Project summary
DocBrain is an open-source AI-powered knowledge layer for any team that ingests, indexes, and provides cited answers from scattered cloud-native documentation.
Project description
As organizations transition to microservices and Kubernetes, technical knowledge becomes fragmented across Slack, GitHub, Jira, and internal wikis. This "documentation debt" creates significant friction for On-Call engineers and DevOps teams. DocBrain is a high-performance, Rust-based system designed to bridge this gap.
By utilizing a multi-layer memory system (Episodic, Semantic, and Procedural) and hybrid search (Vector + Keyword), DocBrain transforms static, rotting documentation into an active knowledge partner. It provides engineers with instant, grounded answers to complex operational questions (e.g., "What are our RDS provisioning standards?") with full traceability to source documents. DocBrain doesn't just surface existing info; its "Autopilot" feature identifies documentation gaps based on failed queries, enabling teams to proactively fix their internal knowledge base before the next incident.
Org repo URL (provide if all repos under the org are in scope of the application)
https://github.com/docbrain-ai
Project repo URL in scope of application
https://github.com/docbrain-ai/docbrain
Additional repos in scope of the application
NA
Website URL
https://docbrainapi.com
Roadmap
https://github.com/docbrain-ai/docbrain/blob/main/ROADMAP.md
Roadmap context
The current roadmap focuses on deepening integrations with the CNCF ecosystem, specifically:
Observability Integration: Directly ingesting Prometheus metrics/Alertmanager logs to correlate documentation with real-time incidents.
Local Inference: Enhancing support for Ollama/vLLM for air-gapped, high-security cloud environments.
Knowledge Graph Expansion: Better extraction of entity relationships from Helm charts and Terraform manifests.
Contributing guide
https://github.com/docbrain-ai/docbrain/blob/main/CONTRIBUTING.md
Code of Conduct (CoC)
https://github.com/docbrain-ai/docbrain/blob/main/CODE_OF_CONDUCT.md
Adopters
https://github.com/docbrain-ai/docbrain/blob/main/ADOPTERS.md
Maintainers file
https://github.com/docbrain-ai/docbrain/blob/main/MAINTAINERS.md
Security policy file
https://github.com/docbrain-ai/docbrain/blob/main/SECURITY.md
Standard or specification?
NA
Business product or service to project separation
DocBrain is an independent open-source project. While the founders may offer a managed "SaaS" version in the future, the core engine, all connectors (Slack, GitHub, Confluence), and the "Autopilot" gap-detection logic remain entirely in the open-source repository. All development occurs in the public repo under a community-driven model.
Why CNCF?
The CNCF is the home of the modern infrastructure stack. DocBrain is designed specifically for the engineers who build and maintain that stack. By joining the CNCF, DocBrain aims to:
Establish a neutral home to encourage contributions from various cloud providers and tool-chain vendors.
Ensure long-term sustainability through the CNCF’s governance and IP management.
Accelerate interoperability with other CNCF projects like Prometheus, Backstage, and Kyverno.
Benefit to the landscape
Currently, the "Observability" and "Operations" categories focus on metrics, logs, and traces. However, the human-readable knowledge required to act on those signals is often missing or stale. DocBrain adds a "Knowledge Layer" to the landscape, reducing the Mean Time to Resolution (MTTR) by connecting technical documentation directly to the engineering workflow.
Cloud native 'fit'
DocBrain exemplifies cloud-native principles through:
Architecture: Written in Rust for high performance and low resource footprint (single 25MB binary).
Deployment: Natively supports Docker and Kubernetes (Helm charts) for self-hosted, scalable deployments.
Interoperability: Uses standard vector databases and provides a REST API and MCP (Model Context Protocol) for IDE integration.
Cloud native 'integration'
Kubernetes: Ingests READMEs and documentation from GitOps repos.
Backstage: Can act as the backend search/AI engine for Backstage catalogs.
Prometheus/Alertmanager: (In Roadmap) Correlating alert rules with runbooks.
Cloud native overlap
There is minor overlap with Backstage Search, but while Backstage is a portal for service discovery, DocBrain is a specialized AI retrieval engine designed for deep-querying across heterogeneous data sources (Slack, Jira, Docs) with a focus on RAG (Retrieval-Augmented Generation).
Similar projects
Danswer (Open Source)
Verba (Weaviate-based)
Backstage Search
Landscape
Not yet listed.
Insights
Not yet listed.
Trademark and accounts
IP policy
Will the project require a license exception?
Yes. The project currently uses BSL 1.1. Upon acceptance into the CNCF Sandbox, the project maintainers commit to converting the license to Apache 2.0 to comply with CNCF requirements.
Project "Domain Technical Review"
The project has not yet formally presented to a TAG. We intend to engage with TAG Contributor Strategy and TAG Runtime post-submission.
Application contact email(s)
delmenw@gmail.com
Contributing or sponsoring entity signatory information
CNCF contacts
No response
Additional information
No response
Project summary
DocBrain is an open-source AI-powered knowledge layer for any team that ingests, indexes, and provides cited answers from scattered cloud-native documentation.
Project description
As organizations transition to microservices and Kubernetes, technical knowledge becomes fragmented across Slack, GitHub, Jira, and internal wikis. This "documentation debt" creates significant friction for On-Call engineers and DevOps teams. DocBrain is a high-performance, Rust-based system designed to bridge this gap.
By utilizing a multi-layer memory system (Episodic, Semantic, and Procedural) and hybrid search (Vector + Keyword), DocBrain transforms static, rotting documentation into an active knowledge partner. It provides engineers with instant, grounded answers to complex operational questions (e.g., "What are our RDS provisioning standards?") with full traceability to source documents. DocBrain doesn't just surface existing info; its "Autopilot" feature identifies documentation gaps based on failed queries, enabling teams to proactively fix their internal knowledge base before the next incident.
Org repo URL (provide if all repos under the org are in scope of the application)
https://github.com/docbrain-ai
Project repo URL in scope of application
https://github.com/docbrain-ai/docbrain
Additional repos in scope of the application
NA
Website URL
https://docbrainapi.com
Roadmap
https://github.com/docbrain-ai/docbrain/blob/main/ROADMAP.md
Roadmap context
The current roadmap focuses on deepening integrations with the CNCF ecosystem, specifically:
Observability Integration: Directly ingesting Prometheus metrics/Alertmanager logs to correlate documentation with real-time incidents.
Local Inference: Enhancing support for Ollama/vLLM for air-gapped, high-security cloud environments.
Knowledge Graph Expansion: Better extraction of entity relationships from Helm charts and Terraform manifests.
Contributing guide
https://github.com/docbrain-ai/docbrain/blob/main/CONTRIBUTING.md
Code of Conduct (CoC)
https://github.com/docbrain-ai/docbrain/blob/main/CODE_OF_CONDUCT.md
Adopters
https://github.com/docbrain-ai/docbrain/blob/main/ADOPTERS.md
Maintainers file
https://github.com/docbrain-ai/docbrain/blob/main/MAINTAINERS.md
Security policy file
https://github.com/docbrain-ai/docbrain/blob/main/SECURITY.md
Standard or specification?
NA
Business product or service to project separation
DocBrain is an independent open-source project. While the founders may offer a managed "SaaS" version in the future, the core engine, all connectors (Slack, GitHub, Confluence), and the "Autopilot" gap-detection logic remain entirely in the open-source repository. All development occurs in the public repo under a community-driven model.
Why CNCF?
The CNCF is the home of the modern infrastructure stack. DocBrain is designed specifically for the engineers who build and maintain that stack. By joining the CNCF, DocBrain aims to:
Establish a neutral home to encourage contributions from various cloud providers and tool-chain vendors.
Ensure long-term sustainability through the CNCF’s governance and IP management.
Accelerate interoperability with other CNCF projects like Prometheus, Backstage, and Kyverno.
Benefit to the landscape
Currently, the "Observability" and "Operations" categories focus on metrics, logs, and traces. However, the human-readable knowledge required to act on those signals is often missing or stale. DocBrain adds a "Knowledge Layer" to the landscape, reducing the Mean Time to Resolution (MTTR) by connecting technical documentation directly to the engineering workflow.
Cloud native 'fit'
DocBrain exemplifies cloud-native principles through:
Architecture: Written in Rust for high performance and low resource footprint (single 25MB binary).
Deployment: Natively supports Docker and Kubernetes (Helm charts) for self-hosted, scalable deployments.
Interoperability: Uses standard vector databases and provides a REST API and MCP (Model Context Protocol) for IDE integration.
Cloud native 'integration'
Kubernetes: Ingests READMEs and documentation from GitOps repos.
Backstage: Can act as the backend search/AI engine for Backstage catalogs.
Prometheus/Alertmanager: (In Roadmap) Correlating alert rules with runbooks.
Cloud native overlap
There is minor overlap with Backstage Search, but while Backstage is a portal for service discovery, DocBrain is a specialized AI retrieval engine designed for deep-querying across heterogeneous data sources (Slack, Jira, Docs) with a focus on RAG (Retrieval-Augmented Generation).
Similar projects
Danswer (Open Source)
Verba (Weaviate-based)
Backstage Search
Landscape
Not yet listed.
Insights
Not yet listed.
Trademark and accounts
IP policy
Will the project require a license exception?
Yes. The project currently uses BSL 1.1. Upon acceptance into the CNCF Sandbox, the project maintainers commit to converting the license to Apache 2.0 to comply with CNCF requirements.
Project "Domain Technical Review"
The project has not yet formally presented to a TAG. We intend to engage with TAG Contributor Strategy and TAG Runtime post-submission.
Application contact email(s)
delmenw@gmail.com
Contributing or sponsoring entity signatory information
CNCF contacts
No response
Additional information
No response