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Warp

Warp Oz agent sessions from Warp's local SQLite database.

  • Source: src/providers/warp.ts
  • Loading: lazy (src/providers/index.ts)
  • Test: tests/providers/warp.test.ts

Where it reads from

A SQLite database in Warp's group container.

Channel Default path
Stable ~/Library/Group Containers/2BBY89MBSN.dev.warp/Library/Application Support/dev.warp.Warp-Stable/warp.sqlite
Preview ~/Library/Group Containers/2BBY89MBSN.dev.warp/Library/Application Support/dev.warp.Warp-Preview/warp.sqlite

Override with WARP_DB_PATH when needed.

Storage format

SQLite. The parser requires these tables:

  • agent_conversations
  • ai_queries
  • blocks

Caching

No provider-specific cache. Standard CodeBurn session cache applies.

Deduplication

Per exchange: warp:<conversationId>:<exchangeId>.

What we extract

codeburn field Warp source
sessionId agent_conversations.conversation_id
timestamp ai_queries.start_ts
userMessage ai_queries.input[0].Query.text (when present)
project / projectPath ai_queries.working_directory
model ai_queries.model_id with fallback to dominant conversation_data.token_usage[*].model_id
tools / bashCommands blocks.stylized_command grouped onto nearest preceding exchange
inputTokens Estimated from prompt-size weighting, normalized to conversation token totals

outputTokens are set to 0 because Warp does not expose a reliable per-exchange input/output split in these tables.

Quirks

  • ai_queries.output_status may be stored as a JSON-quoted string (for example "Completed"). The parser normalizes this before filtering.
  • Warp auto model IDs (auto-efficient, auto-powerful) are resolved using dominant conversation model information when available.
  • Token and cost attribution is estimated. Calls are emitted with costIsEstimated: true.
  • Command blocks are attached to the closest preceding exchange in the same conversation based on start_ts.

When fixing a bug here

  1. Reproduce with a fixture SQLite in tests/providers/warp.test.ts before changing parser logic.
  2. Verify schema compatibility first (agent_conversations, ai_queries, blocks) before debugging parse behavior.
  3. If model/cost looks wrong, inspect both ai_queries.model_id and conversation_data.conversation_usage_metadata.token_usage.
  4. If command attribution is wrong, compare blocks.start_ts ordering against ai_queries.start_ts; attribution is timestamp-based.