hilt-python 0.1.0

HILT: Human-IA Log Trace - Privacy-first logging format for AI interactions

HILT – Human–AI Log Tracing

HILT is a privacy-first, open-source format for logging human–AI interactions. Drop in one line at startup and every LLM call is captured with prompts, completions, metrics, and error context—no refactors, no custom wrappers.

What’s inside today

• One-line auto-instrumentation for the official OpenAI Python SDK (client.chat.completions.create)
• Deterministic conversation threading with prompt/completion links and reply metadata
• Rich telemetry: latency, token usage, cost estimates, HTTP-style status codes, and error surfaces
• Storage backends you control: append-only JSONL files or real-time Google Sheets dashboards
• Thread-safe context management so you can override sessions per request, per worker, or per tenant
• Manual event logging via Session.append() for tool calls, human feedback, or guardrail results

Installation

pip install hilt

Need Google Sheets streaming? Install the Sheets extra:

pip install "hilt[sheets]"

Quick start

from hilt import instrument, uninstrument
from openai import OpenAI

# Enable automatic logging (writes to logs/chat.jsonl by default)
instrument(backend="local", filepath="logs/chat.jsonl")

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Give me three onboarding tips"}],
)

print(response.choices[0].message.content)

# Stop logging when your app shuts down
uninstrument()

After the single instrument() call:

• Prompts and completions are recorded as separate events
• Latency, tokens, cost, and status codes are populated automatically
• Conversation IDs remain stable so you can trace every exchange end to end

Storage options

Local JSONL (default)

instrument(backend="local", filepath="logs/app.jsonl")

• Privacy-first: data never leaves your environment
• Plays nicely with analytics tooling (Python, Pandas, Spark, etc.)

Google Sheets (real time)

See Google Sheets setup guide for credential and sheet ID steps.

instrument(
    backend="sheets",
    sheet_id="1abc...",
    credentials_path="credentials.json",
    worksheet_name="LLM Logs",
    columns=["timestamp", "message", "cost_usd", "status_code"],
)

• Great for support, QA, or cost monitoring teams
• Columns control both ordering and visibility
• Works with credentials_path or in-memory credentials_json

Advanced usage

Provider selection

instrument(
    backend="local",
    filepath="logs/app.jsonl",
    providers=["openai"],  # Anthropic / Gemini planned
)

Passing an empty list opens the session without patching any providers—useful for manual logging scenarios.

Troubleshooting highlights

• Nothing recorded? Ensure instrument() runs before importing the OpenAI client.
• Async apps? Use the same call; the instrumentation is thread-safe and works with AsyncOpenAI.
• Large logs? Rotate files daily (logs/app-YYYY-MM-DD.jsonl) and prune with a cron job.
• Sheets failing? Double-check the service account has editor access and that hilt[sheets] is installed.

See docs/ for deeper guides on privacy, advanced contexts, and FAQ.

Development

Contributions are welcome! Start with CONTRIBUTING.md. The test suite lives in tests/, and linting/type checking is configured via Ruff, Black, and MyPy.

TODO

• Add auto-instrumentation for Anthropic Claude
• Add auto-instrumentation for Google Gemini
• Add auto-instrumentation for LangGraph

License

Released under the Apache License 2.0.