superloglabs/superlog

Open-source observability tool that uses AI agents to self-heal your software

What it solves

Superlog provides an agentic telemetry system to help teams debug production systems. It solves the problem of noisy telemetry signals (traces, logs, and metrics) by grouping them into incidents, allowing developers to move from raw data to actionable insights without manually sifting through logs.

How it works

Superlog acts as an observability workspace for OpenTelemetry (OTLP) data. It uses an OTLP ingest proxy to collect data, worker processes to group signals into incidents, and a combination of Postgres and ClickHouse to store and query telemetry. It includes an agent runner interface that allows for pluggable investigation runtimes to analyze incidents.

Who it’s for

Developers and DevOps teams who need a local-first observability tool to manage and monitor infrastructure and monitor production systems.

Highlights

  • Agentic Telemetry: Uses agents to automate the investigation of incidents.
  • OpenTelemetry Support: Native ingestion of traces, logs, and metrics via OTLP.
  • Incident Grouping: Automatically groups noisy signals into cohesive incidents.
  • Open-Core: Provides a free community edition including the web app, API, and worker processes.

Related

  • Project
    papercomputeco/tapes

    An agentic telemetry system that provides durable storage and OpenTelemetry instrumentation for content-addressable LLM interactions.

  • Dispatch
    Superlog: Moving from Reactive Observability to Automated Resolution

    Superlog aims to automate the entire observability lifecycle, from one-prompt instrumentation to AI-driven bug fixes via pull requests.

  • Project
    Asymptote-Labs/agent-beacon

    Beacon is an open-source memory layer for AI coding agents that captures session history across multiple tools, allowing developers to reuse debugging patterns and workflows across different coding environments.

  • Project
    monocle2ai/monocle

    Monocle is an Apache‑2.0 Python SDK that adds OpenTelemetry‑compatible tracing to GenAI apps and agents. It auto‑instrumentates popular frameworks (LangChain, LlamaIndex, Haystack, Google ADK, Claude CLI, etc.), emits structured spans for prompts, LLM calls, tool use, and vector queries, and integrates with any OTLP collector or local JSON files. A companion `monocle-test-tools` package lets you write pytest‑style tests that assert on those traces (agent runs, tool calls, token/cost limits, evaluation facts). Features include a metamodel for standard span attributes, scope helpers for tagging spans, built‑in secret obfuscation, zero‑code wrapper mode, VS Code visualizer integration, and broad exporter support (file, stdout, S3, Azure Blob, Okahu cloud, PostgreSQL, ClickHouse). Ideal for debugging, cost monitoring, CI testing, and platform‑level observability of AI agents.

  • Project
    comet-ml/opik

    An open-source LLM observability and evaluation platform for AI agent tracing, prompt management, and production monitoring.