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traceburn

PyPIInfrastructure

PulseGate's liveness check found it on 14 Sep 2026; it is registered on GitHub and PyPI and has been in the index since 8 Jul 2026. How this is checked

traceburn is an open-source, local-first tracer and efficiency profiler designed for AI agent workflows. It enables developers to analyze cost and latency using flamegraphs, perform deterministic replay, compare runs, and detect inefficiencies, all stored in a single SQLite file. Ideal for AI developers seeking detailed observability and optimization tools.

Inferred · not functionally tested

Open SourceMITCLISelf-hosted
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Overview

6 features

Purpose: Profiling and analyzing the efficiency and cost of AI agent workflows locally.

Inferred · not functionally tested

Audience: AI developers and researchers

Inferred · not functionally tested

Functions: monitoring, analytics

Inferred · not functionally tested

Interfaces: API: unknown · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: MIT · platforms: CLI · deployment: cli, self_hosted

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org · github.com. These links do not verify the individual claims.

In the Agent monitoring & governance space, traceburn takes a focused approach. Inferred · not functionally tested: It focuses on profiling and analyzing the efficiency and cost of AI agent workflows locally. Inferred · not functionally tested: It is built as an open-source project for AI developers and researchers. Basis unknown · not verified: The project is open source (MIT). Basis unknown · not verified: It ships for the command line, and it can be self-hosted.

Behind traceburn is TommyTranX, and it first shipped in 2026. Development happens publicly on GitHub with 10 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are cost profiling, latency flamegraphs, and deterministic replay.

Summary written by a language model from the project’s public pages.

Tasks: Inferred · not functionally tested

  • Cost profiling
  • Latency flamegraphs
  • Deterministic replay
  • Run diffs
  • Waste detection
  • SQLite storage

Topics: Inferred · not functionally tested

Tags
ai-agent-profilingflamegraphcost-analysislatency-tracingsqlite-integration
AI capabilities
Code

JSON profile · Text profile · Access guide

Built with & integrations

Runs on
CLISelf-hosted

Trust & compliance

License
MIT
Public signals
HTTPSOpen SourceFree tierGitHubActive maintenance

Indexing history

1

What PulseGate has recorded for this listing

  1. Indexed5 Oct · 01:04 UTC
    traceburn seen via PyPI Fresh Feed
    Source: PyPI Fresh Feed · Open

Frequently asked questions about traceburn

What is traceburn?
Inferred · not functionally tested: Traceburn focuses on profiling and analyzing the efficiency and cost of AI agent workflows locally. It is catalogued under Agent monitoring & governance on PulseGate.
Who is traceburn for?
Inferred · not functionally tested: traceburn is an open-source project built for AI developers and researchers.
Is traceburn free?
Basis unknown · not verified: Yes — traceburn is open source under the MIT license and free to use.
What platforms does traceburn run on?
Basis unknown · not verified: traceburn runs on the command line. It can also be self-hosted.
Is traceburn still maintained?
PulseGate's liveness check found it on 14 Sep 2026. Its GitHub repository shows 10 commits in the last 90 days.
What are alternatives to traceburn?
Similar projects tracked by PulseGate include tracemotive, agentburn, and BurnRate.tracemotiveagentburnBurnRate
Who makes traceburn?
traceburn is developed by TommyTranX.
When did traceburn launch?
traceburn first shipped in 2026.

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