coil-python 0.7.0

COIL - Token-optimized structured data encoding for LLM pipelines. Compress JSON into compact, schema-aware blocks that reduce LLM token usage by 40-70%.

🧬 coil_python — COIL v0.7.0

Token-optimized structured data encoding for LLM pipelines.

COIL (Compact Object Input Language) compresses JSON into a compact, schema-aware representation that reduces LLM token usage by 40–70% while remaining fully lossless and reversible.

Unlike general-purpose compression, COIL is designed around how transformers tokenize text. It detects table-shaped arrays, builds a greedy value-alias map using hill-climbing optimisation, and encodes rows as pipe-delimited strings — squeezing the same information into far fewer tokens.

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✨ Features

• 40–70% token reduction on structured JSON payloads
• Lossless round-trip — integers, floats, booleans, None, and arrays all survive encode → decode with their exact Python types
• Nested object support — deep dicts are flattened to dot-notation keys and reconstructed on decode
• Hill-climbing vmap — Algorithm 1 from the COIL paper; only commits substitutions that strictly reduce token count (no negative savings)
• Per-file type registry — input_coil_types.json alongside your data, never a shared global file
• Terminal visuals — bar_chart, graph, report for instant feedback
• matplotlib charts — show_charts for richer visual analysis (optional)
• CLI — encode, decode, and stats from the command line
• tiktoken optional — falls back to a character-length heuristic if not installed

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📦 Installation

pip install coil_python

With real token counting (recommended for production):

pip install "coil_python[tiktoken]"

With matplotlib charts:

pip install "coil_python[all]"

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🚀 Quick Start

import json
import coil_python as coil

# Load your data
with open("data.json") as f:
    data = json.load(f)

# Encode → writes data_coil_types.json alongside the encoded output
type_file = coil.types_file_path("data.json")   # "data_coil_types.json"
encoded   = coil.encode(data, type_file=type_file)

# Save encoded output
with open("data_encoded.json", "w") as f:
    json.dump(encoded, f, indent=2)

# Decode → restores exact types
decoded = coil.decode(encoded, type_file=type_file)

# Verify losslessness
print(coil.is_lossless(data, decoded))  # True

# Stats
s = coil.stats(data, encoded, decoded, out="coil_stats.json")
coil.report(s)

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🖥️ CLI

After installation the coil command is available:

# Encode
coil encode data.json
# → data_encoded.json  +  data_coil_types.json

# Encode with explicit output paths
coil encode data.json -o out_encoded.json --type-file out_types.json

# Decode
coil decode data_encoded.json
# → data_encoded_decoded.json

# Stats dashboard
coil stats data.json data_encoded.json

# Library info
coil info

Or via the module:

python -m coil_python encode data.json
python -m coil_python decode data_encoded.json
python -m coil_python stats data.json data_encoded.json

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📘 API Reference

coil.encode(data, *, type_file="coil_types.json", return_types=False)

Encodes a Python object into COIL format.

COIL automatically:

• Detects table-shaped arrays (list[dict]) and encodes them positionally
• Flattens nested dicts to dot-notation keys ({"a": {"b": 1}} → {"a.b": 1})
• Builds a hill-climbing value-alias map — only commits aliases that save tokens
• Stores precise column types to type_file for lossless decode

encoded = coil.encode(data)

# Per-file type registry (recommended — avoids collisions)
type_file = coil.types_file_path("employees.json")  # "employees_coil_types.json"
encoded   = coil.encode(data, type_file=type_file)

# Get the type registry inline
encoded, registry = coil.encode(data, return_types=True)

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coil.decode(encoded_data, *, type_file="coil_types.json")

Restores original data from COIL encoding.

decoded = coil.decode(encoded)
decoded = coil.decode(encoded, type_file="employees_coil_types.json")

The type_file must match the path used during encode().

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coil.verify(original, encoded, *, type_file="coil_types.json")

Decodes and checks losslessness in one step.

result = coil.verify(data, encoded)
print(result["lossless"])   # True / False
decoded = result["decoded"]

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coil.stats(original, encoded, decoded=None, *, out="coil_stats.json")

Computes compression metrics and saves them to out.

s = coil.stats(data, encoded, decoded)
print(s["comparison"]["token_saving_percent"])   # e.g. 62.79
print(s["comparison"]["compression_ratio"])       # e.g. 2.69
print(s["lossless"])                              # True

Returns a dict with original, encoded, comparison, and optionally lossless.

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coil.is_lossless(original, decoded)

Deep structural equality check for any JSON-compatible object.

print(coil.is_lossless(data, decoded))  # True

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coil.bar_chart(stats, metric="tokens")

Horizontal bar chart in the terminal.

coil.bar_chart(stats, "tokens")   # token count comparison
coil.bar_chart(stats, "bytes")    # byte size comparison
coil.bar_chart(stats, "ratio")    # compression ratio bar
coil.bar_chart(stats, "chars")    # character count

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coil.graph(stats, metric="full")

Multi-metric summary graph.

coil.graph(stats, "savings")   # token & byte savings with colour bars
coil.graph(stats, "size")      # side-by-side size comparison
coil.graph(stats, "full")      # everything: savings + size + quality

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coil.report(stats)

Full compression dashboard — the clearest single-call summary.

coil.report(stats)

╔══════════════════════════════════════════════╗
║           COIL Compression Report            ║
╠══════════════════════════════════════════════╣
║  Original  │ 8154   tokens │ 32614    bytes  ║
║  Encoded   │ 3034   tokens │ 12134    bytes  ║
╠══════════════════════════════════════════════╣
║  Token saved  : █████████████░░░░░░░ 62.79%  ║
║  Bytes saved  : █████████████░░░░░░░ 62.8%   ║
╠══════════════════════════════════════════════╣
║  Compression  : 2.69x                        ║
║  Lossless     : ✅ YES                        ║
╚══════════════════════════════════════════════╝

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coil.show_charts(stats)

Matplotlib bar charts for token count, byte size, and savings summary. Requires pip install "coil_python[charts]".

coil.show_charts(stats)

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coil.types_file_path(input_file)

Derives the type-registry filename from an input file path.

coil.types_file_path("data/employees.json")
# → "data/employees_coil_types.json"

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coil.debug_mode(flag=True)

Enable verbose internal logging.

coil.debug_mode(True)
# [COIL] Encoding → type_file=coil_types.json, model=default
# [COIL] Encoding complete

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coil.set_model(model_name)

Select the tokenizer backend for token counting.

coil.set_model("gpt-4o-mini")   # uses tiktoken if installed
coil.set_model("claude-4")      # anthropic (heuristic fallback)
coil.set_model("default")       # character-length heuristic (no deps)

Supported: "gpt-4o", "gpt-4o-mini", "gpt-4.1", "claude-3", "claude-4", "gemini", "mistral", "default".

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coil.info()

Returns library metadata.

coil.info()
# {
#   "library":   "coil_python",
#   "version":   "0.5.0",
#   "ecosystem": "python",
#   "model":     "default",
#   "debug":     False,
#   "purpose":   "Token-optimized structured data encoding for LLMs"
# }

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🔁 Complete Example

import json
import coil_python as coil

# ── Setup ──────────────────────────────────────────────────────────────────────
coil.set_model("gpt-4o-mini")   # use tiktoken for accurate token counts
coil.debug_mode(False)

# ── Load ───────────────────────────────────────────────────────────────────────
with open("telemetry.json") as f:
    data = json.load(f)

# ── Encode ─────────────────────────────────────────────────────────────────────
type_file = coil.types_file_path("telemetry.json")
encoded   = coil.encode(data, type_file=type_file)

with open("telemetry_encoded.json", "w") as f:
    json.dump(encoded, f, indent=2)

# ── Decode ─────────────────────────────────────────────────────────────────────
decoded = coil.decode(encoded, type_file=type_file)

with open("telemetry_decoded.json", "w") as f:
    json.dump(decoded, f, indent=2)

# ── Verify ─────────────────────────────────────────────────────────────────────
result = coil.verify(data, encoded, type_file=type_file)
print("Lossless:", result["lossless"])

# ── Analyse ────────────────────────────────────────────────────────────────────
s = coil.stats(data, encoded, decoded, out="telemetry_stats.json")

coil.report(s)
coil.bar_chart(s, "tokens")
coil.bar_chart(s, "ratio")
coil.graph(s, "full")
coil.show_charts(s)   # requires matplotlib

print(coil.info())

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🧠 How COIL Works

COIL applies a two-stage algorithm to each table-shaped array it encounters:

Stage 1 — Structural analysis Arrays of uniform dicts are detected as tables. Nested dicts are flattened to dot-notation keys (user.name, location.city) so the entire record fits into a single flat row. The original nesting is reconstructed on decode.

Stage 2 — Hill-climbing vmap (Algorithm 1)

1. Candidate values are sorted by freq(v) × tokenCount(v) descending.
2. Each iteration finds the single best alias assignment.
3. An alias is committed only if it strictly reduces the token count.
4. A stall counter stops the loop after 6 consecutive non-improving candidates.
5. Alias tokens are compact: V1..V9, VA..VZ, VAA...

The result is a META header (column order, alias map) and a BODY of pipe-delimited rows — a format that is dense for transformer attention while remaining fully decodable.

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⚠️ Important: type_file pairing

encode() writes a type registry file and decode() reads it. Both calls must use the same type_file path, and the file must exist when you call decode().

# ✅ Correct — same type_file path
encoded = coil.encode(data, type_file="my_types.json")
decoded = coil.decode(encoded, type_file="my_types.json")

# ❌ Wrong — default path, then missing file on another machine
encoded = coil.encode(data)           # writes coil_types.json locally
decoded = coil.decode(encoded)        # reads coil_types.json — must exist!

For multi-file pipelines, use types_file_path() to generate a unique name per input file automatically.

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📊 Benchmark

Tested on mixed_test.json (smart-city analytics payload with 6 embedded tables, nested objects, mixed types, nulls, and array columns):

Metric	Value
Original tokens	3,000
Encoded tokens	1,823
Token saving	39.2%
Byte saving	39.3%
Compression ratio	1.65×
Lossless	✅

On highly repetitive datasets (sensor logs, transaction records) savings reach 60–70% with compression ratios above 2.5×.

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📋 Known Limitations

• Token counts use a ceil(len / 4) heuristic unless tiktoken is installed. Install with pip install "coil_python[tiktoken]" for accurate counts.
• COIL falls back to the original data when encoding would not reduce size, so not every JSON document will be compressed.
• The type_file must be kept alongside the encoded JSON for decode to work. It is not embedded in the encoded output.

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📜 License

MIT

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👤 Author

Muthukumaran S & Bala Jothi Adithiya S — Creator of the COIL Protocol

Contact - [e-mail] , [e-mail]

If you use COIL in research, please cite it as: COIL — Compact Object Input Language, 2026.