yalc 0.1.3

Yet Another LLM Client

Yet Another LLM Client

An opinionated python wrapper for LLM calls. Supports multiple LLM providers:

• OpenAI
• Anthropic
• more to come...

Uses pydantic models to serialize LLM responses. Every response has to be serialized into a pydantic model.

Full async support.

Usage

Every call to the LLM returns some metadata. Metadata contains token usage, costs, model used and context messages. YALC supports 2 modes of operations for handling metadata.

Metadata return mode

Metadata is returned directly alongside the response as a tuple.

client = create_client(LLMModel.gpt_4o_mini)

result, metadata = await client.structured_response(
    JudgmentResult, messages
)

Advantages:

• Simple, no setup required
• Direct access to metadata at the call site

Disadvantages:

• Must handle metadata manually on every call
• Easy to forget or handle inconsistently across call sites

Strategy metadata mode

A metadata handler strategy is provided during client creation. The strategy is automatically invoked on every call when a context is passed. The provided context is used for any additional data that needs to be used when handling LLM call metadata.

# 1. Define your strategy
class LogStrategy:
    def handle(self, call: ClientCall, context: LLMLogContext):
        print(f"Tokens: {call.input_tokens + call.output_tokens}")
        print(f"Cost: {call.input_tokens_cost + call.output_tokens_cost}")
        db.save(call.model_dump(), context.request_id)

# 2. Create client with the strategy
client = create_client(LLMModel.gpt_4o_mini, log_strategies=[LogStrategy()])

# 3. Pass context to trigger the strategy
result = await client.structured_response(
    JudgmentResult, messages, context=llm_log_context
)

Advantages:

• Metadata handling is set up once and applied consistently
• Call sites stay clean — no need to unpack or handle metadata each time

Disadvantages:

• More initial setup
• Metadata handling is implicit, which can be harder to trace