Building LLM Harnesses#
A harness is the infrastructure that wraps LLM calls into a reliable, testable, and observable workflow. It handles the concerns that a raw API call does not: input preparation, output validation, error recovery, model routing, parallel execution, and quality scoring. Without a harness, you have a script. With one, you have a tool.
Harness Architecture#
Input
│
├── Preprocessing (validate input, select model, prepare prompt)
│
├── Execution (call Ollama with timeout, retry on failure)
│
├── Post-processing (parse output, validate schema, score quality)
│
├── Routing (if quality too low, escalate to larger model or flag)
│
└── Output (structured result + metadata)Core Harness in Python#
import ollama
import json
import time
from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class LLMResult:
content: str
model: str
tokens_in: int
tokens_out: int
duration_ms: int
ttft_ms: int
success: bool
retries: int = 0
score: float | None = None
metadata: dict = field(default_factory=dict)
@dataclass
class HarnessConfig:
model: str = "qwen2.5-coder:7b"
temperature: float = 0.0
max_tokens: int = 1024
json_mode: bool = False
timeout_seconds: int = 120
max_retries: int = 2
retry_delay_seconds: float = 1.0
def call_llm(
messages: list[dict],
config: HarnessConfig,
) -> LLMResult:
"""Make a single LLM call with timing metadata."""
start = time.monotonic()
kwargs = {
"model": config.model,
"messages": messages,
"options": {
"temperature": config.temperature,
"num_predict": config.max_tokens,
},
"stream": False,
}
if config.json_mode:
kwargs["format"] = "json"
try:
response = ollama.chat(**kwargs)
duration = int((time.monotonic() - start) * 1000)
return LLMResult(
content=response["message"]["content"],
model=config.model,
tokens_in=response.get("prompt_eval_count", 0),
tokens_out=response.get("eval_count", 0),
duration_ms=duration,
ttft_ms=int(response.get("prompt_eval_duration", 0) / 1_000_000),
success=True,
)
except Exception as e:
duration = int((time.monotonic() - start) * 1000)
return LLMResult(
content=str(e),
model=config.model,
tokens_in=0,
tokens_out=0,
duration_ms=duration,
ttft_ms=0,
success=False,
)Retry with Validation#
Do not retry blindly. Retry only when the output fails validation: