# Prilog Developer Docs

> Technical guides for connecting production signals to code, generated fixes, and reviewed pull requests.

- [Developer overview](https://prilog.ai/docs/): Connect production signals to source code, investigate errors, and turn generated fixes into pull requests with Prilog. [Markdown](https://prilog.ai/docs/index.md)
- [Quickstart](https://prilog.ai/docs/quickstart/): Connect a GitHub repository, send your first logs, and review a generated fix from your terminal. [Markdown](https://prilog.ai/docs/quickstart.md)
- [Automatic bug fixing](https://prilog.ai/docs/automatic-bug-fixing/): Connect production errors to your codebase, investigate failures, and prepare pull requests with Prilog. [Markdown](https://prilog.ai/docs/automatic-bug-fixing.md)
- [OpenTelemetry to pull request](https://prilog.ai/docs/opentelemetry-to-pull-request/): Use existing OpenTelemetry logs and traces as evidence for repository-aware investigation and proposed code fixes. [Markdown](https://prilog.ai/docs/opentelemetry-to-pull-request.md)
- [GitHub issue to pull request](https://prilog.ai/docs/github-issue-to-pr/): Import an open public GitHub issue for a connected fork, investigate the bug, and prepare an upstream pull request. [Markdown](https://prilog.ai/docs/github-issue-to-pr.md)
- [Continuous bug fixing for small teams](https://prilog.ai/docs/continuous-bug-fixing/): Set up an ongoing production-to-review workflow with monitoring, code investigation, proposed fixes, and usage controls. [Markdown](https://prilog.ai/docs/continuous-bug-fixing.md)
- [Analysis and PR automation](https://prilog.ai/docs/automation/): Configure analysis and review modes, follow asynchronous outcomes, and control automatic pull request publication. [Markdown](https://prilog.ai/docs/automation.md)
- [CLI reference](https://prilog.ai/docs/cli/): Install the Prilog CLI, manage projects, upload logs, import GitHub issues, and automate fixes with structured JSON output. [Markdown](https://prilog.ai/docs/cli.md)
- [Claude & Codex plugins](https://prilog.ai/docs/plugins/): Use Prilog from your coding agent with a shared skill, the local CLI, and an authorized hosted MCP connection. [Markdown](https://prilog.ai/docs/plugins.md)
- [MCP reference](https://prilog.ai/docs/mcp/): Connect to the hosted Prilog MCP endpoint with OAuth and use project-scoped tools to inspect errors, request fixes, and create pull requests. [Markdown](https://prilog.ai/docs/mcp.md)
- [Monitoring overview](https://prilog.ai/docs/monitoring/): Collect application logs, traces, and exceptions with Prilog's OpenTelemetry SDKs or your existing OTLP instrumentation. [Markdown](https://prilog.ai/docs/monitoring.md)
- [Node.js SDK](https://prilog.ai/docs/nodejs/): Instrument Node.js applications with @prilog/monitoring, automatic instrumentation, correlated logs, and exception capture. [Markdown](https://prilog.ai/docs/nodejs.md)
- [Browser & React SDK](https://prilog.ai/docs/browser/): Capture browser errors, page loads, and network traces with @prilog/monitoring-browser and connect frontend activity to backend spans. [Markdown](https://prilog.ai/docs/browser.md)
- [Go SDK](https://prilog.ai/docs/go/): Add OpenTelemetry monitoring to Go services with HTTP instrumentation, structured logging, error capture, and graceful shutdown. [Markdown](https://prilog.ai/docs/go.md)
- [Python SDK](https://prilog.ai/docs/python/): Use the Python SDK archive for application logs, spans, and exceptions while public PyPI publishing awaits approval. [Markdown](https://prilog.ai/docs/python.md)
- [OpenTelemetry & OTLP](https://prilog.ai/docs/otlp/): Send OTLP logs and traces directly to Prilog using HTTP/protobuf or HTTP/JSON, a scoped ingest token, and standard resource attributes. [Markdown](https://prilog.ai/docs/otlp.md)
- [Troubleshooting](https://prilog.ai/docs/troubleshooting/): Resolve authentication, project selection, missing telemetry, asynchronous fix states, and MCP retry problems. [Markdown](https://prilog.ai/docs/troubleshooting.md)
- [Merged public fixes](https://prilog.ai/docs/merged-fixes/): Inspect accepted public bug-fix contributions in Keycloak, Prefect, and Newt, and see what each record establishes. [Markdown](https://prilog.ai/docs/merged-fixes.md)
- [Reading the public results](https://prilog.ai/docs/results/): Understand the curated results collection, GitHub status checks, data downloads, and the scope of its counts. [Markdown](https://prilog.ai/docs/results.md)
- [SWE-bench Verified evaluation](https://prilog.ai/docs/swe-bench-verified/): Prilog reports 91.2% (456/500) in a feedback-guided evaluation. Read the method, artifacts, and pending submission status. [Markdown](https://prilog.ai/docs/swe-bench-verified.md)
- [Prilog vs Sentry Seer](https://prilog.ai/compare/prilog-vs-sentry-seer/): Compare documented inputs, issue investigation, code changes, review controls, and how to evaluate Prilog and Sentry Seer. [Markdown](https://prilog.ai/docs/prilog-vs-sentry-seer.md)
- [Prilog vs Datadog Bits Code](https://prilog.ai/compare/prilog-vs-datadog-bits-code/): Compare production evidence, repository-connected fixes, automation, and evaluation criteria for Prilog and Datadog Bits Code. [Markdown](https://prilog.ai/docs/prilog-vs-datadog-bits-code.md)
- [Prilog vs manual coding-agent workflows](https://prilog.ai/compare/prilog-vs-coding-agents/): Understand how Prilog connects production intake, investigation, and PR review, and how it works with a coding agent. [Markdown](https://prilog.ai/docs/prilog-vs-coding-agents.md)

- [All documentation](https://prilog.ai/llms-full.txt): Complete documentation in plain text.