cc-transcript 0.6.0

Typed events for Claude Code transcripts: discovery, a superset JSONL parser (Python + Rust), and ingestion-state tracking.

cc-transcript

cc-transcript parses Claude Code's on-disk JSONL transcripts into a typed superset event model — every entry type preserved, nothing dropped — so you build on one faithful representation and apply your own semantic filtering on top.

The one property that makes it worth using: the parser is non-lossy. It never silently discards sidechains, synthetic turns, tool results, or unrecognized entry types; filtering is opt-in and lives in your code, not buried in the parser.

Install

uv add cc-transcript        # or: pip install cc-transcript

Quickstart

Discover the transcripts on disk, parse one, and look at the events:

from cc_transcript import TranscriptDiscovery, parse_events, AssistantEvent, UserEvent

events = parse_events(TranscriptDiscovery.find_transcripts()[0])

for event in events:
    match event:
        case UserEvent(text=text):
            print("user:", text[:80])
        case AssistantEvent(model=model, text=text):
            print(f"assistant ({model}):", text[:80])

Compose a filter from small building blocks and apply it. The builders return clauses, build_spec assembles them into a spec, and apply_spec yields the survivors:

from cc_transcript import apply_spec, build_spec, keep_only, drop_junk, drop_short

spec = build_spec(keep_only("user", "assistant"), drop_junk("structural"), drop_short(2))
clean = list(apply_spec(events, spec))

NOISE_SPEC is a ready-made spec for the universal structural noise (system reminders, local-command output, skill banners). For flag-style filtering, FilterConfig is also available — every rule is off by default, so a bare FilterConfig() passes everything through.

What problems does this solve?

• One faithful parse. Anything reading Claude Code transcripts re-implements the same JSONL quirks (str-or-list content, tool results nested two ways, envelope-less mode markers). This is that parser, written once and typed strictly.
• Non-lossy by design. The event model is a superset: sidechains, <synthetic> turns, thinking blocks, and unrecognized entry types all survive parsing. You decide what to drop, via composable filter specs (build_spec) or FilterConfig.
• Incremental ingestion. FileStateStore tracks per-file mtimes in SQLite (WAL, thread-safe) so re-runs only reparse changed files, and you compose your own writes in the same transaction.
• Pluggable backends. A Rust backend (PyO3 + rayon) is the default fast path, with a pure-Python reference parser behind the same Backend protocol as the fallback — both at full event parity.

Docs

Read the docs for the full guides — Getting Started, Filtering events, Scoring sentiment, Rust/Python backends & parity, and Compose your own policy — plus the complete API reference.