@blackwell-systems/gcf weekly downloads statistics
By blackwell-systems · npm · MCP servers
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.
@blackwell-systems/gcf has 5,962 recorded weekly downloads on npm, a decrease of 90.7% over the past 30 days, ranking #1097 of 2,391 tracked listings.
About @blackwell-systems/gcf
gcf-typescript
TypeScript implementation of GCF, the most token-efficient wire format for LLMs. A drop-in alternative to JSON and TOON for any structured data.
<p align="center"> <img src="https://raw.githubusercontent.com/blackwell-systems/gcf-typescript/main/assets/divider-wave-2.png" alt="" width="100%"> </p> <p align="center"> <img src="https://raw.githubusercontent.com/blackwell-systems/gcf-typescript/main/assets/gcf-ts-diagram.png" alt="gcf-typescript" width="80%"> </p> <p align="center"> <img src="https://raw.githubusercontent.com/blackwell-systems/gcf-typescript/main/assets/divider.png" alt="" width="100%"> </p>Built for the agentic loop, where the same structured context crosses the model boundary turn after turn. A single payload is 50-92% smaller than JSON, but GCF also deduplicates repeated structure across turns and sends only deltas when context changes, so by the 5th overlapping call each response costs 99% fewer tokens than JSON, and a 10-call session runs 94.4% cheaper than re-sending JSON every turn. Session dedup and delta both need local IDs and a multi-turn design that neither JSON nor TOON has.
- 100% comprehension on every frontier model, zero training. 29% fewer tokens than TOON and 56% fewer than JSON across 16 datasets; 91.2% on structurally complex code graphs (vs TOON 68.8%, JSON 54.1%).
- Proven lossless across 43,000,000,000+ round-trips in 5 formats and 6 languages. Zero runtime dependencies.
- One format, four properties no other single format holds at once: schema-free, lossless, token-compact (50-92% vs JSON), and model-readable with zero training. JSON is verbose, Protobuf needs a schema, MessagePack is binary, and TOON isn't reliably lossless.
2,500+ LLM evaluations. Full benchmarks.
Docs: gcformat.com · Playground · GCF vs TOON
Install
npm install @blackwell-systems/gcf
Zero dependencies. TypeScript-first. Includes CLI. Don't want to change code? Use the MCP proxy for zero-code adoption.
CLI
npx @blackwell-systems/gcf encode < payload.json # JSON to GCF
npx @blackwell-systems/gcf decode < payload.gcf # GCF to JSON
npx @blackwell-systems/gcf stats < payload.json # token comparison
Payload: 50 symbols, 20 edges
JSON ██████████████████████████████ 4,200 tokens
GCF ████████░░░░░░░░░░░░░░░░░░░░░░ 1,150 tokens
Savings: 73% fewer tokens with GCF
Or install globally: npm install -g @blackwell-systems/gcf then use gcf directly.
Library
Quick Start
import { encodeGeneric } from '@blackwell-systems/gcf';
const output = encodeGeneric({
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
import { decode } from '@blackwell-systems/gcf';
const p = decode(input);
console.log(p.tool, p.symbols.length, 'symbols', p.edges.length, 'edges');
Session Deduplication
Track transmitted symbols across multiple tool responses. Previously-sent symbols become bare references instead of full declarations:
import { Session, encodeWithSession } from '@blackwell-systems/gcf';
const sess = new Session();
const out1 = encodeWithSession(payload1, sess); // full declarations
const out2 = encodeWithSession(payload2, sess); // reused symbols as "@N # previously transmitted"
By the 5th call in a session: 86% fewer tokens than JSON from dedup alone, 99% stacked with delta encoding.
Streaming Encode
Write GCF output incrementally as symbols and edges arrive. Zero buffering, O(1) memory per row. Ideal for MCP servers that walk large graphs or paginate results:
import { StreamEncoder } from '@blackwell-systems/gcf';
const enc = new StreamEncoder(writer, 'context_for_task', { tokenBudget: 5000 });
// Symbols emit immediately as they're discovered.
enc.writeSymbol({ qualifiedName: 'pkg.Auth', kind: 'function', score: 0.95, provenance: 'lsp', distance: 0 });
enc.writeSymbol({ qualifiedName: 'pkg.Server', kind: 'function', score: 0.60, provenance: 'lsp', distance: 1 });
// Edges emit immediately too.
enc.writeEdge({ source: 'pkg.Server', target: 'pkg.Auth', edgeType: 'calls' });
// Close emits the ##! summary trailer with final counts.
enc.close();
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: string) method (Node.js streams, web WritableStreams, or a simple callback). Standard decode() handles streaming output with no changes.
Delta Encoding…
Description from the npm listing.
- gcf
- graph
- compact
- format
- llm
- tokens
- mcp
- wire-format
- delta
- multi-turn
- agents
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Charts
Images: 30d · 1y · all time · 1600×900 dark
Adoption over time
7/30/90-day growth
Ranking history
Listing placement
Listing placement
Filled points are observed values; hollow points come from Internet Archive snapshots of the listing page. Dashed segments span periods with no observations — values between points are not interpolated.
Each band is one page of 20. Weekly downloads on a linear scale. Placement is projected from weekly downloads, so it can differ from the marketplace's own sort order. Hover a dot to see the listing.
Recent observations
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Weekly downloads (trailing 7 complete days) come from the official npm downloads API. Downloads include CI and mirrors and are not equivalent to installs or users. npm publishes no ratings.
Listing: https://www.npmjs.com/package/@blackwell-systems/gcf. First seen Oct 16, 2026; last successful collection Oct 20, 2026. See the methodology.