What is the Logic API?
The logic repo provides composable decision-making modules as pure Python functions and FastAPI endpoints. Every module is open source, stateless by default, and designed to be imported into your own systems.
Available Modules
TCO (Total Cost of Ownership) — Live
| Function |
Description |
calculate_tco() |
Compute total and annual cost with NPV adjustment |
compare_options() |
Rank multiple options by annual cost |
calculate_breakeven() |
Find when a higher upfront cost pays off |
PERT (Three-Point Estimation) — Live
| Function |
Description |
calculate_task() |
Single-task PERT with optional insight tags |
calculate_project() |
Multi-task project estimation with variance aggregation |
EVM (Earned Value Management) — Live
| Function |
Description |
evm_metrics() |
Schedule and cost performance (SV, SPI, CV, CPI, EAC, TCPI) |
health_signal() |
Interpret metrics into actionable health status |
create_baseline() |
Create frozen project baseline from work packages |
evaluate_progress() |
Evaluate actual progress against baseline |
Bayesian (Estimation Calibration) — Live
| Function |
Description |
update_belief() |
Compute posterior from prior belief and observed estimate-vs-actual pairs |
adjust_estimate() |
Apply a learned delay factor to a PERT estimate |
Monte Carlo (Schedule Simulation) — Live
| Function |
Description |
simulate_schedule() |
Run Monte Carlo simulation on a schedule of dependent tasks |
probability_of_completion() |
Probability of completing within a target duration |
compare_with_pert() |
Compare textbook PERT aggregation with Monte Carlo results |
Coming Soon
| Module |
Category |
Description |
| Base-rate |
Forecasting |
Reference class forecasting to reduce subjective bias |
| NPV |
Finance |
Net Present Value analysis |
| IRR |
Finance |
Internal Rate of Return |
Self-Host (Available Now)
Clone the repo and run locally:
git clone https://github.com/lemur47/logic.git && cd logic
uv pip install -e ".[dev]"
uv run uvicorn app.main:app --reload
API available at http://127.0.0.1:8000. Full endpoint docs at /docs (Swagger UI).
TCO Endpoints
| Method |
Path |
Description |
POST |
/tco/calculate |
Stateless TCO calculation |
POST |
/tco/compare |
Compare options, ranked by annual cost |
POST |
/tco/breakeven |
Break-even analysis between two options |
POST |
/tco/scenarios |
Save a scenario |
GET |
/tco/scenarios |
List scenarios (paginated, searchable) |
GET |
/tco/scenarios/{id} |
Get a scenario |
PATCH |
/tco/scenarios/{id} |
Update a scenario (auto-recalculates) |
DELETE |
/tco/scenarios/{id} |
Delete a scenario |
GET |
/tco/scenarios/stats |
Aggregate statistics |
PERT Endpoints
| Method |
Path |
Description |
POST |
/pert/task |
Single-task PERT estimate (with optional insight tags) |
POST |
/pert/project |
Multi-task project estimation |
EVM Endpoints
| Method |
Path |
Description |
POST |
/evm/calculate |
Calculate EVM metrics (stateless) |
POST |
/evm/health |
Health signal from SPI/CPI (stateless) |
POST |
/evm/baselines |
Create a project baseline |
GET |
/evm/baselines |
List baselines (paginated, searchable) |
GET |
/evm/baselines/{id} |
Get a baseline with work packages |
DELETE |
/evm/baselines/{id} |
Delete a baseline |
POST |
/evm/baselines/{id}/evaluate |
Evaluate progress against baseline |
GET |
/evm/baselines/{id}/snapshots |
List evaluation snapshots |
Bayesian Endpoints
| Method |
Path |
Description |
POST |
/bayesian/calculate |
Compute posterior from prior and observations (stateless) |
POST |
/bayesian/adjust |
Apply delay factor to a PERT estimate (stateless) |
POST |
/bayesian/contexts |
Create an estimation context |
GET |
/bayesian/contexts |
List contexts (paginated, searchable) |
GET |
/bayesian/contexts/{id} |
Get a context with current belief |
DELETE |
/bayesian/contexts/{id} |
Delete a context and its observations |
POST |
/bayesian/contexts/{id}/observations |
Add observations to a context |
GET |
/bayesian/contexts/{id}/observations |
List observations (paginated) |
GET |
/bayesian/contexts/{id}/belief |
Get current posterior belief |
POST |
/bayesian/contexts/{id}/adjust |
Adjust a PERT estimate using context belief |
Monte Carlo Endpoints
| Method |
Path |
Description |
POST |
/montecarlo/simulate |
Run Monte Carlo schedule simulation (stateless) |
POST |
/montecarlo/simulate/target |
Probability of completing within a target duration (stateless) |
POST |
/montecarlo/scenarios |
Save a scenario with simulation results |
GET |
/montecarlo/scenarios |
List scenarios (paginated, searchable) |
GET |
/montecarlo/scenarios/{id} |
Get a scenario with cached results |
PATCH |
/montecarlo/scenarios/{id} |
Update a scenario (auto-resimulates) |
DELETE |
/montecarlo/scenarios/{id} |
Delete a scenario |
GET |
/montecarlo/scenarios/stats |
Aggregate statistics |
Other Endpoints
| Method |
Path |
Description |
GET |
/ |
API info |
GET |
/health |
Health check |
Python Import
You can also use the logic directly without the API:
from app.tco.core import calculate_tco
result = calculate_tco(
initial_price=450000,
useful_life_years=12,
residual_value=90000,
annual_maintenance=5000,
)
print(f"Annual cost: {result['annual_cost']:,.0f}")
from app.pert.core import calculate_task
result = calculate_task(optimistic=5, most_likely=8, pessimistic=15)
print(f"Expected: {result['textbook']['expected']:.1f} days")
from app.evm.core import evm_metrics
result = evm_metrics(pv=100000, ev=85000, ac=95000, bac=200000)
print(f"SPI={result['spi']:.2f} CPI={result['cpi']:.2f}")
from app.bayesian.core import Prior, Observation, update_belief, adjust_estimate
prior = Prior(mean=1.0, variance=0.1)
observations = [Observation(estimated=5, actual=7), Observation(estimated=10, actual=13)]
posterior = update_belief(prior, observations)
result = adjust_estimate(pert_expected=8.0, posterior=posterior)
print(f"Adjusted: {result['adjusted_expected']:.1f} days")
from app.montecarlo.core import Task, simulate_schedule
tasks = [
Task(name="Design", optimistic=3, most_likely=5, pessimistic=10),
Task(name="Build", optimistic=8, most_likely=12, pessimistic=20, depends_on=("Design",)),
Task(name="Test", optimistic=2, most_likely=4, pessimistic=8, depends_on=("Build",)),
]
result = simulate_schedule(tasks, n_simulations=10_000, seed=42)
print(f"P85: {result.percentiles['P85']:.1f} days")
Hosted API
A hosted API at api.pmo.run is planned for Phase 2. It will include authentication, rate limits, and saved scenarios with encrypted storage.
For now, self-hosting is the way to go — and it’s free, forever.
Source
github.com/lemur47/logic — MIT License