spdlog

LogDeck is a self-hosted control plane for Docker and Podman, distributed as a single Go binary. It tails container logs into a local SQLite store so history survives restarts, redeployments, and container removal, with full-text search, log-level and time-range filters, live streaming, and downloads. It also manages containers and Compose stacks, provides a WebSocket terminal, resource and environment-variable editing, alert rules, CPU and memory statistics, and connections to local or remote hosts over SSH. An included MCP server lets compatible AI assistants read logs, inspect containers, view events and statistics, and perform container actions according to scoped API-token permissions. A command-line client exposes log, statistics, lifecycle, Compose, and alert operations over the HTTP API. LogDeck supports Docker-compatible Podman endpoints, optional authentication, and read-only mode.

Superlog is an open-core, self-hosted observability workspace for OpenTelemetry data, developed by Superlog Labs. It ingests traces, logs, and metrics, groups noisy signals into incidents, and provides a local-first interface for investigating production systems. The open-source community edition includes a web app and API, an OTLP intake proxy, worker processes for incident grouping and background jobs, a Postgres schema, ClickHouse-backed telemetry queries, and pluggable agent-runner interfaces with a default runner that records a local incident summary. The repository is licensed under Apache License 2.0; a hosted Superlog Cloud edition is also available with a free tier, pay-as-you-go plan, and monthly credit packs.

ctrlb-decompose is a log-processing tool from ctrlb that converts large, repetitive log files into compact, LLM-ready patterns with typed variables, quantiles, anomaly information, severity scores, and example values. Its single-pass pipeline extracts and normalizes timestamps, applies CLP encoding to replace integers, floats, IP addresses, and strings with compact placeholders, clusters log types with the tree-based Drain3 algorithm, and merges wildcard positions with decoded values for semantic typing such as IPv4, UUID, duration, enum, and integer. It accumulates DDSketch quantiles, HyperLogLog cardinality estimates, top-k values, temporal buckets, and reservoir-sampled lines, and provides human-readable and LLM-optimization output modes. The project runs as a CLI, a Rust library, or client-side WebAssembly in the browser; its README states that logs remain on the local machine.

Glyd is a Rust lossless-compression library and command-line tool with a C ABI, streaming interfaces, and Python and Go bindings. It targets object storage, data lakes, logs, telemetry, backups, versioned exports, RPC payloads, and caches as an alternative to LZ4, Snappy, and zstd. Its record mode detects delimited records, SQL dumps, JSON lines, and varying-shape logs, converts fields or template variables into typed streams, and compresses them in parallel. Base mode compresses a new version against an older version by using regions of the old data as history. The format uses independently decodable blocks and parallel paths; higher levels use separated token, offset, length, and literal streams with SIMD-oriented decoding, while long-distance matching can find repeated content up to 128 MB back. The repository also provides shape dictionaries for small objects, packs for many small objects, container and JPEG handling at selected levels, and a cold context-mixing level for infrequently read data. The separate glyd-store crate and CLI select similar stored objects as delta bases using fingerprints, cap delta chains, and support local directories and S3-compatible backends. The codec, CLI, C ABI, and bindings use the BSD 3-Clause License or GPL-2.0; glyd-store uses the Business Source License 1.1, with commercial production use requiring a license and conversion to Apache-2.0 four years after each release.

Lemmalog is a Datalog engine for LLM-agent memory, distributed as a Rust crate with an MCP server, REPL, and agent skill. It treats extracted statements as provenance-tracked base facts, then uses runtime-parsed stratified Datalog to derive temporal projections, closures, contradiction candidates, relevance relationships, and aggregates. The engine supports seminaive incremental fixpoint evaluation, stratified negation with negative-cycle rejection, bi-temporal facts, confidence and provenance annotations, proof trees through why(), scoped retraction recomputation, demand-driven ask_deep queries using magic sets, and persistence of episodes, rules, and base facts while rebuilding derived relations on load. Its AgentMemory facade connects an extractor to deterministic ADD, UPDATE, NOOP, and escalation policies, and assembles budgeted contexts from distilled facts and their source episodes. The MCP server exposes the engine to agent harnesses such as Claude Code and Kimi CLI through tools for observing facts, querying, explaining proofs, retrieving context, saving state, installing rules, and running hypotheticals.