Power your AI agents with live runtime context
Lightrun MCP connects AI agents to live execution data, allowing them to validate code and troubleshoot bugs in production, without a single redeploy
Trusted by engineering teams at Fortune 500s
AI accelerated code generation.
Now make it reliable.
Eliminate long redeploy cycles to approve each AI-generated change. Let your agents validate their work against live production state, instantly.
Proactive intelligence
Detect missing runtime data and gather the contextual evidence that ensures reliable systems.
Deep inspection
Assess live execution behavior from across staging, canary, and production to understand full system architecture.
Zero friction
Validate code and PRs and identify root causes direct from your agent’s chat without context switching between tools.
Trust your software.
See how it actually runs.
Lightrun MCP is built for AI-native engineers. It connects agents to live environments to build, validate, and debug software.
Design features using live
production behavior
Agents query live runtime context to understand real user flows, service interactions, and error patterns as they write and validate new PRs.
Activate autonomous
investigations into failures
Agents analyze execution behavior, trace failing
code paths, and compare runtime conditions across
environments to prove root causes and generate fixes.
Debug production issues live, without redeployments
Agents add dynamic instrumentation, trace execution paths, and inspect stack frames in a read-only sandboxed, running environment, cutting hours spent on manual reproductions.
Fix vulnerabilities
using live runtime context
Lightrun uses the defined root causes to offer verified fixes that consider full system architecture. Every proposal is shared with a verifiable chain of thought to ensure trust.
Ensure deployments behave correctly after release
Agents verify runtime behavior by validating service execution, inspect request flows, and detect anomalies early, before users are impacted by unexpected behavior.
Secure by design, production ready.
Connect to an extensible platform, sandboxed environment,
with data masking and role based permission settings.
Lightrun Sandbox
Read-only execution with instrumentation isolation, with no impact on production.
Enterprise Compliance
ISO 27001 and SOC 2 Type II certified with GDPR and HIPAA alignment. Full RBAC, SSO, and audit logging.
IP & AI Protection
No source code storage, no model training on customer data, and strict execution guardrails.
Data privacy controls
Configurable retention, PII redaction, prompt sanitization, and zero data retention with AI providers.
End-to-end encryption
TLS 1.3 in transit and AES-256 encryption at rest, backed by AWS KMS with annual key rotation.
Secure integrations
Read-only integrations with least- privilege access. Customer data is never modified.
Tenant Isolation
Logical tenant separation, dedicated secret storage & fully isolated AI sandboxes.
“The unique solutions that Lightrun is developing dramatically impact how developers operate.”
Siris Singh, Global Head of Markets Strategic Investments
AT&T reduced Time to Resolve incidents from
5 hours to 30 minutes avoiding costly war rooms
“When it comes to priority-one tickets, customers can’t wait days for a fix. Lightrun helps us reduce that to hours, that’s a huge win for us and for our customers.”
Hood Munaim SVP, Head of Product Engineering
Priceline increased developer productivity
by 30% across workflows over 2000+ services
“Lightrun not only saved us days, if not weeks, of painstaking debugging but provided an efficient approach to tackling complex issues in production.” Tomer Glicksman, SalesForce
Taboola reclaimed 260+ hours of monthly engineering
capacity by eliminating manual reproduction
Inditex engineers used Lightrun’s live, dynamic logs and snapshots directly from their IDE to dig into a critical production issue and uncover a rounding bug quickly.
Drata accelerated incident response velocity by 30% while maintaining strict compliance standards.
Frequently asked questions
about Lightrun MCP
The Lightrun Model Context Protocol (MCP) server connects your AI agent directly to your live application’s runtime. While AI agents usually only see static source code, Lightrun MCP provides live runtime context, real-time variables, stack traces, and logs. By feeding the model actual production facts instead of assumptions, it eliminates AI “hallucinations” and enables instant, data-driven debugging of live environments.
The Lightrun MCP server is built on an open standard, making it compatible with any tool that supports the Model Context Protocol. This includes AI coding assistants like Claude Code, Cursor, GitHub Copilot, Gemini, Kiro, alongside AI-powered IDEs like Antigravity, VS Code, IntelliJ and more. This allows you to access live production context where you are working.
Lightrun MCP’s security is handled through the use of a read-only sandboxed environment. The AI agent can only observe data, it cannot modify your source code or application state. Additionally, Lightrun MCP inherits enterprise-grade protections, including PII Redaction to mask sensitive data before it reaches the AI and Role-Based Access Control (RBAC) to ensure only authorized users can request runtime context.
No. Lightrun uses Dynamic Instrumentation, allowing the MCP server to fetch real-time data without a single line of code change or a service restart. As long as the Lightrun Agent is running in your application (Java, Python, Node.js, or .NET), your AI agent can begin inspecting the runtime environment immediately.
Lightrun MCP is designed for SRE, DevOps, and Platform Engineering teams. It is ideal for the FinTech, E-commerce, and Healthcare industries, where maintaining high uptime across complex microservice or legacy architectures is critical. By empowering on-call engineers to delegate data collection to autonomous AI agents, the platform simplifies code design, validation, and incident response while maintaining the strict PII redaction and RBAC security standards required by highly regulated sectors.
It’s a simple, conversation-based process. You describe a production issue to your AI agent, like Claude or Cursor, and the agent takes action. It automatically identifies the service, sets up the necessary snapshots and traces, and retrieves the live data for you. You get a data-backed root cause analysis in seconds, all without ever leaving your chat window, redeploying, or changing your production code.