Welcome to Agent Logic

🤖 agent-logic

Modular Symbolic Logic for Agent Reasoning, SAT Solving, and LLM-Enhanced Proof Systems

🚀 Overview

agent-logic is a modular Python library for constructing, evaluating, transforming, and proving logical expressions — designed to act as a lightweight symbolic logic and SAT solving engine for LLM-driven agent systems.

It enables:

✅ Propositional Logic (AND, OR, IMPLIES, IFF, NOT)
✅ Predicate Logic (Terms, Predicates, Quantifiers: FORALL ∀, EXISTS ∃)
✅ Formal Proof Validation (Inference Rules, Structured Derivations)
✅ Truth Table Generation and Logical Satisfiability Checking
✅ Logical Transformations (Equivalences, Normal Forms)
✅ Recursive Abstract Syntax Tree (AST) Parsing
✅ Native Pydantic Models and Strong Typing for Safe Structured Outputs

🔗 Structured. Serializable. Reasonable. Agent-Ready.

💪 Current Status

Core symbolic logic (propositions, connectives, predicates, quantifiers) is fully implemented.
Truth tables, tautology/contradiction checking, and core proof validation are working and tested.
Inference rules are largely implemented (Modus Ponens, Modus Tollens, Hypothetical Syllogism, Dilemmas, Biconditional Elimination, etc).
AST parsing, SAT-based search, and deeper quantifier handling are in progress.

⚠️ Note:

Some features (e.g., deeper quantifier transformations, large proof automation) are actively being debugged.
Basic and intermediate logical operations are stable; complex proof search under refinement.

📊 Motivation

Large Language Models can predict, generate, and reflect — but they struggle with formal, structured, symbolic reasoning.

agent-logic empowers:

Agents that perform valid, step-by-step derivations.
LLMs that validate, transform, and construct proofs.
Systems that reason explicitly over symbolic structures, not just language.

By combining a SAT-solving core, formal proof system, and structured Pydantic output models, it provides the foundation for autonomous, interpretable reasoning agents.

“Prediction ends where true reasoning begins.”

💡 Key Features

Feature	Details
Propositional Logic	Build expressions with AND, OR, NOT, IMPLIES, IFF
Predicate Logic	Define predicates, terms, universal and existential quantifiers
Inference System	Apply formal inference rules to derive conclusions
Truth Tables	Generate complete truth tables, detect tautologies and contradictions
AST-Based Parsing	Logical expressions modeled as fully typed recursive trees
Pydantic Models	All structures serializable, introspectable, LLM-compatible
SAT Solver Backbone	Solve satisfiability and consistency of logical expressions (planned)
Type-Safe API	Full typing with Pydantic v2, Literal types, structured validation

💡 Example Usage

from agent_logic.core.operations import Proposition, BinaryOp
from agent_logic.evaluation.truth_table import TruthTable

# Define propositions
p = Proposition(name="P")
q = Proposition(name="Q")

# Create an expression: (P AND Q)
expr = BinaryOp(left=p, right=q, operator="AND")

# Generate a truth table
table = TruthTable(expression=expr)
for row in table.generate():
    print(row)

# Check logical properties
print("Is tautology:", table.is_tautology())
print("Is contradiction:", table.is_contradiction())

📙 LLM and Agent Toolkit Use Cases

Formal proof verification of LLM-generated outputs
Autonomous deduction chains in multi-agent debates
Structured symbolic output parsing for LangChain tools / OpenAI functions
Hypothetical reasoning, consequence checking, and goal validation
Safe, introspectable logical reasoning pipelines for AI agents

All models use Pydantic v2, meaning:

JSON-serializable and function-call ready
Validatable against strict schemas
Compatible with LangChain Structured Tools, OpenAI Tools, JSON mode parsing

“Not just token prediction. Formal reasoning.”

🌟 Roadmap

[x] Propositional and Predicate Logic Core
[x] Truth Tables and Tautology Checking
[x] Structured Proof Validation Engine
[ ] Advanced SAT Solving and Forward/Backward Proof Search
[ ] Quantifier Manipulation (Skolemization, Unification)
[ ] Natural Language to Formal Logic Parsing (Experimental)
[ ] Web Visualizer Playground

🚀 Getting Started

pip install agent-logic

or from git:

git clone https://github.com/pr1m8/agent-logic.git
cd agent-logic
poetry install

👤 Authors

Built by developers passionate about combining symbolic logic, autonomous reasoning, and practical agentic AI design.

Contributions, ideas, and PRs are welcome!

🎉 License

MIT License.

Empower Your Agents with True Reason.

💡 “Teach your models to reason, not just predict.”

API Reference:

API Reference

agent_logic

Agent Logic Package.