glasspipe 0.1.8

The flight recorder for AI agents. Trace, visualize, and share your agent runs in 60 seconds.

GlassPipe — The Flight Recorder for AI Agents

See what your AI agent actually did. Share the trace in one click.

pip install glasspipe

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The problem

You built an AI agent. It takes 47 seconds and costs $3 per run. You have no idea why.

The logs look like soup. You add print statements. You still don't know. You're flying blind.

GlassPipe fixes this in 60 seconds.

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How it works

Add one decorator:

from glasspipe import trace

@trace
def my_agent(question):
    # your existing code, completely untouched
    return answer

Run your agent. Then:

glasspipe dashboard

Every LLM call, every tool, every step — captured and laid out as a visual timeline. Click any span to see exactly what went in and what came out. Share the whole trace with one click.

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Install

pip install glasspipe

Requires Python 3.10+. No account. No API key. No configuration.

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Quickstart

from glasspipe import trace, span

@trace
def research_agent(topic):
    # Manual spans for your own steps
    with span("plan", kind="custom") as s:
        plan = f"I will research: {topic}"
        s.record(input={"topic": topic}, output={"plan": plan})

    # Tool calls
    with span("web_search", kind="tool") as s:
        results = ["Result 1", "Result 2"]
        s.record(input={"query": topic}, output={"results": results})

    return results

research_agent("AI agent observability")

Then open the dashboard:

glasspipe dashboard

Your trace is waiting at http://localhost:3000.

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Auto-instrumentation

GlassPipe automatically records every OpenAI and Anthropic call — no extra code needed:

import openai
from glasspipe import trace

@trace
def my_agent(question):
    # This call is automatically captured — model, tokens, cost, latency
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}]
    )
    return response.choices[0].message.content

What gets captured automatically:

• Model name
• Prompt and completion tokens
• Cost in USD
• Latency
• Full input and output

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Sharing a trace

In the dashboard, click Share on any run.

A preview modal shows you exactly what will be made public. GlassPipe scans for secrets — API keys, tokens, emails, JWTs — and highlights them. Redact anything with one click. Then confirm.

You get a link like:

https://glasspipe.dev/t/a1f9c2

Anyone can open it. No account needed. Ever. Traces expire after 30 days. You get a delete token to remove it early.

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Example traces

• Simple research agent — 3 spans, plan → search → synthesize
• Customer support agent — 4 spans, classify → fetch → draft → review

(Replace these with real shared trace URLs after you run the examples)

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GlassPipe vs Langfuse vs LangSmith

We're going to be honest with you. Pick the right tool.

	GlassPipe	Langfuse	LangSmith
Install time	~60 seconds	~20 minutes	~20 minutes
Account required	Never	Yes	Yes
Share a trace publicly	One click	Several steps	Several steps
Local dashboard	Yes	No	No
Team workspaces	No	Yes	Yes
Production monitoring	No	Yes	Yes
Async support	No	Yes	Yes
Price	Free, open source	Free tier + paid	Free tier + paid

Use GlassPipe if: you're an indie dev or student who wants to install in 60 seconds, see what your agent is doing, and share a trace link without making an account.

Use Langfuse or LangSmith if: you need production monitoring, team features, async support, or enterprise observability.

We're not trying to replace them. We're built for a different moment.

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Limitations — please read before installing

GlassPipe v1 is intentionally minimal. It does not:

• Support async Python (sync only — coming in v1.5)
• Capture streaming responses (final results only)
• Auto-instrument LangChain (raw OpenAI and Anthropic SDKs only)
• Support languages other than Python
• Provide team accounts, alerts, or production monitoring

If you need those things today, use Langfuse, LangSmith, or Arize Phoenix — they're genuinely great tools. GlassPipe is for the 60-second install crowd.

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Examples

Three working examples in the /examples folder:

python examples/hello.py            # minimal — one span
python examples/research_agent.py   # 3 spans — plan, search, synthesize
python examples/customer_support.py # 4 spans — classify, fetch, draft, review

All examples run without a real API key.

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Privacy and security

• Redaction happens on your machine, before upload. The server never sees your original data.
• The pre-share preview modal cannot be bypassed.
• Add custom redaction patterns via GLASSPIPE_REDACT_PATTERNS environment variable.
• Shared traces are public but unlisted — accessible only via direct link.
• All shared traces expire after 30 days.

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Built with

• Python 3.10+
• Flask + HTMX (local dashboard)
• SQLite (local storage)
• SQLAlchemy
• Railway + Postgres (hosted share service)

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License

MIT. Free forever.

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Contributing

Issues and PRs welcome. This is a v1 — there's plenty to improve.

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Built by Jonathan — LinkedIn · glasspipe.dev

(Replace # links with your real LinkedIn URL and domain once live)