Metadata-Version: 2.4
Name: python-token-killer
Version: 0.2.0
Summary: Minimize LLM tokens from Python objects — dicts, code, logs, diffs, and more.
Project-URL: Homepage, https://github.com/amahi2001/python-token-killer
Project-URL: Repository, https://github.com/amahi2001/python-token-killer
Project-URL: Issues, https://github.com/amahi2001/python-token-killer/issues
Project-URL: Changelog, https://github.com/amahi2001/python-token-killer/blob/main/CHANGELOG.md
Author-email: amahi2001 <amahi2001@gmail.com>
License: MIT License
        
        Copyright (c) 2026 ptk contributors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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License-File: LICENSE
Keywords: agents,claude,compression,context-window,langchain,langgraph,llm,nlp,openai,rag,tokens
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing
Classifier: Typing :: Typed
Requires-Python: >=3.10
Provides-Extra: tiktoken
Requires-Dist: tiktoken>=0.7; extra == 'tiktoken'
Description-Content-Type: text/markdown

<p align="center">
  <img src="assets/mascot.png" alt="ptk" width="200"/>
</p>

<p align="center">
  <strong>ptk — Python Token Killer</strong><br/>
  <strong>One call. Any Python object. Fewer tokens.</strong><br/>
  Zero dependencies · Auto type detection · 361 tests
</p>

<table align="center">
  <tr>
    <td align="left" valign="middle">
      <a href="https://github.com/amahi2001/python-token-killer/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/amahi2001/python-token-killer/ci.yml?branch=main&style=flat-square&label=CI" alt="CI"/></a><br/>
      <img src="https://img.shields.io/badge/python-3.10+-3776AB?style=flat-square&logo=python&logoColor=white" alt="Python 3.10+"/><br/>
      <img src="https://img.shields.io/badge/mypy-strict-blue?style=flat-square" alt="mypy strict"/><br/>
      <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-yellow?style=flat-square" alt="License"/></a>
    </td>
  </tr>
</table>

---

## Your LLM calls carry dead weight

A typical API response you feed into an agent:

```json
{
  "user": {
    "id": 8821,
    "name": "Alice Chen",
    "email": "alice@example.com",
    "bio": null,
    "avatar_url": null,
    "phone": null,
    "address": null,
    "metadata": {},
    "preferences": {
      "theme": "dark",
      "notifications": null,
      "newsletter": null
    },
    "created_at": "2024-01-15T10:30:00Z",
    "updated_at": "2024-06-20T14:22:00Z",
    "last_login": null,
    "is_verified": true,
    "is_active": true
  },
  "errors": null,
  "warnings": []
}
```

Seven null fields, two empty containers. Your LLM reads them, bills you for them, learns nothing from them. `ptk` strips the noise:

```python
import ptk
ptk(response)
```

```json
{"user":{"id":8821,"name":"Alice Chen","email":"alice@example.com","preferences":{"theme":"dark"},"created_at":"2024-01-15T10:30:00Z","updated_at":"2024-06-20T14:22:00Z","is_verified":true,"is_active":true}}
```

52% fewer tokens. Same information. No config needed.

```bash
pip install python-token-killer
# or
uv add python-token-killer
```

---

## Benchmarks

Token counts via tiktoken (`cl100k_base`, the tokenizer behind GPT-4 and Claude):

```
Input                          Tokens (before)   Tokens (after)   Saved
─────────────────────────────────────────────────────────────────────────
API response (JSON)                    1,450              792      45%
Python module (code → sigs)            2,734              309      89%
CI log (58 lines, errors only)         1,389              231      83%
50 user records (tabular)              2,774              922      67%
Verbose prose (text)                     101               74      27%
─────────────────────────────────────────────────────────────────────────
Total                                 11,182            2,627      76%
```

At Claude Sonnet 4.6 pricing ($3/1M input tokens), a 76% reduction on 100k tokens/day saves ~$6/month per user. Multiply that by your user base and your agent loop iterations.

Run it yourself: `python benchmarks/bench.py`

---

## How it works

You pass `ptk` any Python object. It detects the content type and picks the right compression strategy:

| Input           | Strategy                                                                                                   | Savings |
| --------------- | ---------------------------------------------------------------------------------------------------------- | ------- |
| `dict` / `list` | Strips `null`, `""`, `[]`, `{}` recursively. Tabular encoding for uniform arrays.                          | 40–70%  |
| Code            | Strips comments (preserves `# noqa`, `# type: ignore`, `TODO`). Collapses docstrings. Extracts signatures. | 25–89%  |
| Logs            | Collapses duplicate lines with counts. Filters to errors and stack traces.                                 | 60–90%  |
| Diffs           | Folds unchanged context. Strips git noise (`index`, `old mode`).                                           | 50–75%  |
| Text            | Abbreviates verbose words (`implementation→impl`, `configuration→config`). Removes filler.                 | 10–30%  |

---

## Usage

```python
import ptk

# ── auto-detected, one call ──────────────────────────────────
ptk.minimize(api_response)        # dict/list → compact JSON, nulls stripped
ptk.minimize(source_code)         # strips comments, collapses docstrings
ptk.minimize(log_output)          # dedup repeated lines, keep errors
ptk.minimize(git_diff)            # fold context, keep changes
ptk.minimize(any_object)          # always returns a string, never raises

# ── aggressive mode: maximum compression ─────────────────────
ptk.minimize(response, aggressive=True)

# ── force content type ───────────────────────────────────────
ptk.minimize(text, content_type="code", mode="signatures")  # sigs only
ptk.minimize(logs, content_type="log", errors_only=True)    # errors only

# ── stats: token counts + savings ────────────────────────────
ptk.stats(response)
# {
#   "output": "...",
#   "original_tokens": 1450,
#   "minimized_tokens": 792,
#   "savings_pct": 45.4,
#   "content_type": "dict"
# }

# ── callable shorthand ───────────────────────────────────────
ptk(response)  # same as ptk.minimize(response)

# ── preserve nulls when they carry meaning ───────────────────
ptk.minimize({"status": "pending", "error": None}, strip_nulls=False)
# → {"status":"pending","error":null}
```

---

## Real-world examples

### RAG pipeline: compress retrieved docs before they hit the prompt

Your retriever returns full documents. The LLM needs the content, not the metadata scaffolding around it.

```python
import ptk

def build_context(docs: list[dict]) -> str:
    """Compress retrieved docs before injecting into an LLM prompt."""
    chunks = []
    for doc in docs:
        content = ptk.minimize(doc["content"])   # strip boilerplate
        chunks.append(f"[{doc['source']}]\n{content}")
    return "\n\n---\n\n".join(chunks)
```

Full working demo with token counts: [`examples/rag_pipeline.py`](examples/rag_pipeline.py)

---

### LangGraph / LangChain: compress tool outputs between nodes

Drop this node between a tool call and the next LLM call. Tool outputs shrink before they re-enter the context window.

```python
import ptk

def compress_tool_output(state: dict) -> dict:
    """Compress the last tool message before the next LLM call."""
    state["messages"][-1]["content"] = ptk.minimize(
        state["messages"][-1]["content"], aggressive=True
    )
    return state
```

Complete agent loop with per-step token savings: [`examples/langgraph_agent.py`](examples/langgraph_agent.py)

---

### Log triage: feed only failures to your LLM

A 10,000-line CI log collapses to the failures and their stack traces.

```python
import ptk

errors = ptk.minimize(ci_log, content_type="log", aggressive=True)
# 80%+ fewer tokens, same diagnostic signal.
```

Before/after demo: [`examples/log_triage.py`](examples/log_triage.py)

---

## API reference

### `ptk.minimize(obj, *, aggressive=False, content_type=None, **kw) → str`

- **`aggressive=True`** maximizes compression: timestamps stripped, signatures-only for code, errors-only for logs
- **`content_type`** overrides auto-detection: `"dict"`, `"list"`, `"code"`, `"log"`, `"diff"`, `"text"`
- **`format`** controls dict output: `"json"` (default), `"kv"`, `"tabular"`
- **`mode`** controls code output: `"clean"` (default) or `"signatures"`
- **`errors_only`** filters logs to errors and stack traces

### `ptk.stats(obj, **kw) → dict`

Same interface as `minimize`. Returns `output`, `original_tokens`, `minimized_tokens`, `savings_pct`, `content_type`.

### `ptk(obj)` callable shorthand

The module itself is callable. `ptk(x)` equals `ptk.minimize(x)`.

---

## Comparison

| Tool                                                              | Type           | Tradeoff                                          |
| ----------------------------------------------------------------- | -------------- | ------------------------------------------------- |
| **ptk**                                                           | Python library | One call, any Python object, zero deps            |
| [RTK](https://github.com/rtk-ai/rtk)                              | Rust CLI       | Compresses shell command output for coding agents |
| [claw-compactor](https://github.com/open-compress/claw-compactor) | Python library | 14-stage AST-aware pipeline, heavier setup        |
| [LLMLingua](https://github.com/microsoft/LLMLingua)               | Python library | Neural compression, requires GPU                  |

---

## Design

- **Zero required dependencies.** Stdlib only. `tiktoken` is optional for exact token counts.
- **Never raises.** Any Python object produces a string. Circular refs, `bytes`, `nan`, generators all handled.
- **Never mutates.** Your input stays untouched.
- **Thread-safe.** Stateless singleton minimizers.
- **Fast.** Precompiled regexes, `frozenset` lookups, single-pass algorithms. Microseconds per call.

---

## Development

```bash
git clone https://github.com/amahi2001/python-token-killer.git
cd python-token-killer
uv sync          # installs all dev dependencies, creates .venv automatically
make check       # lint + typecheck + 361 tests
```

## License

MIT
