Basic Chat Completions Skill

Basic Chat Completions

Installation

# With uv (recommended)
uv add llmring

# With pip
pip install llmring

Provider SDKs (install what you need):

uv add openai>=1.0      # OpenAI
uv add anthropic>=0.67   # Anthropic
uv add google-genai      # Google Gemini
uv add ollama>=0.4       # Ollama

API Overview

This skill covers:

LLMRing - Main service class
LLMRequest - Request configuration
LLMResponse - Response structure
Message - Message format
Resource management with context managers

Quick Start

FIRST: Create your lockfile (required for all real applications):

# Initialize lockfile
llmring lock init

# Check available models (get current names from registry):
llmring list --provider openai
llmring list --provider anthropic

# Bind aliases using CURRENT model names:
llmring bind summarizer anthropic:claude-3-5-haiku-20241022

# Or use interactive configuration (recommended - knows current models):
llmring lock chat

⚠️ Important: Check llmring list for current model names. Models change (e.g., gemini-2.5-pro → gemini-2.5-pro).

THEN: Use in code:

from llmring import LLMRing, LLMRequest, Message

# Use context manager for automatic resource cleanup
async with LLMRing() as service:
    request = LLMRequest(
        model="summarizer",  # YOUR semantic alias (defined in llmring.lock)
        messages=[
            Message(role="system", content="You are a helpful assistant."),
            Message(role="user", content="Hello!")
        ]
    )

    response = await service.chat(request)
    print(response.content)

⚠️ Important: The bundled lockfile that ships with llmring is ONLY for running llmring lock chat. Real applications must create their own lockfile.

Timeout Control

The library enforces a 60-second timeout by default. Override it when processing large documents, running expensive reasoning chains, or forwarding calls to slower local models.

async with LLMRing(timeout=300.0) as service:  # default for this context manager
    request = LLMRequest(
        model="summarizer",
        messages=[Message(role="user", content=huge_thread)],
        timeout=None,                            # disable timeout for this request
    )
    response = await service.chat(request)

You can also set LLMRING_PROVIDER_TIMEOUT_S=120 in the environment to establish a default when you don't pass the constructor argument.

Complete API Documentation

LLMRing

Main service class that manages providers and routes requests.

Constructor:

LLMRing(
    origin: str = "llmring",
    registry_url: Optional[str] = None,
    lockfile_path: Optional[str] = None,
    server_url: Optional[str] = None,
    api_key: Optional[str] = None,
    log_metadata: bool = True,
    log_conversations: bool = False,
    alias_cache_size: int = 100,
    alias_cache_ttl: int = 3600,
    timeout: Optional[float] = 60.0
)

Parameters:

origin (str, default: "llmring"): Origin identifier for tracking
registry_url (str, optional): Custom registry URL for model information
lockfile_path (str, optional): Path to lockfile for alias configuration
server_url (str, optional): llmring-server URL for usage logging
api_key (str, optional): API key for llmring-server
log_metadata (bool, default: True): Enable logging of usage metadata (requires server_url)
log_conversations (bool, default: False): Enable logging of full conversations (requires server_url)
alias_cache_size (int, default: 100): Maximum cached alias resolutions
alias_cache_ttl (int, default: 3600): Cache TTL in seconds
timeout (float | None, default: 60.0): Default request timeout in seconds (None disables)

Example:

from llmring import LLMRing

# Basic initialization (uses environment variables for API keys)
async with LLMRing() as service:
    response = await service.chat(request)

# With custom lockfile
async with LLMRing(lockfile_path="./my-llmring.lock") as service:
    response = await service.chat(request)

LLMRing.chat()

Send a chat completion request and get a response.

Signature:

async def chat(
    request: LLMRequest,
    profile: Optional[str] = None
) -> LLMResponse

Parameters:

request (LLMRequest): Request configuration with messages and parameters
profile (str, optional): Profile name for environment-specific configuration (e.g., "dev", "prod")

Returns:

LLMResponse: Response with content, usage, and metadata

Raises:

ProviderNotFoundError: If provider is not configured
ModelNotFoundError: If model is not available
ProviderAuthenticationError: If API key is invalid
ProviderRateLimitError: If rate limit exceeded

Example:

from llmring import LLMRing, LLMRequest, Message

async with LLMRing() as service:
    request = LLMRequest(
        model="responder",  # Your alias for responses
        messages=[
            Message(role="user", content="What is 2+2?")
        ],
        temperature=0.7,
        max_tokens=100
    )

    response = await service.chat(request)
    print(f"Response: {response.content}")
    print(f"Tokens: {response.total_tokens}")
    print(f"Model: {response.model}")

LLMRequest

Configuration for a chat completion request.

Constructor:

LLMRequest(
    messages: List[Message],
    model: Optional[str] = None,
    temperature: Optional[float] = None,
    max_tokens: Optional[int] = None,
    reasoning_tokens: Optional[int] = None,
    response_format: Optional[Dict[str, Any]] = None,
    tools: Optional[List[Dict[str, Any]]] = None,
    tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
    cache: Optional[Dict[str, Any]] = None,
    metadata: Optional[Dict[str, Any]] = None,
    json_response: Optional[bool] = None,
    timeout: Optional[float] = None,
    extra_params: Dict[str, Any] = {}
)

Parameters:

messages (List[Message], required): Conversation messages
model (str, optional): Model alias (e.g., "fast") or provider:model reference (e.g., "openai:gpt-4o")
temperature (float, optional): Sampling temperature (0.0-2.0). Higher = more random
max_tokens (int, optional): Maximum tokens to generate
reasoning_tokens (int, optional): Token budget for reasoning models (o1, etc.)
response_format (dict, optional): Structured output format (see llmring-structured skill)
tools (list, optional): Available functions (see llmring-tools skill)
tool_choice (str/dict, optional): Tool selection strategy
cache (dict, optional): Caching configuration
metadata (dict, optional): Request metadata
json_response (bool, optional): Request JSON format response
timeout (float | None, optional): Override service-level timeout; None waits indefinitely
extra_params (dict, default: {}): Provider-specific parameters

Example:

from llmring import LLMRequest, Message

# Simple request
request = LLMRequest(
    model="summarizer",  # Your domain-specific alias
    messages=[Message(role="user", content="Hello")]
)

# With parameters
request = LLMRequest(
    model="explainer",  # Another semantic alias you define
    messages=[
        Message(role="system", content="You are a helpful assistant."),
        Message(role="user", content="Explain quantum computing")
    ],
    temperature=0.3,
    max_tokens=500
)

Message

A message in a conversation.

Constructor:

Message(
    role: Literal["system", "user", "assistant", "tool"],
    content: Any,
    tool_calls: Optional[List[Dict[str, Any]]] = None,
    tool_call_id: Optional[str] = None,
    timestamp: Optional[datetime] = None,
    metadata: Optional[Dict[str, Any]] = None
)

Parameters:

role (str, required): Message role - "system", "user", "assistant", or "tool"
content (Any, required): Message content (string or structured content for multimodal)
tool_calls (list, optional): Tool calls made by assistant
tool_call_id (str, optional): ID for tool result messages
timestamp (datetime, optional): Message timestamp
metadata (dict, optional): Provider-specific metadata (e.g., cache_control for Anthropic)

Example:

from llmring import Message

# System message
system_msg = Message(
    role="system",
    content="You are a helpful assistant."
)

# User message
user_msg = Message(
    role="user",
    content="What is the capital of France?"
)

# Assistant response
assistant_msg = Message(
    role="assistant",
    content="The capital of France is Paris."
)

# Anthropic prompt caching
cached_msg = Message(
    role="system",
    content="Very long system prompt...",
    metadata={"cache_control": {"type": "ephemeral"}}
)

LLMResponse

Response from a chat completion.

Attributes:

content (str): Generated text content
model (str): Model that generated the response
usage (dict, optional): Token usage statistics
finish_reason (str, optional): Why generation stopped ("stop", "length", "tool_calls")
tool_calls (list, optional): Tool calls made by model
parsed (dict, optional): Parsed JSON when response_format used

Properties:

total_tokens (int, optional): Total tokens used (prompt + completion)

Example:

response = await service.chat(request)

print(response.content)           # "The capital is Paris."
print(response.model)              # "anthropic:claude-sonnet-4-5-20250929"
print(response.total_tokens)       # 45
print(response.finish_reason)      # "stop"
print(response.usage)              # {"prompt_tokens": 20, "completion_tokens": 25}

Environment Setup

Required environment variables (set API keys for providers you want to use):

# Add to .env file or export
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_GEMINI_API_KEY=AIza...
OLLAMA_BASE_URL=http://localhost:11434  # Optional, default shown

LLMRing automatically initializes providers based on available API keys.

Resource Management

Context Manager (Recommended)

Always use context manager for automatic cleanup:

from llmring import LLMRing, LLMRequest, Message

# Context manager handles cleanup automatically
async with LLMRing() as service:
    request = LLMRequest(
        model="chatbot",  # Your alias for conversational AI
        messages=[Message(role="user", content="Hello")]
    )
    response = await service.chat(request)
    # Resources cleaned up when exiting context

Manual Cleanup

If you can't use context manager:

service = LLMRing()
try:
    response = await service.chat(request)
finally:
    await service.close()  # MUST call close()

Common Patterns

Multi-Turn Conversation

from llmring import LLMRing, LLMRequest, Message

async with LLMRing() as service:
    messages = [
        Message(role="system", content="You are a helpful assistant."),
        Message(role="user", content="What is Python?")
    ]

    # First turn
    request = LLMRequest(model="assistant", messages=messages)
    response = await service.chat(request)

    # Add assistant response to history
    messages.append(Message(role="assistant", content=response.content))

    # Second turn
    messages.append(Message(role="user", content="What about JavaScript?"))
    request = LLMRequest(model="assistant", messages=messages)
    response = await service.chat(request)

    print(response.content)

Using Model Aliases

# Semantic aliases YOU define in your lockfile
request = LLMRequest(
    model="summarizer",  # Alias you configured for this task
    messages=[Message(role="user", content="Hello")]
)

# Use task-based names:
# model="code-reviewer"  - For code review tasks
# model="sql-generator"  - For generating SQL
# model="extractor"      - For extracting structured data
# model="analyzer"       - For analysis tasks

Using Direct Model References

# Direct provider:model format (escape hatch)
request = LLMRequest(
    model="anthropic:claude-sonnet-4-5-20250929",
    messages=[Message(role="user", content="Hello")]
)

# Or specific versions
request = LLMRequest(
    model="openai:gpt-4o",
    messages=[Message(role="user", content="Hello")]
)

Temperature Control

# Creative writing (higher temperature)
request = LLMRequest(
    model="creative-writer",  # Your alias for creative tasks
    messages=[Message(role="user", content="Write a poem")],
    temperature=1.2  # More random/creative
)

# Factual responses (lower temperature)
request = LLMRequest(
    model="factual-responder",  # Your alias for factual tasks
    messages=[Message(role="user", content="What is 2+2?")],
    temperature=0.2  # More deterministic
)

Token Limits

# Limit response length
request = LLMRequest(
    model="summarizer",  # Your summarization alias
    messages=[Message(role="user", content="Summarize this...")],
    max_tokens=100  # Cap at 100 tokens
)

Error Handling

from llmring import (
    LLMRing,
    LLMRequest,
    Message,
    ProviderAuthenticationError,
    ModelNotFoundError,
    ProviderRateLimitError,
    ProviderTimeoutError,
    ProviderNotFoundError
)

async with LLMRing() as service:
    try:
        request = LLMRequest(
            model="chatbot",  # Your conversational alias
            messages=[Message(role="user", content="Hello")]
        )
        response = await service.chat(request)

    except ProviderAuthenticationError:
        print("Invalid API key - check environment variables")

    except ModelNotFoundError as e:
        print(f"Model not available: {e}")

    except ProviderRateLimitError as e:
        print(f"Rate limited - retry after {e.retry_after}s")

    except ProviderTimeoutError:
        print("Request timed out")

    except ProviderNotFoundError:
        print("Provider not configured - check API keys")

Common Mistakes

Wrong: Forgetting Context Manager

# DON'T DO THIS - resources not cleaned up
service = LLMRing()
response = await service.chat(request)
# Forgot to call close()!

Right: Use Context Manager

# DO THIS - automatic cleanup
async with LLMRing() as service:
    response = await service.chat(request)

Wrong: Invalid Message Role

# DON'T DO THIS - invalid role
message = Message(role="admin", content="Hello")

Right: Use Valid Roles

# DO THIS - valid roles only
message = Message(role="user", content="Hello")
# Valid: "system", "user", "assistant", "tool"

Wrong: Missing Model

# DON'T DO THIS - no model specified and no lockfile
request = LLMRequest(
    messages=[Message(role="user", content="Hello")]
)

Right: Use Semantic Alias from Lockfile

# DO THIS - use your semantic alias
request = LLMRequest(
    model="chatbot",  # or "anthropic:claude-sonnet-4-5-20250929" for direct reference
    messages=[Message(role="user", content="Hello")]
)

Profiles: Environment-Specific Configuration

Use different models for different environments:

# Set profile via environment variable
# export LLMRING_PROFILE=dev

# Or in code
async with LLMRing() as service:
    # Uses 'dev' profile bindings (cheaper models)
    response = await service.chat(request, profile="dev")

    # Uses 'prod' profile bindings (higher quality)
    response = await service.chat(request, profile="prod")

See llmring-lockfile skill for full profile documentation.

Related Skills

llmring-streaming - Stream responses for real-time output
llmring-tools - Function calling and tool use
llmring-structured - JSON schema for structured output
llmring-lockfile - Configure aliases and profiles
llmring-providers - Multi-provider patterns and raw SDK access

Provider Support

| Provider | Initialization | Example | |----------|---------------|---------| | OpenAI | Set OPENAI_API_KEY | model="openai:gpt-4o" | | Anthropic | Set ANTHROPIC_API_KEY | model="anthropic:claude-sonnet-4-5-20250929" | | Google | Set GOOGLE_GEMINI_API_KEY | model="google:gemini-2.5-pro" | | Ollama | Runs automatically | model="ollama:llama3" |

All providers work with the same unified API - no code changes needed to switch providers.

Agent Skills: Basic Chat Completions

Skill Files