pretia 1.0.1

Pre-deployment cost intelligence for AI agent workflows

Pretia

Know what your agent will cost before you deploy.

Pre-deployment cost intelligence for AI agent workflows. Two commands, zero config, ~$2. Get distributional cost projections (p50-p99), detect cost time-bombs, and receive dollar-denominated optimization recommendations.

Install

pip install pretia

Quick Start

Zero-cost estimate (static analysis, no execution):

pretia estimate my_agent.py

Full profile (runs your workflow, ~$2, ~3 minutes):

pretia profile run my_agent.py

No config files, no JSONL datasets, no setup. Pretia reads your workflow, generates diverse synthetic inputs, runs 20 profiling runs, detects patterns, and opens an HTML report with projections and recommendations.

Features

Distributional Projections

Cost projections at p50, p75, p90, p95, and p99 — not just averages. For workflows with non-linear behavior (context growth, variable loop counts), Pretia uses Monte Carlo simulation (10K runs) instead of linear scaling.

8 Pattern Detectors

Automatically detects cost risks in your workflow:

• Context growth — input tokens increasing with each iteration
• Loop count variance — unpredictable iteration counts
• High token variance — wide spread between typical and worst-case calls
• Step count variance — routing variability across runs
• Bimodality — two distinct cost clusters (e.g., cache hit vs. miss)
• Cache utilization opportunity — missing prompt caching on supported providers
• Zero-execution steps — workflow paths never triggered during profiling
• Output token budget — wasteful max_tokens settings or truncation risk

6 Optimization Recommendations

Each recommendation comes with estimated monthly savings in dollars:

• Model swap — downshift steps using frontier models for classification tasks
• Loop iteration cap — cap iterations where marginal returns diminish
• Circuit breaker — hard exit for stuck loops consuming >15% of cost
• Enable prompt caching — activate provider caching for repeated system prompts
• Filter tool definitions — remove unused tools from step context
• Cache re-sent context — eliminate redundant system prompts across consecutive steps

Optimization Score

A 0-100 score measuring workflow cost efficiency. Three zones: red (0-40, needs optimization), amber (41-70, room to improve), green (71-100, well optimized).

Five Input Modes

A friction ladder from zero-effort to maximum precision:

Level	Command	What happens	Cost
0	pretia estimate workflow.py	Static code analysis only. No execution.	Free
1	--input "How do I reset my password?"	One run + priors for variance estimation.	~$0.10
2	--auto-generate N (default)	LLM generates diverse inputs from system prompt.	~$2
3	--from-langfuse --last 100	Pull real inputs from Langfuse production traces.	Free
4	--inputs samples.jsonl	User-curated test dataset. Maximum precision.	Execution only

Add to Your CI in 2 Minutes

Pretia ships a GitHub Action that comments on every PR with cost analysis.

Diff-only mode (free, default) — static analysis in seconds:

# .github/workflows/pretia.yml
name: Pretia
on: [pull_request]

jobs:
  cost-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: pretia/pretia/action@v1
        with:
          workflow_path: src/agent.py
          cost_threshold: "20"  # fail if cost increases >20%
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Full profile mode (opt-in, ~$2) — real profiling with recommendations:

      - uses: pretia/pretia/action@v1
        with:
          workflow_path: src/agent.py
          mode: profile
          cost_threshold: "20"
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}  # or your provider key

The PR comment shows: optimization score, projected monthly cost, cost delta vs. baseline, and recommendations in a collapsible section.

CLI Commands

pretia estimate workflow.py             # Instant cost estimate (no execution)
pretia profile run workflow.py          # Full profiling (default: --auto-generate 20)
pretia report profile.json              # Generate HTML report from saved profile
pretia recommend profile.json           # Generate optimization recommendations
pretia analyze --from-langfuse          # Analyze Langfuse traces (no execution)
pretia baseline update profile.json     # Save baseline for CI diffing
pretia diff baseline.json new.json      # Compare profiles, show per-step deltas

Supported Frameworks

Framework	Collection method	Install
LangGraph	Callback handler	pip install pretia[langgraph]
OpenAI Agents SDK	RunHooks lifecycle	pip install pretia[openai]
Qwen-Agent	LLM proxy	pip install pretia[qwen]
Generic	@collector.step() decorator	pip install pretia

How It Works

Data flows through a five-stage pipeline:

1. Collector — Framework adapters instrument your workflow and emit unified StepRecords
2. StepRecord — Frozen dataclass capturing one LLM call: model, tokens, cost, timing, tool usage
3. ProfileStore — Persists profiling sessions as JSON (one workflow x N input runs)
4. Projection — Distributional scaling (p50-p99) for stable workflows, Monte Carlo for non-linear cases
5. Recommendation — Rule-based generators produce dollar-denominated optimization suggestions

The projection engine is validated against 13 real-world workflow archetypes (12/13 within 10% projection error).

Positioning

Langfuse tells you what you spent. Pretia tells you what you'll spend. Use both.

Pretia sits above the LLM tooling stack. It detects when other tools are needed — it doesn't replace them. No proxy (use LiteLLM), no routing (use Martian), no tracing (use Langfuse), no evals (use Braintrust).

Development

uv pip install -e ".[dev]"
pytest tests/unit/ -v
ruff check pretia/ tests/
ruff format pretia/ tests/
pyright pretia/

See CLAUDE.md for architecture details and coding conventions.

Contributing

Issues and PRs welcome. Run pytest tests/unit/ and ruff check pretia/ tests/ before opening a PR.

License

BSL 1.1 (Business Source License). Free for all use except offering Pretia as a commercial hosted service. Converts to Apache 2.0 on 2030-06-13.