llm-context-compressor 0.2.0

Shrink LLM context windows by removing noise, redundancy, and long-tail detail — without losing signal.

Context Compressor

Shrink LLM context windows — removing noise, redundancy, and long-tail detail without losing the signal. Typically 40–80% fewer tokens depending on how repetitive the input is (benchmarks).

Long agent loops, verbose tool output, and 10,000-line security scans blow past even a 200K context window — and every redundant token you send costs latency and money. Context Compressor is a small, fast, rule-based pipeline that strips the fat out of any text before it reaches the model.

from context_compressor import ContextCompressor

compressor = ContextCompressor()
result = compressor.compress(noisy_log_text)

print(result.stats.summary())   # 136 -> 65 tokens (52.2% smaller, backend=tiktoken)
print(result.compressed)        # the cleaned text, ready to send to your LLM

---

Why

Problem	What happens	What this does
Context overflow	Multi-turn agents accumulate history until the window overflows and the run breaks.	Collapse repeated turns and boilerplate phrasing.
Verbose tool output	A vuln scan or SELECT * dumps thousands of near-identical lines, 90% noise.	Drop noise, dedupe rows, trim long-tail detail.
Token cost	Every wasted token is latency + dollars on every call.	40–80% token reduction on noisy input, measured with tiktoken.

It works on anything text: chat transcripts, application logs, JSON blobs, SQL result dumps, and security scanner output.

Highlights

• Zero required dependencies. Pure Python standard library. tiktoken is optional — without it a built-in heuristic counter is used automatically.
• Lossless-leaning by default. Removals are high-precision; counts are preserved (port 22 open [x3]) rather than silently dropped.
• Composable pipeline. Toggle each stage, tune thresholds, or add your own noise patterns via plain dataclass config.
• Measured, not guessed. Every run returns before/after token counts and a per-stage breakdown.
• Security-aware. A dedicated summarizer turns raw scanner output into a severity-ranked brief.
• CLI included. cat scan.log | context-compress --stats.

Install

pip install llm-context-compressor                 # zero dependencies
pip install "llm-context-compressor[tiktoken]"     # exact OpenAI/Anthropic-style token counts

Install name is llm-context-compressor; the import is import context_compressor (like scikit-learn → import sklearn).

Or from source:

git clone https://github.com/uninhibited-scholar/context-compressor
cd context-compressor
pip install -e ".[dev]"
pytest

How it works

The pipeline runs cheap, high-precision stages first, then optionally falls back to extractive summarization only if a target_ratio is requested and the rules didn't get there:

raw text
   │
   ▼  1. NoiseFilter        drop timestamps, progress bars, status chatter, separators
   ▼  2. DetailTrimmer      shorten long strings, cap JSON depth, collapse table runs, strip log metadata
   ▼  3. PatternRemover     collapse repeated agent phrasing ("as I mentioned…")
   ▼  4. RedundancyFilter   dedupe identical/near-identical lines, keep counts
   ▼  5. ExtractiveSummarizer   (optional) TextRank-style, only if still over target
   │
   ▼
compressed text  +  full token/stage metrics

Usage

Presets

from context_compressor import ContextCompressor, CompressionConfig

ContextCompressor(CompressionConfig.conservative())  # safe, lossless-ish
ContextCompressor()                                  # balanced default
ContextCompressor(CompressionConfig.aggressive())    # smallest output

Hit a target size

cfg = CompressionConfig(target_ratio=0.3)   # aim for 30% of the original
result = ContextCompressor(cfg).compress(long_transcript)

Tune any stage

from context_compressor import CompressionConfig, NoiseConfig

cfg = CompressionConfig()
cfg.noise.drop_log_levels = True
cfg.redundancy.near_duplicate = True
cfg.trim.max_list_items = 10
cfg.noise.extra_patterns.append((r"^TRACE:.*$", "trace_line"))  # your own rule

Security scan brief

brief = ContextCompressor().compress_security_scan(nessus_output, examples_per_type=3)
print(brief)

【Security Scan Summary】

Detected 11 findings across 6 categories.

🔴 SQL Injection [CRITICAL]: 2  (CVE-2024-2117)
    1. https://shop.local/search?q=test
    2. https://shop.local/item?id=42
🟠 Cross-Site Scripting (XSS) [HIGH]: 2
    ...
🟢 Open Port / Service [LOW]: 3
    1. 10.0.0.5
    … and 2 more

Command line

context-compress scan.log --stats
cat transcript.txt | context-compress --preset aggressive
context-compress nessus.txt --security
context-compress big.log --target 0.3 > small.log

Compressed text goes to stdout; metrics go to stderr, so pipes stay clean.

Reading the metrics

result = compressor.compress(text)
s = result.stats

s.original_tokens      # 136
s.compressed_tokens    # 65
s.reduction_pct        # 52.2
s.token_backend        # "tiktoken" or "heuristic"
for stage in s.stages:
    print(stage.name, stage.chars_removed, stage.details)

Benchmarks

Reproducible with python benchmarks/benchmark.py (token counts via tiktoken):

Dataset	Tokens before	Tokens after	Reduction	Time
Application log	13,479	7,864	41.7%	30 ms
Security scan	4,625	3,863	16.5%	11 ms
Agent transcript	3,172	2,665	16.0%	8 ms
JSON result dump	1,124	220	80.4%	1 ms

Reduction scales with how repetitive the input is — heavily duplicated logs and scan output compress much further than already-unique prose. See benchmarks/BENCHMARKS.md for the chart.

JSON blobs

result = compressor.compress_json(huge_json_string)   # caps depth, lists, long strings
print(result.compressed)

RAG: LangChain & LlamaIndex

Drop-in adapters compress retrieved chunks before they reach the model. They are dependency-free (they duck-type the document objects), so installing this package never pulls in either framework.

# LangChain — implements the BaseDocumentTransformer interface
from context_compressor.integrations import CompressorDocumentTransformer

transformer = CompressorDocumentTransformer()
smaller_docs = transformer.transform_documents(retrieved_docs)
# each doc.metadata["compression"] now records the token savings

# LlamaIndex — works on nodes / Documents
from context_compressor.integrations import compress_nodes

nodes = compress_nodes(retriever.retrieve("my query"))

Integrating with an agent loop

from context_compressor import ContextCompressor, CompressionConfig

compressor = ContextCompressor(CompressionConfig(target_ratio=0.4))

def before_model_call(history: str) -> str:
    # Compress accumulated context before each turn to stay under the window.
    return compressor.compress(history).compressed

API at a glance

Object	Purpose
ContextCompressor	The pipeline. .compress(text) -> CompressionResult.
CompressionConfig	All knobs; .aggressive() / .conservative() presets.
CompressionResult	.compressed text + .stats.
NoiseFilter, RedundancyFilter, DetailTrimmer, PatternRemover	Stages, usable standalone.
ExtractiveSummarizer	Dependency-free TextRank-style summarizer.
SecuritySummarizer	Scanner output → severity-ranked brief.
TokenCounter	tiktoken-backed counter with heuristic fallback.

Development

pip install -e ".[dev]"
pytest --cov=context_compressor      # 24 tests, ~94% coverage

中文简介

Context Compressor 是一个零依赖的 Python 库，用于在把文本送入大模型之前 压缩上下文：去除噪音（时间戳、进度条、状态消息）、合并重复行、裁剪长字符串/ 深层 JSON、折叠 Agent 重复话术，并可选地做抽取式摘要。典型可减少 50–80% 的 Token，配合 tiktoken 可获得与 OpenAI/Anthropic 对齐的精确计数。内置面向网络 安全扫描结果的专属摘要器，可将上万行扫描日志归纳为按风险等级排序的简报。

License

MIT