- Category
- AI Agents
- Rank
- No. 246Tools index
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Deployment & Operations · Agent Building
- Interfaces
- CLI · MCP · SDK
- Builder
- tracer-cloud
- GitHub
- 11.2k stars
- Latest release
- main-build
- Date
About
An open-source framework for building AI-powered Site Reliability Engineering (SRE) agents that automatically investigate production incidents, correlate evidence across logs and metrics, and provide root cause analysis. Connects to 60+ observability and infrastructure tools to help resolve outages faster.
What it does
OpenSRE gives operators several ways to run incident investigations: an interactive terminal, one-shot alert analysis, remote service checks, a messaging gateway, and an embedded Python interface. Its agent gathers operational evidence, tests hypotheses through tool calls, produces a structured report, and can suggest or optionally perform remediation.
Why it's ranked here
The project has unusual depth for an alpha: a provider-neutral agent loop, broad deployment choices, synthetic root-cause suites, and cloud-backed end-to-end scenarios. The empty benchmark table matters, however. Its evaluation ambitions are concrete, but the supplied repository text offers no published benchmark result to validate investigation quality.
What's good
It treats incident response as a system to test, not merely a chat prompt. Synthetic suites check root-cause accuracy, required evidence, and misleading clues, while end-to-end scenarios cover Kubernetes and several AWS services. Operators also get resumable terminal sessions, cost tracking, optional identifier masking, integration verification, and multiple model providers.
Tradeoffs
The maintainers call it a public alpha and warn that APIs and integrations may change. Normal installation follows the main branch, which raises change-management concerns for production use. The Python interface requires a source checkout, and correct embedding needs explicit process configuration. Hosted persistence also requires separate database and Redis services.
How to use it well
Best suited to SRE or platform teams willing to validate an alpha against known incidents before granting remediation access. Start with one-shot alert files and synthetic scenarios, verify integrations, then add the interactive shell or gateway to incident workflows. It complements observability systems by reasoning over their evidence; it does not replace log, metric, trace, database, or cache infrastructure.
Technical notes+
pyproject.toml defines a Python 3.12+ setuptools package, the opensre console script, core MCP and LLM dependencies, development tooling, and optional database extras. main.py forwards normal execution to the CLI and documents required process configuration for embedded sessions. core/__init__.py and core/agent_harness/__init__.py resolve public exports through module-level __getattr__, so those imports occur when an exported attribute is accessed. core/agent/__init__.py exposes the reusable agent and run result directly. gateway/main.py deliberately rejects bare module execution because production gateway wiring belongs to the CLI composition root. The Makefile exposes synthetic, cloud-backed, chaos, benchmark, lint, type-check, and integration-verification targets.
Observed
- License
- Apache-2.0
- Primary language
- Python
- Python requirement
- Python 3.12 or newer
- Packaging
- setuptools package with a console-script entry point
- Install surfaces
- Installer script for macOS, Linux, and Windows; Homebrew is also documented
- Interfaces
- Interactive CLI, one-shot CLI, Python embedding interface, messaging gateway, and hosted service deployment
- Platform support
- macOS, Linux, and Windows installation instructions are provided
- Test structure
- Repository text documents synthetic suites, cloud-backed end-to-end scenarios, chaos tests, and benchmark targets
Read from README.md, Makefile, pyproject.toml, main.py, core/__init__.py, core/agent/__init__.py, core/state/__init__.py, core/domain/__init__.py, core/messages/__init__.py, core/agent_harness/__init__.py, gateway/main.py, tools/__init__.py, config/__init__.py.
What it can do
Investigate production incidents automatically
Production incident alerts and system data → Investigation results and evidence analysis
Correlate evidence across logs and metrics
Log files and system metrics from multiple sources → Correlated evidence patterns and relationships
Provide root cause analysis
Incident data and correlated evidence → Root cause analysis report with findings
Connect to observability and infrastructure tools
Tool configurations and API credentials → Integrated data feeds from 60+ monitoring tools
Generate synthetic incident simulations
Cloud environment parameters and scenario definitions → Simulated incident scenarios for training
Score root cause analysis accuracy
AI agent RCA outputs and known incident solutions → Performance scores and accuracy metrics
Deploy AI SRE agents to production environments
Agent configurations and deployment parameters → Running AI agents monitoring production systems
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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.