ai-decision-tracer 0.2.0

Zero-dependency local AI agent decision tracer. Records every step an AI agent takes — what it saw, what it decided, why.

ai-trace

Zero-dependency local AI agent decision tracer.

Records every step an AI agent takes — what it saw, what it decided, and why. JSON + Markdown output. No network calls. No cloud. Entirely local.

Part of the AI Agent Infrastructure Stack:

• ai-cost-guard — hard budget caps before the LLM call
• ai-injection-guard — prompt injection scanner
• ai-trace — local decision tracer ← you are here

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Install

pip install ai-decision-tracer

No dependencies. Pure Python stdlib.

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Quickstart

from ai_trace import Tracer

tracer = Tracer("trading_agent", meta={"model": "claude-haiku-4-5"})

with tracer.step("market_scan", symbol="BTCUSDT") as step:
    signal = analyze(market_data)
    step.log(signal=signal, confidence=0.87)

with tracer.step("decision", signal=signal) as step:
    action = decide(signal)
    step.log(action=action, reason="SuperTrend bullish + volume spike")

# Save full trace
tracer.save()             # → traces/trading_agent_20240301_143022.json
tracer.save_markdown()    # → traces/trading_agent_20240301_143022.md

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Why this exists

My trading bot made a bad trade at 3AM. Lost money. I had logs — thousands of lines of print() spam — but I couldn't answer the basic question: what did the agent see at the moment it decided to enter that position?

Was it a bad signal? A stale data feed? A prompt injection that slipped past the scanner? Without a structured decision trace, postmortems are guesswork.

ai-trace records every step an AI agent takes — what it saw, what it decided, why, and how long it took. JSONL auto-save (survives crashes), Markdown reports (human-readable), and a CLI for quick inspection. No cloud. No external service. Everything stays local.

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Features

Feature	Details
Zero dependencies	Pure Python 3.8+ stdlib
Context manager	with tracer.step("name", **ctx) as step:
Auto-save	Appends each step to JSONL as it completes
Atomic writes	JSON/Markdown via temp file + rename — no partial output
CLI viewer	ai-trace view, ai-trace tail, ai-trace stats
Error capture	Full traceback captured on exception, step marked as error
Metadata	Attach model name, version, run ID to the session

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API

Tracer

Tracer(
    agent: str,           # agent name — used in filenames
    trace_dir: str,       # where to write files (default: "traces")
    auto_save: bool,      # append to JSONL after each step (default: True)
    meta: dict,           # session-level metadata (model, version, etc)
)

step(name, **context)

with tracer.step("classify", input_text=text[:50]) as step:
    result = model.classify(text)
    step.log(label=result.label, confidence=result.score)

Or manually:

step = tracer.step("scan")
step.start()
step.log(markets_scanned=142)
step.finish()  # or step.fail(reason="timeout")

Save

tracer.save()           # → JSON (all steps + metadata)
tracer.save_markdown()  # → human-readable Markdown summary
tracer.summary()        # → dict: steps, ok, errors, avg_duration_ms

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CLI

# List all trace sessions
ai-trace list

# View a specific session
ai-trace view trading_agent_20240301_143022.jsonl

# Live tail the latest trace
ai-trace tail -n 20

# Stats across all sessions
ai-trace stats

Custom directory:

ai-trace --dir /var/log/agent/traces list

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Output formats

JSONL (auto-saved, one line per step)

{"name": "market_scan", "context": {"symbol": "BTCUSDT"}, "outcome": "ok", "duration_ms": 142.3, "logs": [{"_t": 1709300422.1, "signal": 0.87}]}

JSON (full session snapshot)

{
  "agent": "trading_agent",
  "session_id": "20240301_143022",
  "meta": {"model": "claude-haiku-4-5"},
  "steps": [...]
}

Markdown (human-readable)

# Trace: trading_agent — 20240301_143022

## Summary
| Steps | OK | Errors | Avg duration |
|---|---|---|---|
| 4 | 3 | 1 | 127.4 ms |

## Steps

### 1. ✅ `market_scan` (142.3 ms)
**Context:**
- `symbol`: 'BTCUSDT'

**Logs:**
- `14:30:22.100Z` — `signal=0.87`

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Use with other stack libraries

from ai_cost_guard import CostGuard
from ai_injection_guard import PromptScanner
from ai_trace import Tracer

guard   = CostGuard(weekly_budget_usd=5.00)
scanner = PromptScanner(threshold="MEDIUM")
tracer  = Tracer("agent", meta={"model": "claude-haiku-4-5"})

@guard.protect(model="anthropic/claude-haiku-4-5-20251001")
@scanner.protect(arg_name="prompt")
def call_llm(prompt):
    with tracer.step("llm_call", prompt_len=len(prompt)) as step:
        response = client.messages.create(...)
        step.log(tokens=response.usage.input_tokens)
    return response

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License

MIT