gcf-python 2.4.0

The AI-native wire format for structured data. 50-92% fewer tokens than JSON, with multi-turn delta encoding for agent loops. 100% comprehension on every frontier model. Zero dependencies.

gcf-python

Python implementation of GCF — the most token-efficient wire format for LLMs. A drop-in alternative to JSON and TOON for any structured data.

100% comprehension on every frontier model tested. 29% fewer tokens than TOON, 56% fewer than JSON across 16 datasets. 91.2% on structurally complex code graphs (vs TOON 68.8%, JSON 54.1%). 2,400+ LLM evaluations. Zero training.

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_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

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.

Streaming Encode

Write GCF output incrementally as symbols and edges arrive. Zero buffering, O(1) memory per row:

from gcf import StreamEncoder, Symbol, Edge

enc = StreamEncoder(sys.stdout, "context_for_task", token_budget=5000)

enc.write_symbol(Symbol(qualified_name="pkg.Auth", kind="function", score=0.95, provenance="lsp", distance=0))
enc.write_symbol(Symbol(qualified_name="pkg.Server", kind="function", score=0.60, provenance="lsp", distance=1))
enc.write_edge(Edge(source="pkg.Server", target="pkg.Auth", edge_type="calls"))
enc.close()  # emits ##! summary trailer

Output:

GCF tool=context_for_task budget=5000
## targets
@0 fn pkg.Auth 0.95 lsp
## related
@1 fn pkg.Server 0.60 lsp
## edges [?]
@0<@1 calls
##! summary symbols=2 edges=1 counts=1,1,1

The writer is any object with a write(s: str) method. Thread-safe. Standard decode() handles streaming output with no changes.

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.

Generic-Profile Delta (multi-turn)

In an agent loop the same keyed table gets re-queried turn after turn. Instead of re-sending the whole table each time, send only the changed rows (SPEC §10a):

from gcf import GenericSet, diff_generic_sets, encode_generic_delta, verify_generic_delta

base = GenericSet(key="id", fields=["id", "status"], rows=[
    {"id": 1001, "status": "pending"},
    {"id": 1002, "status": "shipped"},
])
nxt = GenericSet(key="id", fields=["id", "status"], rows=[
    {"id": 1001, "status": "shipped"},   # changed
    {"id": 1003, "status": "pending"},   # added (1002 removed)
])

d = diff_generic_sets(base, nxt)
wire = encode_generic_delta(d)                       # ## added / ## changed / ## removed
held = verify_generic_delta(base, d, d.new_root)     # atomic apply + new_root verification

Opt-in and bilateral, keyed on content-addressed pack roots. By the 5th overlapping call, ~97% fewer tokens than re-sending JSON.

Re-anchor session helper

GenericDeltaSession manages the delta/re-anchor cadence for you: each next() returns either a compact delta or, on its cadence, a full re-anchor (which re-grounds the consumer), updating its held base.

from gcf import GenericDeltaSession, fixed_n, size_guard

sess = GenericDeltaSession(base, tool="orders", policy=size_guard())
wire = sess.current_full()                # transmit the base once to establish it
for snapshot in stream:                   # each turn's current GenericSet
    wire, is_full = sess.next(snapshot)    # a compact delta, or a periodic full re-anchor

fixed_n(15) re-anchors every N turns; size_guard() (recommended) re-anchors once the cumulative delta reaches a full payload's size. It introduces no new wire syntax and the decoder stays cadence-agnostic, so a re-anchor is just the protocol's "full" outcome on a schedule.

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 graph delta (added/removed only)
diff_generic_sets(base, next) -> GenericDeltaPayload	Diff two keyed record sets (generic profile)
encode_generic_delta(d) -> str / decode_generic_delta(s)	Generic-profile delta wire (§10a)
verify_generic_delta(base, d, root) -> GenericSet	Atomic apply + new_root verification
GenericDeltaSession(base, tool, policy)	Producer-side re-anchor cadence helper (§10a.8)
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 graph packs: added/removed symbols and edges
GenericSet / GenericDeltaPayload	Keyed record set and its generic-profile diff (§10a)
GenericDeltaSession	Stateful producer that schedules delta vs full re-anchor (§10a.8)
Session	Thread-safe tracker for multi-call deduplication
KIND_ABBREV / KIND_EXPAND	Bidirectional kind abbreviation dicts

Benchmarks

2,400+ LLM evaluations across 10 models, 3 providers, and 51 independent test runs.

	GCF	TOON	JSON
Comprehension (23 runs, 10 models)	91.2%	68.8%	54.1%
Generation (28 runs, 9 models)	5/5	1.0/5	5.0/5
Input tokens (500 symbols)	11,090	16,378	53,341
Output tokens (100 symbols)	5,976	8,937	16,121

GCF wins 15/16 datasets on the expanded token efficiency benchmark. Full results: gcformat.com/guide/benchmarks

Implementations

Language	Package	Repository
Go	go get github.com/blackwell-systems/gcf-go	gcf-go
TypeScript	npm install @blackwell-systems/gcf	gcf-typescript
Python	pip install gcf-python	gcf-python
Rust	cargo add gcf	gcf-rust
Swift	Swift Package Manager	gcf-swift
Kotlin	JitPack	gcf-kotlin
MCP Proxy	pip install gcf-proxy	gcf-proxy (bidirectional, session dedup, HTTP frontend)
Claude Code Plugin	/plugin install	gcf-claude-plugin (one-command install, session stats hook)
Codex Plugin	codex plugin add	gcf-codex-plugin (one-command install, session stats hook)
VS Code	ext install blackwell-systems.gcf-vscode	gcf-vscode (syntax highlighting)
n8n	npm install n8n-nodes-gcf	gcf-n8n-nodes (workflow encode/decode)
Tree-sitter	npm install tree-sitter-gcf	tree-sitter-gcf

Zero runtime dependencies. Permanently. All six implementations depend only on their language's standard library. No transitive dependencies. No supply chain risk. This is a permanent commitment: GCF will never take on external runtime dependencies. MIT licensed. All implementations support both generic profile (encodeGeneric) and graph profile (encode). CLI included in all 6 languages.

Specification: SPEC v3.2 Stable with 174 conformance fixtures, 43,000,000,000+ lossless round-trips verified across 5 formats and 6 languages. All implementations at v2.2.1+ (Go v1.3.1). Cross-language 6x6 matrix verified.

Adopted by

Chrome DevTools MCP (46K stars, Google Chrome DevTools team) · Speakeasy (API tooling, customers include Google, Verizon, Mistral AI, DocuSign, Vercel) · OmniRoute (6.1K stars) · NetClaw (556 stars) · ctx (510 stars) · NeuroNest · Open Data Products SDK (Linux Foundation) · Raycast · and more

License

MIT - Dayna Blackwell