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"""
anthropic_to_lmstudio.py
Converts an Anthropic export (conversations.json) into a set of LMStudio
conversation files.
Usage:
python anthropic_to_lmstudio.py --input path/to/conversations.json \
--output-dir ./lmstudio_convos
The output will contain one .conversation.json file per conversation,
named <epoch_ms>.conversation.json. Empty conversations are ignored.
"""
import json
import argparse
import os
from datetime import datetime, timezone
from pathlib import Path
from typing import List, Dict, Any
def iso_to_epoch_ms(iso_str: str) -> int:
"""Convert an ISO‑8601 timestamp (with trailing Z) to Unix epoch in ms."""
dt = datetime.fromisoformat(iso_str.replace("Z", "+00:00"))
return int(dt.timestamp() * 1000)
def build_lmstudio_conversation(convo: Dict[str, Any]) -> Dict[str, Any]:
"""
Build a minimal LMStudio conversation dict from an Anthropic conversation.
"""
created_at_ms = iso_to_epoch_ms(convo["created_at"])
name = convo.get("name") or f"Conversation {created_at_ms}"
messages: List[Dict[str, Any]] = []
for msg in convo.get("chat_messages", []):
role = msg["sender"]
content_text = ""
if msg.get("content"):
for part in msg["content"]:
if part["type"] == "text":
content_text += part["text"]
if not content_text.strip():
continue
if role == "human":
messages.append(
{
"versions": [
{
"type": "singleStep",
"role": "user",
"content": [{"type": "text", "text": content_text}],
}
],
"currentlySelected": 0,
}
)
elif role == "assistant":
messages.append(
{
"versions": [
{
"type": "multiStep",
"role": "assistant",
"senderInfo": {"senderName": convo["account"]["uuid"]},
"steps": [
{
"type": "contentBlock",
"stepIdentifier": f"{created_at_ms}-{hash(content_text) % 1_000_000}",
"content": [
{
"type": "text",
"text": content_text,
"fromDraftModel": False,
"tokensCount": None,
"isStructural": False,
}
],
"defaultShouldIncludeInContext": True,
"shouldIncludeInContext": True,
}
],
}
],
"currentlySelected": 0,
}
)
else:
continue
if not messages:
return None
lm_convo = {
"name": name,
"pinned": False,
"createdAt": created_at_ms,
"preset": "",
"tokenCount": 0,
"systemPrompt": "",
"messages": messages,
"usePerChatPredictionConfig": True,
"perChatPredictionConfig": {"fields": []},
"clientInput": "",
"clientInputFiles": [],
"userFilesSizeBytes": 0,
"lastUsedModel": {
"indexedModelIdentifier": "openai/gpt-oss-20b",
"identifier": "openai/gpt-oss-20b",
"instanceLoadTimeConfig": {"fields": []},
"instanceOperationTimeConfig": {"fields": []},
},
"notes": [],
"plugins": [],
"pluginConfigs": {},
"disabledPluginTools": [],
"looseFiles": [],
}
return lm_convo
def main():
parser = argparse.ArgumentParser(description="Anthropic → LMStudio converter")
parser.add_argument(
"--input",
"-i",
required=True,
help="Path to the Anthropic conversations.json file",
)
parser.add_argument(
"--output-dir",
"-o",
default="./lmstudio_convos",
help="Directory where .conversation.json files will be written",
)
args = parser.parse_args()
with open(args.input, "r", encoding="utf-8") as f:
anth_convs = json.load(f)
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
for convo in anth_convs:
lm_convo = build_lmstudio_conversation(convo)
if lm_convo is None:
continue
filename = f"{iso_to_epoch_ms(convo['created_at'])}.conversation.json"
out_path = out_dir / filename
with open(out_path, "w", encoding="utf-8") as out_f:
json.dump(lm_convo, out_f, indent=2)
print(f"Written {out_path}")
print("Conversion complete.")
if __name__ == "__main__":
main()
|
