same connections, coding tools, trace view, and rollout path across every agent system.
Use Tool-Centric architecture when the agent should choose from approved tools, run specific tool calls, and check each step. Ideal for high-trust workflow runs, CRM updates, support triage, and internal automation where reliability matters more than open-ended reasoning.
Bring together REST APIs, GraphQL, webhooks, internal tools, and one tool server in one runtime. Build an AI system that can choose tools deliberately instead of guessing what to do next.
Add approval gates, permissions, rule-based branching, and checks before any write action. Check input, constrain tool access, and keep agent behavior aligned with enterprise-grade policy.
Use low-code blocks for fast setup, then add JavaScript or Python to handle custom logic, transforms, and script-based connections. This builder helps developers refine complex systems without abandoning the visual flow.
Inspect every interaction, tool call, parameter, and model response with preview tools and detailed run history. Quickly validate prompts, debug failure paths, and improve agent performance before users ever see the agent.
Ship agents as internal tools, API endpoints, scheduled jobs, or embedded actions. The same workflow you use to build AI agents is the one you use to ship production-ready agents with less rollout effort.
Use Sub-Agent Teams when one model should not handle the whole job alone. Break work into specialist roles for research, planning, execution, QA, and reporting, then coordinate them in one workflow with clear control.
Create specialist AI agents for research, retrieval, action execution, and reviewer checks. The parent flow coordinates handoffs so no single agent carries every tool, model, and responsibility at once.
Give each sub-agent access to the same APIs, retrieval layers, tool server connections, and reusable blocks. Reuse starter patterns across sales, support, and ops instead of rebuilding every flow from scratch.
Handle uncertain steps with explicit loop control, retries, and fallback branches. Keep reliability high while still letting the team make decisions in the right places.
Keep run history and preview output for each agent in the system. That makes it easier to inspect agent behavior, debug failure paths, tune prompts, and compare agent performance across tasks.
Start with one assistant and evolve into an agent system as requirements grow. Because connections, rollout controls, traces, and permissions stay centralized, scaling does not mean a second architecture or a second product.
Use this architecture when speed matters and the next step should be rule-based. Classify the request, pick the right tool or branch, check the result, and execute the action without forcing every task through a full reasoning loop.
Classify the request, choose the right path, and execute the next action instantly. This pattern is ideal for support triage, lead qualification, internal copilots, and embedded AI agents that need fast answers with policies.
Use an LLM to interpret intent, then hand off to controlled branches for tool calls, permission checks, and script execution. This pattern gives you flexibility at the edge and predictability in the core flow.
Map decisions to explicit branches so the agent can use APIs, MCP tools, or scripts only when the right condition is met. That helps teams automate real-world tasks with less risk and less manual handoff.
Before the agent writes to a CRM, posts to chat, updates tickets, or triggers a tool server call, add validation, checks, and preview checkpoints. Quickly validate sensitive actions and keep live agents safe.
Start from starter agent workflows for sales ops, support, DevOps, research, and internal copilots. Reuse the same decision shape across many tools, data sources, and workflow patterns to simplify agent development.
Powerful enough for real-world agent systems. Simple enough to get started with a starter pattern, add coding where you need it, and ship with confidence.
Connect frameworks like LangChain, knowledge stores, APIs, and every tool server you need. Bring together tools and data so your AI agents can act on real systems without brittle glue code.
Start with reusable blocks, then add checks, loops, and script steps in the same visual builder. The result is a workflow you can understand, reuse, and tune.
Preview outputs, inspect traces, and compare run history before rollout. Check each interaction, tune prompts, and improve agent performance with observability built in.
Ship live agents as endpoints, schedules, or embedded actions. Add enterprise-grade permissions, runtime controls, and centralized deployment controls as usage grows.
Connect every agent to Salesforce, Slack, GitHub, Jira, databases, LLMs, and custom data sources. Use API, webhook, or tool-server patterns with providers like Anthropic and OpenAI supported out-of-the-box.
Use Latenode's script blocks, API steps, and tool-server support to connect any internal system, endpoint, or data source — even when there is no starter connection yet.
Ready-to-deploy
Start with a starter pattern, then tune prompts, tools, and decision logic for your use case. Starter patterns help you create agents faster without locking you into one framework.
AIAI agent that retrieves live weather data via API.
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At 1 million operations, Latenode costs $59/month vs. $3,749 on Zapier — without sacrificing speed or control. Proven savings for AI and engineering teams, at a fraction of the cost.
| Volume / Capability | Latenode | Zapier | Make | n8n Cloud |
|---|---|---|---|---|
| 100,000 ops/mo | $19 | $733–$898 | $64–$203 | $50+ |
| 1M ops/mo | $59 | $3,299–$3,749 | $880–$1,647 | $500+ |
| Apps & LLMs | 1,200+ | Separate cost | Separate cost | Limited |
| Custom JavaScript | Full support | Paid add-on | Limited | Supported |
| API key management | None | Per model | Per model | Per model |
Import your existing workflows in 15 minutes. Your automations keep running without interruption — same logic, lower price. We help your team move more efficiently at no migration cost.
Pricing
Start with starter patterns, create agents from scratch when needed, and scale into enterprise-grade rollout as your workflow graphs grow.
Free
/month
For exploring and building your first AI agent workflows. Always free.
No credit card required
Start
/month
For technical teams running live AI agent workflows with production-ready rollout and monitoring.
Community forum + email support
Enterprise
/month
For organizations shipping agent systems with enterprise-grade governance and rollout controls.
Custom SLA · Priority support
Latenode is not just a chat wrapper or a generic automation canvas. It supports Tool-Centric, Sub-Agent Teams, and Action Selection architectures in one visual builder, with APIs, MCP, coding, checks, and rollout controls in the same system.
Yes. You can get started with starter blocks, drag-and-drop steps, and agent templates. When you need deeper control, add JavaScript, Python, or direct API calls without leaving the builder.
Use sandbox, run history, checks, permissions, and guided traces to check behavior before an agent writes to external systems. That helps you inspect, tune, and reduce operational risk.
Yes. Latenode supports APIs, webhooks, models, knowledge stores, and tool servers. You can connect Salesforce and internal tools to build real-world agent systems.
Yes. Latenode is designed for production-ready agents with enterprise-grade permissions, centralized rollout controls, inspection tooling, and reusable workflow patterns that can scale from one agent to agent teams.
Join technical teams using Latenode to build AI agents, check behavior, tune flows, and ship live agents without splitting work across disconnected tools.