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🌐 Agentics

Agentics is a lightweight, Python-native framework for building structured and massively parallel agentic workflows using Pydantic models and transducible functions.

📚 Documentation Overview

Core Documentation

  • Getting Started 🚀 Install Agentics, set up your environment, and run your first transducible function over a small dataset.

  • Core Concepts 🧠 Pydantic types, transducible functions, typed state containers, Logical Transduction Algebra (LTA), and Map–Reduce.

  • Transducible Functions ⚙️ How to define, configure, and execute transducible functions. Understanding dynamic generation and composition of transducible functions, batch processing, and provenance of generation.

  • Map-Reduce Operations 🔁 Scaling transducible functions with map and reduce operations, batch processing patterns, and best practices.

  • Semantic Operators 🔍 High-level declarative API for data transformations using natural language. Includes sem_map, sem_filter, sem_agg, and more LOTUS-style operations.

  • Agentics (AG) 🧬 Working with AG typed state containers, loading data from JSON/CSV/DataFrames, and preserving type information across the pipeline.

Advanced Topics

  • Performance Optimization ⚡ Batch size tuning, persisting intermediate results, performance optimization strategies, performance benchmarking, error handling, and best practices.

  • Tool Integration 🔌 Using MCP tools, tool usage patterns, custom tools, and best practices.

Tutorials & Examples

  • Logical Transduction Algebra 🔁 Interactive tutorial: Chaining transducible functions, branching, fan-in/fan-out patterns, and building reusable pipeline components.

  • Map-Reduce Tutorial 🚀 Interactive tutorial: Using amap and areduce for large-scale runs, batching strategies, handling failures, and performance considerations.

  • Examples & Use Cases 📘 End-to-end examples: text-to-SQL, data extraction and enrichment, classification, document workflows, evaluation pipelines, and more.

How to Cite Agentics

  • References 📚 Academic papers and research that form the foundation of Agentics, including transduction algebra, agentic AI, and applications.

📖 Glossary

AG (Agentics)
Short for "Agentics". A typed state container that wraps a list of Pydantic objects, enabling structured transductions. Used as AG[Type] or simply AG(atype=Type). The recommended way to work with collections of typed data.

Agentics
The full name of the framework and the class name for typed state containers. In code, typically imported and used as AG for brevity.

Transducible Function
A typed, explainable function that maps inputs of type Source to outputs of type Target. Defined using the @transducible() decorator or dynamically with the << operator. Guarantees totality, local evidence, and slot-level provenance.

Transduction
The process of transforming data from one typed structure to another using LLM-powered reasoning. Unlike simple mapping, transduction preserves semantic relationships and provides explainability.

Logical Transduction Algebra (LTA)
The formal mathematical framework underlying Agentics. Treats transductions as morphisms between types, enabling composition, explainability, and stability guarantees.

<< Operator (Left Shift)
The transduction operator. Target << Source creates a transducible function that maps Source to Target. Can be used with types, instances, or existing functions for composition.

With() Function
A helper that wraps a source type with configuration parameters. Used as Target << With(Source, instructions="...", tools=[...]) to create configured transducible functions dynamically.

TransductionResult
A wrapper object returned when provide_explanation=True. Supports automatic unpacking into (value, explanation) tuples or single value assignment.

AType
Short for "Agentics Type". The Pydantic model class that defines the schema for all instances in an AG container. Accessed via ag.atype.

Map-Reduce
The execution pattern for scaling transductions. amap applies a function to each element in parallel; areduce aggregates results into a summary.

MCP (Model Context Protocol)
A standard protocol for exposing tools (web search, databases, APIs) to LLMs. Agentics supports MCP tools via the tools parameter.

Evidence
The subset of input fields that contributed to generating a specific output field. Tracked automatically to enable explainability and provenance.

Slot
A field in a Pydantic model. "Slot-level provenance" means tracking which input slots contributed to each output slot.

Documentation

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