Open-Source Observability · itamarst

eliot

Eliot is a Python logging library that captures causal chains of actions across your application, showing not just what happened but why. It traces how operations relate to each other, particularly useful for debugging distributed systems and understanding performance bottlenecks.

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Source: GitHub — github.com/itamarst/eliot

1.2k

GitHub stars

Forks

Python

Primary language

Apache-2.0

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	itamarst/eliot
Owner	itamarst
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	1.2k
Forks	74
Open issues	120
Latest release	1.18.0 (2026-05-07)
Last updated	2026-05-07
Source	https://github.com/itamarst/eliot

What eliot is

Eliot structures logs as causal chains of actions that can spawn sub-actions and succeed or fail, providing distributed tracing across single-process and multi-machine deployments. It integrates with asyncio, Twisted, Dask, NumPy, and can output to ElasticSearch, journald, and other backends.

Quickstart

Get the eliot source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/itamarst/eliot.git2cd eliot3# follow the project's README for install & configuration

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

Best use cases

Distributed System Debugging

Trace causal relationships across microservices and multiple processes. Use the Eliot task UUID to correlate logs in Sentry, ElasticSearch, or other aggregation systems and quickly identify which user action caused a failure.

Async/Concurrent Application Tracing

Track operation chains in asyncio, Trio, and Twisted applications where traditional line-by-line logging fails to show causality. Understand which coroutine spawned which operations and in what order they completed.

Scientific Computing Performance Analysis

Profile and debug NumPy/Dask-heavy workloads by logging computation actions and their relationships. Identify bottlenecks and understand why specific code paths were chosen in parallel or distributed computations.

Implementation considerations

Design action hierarchies that meaningfully represent your application's control flow; overly granular or poorly structured actions reduce debuggability.
Plan for log output backends (ElasticSearch, journald, or custom) early; Eliot generates structured JSON, not text, and requires proper aggregation to be valuable.
Instrument async/await boundaries, service calls, and error paths with context preservation; automatic context propagation is available for asyncio and Twisted but may require manual setup for other frameworks.
Account for the performance overhead of structured logging and causal tracking; profile in your target deployment environment.
Set up log retention and rotation policies; causal tracing can generate verbose output, especially with metadata included at each action.

When to avoid it — and what to weigh

Simple Single-Process Logging — If you only need basic stdout/file logging in a single-threaded application, Eliot's causal-chain model adds unnecessary complexity. Python's built-in logging or structlog may be more suitable.
Real-Time, Ultra-High-Frequency Logging — Eliot's action-based structure and causal tracking incur overhead. For extremely high-throughput scenarios (millions of events/sec), lower-level libraries or sampling strategies may be more appropriate.
No Log Aggregation Infrastructure — Eliot alone generates structured logs; effective use typically requires ElasticSearch, Logstash, or similar aggregation. Without infrastructure to consume and correlate logs, the benefit is diminished.
Team Unfamiliar with Structured Logging Concepts — Eliot requires understanding of action hierarchies, task UUIDs, and causal relationships. Teams accustomed to traditional logging may find the learning curve significant.

License & commercial use

Eliot is licensed under Apache 2.0 (Apache License 2.0), a permissive OSI-approved license permitting commercial use, modification, and distribution with conditions including retention of license/copyright notice and a liability disclaimer.

Apache 2.0 permits commercial use without restriction. The maintainer offers commercial support through Python⇒Speed; consult their website for support terms. Use in proprietary applications is allowed, but derivative works distributed must retain the Apache 2.0 license.

DEV.co evaluation signals

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

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

Security considerations

Eliot logs structured data including application state and action metadata; ensure sensitive data (credentials, PII) is not logged via action contexts. Review log output carefully before sending to external aggregation services. No inherent encryption; rely on transport security (TLS) for log transmission.

Alternatives to consider

structlog

Structured logging library with similar JSON output but without causal-chain semantics; simpler and lower-overhead for applications not requiring distributed tracing.

OpenTelemetry (Python SDK)

Industry-standard instrumentation and tracing framework with broader ecosystem integration; steeper learning curve but more portable across tools and languages.

Jaeger or Zipkin (with auto-instrumentation)

Dedicated distributed tracing systems; more complex but purpose-built for microservices observability; requires agent/collector infrastructure.

Software development agency

Build on eliot with DEV.co software developers

Evaluate Eliot for your distributed logging needs. Assess log aggregation infrastructure requirements, team familiarity with structured logging, and performance overhead in a sandbox environment. Reach out for guidance on deployment architecture.

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eliot FAQ

Does Eliot replace my standard logging?

Eliot supplements or replaces application logging by providing causal tracing. It outputs structured JSON and is most effective paired with a log aggregation system. Standard logging (e.g., Python's logging module) may still be used for low-level infrastructure logs.

How does Eliot handle context propagation in async code?

Eliot has built-in support for asyncio and Trio via context variables; Twisted requires explicit integration. For other async frameworks, you may need manual context management. Distributed tracing requires explicit tracing headers passed across service boundaries.

What is the performance impact?

Overhead depends on action granularity and output backend. Structured logging and causal tracking add CPU/memory cost; profile in your environment. Asynchronous log output can mitigate impact in I/O-bound applications.

Can I use Eliot without ElasticSearch?

Yes. Eliot supports multiple backends (journald, file, stdout, custom). However, full causal-tracing benefits (querying, correlating across services) are best realized with a log aggregation system like ElasticSearch or similar.

Custom software development services

Adopting eliot is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source observability software in production.

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