boolean-algebra-engine 0.1.8

Deterministic logic layer for AI agents — catch logical contradictions in system prompts, rules, and agent reasoning

boolean-algebra-engine

The logic layer your AI is missing.

AI agents hallucinate on boolean logic — not sometimes, reliably. They predict the next token. They don't compute. This engine does. Deterministic, exhaustive, under 10ms. It sits inside your agent pipeline and makes one guarantee the model cannot make itself: that its reasoning is logically consistent.

pip install boolean-algebra-engine

---

Quick start

Zero dependencies. Works immediately after install.

from core.evaluator import evaluate
from core.synthesizer import synthesize

# Does a contradiction exist?
table, _ = evaluate("A.!A")
print(table.satisfiable)   # False — always a contradiction

# Can two rules both be true simultaneously?
table, _ = evaluate("(A.B).(!A)")
print(table.satisfiable)   # False — A and !A can't both hold

# Full truth table
table, _ = evaluate("A.(B+C)")
print(table.variables)     # ['A', 'B', 'C']
print(table.minterms)      # [5, 6, 7]
print(table.satisfiable)   # True

# Simplify to minimal form
minimal, _ = synthesize(table)
print(minimal)             # A.C+A.B

---

The problem

Six rules. Three variables. Written by four people over six months.

A fintech AI agent auto-approves or rejects loan applications based on these rules — nobody ever verified them together. The engine checks all 8 input combinations for every rule, in every combination:

# pip install boolean-algebra-engine[mcp]
from mcp_server.server import check_prompt_logic

result = check_prompt_logic([
    "A.B",  # approve: good credit AND income verified
    "!A",   # reject:  bad credit
    "C",    # approve: collateral exists
    "!C",   # reject:  no collateral
])

print(result["summary"])
# {'total': 4, 'contradictions': 0, 'tautologies': 0,
#  'equivalent_pairs': 0, 'conflicting_pairs': 2}

print([(p["rule1"], p["rule2"]) for p in result["pairwise"] if p["always_conflict"]])
# [('A.B', '!A'), ('C', '!C')]

What it found:

• A.B and !A conflict — good credit approval and bad credit rejection fire simultaneously when A=1. The agent picks a winner arbitrarily.
• C and !C conflict — collateral approval and no-collateral rejection are mutually exclusive by definition. Both rules can never apply at the same time.

Nobody caught these by reading the rules. The engine caught them by checking every combination.

---

The benchmark

The engine is the oracle — ground truth is computed by exhaustive enumeration, not guessed. Every LLM disagreement is a provable hallucination.

Methodology: generate pairs of boolean expressions where the correct answer (satisfiable or not) is known exactly. Ask the LLM. Compare. No ambiguity, no human labeling, no interpretation.

python3 benchmark.py --provider ollama --model tinyllama --cases 20
python3 benchmark.py --provider ollama --model llama3.2:3b --cases 20

tinyllama — 1.1B parameters

⬡ z3  verifying 20 ground truth labels... ✓  all 20 cases agree

╭───────────── benchmark config ──────────────╮
│ model        ollama/tinyllama               │
│ cases        20  (10 conflict · 10 compat)  │
│ variables    3  (A, B, C)                   │
│ temperature  0  (deterministic)             │
│ max tokens   5  (yes / no)                  │
│ workers      8  parallel                    │
╰─────────────────────────────────────────────╯

  ollama/tinyllama — 20/20 cases | 50.0% hallucination rate

  #      Rule 1          Rule 2          vars    engine  llm
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  1  ✗   B               !B              B         no    yes
  2  ✗   A.B+C           !A.!B.!C        A B C     no    yes
  3  ✗   A.B             A.!B            A B       no    yes
  4  ✓   A+!B            A.(B+C)         A B C    yes    yes
  5  ✗   A.B             A^B             A B       no    yes
  6  ✓   !A+B.C          B               A B C    yes    yes
  7  ✓   A.B+C           A+B             A B C    yes    yes
  8  ✓   A+B.C.D         C               A B C D  yes    yes
  9  ✓   A.B             B               A B      yes    yes
 10  ✓   !C              !B              B C      yes    yes
 ...

╭─────────── results — ollama/tinyllama ─────────────╮
│ model               ollama/tinyllama               │
│ total cases         20  (10 conflict · 10 compat)  │
│ variables           3  (A, B, C)                   │
│ temperature         0  (deterministic)             │
│ max tokens          5                              │
│ correct             10                             │
│ hallucinated        10                             │
│ hallucination rate  50.0%                          │
│ missed conflicts    10/10  (100.0%)                │
│ missed compatibles  0/10   (0.0%)                  │
╰────────────────────────────────────────────────────╯

llama3.2:3b — 3B parameters

⬡ z3  verifying 20 ground truth labels... ✓  all 20 cases agree

╭───────────── benchmark config ──────────────╮
│ model        ollama/llama3.2:3b             │
│ cases        20  (10 conflict · 10 compat)  │
│ variables    4  (A, B, C, D)                │
│ temperature  0  (deterministic)             │
│ max tokens   5  (yes / no)                  │
│ workers      8  parallel                    │
╰─────────────────────────────────────────────╯

  ollama/llama3.2:3b — 20/20 cases | 50.0% hallucination rate

  #      Rule 1          Rule 2          vars    engine  llm
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  1  ✓   B               !B              B         no     no
  2  ✓   A.B+C           !A.!B.!C        A B C     no     no
  3  ✓   A.B             A.!B            A B       no     no
  4  ✗   A+!B            A.(B+C)         A B C    yes     no
  5  ✓   A.B             A^B             A B       no     no
  6  ✗   !A+B.C          B               A B C    yes     no
  7  ✗   A.B+C           A+B             A B C    yes     no
  8  ✗   A+B.C.D         C               A B C D  yes     no
  9  ✗   A.B             B               A B      yes     no
 10  ✗   !C              !B              B C      yes     no
 ...

╭─────────── results — ollama/llama3.2:3b ───────────╮
│ model               ollama/llama3.2:3b             │
│ total cases         20  (10 conflict · 10 compat)  │
│ variables           4  (A, B, C, D)                │
│ temperature         0  (deterministic)             │
│ max tokens          5                              │
│ correct             10                             │
│ hallucinated        10                             │
│ hallucination rate  50.0%                          │
│ missed conflicts    0/10   (0.0%)                  │
│ missed compatibles  10/10  (100.0%)                │
╰────────────────────────────────────────────────────╯

Both models score 50% — equal to a coin flip — but in opposite directions. tinyllama always answers "yes", llama3.2:3b always answers "no". Neither is reasoning. Both are outputting a constant.

The vars column shows how many variables each case involves. The engine column is ground truth. Every mismatch with llm is a provable hallucination — not an opinion.

Per-case strips (bottom row of the chart): every conflict cell is uniformly one colour per model, every compatible cell is the opposite. No case-by-case variation — no reasoning happening at all.

---

Install

# Core engine — zero dependencies
pip install boolean-algebra-engine

# With CLI
pip install "boolean-algebra-engine[cli]"

# With MCP server (for Claude Desktop)
pip install "boolean-algebra-engine[mcp]"

# With REST API
pip install "boolean-algebra-engine[api]"

# With NL layer (Anthropic)
pip install "boolean-algebra-engine[nl-anthropic]"

# With NL layer (OpenAI)
pip install "boolean-algebra-engine[nl-openai]"

---

Core API

from core.evaluator import evaluate
from core.synthesizer import synthesize

# Forward: expression → truth table
table, _ = evaluate("A.(B+C)")
print(table.variables)    # ['A', 'B', 'C']
print(table.minterms)     # [5, 6, 7]
print(table.satisfiable)  # True

# Inverse: truth table → minimal expression
minimal, _ = synthesize(table)
print(minimal)            # A.C+A.B

# Equivalence and satisfiability (via MCP server functions — no HTTP, direct call)
# pip install boolean-algebra-engine[mcp]
from mcp_server.server import equivalent, satisfiable

print(equivalent("A.(B+C)", "A.B+A.C")["equivalent"])  # True — distributive law
print(satisfiable("A.!A")["satisfiable"])               # False — contradiction

core/ has zero external dependencies. Import it into any Python project.

---

MCP — Claude calls the engine

Wire the engine into Claude Desktop and Claude stops predicting boolean logic. It computes it.

{
  "mcpServers": {
    "boolean-algebra-engine": {
      "command": "python",
      "args": ["-m", "mcp_server.server"]
    }
  }
}

Five tools Claude can call mid-conversation:

• evaluate — expression → truth table
• simplify — expression → minimal form
• equivalent — are two expressions identical?
• satisfiable — does any input make this true?
• check_prompt_logic — audit a full rule set for contradictions, tautologies, conflicts, duplicates

---

Operators

Symbol	Operation	Precedence
!	NOT	4 (highest)
.	AND	3
^	XOR	2
+	OR	1 (lowest)

Variables: uppercase A–Z. Parentheses override precedence. Up to 26 variables, arbitrary nesting.

---

Interfaces

Interface	How
Python library	from core.evaluator import evaluate — embed in any project
CLI / REPL	boolcalc "A.B+!A.C" — instant truth table in terminal
MCP server	Claude Desktop plugin — plug and play
REST API	POST /check-rules — callable from any language or stack
NL layer	Plain English → expression → verified result (Anthropic, OpenAI, Ollama, any OpenAI-compat)
Streamlit UI	Three modes: Expression, Rule Auditor, Plain English

---

vs SymPy and boolean.py

SymPy (sympy.logic) is more powerful for pure boolean mathematics — its DPLL-based satisfiable() scales better beyond 15 variables, and simplify_logic() covers similar minimization ground. If you're doing symbolic mathematics, use SymPy.

boolean.py handles expression parsing and symbolic simplification cleanly. If you need to manipulate boolean expressions as objects, it's the right tool.

This engine is different in three ways:

1. Zero-dependency core. SymPy pulls in numpy, mpmath, and the full symbolic stack. core/ is plain Python — no install side-effects, embeds anywhere.

2. Built for AI pipelines, not mathematics. check_prompt_logic audits a set of rules for pairwise conflicts — the kind of check you run on a system prompt or a business rule engine before an agent acts on it. Neither SymPy nor boolean.py has this concept.

3. The integration layer. MCP server for Claude Desktop, NL layer for plain English input, REST API, benchmark against LLMs — none of this exists in math-focused libraries because it's not a math problem. It's an AI reliability problem.

If you want to do boolean algebra, SymPy is the answer. If you want to verify that your AI agent's rules don't contradict each other, this is built for that.

---

Credibility

The engine does not sample, approximate, or predict. It evaluates every possible input combination:

• Satisfiable — an actual row where output = 1 was found
• Contradiction — every row was checked, all were 0
• Equivalent — output columns compared row-by-row across the full truth table
• Conflict — conjunction of both rules evaluated for every input, always returned 0

The core evaluator is 15 lines (core/evaluator.py). No black box, no model weights, no probability — just arithmetic. This is a stronger correctness claim than any probabilistic tool can make.

90 tests across unit, integration, edge cases, and round-trips. All passing.