ptm-client 0.1.2

Lightweight PTM API client for integration with external Python services

ptm-client

Lightweight Python client for the Prompt Test Manager (PTM) API. Zero dependencies beyond requests.

Install

From PyPI (when published)

pip install ptm-client

From source (development)

pip install -e packages/ptm-client
# or with dev/test dependencies:
pip install -e "packages/ptm-client[dev]"

Docker mount (no install needed)

# docker-compose.override.yml
services:
  app:
    volumes:
      - /path/to/prompt-test-manager/packages/ptm-client/src:/opt/ptm-client-src:ro
    environment:
      PYTHONPATH: /opt/ptm-client-src:/app

Quick Start

from ptm_client import PTMClient

client = PTMClient(base_url="http://localhost:8010", token="your-api-token")

# List prompts
prompts = client.list_prompts(tag="my_team")

# Get prompt detail
detail = client.get_prompt("my_team.summarizer")

# Get prompt test cases
tests = client.get_prompt_tests("my_team.summarizer")

# Run a repository evaluation
run = client.run_eval(
    prompt_ids=["my_team.summarizer"],
    provider_ids=["openai_gpt41_mini"],
)

# Run a manual evaluation
run = client.run_manual_eval({
    "prompt_text": "...",
    "tests": [{"description": "test", "vars": {"name": "World"}}],
    "provider_profiles": ["openai_gpt41_mini"],
    "visibility_scope": "org_visible",
})

# Wait for completion
result = client.wait_for_run(run["run_key"], timeout=120)

# Get HTML report
html = client.run_report(run["run_key"])

# Get JSON report
json_report = client.run_report(run["run_key"], format="json")

API Reference

PTMClient(base_url, token, timeout=30)

Create a client. token is a PTM personal access token (ptm_u_...) or service account token (ptm_sa_...). timeout is the HTTP request timeout in seconds.

Prompts

• list_prompts(tag=None) — list all prompts, optionally filtered by tag
• get_prompt(prompt_id) — get full prompt detail (prompt_text, tags, metadata)
• get_prompt_tests(prompt_id) — get test cases, deepeval metrics, KPIs

Providers

• list_providers() — list available LLM provider profiles

Evaluations

• run_eval(prompt_ids, provider_ids, **kwargs) — submit repository evaluation
• run_manual_eval(payload) — submit manual evaluation with custom prompt + tests
• run_prompt_eval(prompt_id, provider_ids, *, inject_vars=None, extra_tests=None, visibility_scope="org_visible", label=None) — fetch a prompt from PTM, merge runtime vars/tests, and submit (recommended for service integrations)

Runs

• get_run(run_key) — get run status (includes score, passed_tests, total_tests)
• wait_for_run(run_key, timeout=300, poll_interval=5) — block until terminal state
• run_report(run_key, format="html") — get report (html, json, markdown, csv)

Test Cases and Scoring

PTM evaluates with up to three scoring layers. Use any combination.

Promptfoo assertions — deterministic pass/fail checks

Go in the assert array inside each test case:

{
    "description": "test case with assertions",
    "vars": {"transcript": "..."},
    "assert": [
        {"type": "javascript", "value": "/meeting purpose/i.test(output)", "description": "has_purpose"},
        {"type": "icontains", "value": "API migration", "description": "mentions_topic"},
        {"type": "javascript", "value": "output.length >= 100", "description": "min_length"},
    ],
}

DeepEval metrics — semantic quality scoring via judge LLM

Go in additional_metrics at the payload root:

{
    "additional_metrics": [
        {"name": "relevance", "criteria": "Output addresses the input topic with specific details.", "threshold": 0.7},
        {"name": "structure", "criteria": "Output has clear sections and logical flow.", "threshold": 0.7},
    ],
    "judge_profile": "openai_gpt41_mini",
}

KPI configs — custom weighted expressions

Go in additional_kpis at the payload root:

{
    "additional_kpis": [
        {"name": "cost_ok", "description": "Under $0.05", "expression": "1 if cost < 0.05 else 0", "weight": 1.0},
        {"name": "fast", "description": "Under 10s", "expression": "1 if latency_ms < 10000 else 0", "weight": 1.0},
    ],
}

Common patterns

# Promptfoo only (no judge LLM needed)
client.run_manual_eval({"tests": [{"vars": {...}, "assert": [...]}], ...})

# DeepEval only (semantic scoring, no deterministic checks)
client.run_manual_eval({"tests": [{"vars": {...}}], "additional_metrics": [...], ...})

# All three layers
client.run_manual_eval({"tests": [{"vars": {...}, "assert": [...]}], "additional_metrics": [...], "additional_kpis": [...], ...})

# No scoring (just run prompt, capture output)
client.run_manual_eval({"tests": [{"vars": {...}}], ...})

See docs/ptm-client-integration.md for the full test case reference with all assertion types, metric fields, and KPI variables.

Inline Test Cases

run_manual_eval — full control

run = client.run_manual_eval({
    "label": "my_custom_eval",
    "prompt_text": '[{"role": "system", "content": "Summarize."}, {"role": "user", "content": "{{text}}"}]',
    "tests": [
        {"description": "short text", "vars": {"text": "The quick brown fox."}},
    ],
    "provider_profiles": ["openai_gpt41_mini"],
    "visibility_scope": "org_visible",
    "cost_threshold": 1.0,
    "latency_threshold_ms": 30000,
})

run_prompt_eval — fetch prompt from PTM + inject live data

Recommended for service integrations:

run = client.run_prompt_eval(
    prompt_id="my_team.summarizer",
    provider_ids=["openai_gpt41_mini"],
    inject_vars={"transcript": real_transcript, "meeting_title": "Weekly 1:1"},
)
result = client.wait_for_run(run["run_key"], timeout=120)

With extra test cases:

run = client.run_prompt_eval(
    prompt_id="my_team.summarizer",
    provider_ids=["openai_gpt41_mini"],
    extra_tests=[
        {"description": "edge case", "vars": {"transcript": edge_case_text}},
    ],
    visibility_scope="private_only",
    label="meeting_recap_edge_cases",
)

Error Handling

from ptm_client import PTMClient, PTMError, PTMTimeoutError

try:
    result = client.wait_for_run(run_key, timeout=60)
except PTMTimeoutError:
    print("Run did not complete in time")
except PTMError as e:
    print(f"PTM API error ({e.status_code}): {e}")

More

• Integration guide — install methods, test case types, scoring layers, Django/FastAPI examples, chained evals
• Examples — runnable Python scripts for every use case

Dependencies

requests only. No FastAPI, SQLAlchemy, Streamlit, or other PTM server deps.

Development

pip install -e "packages/ptm-client[dev]"
cd packages/ptm-client
pytest tests/ -v
ruff check src/ tests/
ruff format src/ tests/