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Basic generation

Use ctx.llm.generate() for text completion: 
from friday_agent_sdk import agent, ok

@agent(id="writer", version="1.0.0", description="Writes documentation")
def execute(prompt, ctx):
    result = ctx.llm.generate(
        messages=[{"role": "user", "content": f"Write docs for: {prompt}"}],
        model="anthropic:claude-sonnet-4-6",
    )
    return ok({"output": result.text})

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Model Resolution

You can specify models in three ways: 
# Fully qualified - uses this exact model
ctx.llm.generate(..., model="anthropic:claude-sonnet-4-6")

# Bare model name with decorator provider - resolves automatically
@agent(..., llm={"provider": "anthropic", "model": "claude-sonnet-4-6"})
def execute(prompt, ctx):
    # No model arg - uses decorator default
    result = ctx.llm.generate(...)

    # Override per-call
    result = ctx.llm.generate(..., model="claude-haiku-4-5")
Resolution order (first match wins):
  1. Fully qualified per-call (provider:model) - use directly
  2. Bare per-call + decorator provider - resolve
  3. No per-call model + decorator default - use default
  4. Nothing configured - error

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Structured Output

Use ctx.llm.generate_object() for JSON Schema-constrained output: 
schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "tags": {"type": "array", "items": {"type": "string"}},
        "complexity": {"type": "string", "enum": ["low", "medium", "high"]},
    },
    "required": ["title", "complexity"],
}

result = ctx.llm.generate_object(
    messages=[{"role": "user", "content": "Analyse this task"}],
    schema=schema,
    model="anthropic:claude-sonnet-4-6",
)

# result.object contains the parsed JSON
data = result.object

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Response Fields

The LlmResponse object contains: 
result.text          # Generated text (None for generate_object)
result.object        # Structured output dict (None for generate)
result.model         # Model identifier used (e.g., "anthropic:claude-sonnet-4-6")
result.usage         # {"input_tokens": 120, "output_tokens": 250}
result.finish_reason # "stop", "length", etc.

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Error Handling

LLM errors raise LlmError: 
from friday_agent_sdk import LlmError, agent, err, ok

@agent(id="retry-agent", version="1.0.0", description="Retries on failure")
def execute(prompt, ctx):
    try:
        result = ctx.llm.generate(..., model="expensive-model")
    except LlmError as e:
        # Fallback to cheaper model
        result = ctx.llm.generate(..., model="claude-haiku-4-5")
    return ok({"output": result.text})

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Advanced Options

result = ctx.llm.generate(
    messages=[...],
    model="anthropic:claude-sonnet-4-6",
    max_tokens=2000,           # Limit response length
    temperature=0.7,           # Sampling temperature (0-2)
    provider_options={          # Provider-specific passthrough
        "anthropic": {
            "thinking": {"type": "enabled", "budgetTokens": 4000},
        },
    },
)

ctx.llm reference

Full API reference for LLM generation.

How agents work

Why the SDK uses host capabilities instead of direct imports.