prompt-flamegraph

Lightweight, zero-dependency Python package to profile LLM prompt tokens with interactive flamegraphs, waste detection, prompt diffs and HTML/SVG/Markdown/terminal exports.

What it does

prompt-flamegraph takes a structured prompt (system prompt, tools, RAG context, chat history) and shows you where the tokens go, in an interactive flamegraph.

It is intentionally lightweight: no proxy, no server, no dashboard, no telemetry. One function call, one output file.

Install

pip install prompt-flamegraph

Extras:

pip install prompt-flamegraph[tiktoken]   # accurate OpenAI-style token counts
pip install prompt-flamegraph[rich]       # prettier terminal output

Quick start

from prompt_flamegraph import profile_prompt

prompt = {
    "system_prompt": "You are a helpful coding assistant.",
    "tools": ["..."],
    "rag_context": {"doc_1": "..."},
    "chat_history": ["..."],
}

profile_prompt(prompt, output="context.html")

Open context.html in your browser.

Waste detection

Identify token waste before sending the prompt to an API:

from prompt_flamegraph import build_tree, detect_waste

prompt = {
    "system_prompt": "You are a helpful coding assistant.",
    "tools": ["read_file", "write_file", "run_command", "search_web", "send_email", "create_ticket"],
    "rag_context": {
        "doc_1.py": "def helper():\n    return 'value'\n",
        "doc_2.py": "def helper():\n    return 'value'\n",
        "doc_3.py": "def helper():\n    return 'value'\n",
    },
    "chat_history": ["Hi!"] * 10,
}

tree = build_tree(prompt, name="prompt")
report = detect_waste(tree)

print(f"Wasted: {report.wasted_tokens} / {report.total_tokens} tokens ({report.waste_ratio:.1%})")
for finding in report.findings:
    print(f"- {finding.kind}: {finding.message}")

Example output:

Wasted: 26 / 88 tokens (29.5%)
- duplicate: 3× duplicate text ('def helper():     return 'value' ') — keep only one
- duplicate: 5× duplicate text ('Hi!') — keep only one
- too_many_tools: 6 tools defined — only declare the ones the model actually calls

Pass detect_waste=True to profile_prompt() to include findings directly in the HTML report.

CLI

# HTML flamegraph
prompt-flamegraph prompt.json -o context.html --cost 1.5e-6

# Terminal bar chart
prompt-flamegraph prompt.json --terminal

# Diff between two prompts (green = added, red = removed, orange = changed)
prompt-flamegraph v1.json --diff v2.json -o diff.html

# SVG or Markdown export
prompt-flamegraph prompt.json --format svg -o context.svg
prompt-flamegraph prompt.json --format md -o context.md

# Demo
prompt-flamegraph --demo --cost 1.5e-6

Terminal example

────────────────────────────── Prompt Flamegraph ──────────────────────────────
Total: 102 tokens
 Category         Tokens      %  Visual
 system_prompt        18  17.6%  ████
 tools                41  40.2%  ██████████
 rag_context          22  21.6%  █████
 chat_history         21  20.6%  █████

Features

• Pure Python, no required dependencies.
• Optional tiktoken support.
• Pluggable tokenizer.
• Cost estimation.
• Token waste detection: duplicates, oversized RAG, long history, too many tools.
• Prompt diff: compare two prompts and visualize token changes.
• Terminal output: colored ASCII/Rich bar chart.
• Export formats: HTML, SVG, Markdown.
• Works with nested dict, list and str structures.

API

profile_prompt(data, output, title, tokenizer, cost_per_token, detect_waste, width, height)

Build and render a prompt flamegraph to HTML.

diff_prompts(v1, v2, output, title, ...)

Render a diff flamegraph between two prompts.

detect_waste(tree)

Analyze a tree and return a WasteReport with findings.

build_tree(data, name, tokenizer)

Build the internal token tree without rendering.

Resources

• Dev.to article with a step-by-step walkthrough: Stop Guessing Where Your LLM Prompt Tokens Go: prompt-flamegraph
• PyPI package page: https://pypi.org/project/prompt-flamegraph/
• Companion optimizer: prompt-optimizer — generate optimization recommendations and trim your prompts automatically.

Source

https://github.com/fjjjuv/prompt-flamegraph

License

This project is licensed under the GNU General Public License v3.0 or later.

See the LICENSE file for details.