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TDF — Timepoint Data Format

The universal data contract. JSON-LD interchange format for temporal causal data. Every service in the Timepoint suite speaks TDF. Content-addressed via SHA-256 hash.

GitHub

timepoint-ai/timepoint-tdf — Apache-2.0, Pure Python, Pydantic

Detailed Docs

Full schema reference, transforms, and integration docs

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Record Structure

TDFRecord:
    id: str           # Clockchain canonical URL or Flash/Pro UUID
    version: str      # "1.0.0"
    source: str       # "clockchain" | "flash" | "pro" | "proteus" | "snag-bench"
    timestamp: datetime
    provenance: TDFProvenance
        generator: str
        run_id: str
        confidence: float
        flash_id: str | None
    payload: dict     # Source-specific content
    tdf_hash: str     # SHA-256 of canonicalized payload

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Transforms

Convert service-specific output to TDF: 
from timepoint_tdf import from_flash, from_clockchain, from_pro

# From Flash scene
tdf_record = from_flash(flash_output)

# From Clockchain node
tdf_record = from_clockchain(node_data)

# From Pro simulation
tdf_record = from_pro(pro_output)

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Content Addressing

Every TDF record includes a tdf_hash — the SHA-256 of the canonicalized payload. This enables:
  • Deduplication across services
  • Integrity verification end-to-end
  • Immutable references between graph nodes

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Installation

pip install -e "git+https://github.com/timepoint-ai/timepoint-tdf.git#egg=timepoint-tdf"
Single dependency: pydantic>=2.0

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Service Integration

                ┌──────────────┐
                │  TDF v1.0.0  │
                │  (pure lib)  │
                └──────┬───────┘
                       │
          ┌────────────┼────────────┬──────────┐
          │            │            │          │
     from_flash() from_clockchain() from_pro() (planned)
          │            │            │          │
     ┌────▼───┐  ┌────▼──────┐ ┌──▼───┐ ┌───▼─────┐
     │ Flash  │  │Clockchain │ │ Pro  │ │Proteus  │
     └────────┘  └───────────┘ └──────┘ └─────────┘