pretia 1.1.2

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 risks, and see exactly where the money goes.

Install

pip install pretia

Quick Start

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

pretia estimate my_agent.py

This is a conservative upper bound, not a precise prediction. Static analysis reads your code but can't know runtime behavior (which branches execute, actual output lengths, caching effects). Expect estimates 2-5x higher than real costs. Use it to get a ballpark before committing to a full profile.

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

pretia profile run my_agent.py

This runs your workflow with real API calls and gives accurate distributional projections (typically within 10% of production costs). No config files, no JSONL datasets, no setup. Pretia reads your workflow, generates diverse synthetic inputs, runs 50 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 averages. For workflows with non-linear behavior (context growth, variable loop counts), Pretia uses Monte Carlo simulation (10,000 runs) instead of linear scaling.

Automatic Pattern Detection

Pretia scans your profiling data for cost risks: context windows that grow with each iteration, unpredictable retry loops, wide variance between typical and worst-case runs, routing branches that change cost profiles, and bimodal distributions where a cheap path and an expensive path create two distinct cost clusters. If something will surprise you at scale, the report flags it.

Optimization Recommendations

Each recommendation comes with estimated monthly savings in dollars. Pretia identifies where you're overspending and suggests specific changes to bring costs down.

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]

permissions:
  contents: read
  pull-requests: write  # required for PR comments

jobs:
  cost-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: pretia-ai/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-ai/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 the 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 50)
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. 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.