gcf-python
Python implementation of GCF (Graph Compact Format) — the most token-efficient wire format for LLMs. A drop-in alternative to JSON and TOON for any structured data.
79% fewer input tokens than JSON. 75% fewer output tokens. 52% smaller than TOON. 100% LLM comprehension at 500 symbols, where JSON scores 76.9% and TOON scores 92.3%.
Docs: gcformat.com · Playground · GCF vs TOON
Install
pip install gcf-python
Zero dependencies. Pure Python. Python 3.9+. Includes CLI. Don't want to change code? Use the MCP proxy for zero-code adoption.
CLI
gcf encode < payload.json # JSON to GCF
gcf decode < payload.gcf # GCF to JSON
gcf stats < payload.json # token comparison with visual bar
Payload: 50 symbols, 20 edges
JSON ██████████████████████████████ 4,200 tokens
GCF ████████░░░░░░░░░░░░░░░░░░░░░░ 1,150 tokens
Savings: 73% fewer tokens with GCF
Library
Quick Start
from gcf import encode, Payload, Symbol, Edge
p = Payload(
tool="context_for_task",
token_budget=5000,
tokens_used=1847,
symbols=[
Symbol(qualified_name="pkg.AuthMiddleware", kind="function", score=0.78, provenance="lsp_resolved", distance=0),
Symbol(qualified_name="pkg.NewServer", kind="function", score=0.54, provenance="lsp_resolved", distance=1),
],
edges=[
Edge(source="pkg.NewServer", target="pkg.AuthMiddleware", edge_type="calls"),
],
)
output = encode(p)
Output:
GCF tool=context_for_task budget=5000 tokens=1847 symbols=2 edges=1
## targets
@0 fn pkg.AuthMiddleware 0.78 lsp_resolved
## related
@1 fn pkg.NewServer 0.54 lsp_resolved
## edges [1]
@0<@1 calls
Decode
from gcf import decode
p = decode(input_text)
print(p.tool, len(p.symbols), "symbols", len(p.edges), "edges")
Session Deduplication
Track transmitted symbols across multiple tool responses. Previously-sent symbols become bare references instead of full declarations:
from gcf import encode_with_session, Session, Payload, Symbol
sess = Session()
out1 = encode_with_session(payload1, sess) # full declarations
out2 = encode_with_session(payload2, sess) # reused symbols as "@N # previously transmitted"
By the 5th call in a session: 92.7% token savings vs JSON.
Delta Encoding
When the consumer already has a prior context pack, send only what changed:
from gcf import encode_delta, DeltaPayload, Symbol, Edge
delta = DeltaPayload(
tool="context_for_task",
base_root="aaa111",
new_root="bbb222",
removed=[Symbol(qualified_name="pkg.OldFunc", kind="function")],
added=[Symbol(qualified_name="pkg.NewFunc", kind="function", score=0.85, provenance="rwr")],
delta_tokens=30,
full_tokens=200,
)
output = encode_delta(delta)
81.2% savings on re-queries where the pack changed slightly.
Generic Encoding
Encode any Python value (not just graph payloads) into GCF tabular format:
from gcf import encode_generic
output = encode_generic({
"employees": [
{"id": 1, "name": "Alice", "department": "Engineering", "salary": 95000},
{"id": 2, "name": "Bob", "department": "Sales", "salary": 72000},
],
})
Output:
## employees [2]{id,name,department,salary}
1|Alice|Engineering|95000
2|Bob|Sales|72000
Works on dicts, lists, and primitives. Lists of uniform dicts get tabular rows. Nested dicts use ## key section headers.
API
| Function | Description |
|---|---|
encode(p: Payload) -> str |
Encode a graph payload to GCF text |
encode_generic(data: Any) -> str |
Encode any value to GCF tabular format |
decode(input_text: str) -> Payload |
Parse GCF text back to a Payload |
encode_with_session(p: Payload, s: Session) -> str |
Encode with session deduplication |
encode_delta(d: DeltaPayload) -> str |
Encode a delta (added/removed only) |
Session() |
Create a new session tracker (thread-safe) |
Types
| Type | Purpose |
|---|---|
Payload |
Full GCF payload: tool, budget, symbols, edges, pack root |
Symbol |
Graph node: qualified name, kind, score, provenance, distance |
Edge |
Directed relationship: source, target, edge type |
DeltaPayload |
Diff between two packs: added/removed symbols and edges |
Session |
Thread-safe tracker for multi-call deduplication |
KIND_ABBREV / KIND_EXPAND |
Bidirectional kind abbreviation dicts |
Comprehension Eval
Rigorous 3-way benchmark (GCF vs TOON vs JSON) at 500 symbols, 200 edges. 13 structured extraction questions sent to an LLM with zero format instructions:
| Format | Accuracy | Tokens | vs JSON |
|---|---|---|---|
| GCF | 100% (13/13) | 11,090 | 79% fewer |
| TOON | 92.3% (12/13) | 16,378 | 69% fewer |
| JSON | 76.9% (10/13) | 53,341 | baseline |
GCF is the only format with perfect accuracy at scale, at 32% fewer tokens than TOON.
Reproduce: git clone https://github.com/blackwell-systems/gcf-go && cd gcf-go/eval && GOWORK=off go test -run TestComprehension -v -timeout 0
Token Efficiency (TOON's Own Benchmark)
Running TOON's benchmark harness with GCF inserted (their datasets, their tokenizer):
| Track | GCF | TOON | Result |
|---|---|---|---|
| Mixed-structure (nested, semi-uniform) | 170,367 | 227,896 | GCF 34% smaller |
| Flat-only (tabular) | 66,029 | 67,837 | GCF 3% smaller |
| Semi-uniform event logs | 108,158 | 154,032 | GCF 42% smaller |
GCF wins all 6 datasets. On semi-uniform data (the most common real-world pattern), GCF uses 42% fewer tokens than TOON.
Reproduce: git clone https://github.com/blackwell-systems/toon && cd toon && git checkout gcf-comparison && cd benchmarks && pnpm install && pnpm benchmark:tokens
Links
- Documentation
- Playground
- Specification
- Go library
- TypeScript library
- MCP Proxy (zero-code adoption)
- GCF vs TOON
- TOON benchmark fork
License
MIT
Metadata
Release files for gcf-python 0.3.1
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|---|---|---|---|---|
| gcf_python-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 34.5 kB
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