See how teams worldwide are using Termitty to build the next generation of AI-powered infrastructure automation.100% open source and battle-tested in production.
Enable GitHub Copilot, Cursor, and other AI tools to deploy code and manage servers autonomously.
AI assistants can now handle the complete development lifecycle - from writing code to deploying it in production.
AI writes code, creates tests, deploys to staging, runs integration tests, and promotes to production automatically.
# AI handles full deployment pipeline
def ai_deploy_feature(feature_branch):
# AI writes and tests code
ai_write_feature(feature_branch)
# Deploy with Termitty
with TermittySession() as session:
session.connect('staging-server')
session.execute(f'git checkout {feature_branch}')
session.execute('./deploy.sh')
# AI monitors deployment
session.wait_until(OutputContains('Deployment successful'))
# Run integration tests
if ai_run_integration_tests(session):
ai_promote_to_production(feature_branch)AI detects issues in production and automatically applies fixes without human intervention.
# AI monitors and fixes issues
def ai_health_monitor():
with TermittySession() as session:
session.connect('prod-server')
# Check application health
result = session.execute('curl -s localhost/health')
if 'error' in result.output:
# AI diagnoses and fixes
diagnosis = ai_diagnose_issue(result.output)
fix = ai_generate_fix(diagnosis)
session.execute(fix)
# Verify fix worked
session.wait_until(
lambda s: 'healthy' in s.execute('curl -s localhost/health').output
)"AI agents can now handle complex deployment workflows with minimal human oversight."
Build AI agents that manage infrastructure 24/7, handling scaling, updates, and incident response without human oversight.
Companies are reducing operations costs by 70% while improving uptime to 99.99% with AI-powered infrastructure management.
AI analyzes traffic patterns and scales infrastructure proactively, not reactively.
class AIInfrastructureManager:
def intelligent_scaling(self):
# AI predicts traffic based on patterns
predicted_load = self.ai.predict_traffic()
if predicted_load > current_capacity * 0.8:
# Scale up proactively
with TermittySession() as session:
session.connect('k8s-master')
session.execute(f'kubectl scale deployment app --replicas={predicted_load // 100}')
# Wait for pods to be ready
session.wait_until(
lambda s: 'Running' in s.execute('kubectl get pods').output
)
# AI monitors and adjusts continuouslyAI detects potential failures before they happen and takes preventive action.
def ai_predictive_maintenance():
health_metrics = collect_system_metrics()
# AI predicts failures
failure_risk = ai_model.predict_failure_risk(health_metrics)
if failure_risk > 0.8:
with TermittySession() as session:
session.connect('affected-server')
# AI takes preventive action
if failure_risk.component == 'disk':
session.execute('sudo systemctl stop non-critical-services')
session.execute('sudo find /tmp -delete')
elif failure_risk.component == 'memory':
session.execute('sudo systemctl restart memory-heavy-app')
# Verify issue resolved
session.wait_until(lambda s: check_health_improved(s))"AI-powered infrastructure management reduces operational overhead and enables teams to focus on innovation."
Create AI that can SSH into servers, diagnose complex issues, and implement fixes faster than human engineers.
Mean time to resolution (MTTR) reduced from hours to minutes, with 24/7 intelligent monitoring and automated issue resolution.
AI investigates production issues by analyzing logs, system state, and correlating events across services.
class AIDebugger:
def investigate_issue(self, issue_description):
investigation_plan = self.ai.create_debug_plan(issue_description)
with TermittySession() as session:
session.connect('prod-server')
session.start_recording(f'debug_{timestamp}.json')
findings = []
for step in investigation_plan:
result = session.execute(step['command'])
analysis = self.ai.analyze_output(result.output)
findings.append(analysis)
if analysis.root_cause_found:
# AI implements fix
fix = self.ai.generate_fix(analysis)
session.execute(fix.command)
break
session.stop_recording()
return self.ai.generate_report(findings)"Proactive AI debugging can identify and resolve issues before they impact users."
Seamlessly integrate terminal control into LangChain, AutoGPT, and custom AI agent frameworks.
AI researchers and developers can now build agents that interact with real systems, not just text and APIs.
Create AI agents that can execute terminal commands as part of their reasoning process.
from langchain.agents import Tool
from termitty import TermittySession
def create_terminal_tool():
def terminal_execute(command: str) -> str:
with TermittySession() as session:
session.connect('target-server')
result = session.execute(command)
return {
'output': result.output,
'exit_code': result.exit_code,
'terminal_state': session.state.terminal.get_structured_state()
}
return Tool(
name="ServerTerminal",
func=terminal_execute,
description="Execute commands on production servers"
)
# AI agent can now manage servers
agent = initialize_agent([create_terminal_tool()], llm)
agent.run("Deploy the latest version and verify it's working correctly")"Proper terminal integration enables AI agents to manage real infrastructure effectively."
Deploy AI-managed infrastructure at scale with enterprise security, compliance, and audit capabilities.
Enterprise organizations are achieving 99.99% uptime while reducing operational costs by 60% through AI-powered infrastructure management.
AI ensures all infrastructure changes meet compliance requirements and maintains audit trails.
class EnterpriseAIOperations:
def compliant_deployment(self, change_request):
# AI validates compliance
compliance_check = self.ai.validate_compliance(change_request)
if not compliance_check.approved:
return self.escalate_to_human(compliance_check.issues)
with TermittySession() as session:
session.start_recording(f'audit_{change_request.id}.json')
session.connect('prod-server', audit_mode=True)
# Execute with full audit trail
for command in change_request.commands:
result = session.execute(command)
self.audit_logger.log_action(command, result, compliance_check)
session.stop_recording()
return self.generate_compliance_report()"AI agents can manage large-scale infrastructure while maintaining proper audit trails and compliance."
Build products where AI agents are first-class infrastructure managers, creating entirely new categories of automation products.
New startups are emerging that offer "AI DevOps as a Service" - fully autonomous infrastructure management powered by AI agents.
Startups building platforms where AI manages customer infrastructure with zero human intervention.
class AIPlatformService:
def manage_customer_infrastructure(self, customer_id):
customer_config = self.get_customer_config(customer_id)
# AI creates deployment strategy
strategy = self.ai.optimize_deployment(customer_config)
# Execute across customer's infrastructure
for server in customer_config.servers:
with TermittySession() as session:
session.connect(server, key=customer_config.ssh_key)
# AI customizes deployment for each server
customized_commands = self.ai.customize_for_server(
strategy, server.capabilities
)
for command in customized_commands:
session.execute(command)
# AI verifies deployment success
success = self.ai.verify_deployment(session)
self.notify_customer(customer_id, success)"AI-powered infrastructure platforms can provide reliable automation for complex deployments."
These use cases are just the beginning. As AI becomes more capable, the possibilities for autonomous infrastructure management are limitless. Join the open source community building this future.
AI assists with deployments
AI manages infrastructure autonomously
AI-first infrastructure is the standard