foldback-ai 0.1.0

Lossless context compression for LLM agents. Fold it down, fold it back. No data dropped, prompt cache preserved.

FoldBack

Context compression for LLM agents. Fold it down, fold it back.

FoldBack shrinks what your agent sends to the model — JSON tool outputs, logs, search results — using content-preserving transforms. No row is dropped, no value is discarded, and the provider prompt cache keeps hitting.

from foldback import compress

result = compress(messages, model="gpt-4o")
result.messages       # same format, fewer tokens — send these to the model
result.tokens_saved   # tokens saved
result.ratio          # 0.45 == 55% saved; 1.0 == nothing changed
result.transforms     # e.g. ["json:columnar"]

Why it exists

Most "context compression" tools made two expensive mistakes:

1. They compressed conversation history, dropping old messages — which busts the provider prompt cache on every call. On Anthropic that's a 90% discount thrown away.
2. They dropped data and hoped the model would ask for it back via a retrieval tool. If it doesn't realize data is missing, you get a confidently wrong answer with no error.

FoldBack refuses both:

• Passthrough is sacred. Everything before the last cache_control breakpoint is forwarded as the same objects — never copied, never re-serialized — so caches keep hitting.
• Only the live zone is touched (the latest user message / tool result).
• Token-gated. A transform is applied only when it actually reduces tokens. Otherwise the original is returned unchanged.

Guarantees, stated honestly

Two transform categories with different promises:

Content	Transform	Guarantee
JSON array of uniform objects	columnar (keys written once, rows as JSON arrays)	Reversible — exact round-trip, proven by property tests. restore_columnar() reconstructs the original.
Logs	strip ANSI, run-length-collapse consecutive identical lines to (xN)	Normalizing — no textual content lost; removes non-semantic bytes. Not byte-reversible.
Plain text	trim trailing whitespace, collapse blank-line runs	Normalizing — words and punctuation untouched.

The columnar transform only fires on uniform-schema arrays, so each row maps back to its keys unambiguously and "1" (string) never collides with 1 (number). Mixed-schema arrays are left untouched rather than compacted lossily.

from foldback import compress, restore_columnar
# round-trip proof
compressed = compress(messages).messages
# any columnar block is exactly restorable:
#   json.loads(restore_columnar(block)) == original_rows

Measured savings

Reproduce with python benchmarks/run.py --model gpt-4o (exact gpt-4o tokens):

Workload	Before	After	Saved
API response (100 rows)	2,803	1,421	49%
Build log (200 lines)	2,729	499	82%
Code search (50 hits)	1,892	1,159	39%

No marketing numbers — these come straight from the benchmark script.

Install

pip install foldback                 # zero dependencies
pip install "foldback[exact]"        # + tiktoken for exact token counts
pip install "foldback[anthropic]"    # + Anthropic SDK for the wrapper
pip install "foldback[openai]"       # + OpenAI SDK for the wrapper

Use it

Inline:

from foldback import compress, CompressConfig

result = compress(messages, model="claude-sonnet-4-5")
# or with options:
cfg = CompressConfig(model="gpt-4o", min_savings=0.2)  # require >=20% win
result = compress(messages, config=cfg)

Drop-in SDK wrappers (system prompt / tool defs stay frozen → cache-safe):

from anthropic import Anthropic
from foldback.integrations import with_anthropic

client = with_anthropic(Anthropic())
client.messages.create(model="claude-sonnet-4-5", messages=[...])  # auto-compressed

from openai import OpenAI
from foldback.integrations import with_openai

client = with_openai(OpenAI())
client.chat.completions.create(model="gpt-4o", messages=[...])     # auto-compressed

Develop

pip install -e ".[dev,exact]"
pytest                       # tests + coverage
ruff check foldback tests    # lint
mypy foldback                # strict type-check
python benchmarks/run.py     # savings table
python examples/demo.py

Deliberately NOT built

A network proxy, SSE streaming parser, Bedrock/Vertex signing, message scoring / relevance, a HuggingFace compression model, lossy row-dropping with retrieval. FoldBack is a library you call before your own SDK call — so it can never corrupt the wire.

Roadmap

• Diff / patch compaction
• CSV / Markdown-table input detection
• Rust core for the columnar path (only if profiling demands it)

Apache 2.0.