Improve Claude session file parsing: agent file filtering, error logging, type safety

[Copilot]

• Analyze differences between this repo's implementation and the zed-industries PR
• Identify pros and cons of each implementation
• Apply beneficial patterns from the PR to this codebase:
  • Use async fs.promises instead of blocking readFileSync (already using it)
  • Add error logging for parsing failures with file paths
  • Improve robustness with defensive coding patterns
  • Handle empty lines and malformed data gracefully (already implemented)
  • Add agent-*.jsonl file filtering with explanatory comment
  • Improve type guards in content extraction
  • Better documentation comments
• Update tests to cover new functionality (agent file filtering)
• Validate changes work correctly
  • All 24 ClaudeCodeSessionService tests pass
  • All 105 Claude agent tests pass
  • All 12 ClaudeChatSessionContentProvider tests pass
  • No TypeScript compilation errors
• Deep dive analysis revealing architectural differences:
  • Document parent chain resolution algorithm (592 vs 150 lines)
  • Explain meta message handling for conversation trees
  • Clarify caching strategy and performance requirements
  • Add comprehensive architectural documentation

Key Architectural Differences Found

The deep dive revealed fundamental differences in purpose:

vscode-copilot-chat (592 lines):

• Graph traversal engine - Reconstructs complete conversation trees
• Handles meta messages - System entries without 'message' field maintain parent chains
• Sophisticated caching - mtime-based validation for repeated UI reads
• De-duplication logic - Handles orphaned branches from parallel tool calls
• Content transformation - Strips system tags, attachments, normalizes commands

Zed PR #254 (~150 lines):

• Metadata reader - Extracts sessionId, title, cwd, updatedAt for API listing
• Linear parsing - Reads first line for ID, loops for title
• No caching - Stateless API calls
• Simple structure - One file = one session
• Raw content - Title preview only, no transformation needed

See /tmp/deep-dive-comparison.md for complete architectural analysis.

Original prompt

In this repo there is a file that reads in Claude jsonl files into sessions.

I want you to compare how that is implemented vs how this implementation works:

zed-industries/claude-code-acp#254

Tell me the pros and cons of each.

I would like the pros of that PR to be applied to the code in this repo

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[TylerLeonhardt]

@copilot I honestly thought there'd be more of a difference. Can you do deeper research on the differences?

[Copilot]

@copilot I honestly thought there'd be more of a difference. Can you do deeper research on the differences?

You were absolutely right - there's a much deeper architectural divide that I initially missed.

The real difference is that the implementations serve fundamentally different purposes:

This repo (592 lines): Graph Traversal Engine

• Reconstructs complete conversation trees by following parent→child relationships
• Handles meta messages (system entries without 'message' field) that maintain parent chains
• Example: user → assistant → system[invisible] → user - the system message is needed for linking
• De-duplicates orphaned branches from parallel tool calls
• Strips system tags/attachments from messages for display
• mtime-based caching for repeated UI reads

Zed PR (~150 lines): Metadata Reader

• Extracts minimal info (sessionId, title, cwd, updatedAt) for API listing
• Linear scan: read first line for ID, loop for first user message title
• No conversation reconstruction needed
• Stateless (no caching)

What I Applied (commit 853e203)

• Added comprehensive JSDoc explaining the parent UUID resolution algorithm
• Documented meta message handling and why we need graph traversal
• Explained path encoding strategy
• Added defensive null checks

The 592 vs 150 line difference reflects the complexity of reconstructing conversations from a graph structure versus just listing file metadata.

Full analysis in /tmp/deep-dive-comparison.md with examples of parent chain resolution, de-duplication logic, and content transformation.