RAG Frameworks · derisk-ai

OpenDerisk

OpenDeRisk is an AI-native risk intelligence system that uses multi-agent collaboration to perform root cause analysis (RCA) on application incidents by analyzing logs, traces, and code. It provides 24/7 monitoring and incident diagnosis through coordinated SRE, Code, Report, Visualization, and Data agents built on Python with MIT licensing.

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Source: GitHub — github.com/derisk-ai/OpenDerisk

956

GitHub stars

130

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	derisk-ai/OpenDerisk
Owner	derisk-ai
Primary language	Python
License	MIT — OSI-approved
Stars	956
Forks	130
Open issues	12
Latest release	v0.3.0 (2026-03-31)
Last updated	2026-04-26
Source	https://github.com/derisk-ai/OpenDerisk

What OpenDerisk is

OpenDeRisk implements a multi-agent architecture using Python that performs deep RCA via the OpenRCA dataset (20GB). Agents dynamically write and execute code for analysis, visualize evidence chains, and coordinate across SRE, data, and code domains. It integrates with external LLM APIs and supports flame graph analysis, metrics ingestion, and conversation-driven data exploration.

Quickstart

Get the OpenDerisk source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/derisk-ai/OpenDerisk.git2cd OpenDerisk3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Post-Incident Root Cause Analysis

Rapidly identify root causes of production incidents by correlating logs, distributed traces, and code changes. Multi-agent collaboration surfaces evidence chains faster than manual investigation.

SRE Automation & Alert Triage

Automate initial incident response and alert correlation across infrastructure and application layers. Reduces MTTR by directing engineers to likely failure points with supporting evidence.

Flame Graph & Performance Data Analysis

Upload Java/Python flame graphs or performance metrics for AI-driven interpretation and anomaly detection without manual profiling expertise.

Implementation considerations

Large dataset dependency: 20GB OpenRCA dataset must be downloaded and decompressed locally; requires disk space and initial setup time.
LLM API configuration mandatory: System requires external API keys (e.g., OpenAI via proxy). No model bundled; cost and latency depend on configured provider.
Multi-agent orchestration complexity: Requires understanding agent roles (SRE-Agent, Code-Agent, ReportAgent, etc.) and their interaction patterns for customization.
Python and uv toolchain requirement: Development and deployment use Python with uv dependency manager; ensure compatibility with existing CI/CD.
Web UI optional but recommended: Quickstart mode offers zero-config web interface on localhost:7777; CLI-only operation possible but less convenient.

When to avoid it — and what to weigh

Offline or air-gapped environments — Requires external LLM API connectivity (OpenAI proxy configured). No clear support for on-premises or disconnected deployments.
Real-time critical systems under SLA constraints — Multi-agent orchestration and dataset processing (20GB OpenRCA) introduce latency. Not suitable if RCA must complete in seconds.
Small teams without SRE/DevOps expertise — Requires configuration of LLM APIs, dataset download/decompression, and understanding of multi-agent workflows. Operational overhead may not justify ROI for small deployments.
Highly regulated environments without vendor support — No clear commercial support, SLA guarantees, or security audit documentation visible. Community project with MIT license—requires internal security review.

License & commercial use

MIT License (MIT). Permissive open-source license allowing commercial use, modification, and distribution. No copyleft restrictions. Requires attribution and inclusion of license text.

MIT License explicitly permits commercial use. However, no commercial support, liability guarantees, or SLA documentation provided by OpenDeRisk project. Organizations using in production should conduct internal security review, establish internal support processes, and understand that community support via Discord and GitHub issues is the primary recourse. Commercial viability depends on internal SRE maturity and risk tolerance.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

Community open-source project with no public security audit or vulnerability disclosure policy documented. Key risk areas: (1) External LLM API connectivity exposes prompt injection risk if user data is sent verbatim. (2) Code-Agent dynamically writes and executes Python code—supply chain or prompt injection could lead to arbitrary code execution. (3) 20GB dataset sourcing and integrity not clearly validated. (4) No authentication/authorization framework visible; assume single-user or localhost deployment. Internal security review recommended before production use with sensitive data.

Alternatives to consider

Elastic Observability + AI Assistant

Mature commercial platform with built-in RCA, log/trace correlation, and enterprise security. Higher cost but vendor support and SLA guarantees.

Datadog Incident Response

Cloud-native observability with automated incident detection and RCA suggestions. Integrated alerting and chat; requires Datadog adoption.

Splunk On-Call + Custom Automation

Enterprise incident management with runbook automation. Lower AI sophistication than OpenDeRisk but proven SRE workflow integration and support.

Software development agency

Build on OpenDerisk with DEV.co software developers

OpenDeRisk offers promise for teams seeking AI-driven incident analysis, but production deployment requires careful assessment of LLM API costs, data privacy, code execution risks, and lack of vendor support. Start with a sandbox evaluation of the Flame Graph or DataExpert modes before committing to full RCA workflows.

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OpenDerisk FAQ

Do I need to download the full 20GB OpenRCA dataset?

Yes, for AI-SRE (RCA) mode. The system is trained/evaluated on this dataset. Flame Graph and DataExpert modes do not require it, but are less documented.

What LLM APIs are supported?

OpenAI format (via proxy config, e.g., aliyun.toml) is explicitly mentioned. Other providers require proxy or custom integration. Specific model recommendations not stated.

Can I run this without internet access?

Not currently. System requires external LLM API calls. Offline/on-premises deployment is not documented and would require significant refactoring.

Is there commercial support or an enterprise version?

No. Community-only project with MIT license. Support via Discord and GitHub issues. Organizations needing SLA/liability must evaluate risk tolerance independently.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like OpenDerisk. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across rag frameworks and beyond.

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