glasspipe 0.2.0

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.

No agent handy? Seed realistic sample traces and explore:

glasspipe demo && glasspipe dashboard

What you get:

• Waterfall timeline — every span with duration, offset, and a click-through inspector
• Cost & token tracking — per-call and per-run, with live cost ticker for in-flight runs
• Run diffing — select two runs, see exactly which steps appeared, vanished, or slowed down
• Agent versions — tag runs with @trace(version="v1.3.0"), filter the run list by version
• Anomaly watch — flags suspected tool loops, cost spikes, and runaway step counts while a run is live
• Trace replay — replay a run's waterfall in real time
• One-click sharing — mandatory redaction preview, then a public link; no account, ever

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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, credit cards — and auto-redacts every match before anything leaves your machine. Review the redacted preview, then confirm.

You get a link like:

https://glasspipe.dev/t/7sq3QX

Anyone can open it — try that one, it's live. No account, ever. Traces expire after 30 days, and sharing gives you a delete token to revoke a trace early:

curl -X DELETE "https://glasspipe.dev/v1/trace/<id>?token=<delete-token>"

Privacy guarantees:

• Redaction happens on your machine, before upload. The server never sees the original data.
• The dashboard's share flow always routes through the redacted preview.
• Custom redaction patterns: set GLASSPIPE_REDACT_PATTERNS as a JSON dict in your environment.
• Shared traces are public but unlisted — accessible only by direct link.

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[ EXAMPLES ] Examples

Live shared traces (no install needed):

• Competitive Intel Agent — 8 spans: plan → analyze competitors → synthesize → draft brief → review
• Support Agent — 4 spans: classify → fetch → draft → review

Working examples in the /examples folder:

python examples/hello.py                    # minimal — two spans
python examples/research_agent.py           # 3 spans: plan, search, synthesize
python examples/customer_support.py         # 4 spans: classify, fetch, draft, review
python examples/competitive_intel_agent.py  # 8 spans with realistic token/cost data

All of the above run without an API key. There's also a before/after pair (live_customer_support_agent_*.py) showing a real OpenAI-backed agent with and without instrumentation — those two need OPENAI_API_KEY.

Or skip the files entirely: glasspipe demo seeds four sample runs, including a failing one and two versions of the same agent so you can try run comparison and version filtering.

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[ COMPARE ] GlassPipe vs Langfuse vs LangSmith

Honest comparison. Pick the right tool for the job.

	GlassPipe	Langfuse	LangSmith
Install time	~60 seconds	~20 minutes	~20 minutes
Account required	Never	Yes	Yes
Public share in one click	Yes	No	No
Local dashboard	Yes	No	No
Team workspaces	No	Yes	Yes
Production monitoring	No	Yes	Yes
Async support	No	Yes	Yes
Price	Free, OSS	Free tier + paid	Free tier + paid

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[ LIMITS ] What v1 doesn't do

We'd rather you know now than discover it ten minutes in. 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, alerting, or production monitoring

If you need any of these today, use Langfuse, LangSmith, or Arize Phoenix. They're genuinely great tools.

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[ DEV ] Built with

Python 3.10+ · Flask · HTMX · SQLite · SQLAlchemy
Hosted share service: Railway + Postgres

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[ LIC ] License

MIT. Free forever. See LICENSE.

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[ CTRB ] Contributing

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

• Open an issue
• Start a discussion

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Built by Yonatan Michelson · glasspipe.dev