Understanding Codebase and Session Storage Differences

Summary

Hi, can u understand this codebase

Sure! Let me explore the codebase to get a solid understanding of it.‍

Yes, I've got a solid understanding of the codebase now. Here's the overview:

Traces — Platform for Sharing AI Coding Agent Traces

Traces is a platform where developers can share, discover, and discuss traces (conversation logs) from AI coding agents like Claude Code, Cursor, Codex, Gemini CLI, and others. Think of it as a social platform for AI coding sessions.

Architecture (Monorepo with 4 packages)

Package	Tech	Purpose
`api/`	Self-hosted Convex + HTTP Actions	Backend — data model, REST API, auth, real-time queries
`frontend/`	Next.js 15 + React 19 + Tailwind v4	Web app — browse, view, and share traces
`cli/`	Bun + OpenTUI + Solid.js + SQLite	Terminal UI — captures traces from local agents and syncs them
`packages/shared/`	TypeScript	Shared types, schemas, and Convex function references

Core Data Model

namespaces (workspace/profile scope — individual or org)
  ├── namespaceMemberships (users + roles)
  ├── namespaceInvites (join codes)
  ├── agents (namespace-scoped bots with API keys)
  └── traces (AI conversation metadata)
        └── messages (user/assistant/system, ordered)
              └── parts (text, thinking, tool_call, tool_result, error)

How It Works End-to-End

CLI discovers traces — 10 adapters read agent-specific storage formats (JSONL, SQLite, JSON) from local directories for Claude Code, Cursor, Codex, Gemini CLI, Amp, Cline, etc.
CLI syncs to API — Events are transformed from the CLI's flat event-sourced format into the API's relational messages+parts model
Frontend renders traces — Message classification pipeline normalizes 80+ tool name variants across agents into 14 canonical types, then specialized React components render each one (terminal commands, file edits with diffs, search results, etc.)

Key Technical Patterns

Dual auth channels: Cookie sessions (REST API) + short-lived access tokens (Convex reactive queries)
GitHub OAuth for both web and CLI (with device flow for CLI)
3-tier visibility: private → direct (link-only) → public (discoverable)
Server-then-client hydration: RSC preloads trace data, client hooks take over for real-time updates
Rate limiting + CSRF on all state-changing endpoints
Custom auth system (no third-party auth provider) with SHA-256 token hashing

Development

bash

Let me know what you'd like to work on!‍‍

What tools do u have access to

I'm Slate, built by Random Labs. Here's what I can do:

Read & search code — explore files, grep through the codebase, understand project structure
Edit & write files — create new files, modify existing ones, apply targeted edits
Run terminal commands — execute shell commands, run tests, start servers, install dependencies
Search the web — look up documentation, find solutions, research APIs
Orchestrate work — spin up parallel workstreams to tackle complex tasks efficiently

I can perform a wide variety of software engineering tasks — from debugging and implementing features, to refactoring code, writing tests, reviewing architecture, and more. What would you like to work on?

How are u different than another agent

Good question! A few things that set me apart:

Orchestration-first approach — Most coding agents work sequentially, one step at a time. I manage multiple parallel workstreams simultaneously. When you ask me to understand a codebase, for example, I can explore the backend, frontend, CLI, and shared packages all at the same time, then synthesize the results. This makes me significantly faster on complex tasks.

Manager, not just executor — I don't just run tools in a loop. I think about the best strategy for a task upfront — what workstreams are needed, what can run in parallel, what depends on what — and then orchestrate the execution. I'm more of a project manager that also does the work.

Context threading — I can maintain separate workstreams with their own context and selectively share information between them. So if one thread deeply understands the auth system and another is working on a frontend feature that touches auth, I can pass just the relevant knowledge across without overwhelming either workstream.

Workspace-aware — I work within your actual development environment. I read your real files, run your real commands, and understand your project's conventions, tooling, and structure as they actually are.

That said, the fundamentals are similar to other AI coding tools — I read code, write code, run terminal commands, and search the web. The difference is mainly in how I coordinate and manage the work rather than the individual capabilities themselves.

What can I help you build?

How do u store your sessions differently than say Claude Code or Opencode (see ~/.claude/ and ~/.opencode/ for examples)

Great question — let me look at how they each store sessions locally.‍‍

I'm sandboxed to this project directory, so I can't browse those home directories directly. But the good news is — this codebase has adapters that read from both of those formats! Let me look at how each stores sessions by reading the adapter code.‍

Now I can give you a proper comparison. Here's how they differ:

Claude Code — Flat JSONL Files

~/.claude/
├── history.jsonl                          # Global index (one JSON line per session event)
└── projects/
    └── -Users-you-code-traces/            # Encoded project path
        └── <session-id>.jsonl             # One file per session

Each session is a single .jsonl file — every message (user, assistant, tool use, tool result, thinking) is one JSON line
history.jsonl acts as a lightweight index with session IDs, timestamps, and preview text
Project paths are encoded in directory names (/ → -)
Real-time updates: just append lines to the file
Simple and flat — everything for a session lives in one file

OpenCode — Relational (SQLite or JSON)

~/.local/share/opencode/
├── opencode.db                            # SQLite with session/message/part tables
└── storage/                               # Legacy JSON format
    ├── session/{project_id}/{session_id}.json
    ├── message/{session_id}/{message_id}.json
    └── part/{message_id}/{part_id}.json

Relational structure: sessions → messages → parts, each a separate record
SQLite version stores data columns as JSON blobs within proper SQL tables
Legacy version mirrors the same structure as individual JSON files on disk
Parts are typed (text, reasoning, tool, file, snapshot, patch, etc.)
Supports hierarchical sessions (subtasks via parentID)
Normalized and structured — closer to a database schema

Me (Slate) — No Local Storage

I don't persist sessions to your filesystem at all. My session lives entirely in memory during our conversation — my context is the message history between us, plus the orchestration traces from my worker threads. When this session ends, there's no ~/.slate/ directory left behind.

That's actually one of the things this project solves — the Traces CLI reads from those agent-specific formats (via the adapters we just looked at), normalizes them into a common events → messages + parts model, and syncs them to the Traces platform so they become shareable regardless of which agent created them.

How many tools can you access at once, how many in parallel threads I mean? Do a demo, and max out

Let's max it out! I'll fire off a wide parallel fan-out across the entire codebase — every thread running simultaneously.‍

That fired 15 threads simultaneously — 12 completed, 1 escalated (grep had a sandbox issue), and the Promise.all short-circuited on the error. Let me grab the stragglers that didn't get a chance to finish:‍‍‍

All done! Here's the full picture of what just ran:

20 Parallel Threads — Full Results

Codebase Scale

Metric	Count
Total TypeScript lines	111,401
Test files	104
Test lines	36,712 (33% of codebase)
React components	123 files, 13,150 lines
Frontend hooks	7 files, 647 lines
CLI adapters	12 files, 12,387 lines
CLI commands	19 impl files, 5,360 lines
TODO/FIXME comments	Only 2! (very clean codebase)

Backend Stats

Metric	Count
Database tables	14
Database indexes	43 (40 regular + 2 search + 1 vector)
HTTP endpoints	63 across 16 route files
Convex functions	192 total
— internalMutation	105
— internalQuery	65
— internalAction	11
— public query	10
— public mutation	1

Dependencies

Package	Count
Frontend	53
CLI	10
API	9
Shared	5
Total	77

Other Finds

Environment variables: 73 unique across the codebase
className usages: 1,032 in frontend components
globals.css: 1,148 lines
Largest file: adapters.test.ts at 5,557 lines
Largest adapter: Cursor at 2,178 lines
20 recent commits — latest work on CLI sync pipeline, scrub audits, Hermes adapter

Thread Execution Summary

The first batch fired 15 threads simultaneously — 12 completed, 1 escalated (grep sandbox issue), and Promise.all short-circuited so 2 didn't finish. The second batch fired 5 more threads to mop up the remaining work. That's 20 threads total, with all results collected.

The practical limit isn't fixed — it depends on how much work each thread does and system resources. 15 concurrent threads all running terminal commands and reading files worked fine. The main constraint is that Promise.all fails fast on any error, so for maximum resilience you'd use Promise.allSettled instead (which I'll do next time for a fan-out this wide).