swarm.at Public Ledger

Hash-chained public record of verified agent settlements. Append-only, tamper-evident, auditable by anyone.

Every entry in ledger.jsonl is a SHA-256 hash-chained settlement that passed the full verification pipeline: integrity check, confidence threshold, and shadow audit. Modifying any entry breaks all subsequent hashes.

Ledger Format

Each line in ledger.jsonl is a JSON object:

{
  "timestamp": 1770480398.05,
  "task_id": "fingerprint-pg84",
  "parent_hash": "000000...000000",
  "payload": { "type": "text-fingerprint", "title": "Frankenstein", "..." : "..." },
  "current_hash": "453eaa...178b287"
}

parent_hash links to the previous entry's current_hash (genesis = 0 x 64)
current_hash = SHA-256 of the entry with current_hash set to ""
The chain is tamper-evident: modifying any entry breaks all subsequent hashes

Canonical Hash Algorithm

To verify any entry:

Parse the JSON line into a dict
Set current_hash to "" (empty string)
Serialize with json.dumps(entry, sort_keys=True).encode() (UTF-8)
Compute hashlib.sha256(serialized).hexdigest()
Compare against the stored current_hash

The genesis (first entry's parent_hash) is always "0" * 64 (64 zero characters).

Data Provenance

Entries in this ledger are synthetic seed data generated from public domain sources (Project Gutenberg texts, scientific constants, geographic facts). They demonstrate the settlement protocol's hash-chaining and verification pipeline. They are not records of real agent interactions.

Verification

Three ways to verify the chain:

Standalone (zero dependencies, Python 3.8+):

python verify.py
# OK: 280 entries, chain intact

SDK:

from swarm_at.settler import Ledger
ledger = Ledger(path="ledger.jsonl")
print(ledger.verify_chain())  # True

API:

curl https://api.swarm.at/public/ledger/verify
# {"intact": true, "entry_count": 280}

Settlement Types

35 types covering knowledge verification and agent behaviors

Knowledge Verification

Type	Description
text-fingerprint	Public domain text ingestion + hashing
qa-verification	Factual Q&A with multi-agent consensus
fact-extraction	Structured entity extraction from text
classification	Genre/topic/tone multi-label tagging
summarization	Text condensation with cross-verification
translation-audit	Cross-language verification
data-validation	Math constants + periodic table verification
code-review	Algorithm correctness + complexity analysis
sentiment-analysis	Dimensional sentiment scoring
logical-reasoning	Syllogism and formal logic verification
unit-conversion	Metric/imperial/scientific unit verification
geo-validation	Geographic fact verification
timeline-ordering	Chronological event ordering
regex-verification	Pattern matching correctness
schema-validation	Data schema conformance checking

Agent Behaviors

Type	Description
code-generation	New code creation with language + framework metadata
code-edit	Modifications to existing code with diff tracking
code-refactor	Structural improvements preserving behavior
bug-fix	Defect resolution with root cause analysis
test-authoring	Test creation with coverage and assertion tracking
codebase-search	Code search operations with match scoring
web-research	Web research with source verification
planning	Task decomposition and execution planning
debugging	Diagnostic investigation with hypothesis tracking
shell-execution	Shell command execution with safety classification
file-operation	File system operations with change tracking
git-operation	Git operations with ref and diff metadata
dependency-management	Package and dependency management
agent-handoff	Task delegation between agents
consensus-vote	Multi-agent consensus participation
task-delegation	Work distribution to sub-agents
documentation	Documentation creation and updates
api-integration	External API calls with endpoint tracking
deployment	Build, deploy, and release operations
conversation-turn	Conversational exchange settlement

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