# LexrChainer Client
A Python client library for interacting with the LexrChainer API.
## Installation
```bash
pip install lexrchainer-client
```
## Configuration
Configure the client using environment variables:
```bash
# API Key authentication
LEXRCHAINER_API_KEY=your_api_key
# Or JWT token authentication
LEXRCHAINER_JWT_TOKEN=your_jwt_token
# API URL (default: http://localhost:8000)
LEXRCHAINER_API_URL=http://your-api-url
```
## Usage
### 1. Agent Builder Interface
#### Creating a Single Agent
```python
from lexrchainer.client import AgentBuilder
# Create an agent with basic configuration
agent = (AgentBuilder("Simple Assistant")
.with_model("gpt-4")
.with_system_prompt("You are a helpful assistant")
.with_description("A simple assistant for basic tasks")
.create_agent())
# Send a message to the agent
response = agent.send_message("Hello, how are you?")
```
#### Advanced Agent Configuration
```python
agent = (AgentBuilder("Advanced Assistant")
.with_model("gpt-4", temperature=0.7, max_tokens=1000) # Model with parameters
.with_system_prompt("You are a helpful assistant")
.with_description("An advanced assistant with multiple tools")
.with_tool("search") # Add a tool
.with_tool("calculator", credentials={"api_key": "your_key"}) # Add a tool with credentials
.with_static_meta({"version": "1.0", "category": "general"}) # Add static metadata
.create_agent())
```
#### Custom Steps Configuration
```python
agent = (AgentBuilder("Step-Based Assistant")
.with_model("gpt-4")
.add_step(
name="Research Step",
prompt="Research the given topic thoroughly.",
type="HIDDEN_TURN_USER",
flow="TO_USER",
flow_type="AT_ONCE",
tool_use=True
)
.add_step(
name="Summary Step",
prompt="Summarize the research findings.",
flow_state="CONTINUE",
response_treatment="APPEND"
)
.create_agent())
```
#### Managing Existing Agents
```python
# Load an existing agent
agent = AgentBuilder("Existing Assistant").load_agent(agent_id="agent_123", conversation_id="conv_456")
# Update an existing agent
updated_agent = (AgentBuilder("Updated Assistant")
.with_model("gpt-4")
.with_system_prompt("New system prompt")
.update_agent(agent_id="agent_123"))
# Get agent conversations
conversations = agent.get_agent_conversations(medium="WHATSAPP")
# Get all available agents
agents = agent.get_agents()
```
#### Creating Multiple Agents
```python
from lexrchainer.client import MultiAgentBuilder
# Create multiple agents
multi_agent = MultiAgentBuilder()
# Configure first agent
assistant = multi_agent.add_agent("Assistant")
assistant.with_model("gpt-4").with_system_prompt("You are a helpful assistant")
# Configure second agent
expert = multi_agent.add_agent("Expert")
expert.with_model("gpt-4").with_system_prompt("You are an expert in your field")
# Create all agents and start conversation
agents = multi_agent.create_agents()
# Send a message to all agents
responses = agents.send_message("Hello everyone!")
```
### 2. Conversation API
```python
from lexrchainer.client import ClientInterface
client = ClientInterface()
# Create a conversation
conversation = client.create_conversation({
"medium": "WHATSAPP",
"members": [...],
"turn_type": "SEQUENTIAL",
"iteration_end_criteria": "ALL_TURNS_DONE"
})
# Send a message
response = client.send_message(
conversation_id="conv_123",
messages=[...],
streaming=True
)
# Add/remove members
client.add_conversation_member("conv_123", "user_456", "ACTIVE_PARTICIPATION")
client.remove_conversation_member("conv_123", "user_456")
# Get conversation messages
messages = client.get_conversation_messages("conv_123")
# Send message to specific agent
response = client.send_message_to_agent("agent_name", {
"messages": [...],
"streaming": True
})
# Send message to public agent
response = client.send_public_agent_message("public_agent", {
"messages": [...],
"streaming": True
})
```
### 3. User API
```python
from lexrchainer.client import ClientInterface
client = ClientInterface()
# Create a user
user = client.create_user({
"username": "john_doe",
"email": "john@example.com",
"phone": "+1234567890",
"user_type": "HUMAN"
})
# Get user details
user = client.get_user("user_123")
# Update user
updated_user = client.update_user("user_123", {
"email": "new_email@example.com"
})
# Delete user
client.delete_user("user_123")
# List users
users = client.list_users(skip=0, limit=100)
# Get current user
current_user = client.get_current_user()
```
### 4. Organization API
```python
from lexrchainer.client import ClientInterface
client = ClientInterface()
# Create organization
org = client.create_organization({
"name": "My Organization"
})
# Update organization
updated_org = client.update_organization("org_123", {
"name": "Updated Organization Name"
})
```
### 5. Workspace API
```python
from lexrchainer.client import ClientInterface
client = ClientInterface()
# Create workspace
workspace = client.create_workspace({
"name": "My Workspace",
"description": "A workspace for collaboration",
"is_private": True
})
# Get workspace
workspace = client.get_workspace("workspace_123")
# Update workspace
updated_workspace = client.update_workspace("workspace_123", {
"name": "Updated Workspace Name"
})
# Delete workspace
client.delete_workspace("workspace_123")
# List workspaces
workspaces = client.list_workspaces(skip=0, limit=100)
# Manage workspace members
members = client.list_workspace_members("workspace_123")
client.add_workspace_member("workspace_123", {
"user_id": "user_456",
"role": "member"
})
client.remove_workspace_member("workspace_123", "user_456")
```
### 6. Chain API
```python
from lexrchainer.client import ClientInterface
client = ClientInterface()
# Create chain
chain = client.create_chain({
"name": "My Chain",
"description": "A custom chain",
"json_content": {...}
})
# Get chain
chain = client.get_chain("chain_123")
# Update chain
updated_chain = client.update_chain("chain_123", {
"description": "Updated description"
})
# Delete chain
client.delete_chain("chain_123")
# List chains
chains = client.list_chains(skip=0, limit=100)
# Trigger chain execution
result = client.trigger_chain("chain_123", {
"message": "Hello",
"meta_data": {...}
})
# Schedule chain execution
schedule = client.schedule_chain("chain_123", {
"cron": "0 0 * * *",
"message": "Scheduled message"
})
```
## Features
- Simple and intuitive API
- Support for single and multi-agent conversations
- Advanced tool integration with credential support
- Custom step configuration with flow control
- Static metadata support
- Agent management (create, update, load)
- Streaming responses
- Authentication via API key or JWT token
- Error handling and validation
- Complete coverage of all API endpoints
- Type hints and documentation
## License
MIT License
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"description": "# LexrChainer Client\n\nA Python client library for interacting with the LexrChainer API.\n\n## Installation\n\n```bash\npip install lexrchainer-client\n```\n\n## Configuration\n\nConfigure the client using environment variables:\n\n```bash\n# API Key authentication\nLEXRCHAINER_API_KEY=your_api_key\n\n# Or JWT token authentication\nLEXRCHAINER_JWT_TOKEN=your_jwt_token\n\n# API URL (default: http://localhost:8000)\nLEXRCHAINER_API_URL=http://your-api-url\n```\n\n## Usage\n\n### 1. Agent Builder Interface\n\n#### Creating a Single Agent\n\n```python\nfrom lexrchainer.client import AgentBuilder\n\n# Create an agent with basic configuration\nagent = (AgentBuilder(\"Simple Assistant\")\n .with_model(\"gpt-4\")\n .with_system_prompt(\"You are a helpful assistant\")\n .with_description(\"A simple assistant for basic tasks\")\n .create_agent())\n\n# Send a message to the agent\nresponse = agent.send_message(\"Hello, how are you?\")\n```\n\n#### Advanced Agent Configuration\n\n```python\nagent = (AgentBuilder(\"Advanced Assistant\")\n .with_model(\"gpt-4\", temperature=0.7, max_tokens=1000) # Model with parameters\n .with_system_prompt(\"You are a helpful assistant\")\n .with_description(\"An advanced assistant with multiple tools\")\n .with_tool(\"search\") # Add a tool\n .with_tool(\"calculator\", credentials={\"api_key\": \"your_key\"}) # Add a tool with credentials\n .with_static_meta({\"version\": \"1.0\", \"category\": \"general\"}) # Add static metadata\n .create_agent())\n```\n\n#### Custom Steps Configuration\n\n```python\nagent = (AgentBuilder(\"Step-Based Assistant\")\n .with_model(\"gpt-4\")\n .add_step(\n name=\"Research Step\",\n prompt=\"Research the given topic thoroughly.\",\n type=\"HIDDEN_TURN_USER\",\n flow=\"TO_USER\",\n flow_type=\"AT_ONCE\",\n tool_use=True\n )\n .add_step(\n name=\"Summary Step\",\n prompt=\"Summarize the research findings.\",\n flow_state=\"CONTINUE\",\n response_treatment=\"APPEND\"\n )\n .create_agent())\n```\n\n#### Managing Existing Agents\n\n```python\n# Load an existing agent\nagent = AgentBuilder(\"Existing Assistant\").load_agent(agent_id=\"agent_123\", conversation_id=\"conv_456\")\n\n# Update an existing agent\nupdated_agent = (AgentBuilder(\"Updated Assistant\")\n .with_model(\"gpt-4\")\n .with_system_prompt(\"New system prompt\")\n .update_agent(agent_id=\"agent_123\"))\n\n# Get agent conversations\nconversations = agent.get_agent_conversations(medium=\"WHATSAPP\")\n\n# Get all available agents\nagents = agent.get_agents()\n```\n\n#### Creating Multiple Agents\n\n```python\nfrom lexrchainer.client import MultiAgentBuilder\n\n# Create multiple agents\nmulti_agent = MultiAgentBuilder()\n\n# Configure first agent\nassistant = multi_agent.add_agent(\"Assistant\")\nassistant.with_model(\"gpt-4\").with_system_prompt(\"You are a helpful assistant\")\n\n# Configure second agent\nexpert = multi_agent.add_agent(\"Expert\")\nexpert.with_model(\"gpt-4\").with_system_prompt(\"You are an expert in your field\")\n\n# Create all agents and start conversation\nagents = multi_agent.create_agents()\n\n# Send a message to all agents\nresponses = agents.send_message(\"Hello everyone!\")\n```\n\n### 2. Conversation API\n\n```python\nfrom lexrchainer.client import ClientInterface\n\nclient = ClientInterface()\n\n# Create a conversation\nconversation = client.create_conversation({\n \"medium\": \"WHATSAPP\",\n \"members\": [...],\n \"turn_type\": \"SEQUENTIAL\",\n \"iteration_end_criteria\": \"ALL_TURNS_DONE\"\n})\n\n# Send a message\nresponse = client.send_message(\n conversation_id=\"conv_123\",\n messages=[...],\n streaming=True\n)\n\n# Add/remove members\nclient.add_conversation_member(\"conv_123\", \"user_456\", \"ACTIVE_PARTICIPATION\")\nclient.remove_conversation_member(\"conv_123\", \"user_456\")\n\n# Get conversation messages\nmessages = client.get_conversation_messages(\"conv_123\")\n\n# Send message to specific agent\nresponse = client.send_message_to_agent(\"agent_name\", {\n \"messages\": [...],\n \"streaming\": True\n})\n\n# Send message to public agent\nresponse = client.send_public_agent_message(\"public_agent\", {\n \"messages\": [...],\n \"streaming\": True\n})\n```\n\n### 3. User API\n\n```python\nfrom lexrchainer.client import ClientInterface\n\nclient = ClientInterface()\n\n# Create a user\nuser = client.create_user({\n \"username\": \"john_doe\",\n \"email\": \"john@example.com\",\n \"phone\": \"+1234567890\",\n \"user_type\": \"HUMAN\"\n})\n\n# Get user details\nuser = client.get_user(\"user_123\")\n\n# Update user\nupdated_user = client.update_user(\"user_123\", {\n \"email\": \"new_email@example.com\"\n})\n\n# Delete user\nclient.delete_user(\"user_123\")\n\n# List users\nusers = client.list_users(skip=0, limit=100)\n\n# Get current user\ncurrent_user = client.get_current_user()\n```\n\n### 4. Organization API\n\n```python\nfrom lexrchainer.client import ClientInterface\n\nclient = ClientInterface()\n\n# Create organization\norg = client.create_organization({\n \"name\": \"My Organization\"\n})\n\n# Update organization\nupdated_org = client.update_organization(\"org_123\", {\n \"name\": \"Updated Organization Name\"\n})\n```\n\n### 5. Workspace API\n\n```python\nfrom lexrchainer.client import ClientInterface\n\nclient = ClientInterface()\n\n# Create workspace\nworkspace = client.create_workspace({\n \"name\": \"My Workspace\",\n \"description\": \"A workspace for collaboration\",\n \"is_private\": True\n})\n\n# Get workspace\nworkspace = client.get_workspace(\"workspace_123\")\n\n# Update workspace\nupdated_workspace = client.update_workspace(\"workspace_123\", {\n \"name\": \"Updated Workspace Name\"\n})\n\n# Delete workspace\nclient.delete_workspace(\"workspace_123\")\n\n# List workspaces\nworkspaces = client.list_workspaces(skip=0, limit=100)\n\n# Manage workspace members\nmembers = client.list_workspace_members(\"workspace_123\")\nclient.add_workspace_member(\"workspace_123\", {\n \"user_id\": \"user_456\",\n \"role\": \"member\"\n})\nclient.remove_workspace_member(\"workspace_123\", \"user_456\")\n```\n\n### 6. 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