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Devgraph.ai
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About Devgraph.ai
Devgraph.ai is an AI-powered ontology engine designed to map code, infrastructure, tickets, and conversations into a live knowledge graph. The platform connects developer tools such as GitHub, Jira, Slack, Kubernetes, and dozens more to create a unified view of an organization’s technical environment. By grounding LLMs and automation with real-time context, Devgraph enables teams to perform unified searches across their entire stack, conduct impact analysis before deployments, and accelerate onboarding for new engineers. It surfaces tribal knowledge by linking discussions to code and tickets, replacing outdated documentation with real-time system context. The tool supports any LLM provider, including self-hosted models, and offers flexible deployment options such as air-gapped environments to meet security and privacy requirements. Devgraph is particularly suited for engineering teams, DevOps, and platform engineers who need to understand dependencies, trace changes, and deploy with confidence while reducing operational overhead.
Key features
- Continuous mapping of code, infrastructure, tickets, and conversations into a live knowledge graph
- Real-time context integration from GitHub, Jira, Slack, Kubernetes, and other developer tools
- Unified search across the entire engineering stack
- Impact analysis before deployments
- Accelerated onboarding for new team members
- Surface tribal knowledge within the organization
- Enable AI agents to answer questions or take actions reliably
- Support for any LLM, MCP, and self-hosted/air-gapped deployments
Use cases
- Unified search across engineering stacks to quickly locate code, issues, or documentation
- Performing impact analysis before deploying changes to assess potential risks
- Speeding up onboarding by surfacing relevant context and tribal knowledge for new hires
Pros
- Maps relationships between code, infrastructure, and teams in real time
- Supports dozens of integrations with developer tools like GitHub, Jira, Slack, and Kubernetes
- Enables unified search across an entire tech stack
- Allows teams to perform impact analysis before deployments
- Supports any LLM, MCP, and self-hosted or air-gapped deployments
Cons
- May require initial setup to connect all tools and build the ontology
- Complexity could pose a learning curve for new users
- Pricing tiers may limit smaller teams or individual users
Frequently asked questions about Devgraph.ai
What does Devgraph.ai do?
Devgraph.ai builds a live ontology of code, infrastructure, and tools to help AI and teams understand system relationships in real time. It connects developer tools into a unified knowledge graph for search, impact analysis, and AI-driven decision-making.
Who is Devgraph.ai for?
Devgraph.ai is designed for engineering teams, DevOps, platform engineers, and organizations that need to map dependencies, trace changes, and deploy with confidence. It suits teams using multiple developer tools who want to reduce operational overhead and improve onboarding.
Does Devgraph.ai support self-hosted models?
Yes, Devgraph.ai supports self-hosted LLMs and offers air-gapped deployment options, allowing organizations to maintain control over data, privacy, and infrastructure while using the platform.
What integrations does Devgraph.ai support?
Devgraph.ai supports dozens of integrations, including GitHub, GitLab, Jira, Vercel, Kubernetes, Argo, FOSSA, Grafana, Slack, and PagerDuty. It also provides a flexible API for custom integrations.
How does Devgraph.ai help with deployments?
Devgraph.ai maps service dependencies to help teams understand what might break before deploying changes. This enables impact analysis and reduces the risk of unexpected outages or incidents.
How do I get started with Devgraph.ai?
Teams can sign up for Devgraph.ai and connect their developer tools to build a custom ontology. The platform offers multiple pricing tiers, including a free trial, and provides documentation and support to help users get started.
