minimax-m2.7-agent

# Agentic Training Traces This directory contains raw agent trace files generated by agentic-datagen. All assistant responses were generated by **minimax/minimax-m2.7**. Trace files: 20 ## Training-ready tools Use this `tools` payload when rendering converted examples through your training chat template. The same structure is emitted on each converted example as the `tools` field. ```json [ { "type": "function", "function": { "name": "bash", "parameters": { "type": "object", "properties": { "command": { "type": "string" }, "timeout": { "type": "integer" } }, "additionalProperties": true, "required": [ "command" ] }, "description": "Execute bash commands (ls, grep, find, etc.)" } }, { "type": "function", "function": { "name": "edit", "parameters": { "type": "object", "properties": { "edits": { "type": "array", "items": { "type": "object", "properties": { "newText": { "type": "string" }, "oldText": { "type": "string" } }, "additionalProperties": true, "required": [ "newText", "oldText" ] } }, "newText": { "type": "string" }, "oldText": { "type": "string" }, "path": { "type": "string" } }, "additionalProperties": true, "required": [ "path" ] }, "description": "Make precise file edits with exact text replacement, including multiple disjoint edits in one call" } }, { "type": "function", "function": { "name": "read", "parameters": { "type": "object", "properties": { "limit": { "type": "integer" }, "offset": { "type": "integer" }, "path": { "type": "string" } }, "additionalProperties": true, "required": [ "path" ] }, "description": "Read file contents" } }, { "type": "function", "function": { "name": "write", "parameters": { "type": "object", "properties": { "content": { "type": "string" }, "path": { "type": "string" } }, "additionalProperties": true, "required": [ "content", "path" ] }, "description": "Create or overwrite files" } } ] ``` ## 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. Common top-level event groups: - `session_meta` - `turn_context` - `event_msg` - `response_item` - `session` - `message` - `session_info` - `model_change` - `thinking_level_change` ## Example ```json {"type":"session","version":3,"id":"019ddbe5-98a9-73fb-aa65-284362c61fc8","timestamp":"2026-04-30T00:59:10.377Z","cwd":"/workspace"} {"type":"model_change","id":"0bfc0493","parentId":null,"timestamp":"2026-04-30T00:59:10.429Z","modelId":"minimax/minimax-m2.7"} {"type":"thinking_level_change","id":"4e83da40","parentId":"0bfc0493","timestamp":"2026-04-30T00:59:10.430Z","thinkingLevel":"medium"} ``` ## Conversion You can convert these raw traces into training examples with: ```python from pathlib import Path from agentic_datagen import convert_traces_to_training_data examples = convert_traces_to_training_data(Path('.')) ```