fugacio-copilot

Chemical-engineering design copilot/agent for the Fugacio stack. It sits on top of fugacio.sim and turns natural-language design goals into engineering calculations: flowsheets, equipment sizing, and (eventually) techno-economic / life-cycle analysis.

The bridge between a language model and the differentiable engine is a tool registry, deterministic, JSON-in/JSON-out functions exposed with the same function-calling schemas OpenAI/Anthropic expect:

• Properties & equilibrium: list_components, component_properties, saturation_pressure, bubble_pressure, flash_drum.
• Molecular PC-SAFT: saft_flash, saft_density, saft_saturation_pressure, saft_bubble_pressure, and saft_residual_enthalpy, the molecular EOS preferred for associating fluids (water, alcohols).
• Unit operations: heat_exchanger, compressor (and turbine), pump, valve, each closing a rigorous energy balance.
• Distillation: shortcut_distillation (Fenske-Underwood-Gilliland) and rigorous_distillation (multistage column with duties).
• Gradient-based optimization: optimize_flash_temperature and optimize_column_reflux solve for the operating variable that hits a target by differentiating straight through the equilibrium flash and the column.

A model-agnostic agent loop (run_agent) drives plan→act→answer; the planner is injected, so the loop is fully testable with a scripted planner while a real LLM drops in behind the optional llm extra.

from fugacio.copilot import call_tool, run_agent, tool_schemas

# Call an engine-backed tool directly (JSON in / JSON out):
call_tool("saturation_pressure", {"component": "propane", "temperature": 300.0})

# Or drive the agent loop with your own planner (an LLM in production):
def planner(goal, tools, transcript):
    if not transcript:
        return {"tool": "flash_drum", "arguments": {
            "components": ["methane", "propane", "n-pentane"],
            "z": [0.5, 0.3, 0.2], "flow": 100.0,
            "temperature": 320.0, "pressure": 20e5,
        }}
    vf = transcript[-1]["result"]["vapor_fraction"]
    return {"final_answer": f"vapor fraction = {vf:.3f}"}

run_agent("Flash this feed", planner).answer  # 'vapor fraction = 0.747'

tool_schemas() returns the schemas to hand an LLM. LLM-backed planning lives behind the optional llm extra:

pip install "fugacio-copilot[llm]"

Part of the fugacio namespace; installs independently: pip install fugacio-copilot.