steplight 0.2.0

Local-first trace inspector for LLM agents and tool-driven workflows.

Steplight

Local-first trace inspector for LLM agents and tool-driven workflows.

Load a trace. See what happened. Understand why.

Steplight is a terminal-native tool for inspecting LLM runs, tool calls, retries, token usage, errors, and execution bottlenecks without sending trace data to a third-party platform.

It is designed for developers building with OpenAI-style workflows, LangChain, MCP tooling, or custom JSON/YAML traces who want a fast local debugging loop and a shareable HTML report.

Why Steplight

• Understand what your agent actually did, step by step
• Spot slow tools, retry loops, and cost spikes quickly
• Keep sensitive traces on your own machine
• Stay in the terminal instead of digging through raw JSON by hand
• Share a polished HTML report when you need feedback from teammates

Features

• Interactive Textual inspector for timelines, details, and diagnostics
• Rich terminal summary for quick debugging and CI-friendly output
• Static HTML export for sharing a run with other people
• Built-in diagnostics for common agent failure patterns
• Support for OpenAI-style traces, LangChain callbacks, MCP logs, and generic JSON/YAML
• Extensible adapters and rules so you can grow it with your workflow

Install

pip install steplight

The package installs both steplight and the shorter slt alias. The examples below use slt to avoid PowerShell's reserved sl alias.

Quick Start

slt summary sample_traces/agent_with_tools.json
slt validate sample_traces/simple_qa.json
slt export sample_traces/expensive_run.json -o report.html
slt inspect sample_traces/agent_with_tools.json

Example Summary Output

+------------------------ Steplight Summary ------------------------+
| Run: Find compliance gaps in policy                               |
| Source: openai                                                    |
| Overview: Duration: 14.0s | Steps: 4 | Tool calls: 2 | Retries: 0 |
| Tokens: 4,230 in / 670 out | Est. cost: $0.0028                   |
+-------------------------------------------------------------------+

Diagnostics
- WARNING: Step 'web_search' took 68.6% of total runtime.
- WARNING: Tool 'web_search' took 9.6s. Check timeouts, caching, or external latency.
- INFO: Input tokens grew 4.2x between 'Draft plan' and 'Final answer'.
Bottleneck: web_search (68.6% of total runtime)

Supported Trace Formats

• OpenAI-style step traces
• LangChain callback event exports
• MCP tool call logs
• Generic JSON or YAML with an optional steplight.yaml mapping file

Diagnostics

Steplight currently flags:

• bottlenecks
• retry loops
• context growth
• repeated tool calls
• silent errors
• slow tools
• high-cost runs
• empty completions

Custom Mapping Example

mapping:
  steps_path: "$.events"
  timestamp_field: "ts"
  type_field: "event_type"
  type_values:
    prompt: "llm_start"
    completion: "llm_end"
    tool_call: "tool_start"
    tool_result: "tool_end"

Commands

• slt inspect <file> opens the interactive Textual UI
• slt summary <file> prints a non-interactive terminal summary
• slt export <file> -o report.html creates a static HTML report
• slt validate <file> checks whether a trace can be parsed successfully

Docs

• Roadmap
• Versioning and Releases
• Release Process

Repository Layout

steplight/
  cli/
  core/
  adapters/
  tui/
  export/
sample_traces/
tests/

Development

python -m pip install -e .[dev]
pytest
python -m steplight.cli.main --help

Release Checklist

• Run pytest
• Build the distribution with python -m build
• Update CHANGELOG.md
• Review the generated wheel and sdist
• Publish the repository to GitHub
• Publish the package to PyPI when ready

Status

Steplight is currently an alpha-stage project focused on a clean local-first inspection experience, a solid parser surface, and practical diagnostics.

License

MIT