DedeProGames/codex-traces-pt-br

This dataset was generated using teich by TeichAI

codex Agent Traces

This directory contains raw agent trace files generated by teich.

JSONL files: 359

Training-ready tools

Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them. Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP schemas, when the raw transcript only records tool names or calls. A complete dataset-level tools schema snapshot is stored in tools.json to keep this dataset card upload-safe. load_traces applies the dataset snapshot to each loaded example as a fallback tools field.

Create A Similar Dataset

This dataset was staged from existing local agent sessions with teich extract. To build your own local dataset, install Teich and point it at one of the supported providers:

teich extract codex --out data

Use --sessions-dir /path/to/store when your sessions are not in the default location, and --model <substring> when you only want sessions whose model metadata matches a value.

Format

Each file is newline-delimited JSON representing a single captured agent session. The trace schema is designed for upload-first preservation so you can keep the original session history and convert it later for training. Teich normalizes split assistant fragments during trace copy and conversion so the semantic order is reasoning first, optional assistant text second, and tool calls last. Native Claude Code conversion also preserves runtime context such as skills, MCP instructions, hook context, permission state, date changes, and session recaps as masked system messages when the raw transcript provides them.

Common top-level event groups:

• session_meta
• turn_context
• event_msg
• response_item
• session
• message
• session_info
• model_change
• thinking_level_change
• external_session_meta
• external_message
• external_stderr

Example

{"timestamp": "2025-12-01T18:44:41.452Z", "type": "session_meta", "payload": {"id": "019adb3b-1fb2-7a80-a940-625566dbc902", "timestamp": "2025-12-01T18:44:36.402Z", "cwd": "c:\\Users\\user1\\AppData\\Local\\RLBotGUIX\\MyBots\\DropBot\\src", "originator": "codex_vscode", "cli_version": "0.61.1-alpha.1", "instructions": null, "__truncated__": "2 keys omitted"}}

Training

Use this dataset as username/repo with Teich's data preparation and training utilities. If you do not want Teich to handle chat-template formatting or masking, run teich convert to write standalone OpenAI-style JSONL rows with prompt, messages, tools, and metadata. Training setup details evolve over time, so the maintained guide lives in the Teich training docs. For loading, mixing, converting, and validating Teich datasets, see Preparing Data.

Tool schema snapshot

The complete dataset-level tool schema snapshot was written to tools.json because it is too large to embed safely in the Hugging Face dataset card.

Tool names in snapshot

{
  "tool_count": 27,
  "tools": [
    "_fetch_file",
    "_hf_doc_fetch",
    "_hf_doc_search",
    "_hub_repo_details",
    "_search",
    "apply_patch",
    "bash",
    "exec",
    "exec_command",
    "js",
    "js_reset",
    "list_mcp_resources",
    "load_workspace_dependencies",
    "mcp__codex_apps__base44_create_base44_app",
    "mcp__codex_apps__github_fetch_file",
    "mcp__codex_apps__hugging_face_dataset_search",
    "mcp__codex_apps__hugging_face_hf_doc_search",
    "mcp__codex_apps__hugging_face_hub_repo_details",
    "read_mcp_resource",
    "read_thread_terminal",
    "request_user_input",
    "shell",
    "shell_command",
    "update_plan",
    "view_image",
    "wait",
    "web_search"
  ]
}