While others build simple if-this-then-that automations, we built the infrastructure for deep agents - AI that can plan multi-day projects, coordinate teams of specialized agents, remember everything it learns, and improve over time. Think Claude Code, but for your entire business.
# Without Lumnis: 500+ lines of code, 10 different APIs, weeks of integration work# With Lumnis: Just describe what you want in plain English# Install: pip install lumnisaiimport lumnisai# Create client and execute complex multi-step workflowagent = lumnisai.AsyncClient()response = await agent.invoke( "Pull customer tickets from Zendesk, extract product issues, " + "query PostgreSQL for affected customers, analyze sentiment with LLM, " + "create priority matrix in Google Sheets, generate executive report, " + "update JIRA tickets with findings, schedule follow-up meetings in Calendar, " + "draft personalized emails for high-priority customers, and post summary to Slack", user_id="alice@company.com", show_progress=True,)print(response.output_text)# That's it! Lumnis handles planning, orchestration, error recovery, and coordination
Python only. Other languages coming soon.
→ View full documentationShallow tools fail at complex tasks. Deep agents think, plan, remember, and improve - handling work that requires real intelligence.
AI that thinks before acting - creates multi-step strategies for complex tasks
Research competitors, analyze market trends, create strategic plan, monitor progress
Spawns specialized agents that work together with perfect coordination
Research Agent → Analysis Agent → Writing Agent → Review Agent
Persistent memory means agents get smarter over time
Remembers past analyses, learns patterns, improves recommendations
Deep agents don't just connect - they understand and operate your tools with deep understanding. 1000+ integrations and growing.
Agents that think before acting, creating sophisticated multi-step strategies
Divide complex tasks among specialized agents working in parallel
Remember context, learn from past actions, and improve over time
Deep access to your entire enterprise stack, not just webhooks
From natural language to complete execution without manual setup
Production-grade infrastructure with security, monitoring, and scale
While others offer simple if-this-then-that workflows, Lumnis AI enables deep agents - AI that plans multi-day projects, coordinates teams of specialized agents, remembers everything it learns, and improves over time. The difference between a trigger and a strategist.
Manage API users, permissions, and monitor usage in real-time.
1,234
100%
342
alice@company.com
Created agent workflow
bob@company.com
Updated SQL connection
sarah@company.com
Deployed to production
Watch your AI agent plan and execute multi-step workflows automatically.
Parse natural language intent
Determine tools and sequence
Authenticate with services
Run workflow steps
Verify results
"Analyze customer feedback and create summary report"
Track agent performance, API usage, and business metrics in real-time.
Connect your entire stack. Just pass tool names to enable them.
Agents remember user preferences, past conversations, and domain knowledge automatically.
Sales Analysis Thread
2 min agoAPI Development
5 min agoMonthly Reports
10 min agoDeep agents don't just react - they think, plan, and evolve. With persistent memory and sub-agent coordination, they handle complex workflows that require real intelligence, not just automation.
Powered by Lumnis Agents
Growth Rate
+127% YoY
Team Size
850+ employees
Market Cap
$2.25B
Try it yourself:
await agent.invoke("Research Vercel and find sales opportunities")
See why deep agents are fundamentally different from traditional automation tools
Shallow Tools
Linear, predetermined paths only
Deep Agents
Strategic multi-step planning that adapts
Shallow Tools
Stateless - forgets after each run
Deep Agents
Persistent memory across sessions
Shallow Tools
Simple if-this-then-that triggers
Deep Agents
Multi-day projects with sub-tasks
Shallow Tools
Fixed logic, no improvement
Deep Agents
Learns from each execution
Shallow Tools
Fails on edge cases
Deep Agents
Self-healing and adaptive
Shallow Tools
Single linear flow
Deep Agents
Spawns and coordinates sub-agents
Shallow Tools
Basic webhooks and APIs
Deep Agents
Deep understanding of tool capabilities
Shallow Tools
Simple, repetitive tasks
Deep Agents
Complex workflows requiring intelligence
Capability | Shallow Tools(n8n, Zapier, Basic LangGraph) | Deep Agents(Lumnis) |
---|---|---|
Planning Capability | Linear, predetermined paths only | Strategic multi-step planning that adapts |
Memory & Context | Stateless - forgets after each run | Persistent memory across sessions |
Task Complexity | Simple if-this-then-that triggers | Multi-day projects with sub-tasks |
Learning Ability | Fixed logic, no improvement | Learns from each execution |
Error Handling | Fails on edge cases | Self-healing and adaptive |
Agent Coordination | Single linear flow | Spawns and coordinates sub-agents |
Tool Integration | Basic webhooks and APIs | Deep understanding of tool capabilities |
Best For | Simple, repetitive tasks | Complex workflows requiring intelligence |
The difference is clear: shallow tools handle triggers, deep agents handle complexity. If your workflows require planning, memory, and continuous improvement, you need deep agents.
Measurable business outcomes, not just technical capabilities
What took weeks now takes hours. Ship AI features faster.
Handle workflows impossible with simple automations.
Deep agents work continuously, improving with each run.
Self-healing agents that adapt to edge cases.
Deep agents aren't just faster than manual work - they enable entirely new workflows that were previously impossible. The ROI isn't just in time saved, but in capabilities gained.
Deep agents aren't for everyone. They're for teams tackling complexity that shallow automations can't handle.
Tasked with "bringing AI to production" but drowning in infrastructure work? Get deep agents without building the plumbing.
Spending more time on integrations than core product? Deep agents handle the orchestration so you can focus on what matters.
Hit the limits of if-this-then-that tools? Deep agents handle multi-day projects, strategic planning, and continuous improvement.
Need sophisticated AI automation but can't hire an AI team? Enterprise-ready deep agents, no AI expertise required.
Built with planning, memory, coordination, integrations, and MCP-powered reasoning - these five pillars enable deep agents to handle complex tasks that simple automations can't even attempt.
Deep agents plan before acting, creating sophisticated multi-step strategies
Divide and conquer complex tasks with specialized agents working together
Remember, learn, and improve over time through continuous experience
True integration means understanding and operating tools like an expert user
Leverage Model Context Protocol servers for advanced multi-step problem solving
Stop building AI infrastructure. Start shipping deep agents that plan, remember, and improve. See the difference in minutes.
The only infrastructure built specifically for deep agents - with planning, memory, coordination, and scale that shallow automations can't match.
Planning Depth
Multi-day strategies
Sub-Agent Spawning
Parallel execution
Memory Persistence
Cross-session learning
Integration Depth
Enterprise tools
Concurrent Agents
Per organization
Learning Cycles
Self-improvement
Deep agents handle multi-day projects, strategic planning, and continuous learning - work that would fail with simple if-this-then-that automations