chatroutes
Advanced tools
Official Python SDK for ChatRoutes API - Conversation branching, AutoBranch, and multi-model AI chat
Official Python SDK for the ChatRoutes API - A powerful conversation management platform with advanced branching capabilities.
⚠️ Beta Release: ChatRoutes is currently in beta. The API may change without maintaining backward compatibility. Please use with caution in production environments.
Want to try ChatRoutes immediately? Click the badge below to open an interactive notebook in Google Colab:
No installation required - just run the cells and start experimenting!
Want to see ALL features in action? Watch branching, checkpoints, tree visualization, and immutability:
Perfect for sales demos, presentations, and comprehensive exploration!
pip install chatroutes
IMPORTANT: Before you can use ChatRoutes, you must obtain an API key:
from chatroutes import ChatRoutes
client = ChatRoutes(api_key="your-api-key")
conversation = client.conversations.create({
'title': 'My First Conversation',
'model': 'gpt-5' # or 'claude-opus-4-1', 'claude-sonnet-4-5', etc.
})
response = client.messages.send(
conversation['id'],
{
'content': 'Hello, how are you?',
'model': 'gpt-5'
}
)
print(response['message']['content'])
ChatRoutes currently supports the following AI models:
OpenAI:
gpt-5 (default) - OpenAI's GPT-5Anthropic Claude 4:
claude-opus-4-1 - Claude Opus 4.1 (most capable)claude-opus-4 - Claude Opus 4claude-opus-4-0 - Claude Opus 4.0claude-sonnet-4-5 - Claude Sonnet 4.5 (best for coding)claude-sonnet-4-0 - Claude Sonnet 4.0Anthropic Claude 3:
claude-3-7-sonnet-latest - Claude 3.7 Sonnet (latest)claude-3-5-haiku-latest - Claude 3.5 Haiku (fastest)Important: Use these exact model names. Other model names (e.g., gpt-4o, gpt-4o-mini, claude-sonnet-4) are not supported and will result in an error.
conversation = client.conversations.create({
'title': 'Product Discussion',
'model': 'gpt-5'
})
response = client.messages.send(
conversation_id='conv_123',
data={
'content': 'What are the key features?',
'model': 'gpt-5',
'temperature': 0.7
}
)
print(response['message']['content']) # AI response
print(f"Tokens used: {response['usage']['totalTokens']}")
def on_chunk(chunk):
if chunk.get('type') == 'content' and chunk.get('content'):
print(chunk['content'], end='', flush=True)
def on_complete(message):
print(f"\n\nMessage ID: {message['id']}")
client.messages.stream(
conversation_id='conv_123',
data={'content': 'Tell me a story'},
on_chunk=on_chunk,
on_complete=on_complete
)
branch = client.branches.create(
conversation_id='conv_123',
data={
'title': 'Alternative Response',
'contextMode': 'FULL'
}
)
fork = client.branches.fork(
conversation_id='conv_123',
data={
'forkPointMessageId': 'msg_456',
'title': 'Exploring Different Approach'
}
)
result = client.conversations.list({
'page': 1,
'limit': 10,
'filter': 'all'
})
for conv in result['data']:
print(f"{conv['title']} - {conv['createdAt']}")
AutoBranch automatically detects opportunities for conversation branching using AI:
# Check AutoBranch service health
health = client.autobranch.health()
print(f"Status: {health['status']}")
# Analyze text for potential branch points
text = """
I need help with billing and also have a technical question about the API.
Can you help with both?
"""
suggestions = client.autobranch.suggest_branches(
text=text,
suggestions_count=3,
hybrid_detection=False, # Set True to use LLM enhancement
threshold=0.7
)
# Review suggestions
for suggestion in suggestions['suggestions']:
print(f"Branch: {suggestion['title']}")
print(f" Description: {suggestion['description']}")
print(f" Confidence: {suggestion['confidence']:.0%}")
print(f" Trigger: '{suggestion['triggerText']}'")
# Alternative: Use analyze_text alias
analysis = client.autobranch.analyze_text(
text="How do I reset my password?",
suggestions_count=2
)
AutoBranch Use Cases:
Checkpoints allow you to save conversation context at specific points and manage long conversations efficiently:
branches = client.branches.list(conversation_id='conv_123')
main_branch = next(b for b in branches if b['isMain'])
checkpoint = client.checkpoints.create(
conversation_id='conv_123',
branch_id=main_branch['id'],
anchor_message_id='msg_456'
)
print(f"Checkpoint created: {checkpoint['id']}")
print(f"Summary: {checkpoint['summary']}")
print(f"Token count: {checkpoint['token_count']}")
checkpoints = client.checkpoints.list('conv_123')
for cp in checkpoints:
print(f"{cp['id']}: {cp['summary']}")
response = client.messages.send(
conversation_id='conv_123',
data={'content': 'Continue the conversation'}
)
metadata = response['message'].get('metadata', {})
if metadata.get('checkpoint_used'):
print(f"Checkpoint was used for this response")
print(f"Context messages: {metadata.get('context_message_count')}")
The SDK provides specific exception types for different error scenarios:
from chatroutes import (
ChatRoutesError,
AuthenticationError,
RateLimitError,
ValidationError,
NotFoundError,
ServerError
)
try:
conversation = client.conversations.get('conv_123')
except AuthenticationError:
print("Invalid API key")
except NotFoundError:
print("Conversation not found")
except RateLimitError as e:
print(f"Rate limited. Retry after {e.retry_after} seconds")
except ChatRoutesError as e:
print(f"Error: {e.message}")
client = ChatRoutes(
api_key="your-api-key",
base_url="https://api.chatroutes.com/api/v1", # optional
timeout=30, # optional, in seconds
retry_attempts=3, # optional
retry_delay=1.0 # optional, in seconds
)
create(data: CreateConversationRequest) -> Conversationlist(params: ListConversationsParams) -> PaginatedResponseget(conversation_id: str) -> Conversationupdate(conversation_id: str, data: dict) -> Conversationdelete(conversation_id: str) -> Noneget_tree(conversation_id: str) -> ConversationTreesend(conversation_id: str, data: SendMessageRequest) -> SendMessageResponsestream(conversation_id: str, data: SendMessageRequest, on_chunk: Callable, on_complete: Callable) -> Nonelist(conversation_id: str, branch_id: str) -> List[Message]update(message_id: str, content: str) -> Messagedelete(message_id: str) -> Nonelist(conversation_id: str) -> List[Branch]create(conversation_id: str, data: CreateBranchRequest) -> Branchfork(conversation_id: str, data: ForkConversationRequest) -> Branchupdate(conversation_id: str, branch_id: str, data: dict) -> Branchdelete(conversation_id: str, branch_id: str) -> Noneget_messages(conversation_id: str, branch_id: str) -> List[Message]merge(conversation_id: str, branch_id: str) -> Branchlist(conversation_id: str, branch_id: Optional[str] = None) -> List[Checkpoint]create(conversation_id: str, branch_id: str, anchor_message_id: str) -> Checkpointdelete(checkpoint_id: str) -> Nonerecreate(checkpoint_id: str) -> Checkpointsuggest_branches(text: str, suggestions_count: int = 3, hybrid_detection: bool = False, threshold: float = 0.7, llm_model: Optional[str] = None, llm_provider: Optional[str] = None, llm_api_key: Optional[str] = None) -> SuggestBranchesResponseanalyze_text(text: str, suggestions_count: int = 3, hybrid_detection: bool = False, threshold: float = 0.7, llm_model: Optional[str] = None) -> SuggestBranchesResponsehealth() -> HealthResponseThe SDK includes comprehensive type definitions using TypedDict:
ConversationMessageMessageMetadata (includes checkpoint-related fields)BranchCheckpointCreateConversationRequestSendMessageRequestSendMessageResponseCreateBranchRequestForkConversationRequestCheckpointCreateRequestCheckpointListResponseConversationTreeTreeNodeListConversationsParamsPaginatedResponseStreamChunkBranchPoint 🆕BranchSuggestion 🆕SuggestionMetadata 🆕SuggestBranchesRequest 🆕SuggestBranchesResponse 🆕HealthResponse 🆕git clone https://github.com/chatroutes/chatroutes-python-sdk.git
cd chatroutes-python-sdk
pip install -e ".[dev]"
pytest
mypy chatroutes
black chatroutes
MIT License - see LICENSE file for details
FAQs
Official Python SDK for ChatRoutes API - Conversation branching, AutoBranch, and multi-model AI chat
We found that chatroutes demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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