A Modular Framework for Baselith-Core Orchestration featuring World Models (MCTS), Swarm, and Native MCP.

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  • License: GNU Affero General Public License v3 (AGPL-3.0)
  • Author: Gippo
  • Tags ai , agents , framework
  • Requires: Python <3.13, >=3.10
  • Provides-Extra: adapters , test
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Project description

BaselithCore Logo

BaselithCore

The Research-Backed Engine for Production-Grade Agentic AI.

Python 3.10+ License: AGPL v3 Code Style: Ruff Checked with mypy Tests: 1804/1804 | 69% PyPI version

World Model: MCTS Swarm Intelligence Agentic Patterns Native MCP Docker Ready

BaselithCore is a high-performance orchestration engine designed to transition agentic AI from experimental prototypes to resilient, production-ready infrastructure. Built on a modular architecture, it provides an agnostic foundation for engineering scalable multi-agent systems.

Core Philosophy

BaselithCore is governed by a strict architectural separation:

  1. Sacred Core: The core/ directory contains exclusively agnostic logic—orchestration, infrastructure, and utilities. It remains untainted by domain-specific logic.
  2. Plugin-First: All business logic, external integrations, and specialized capabilities are implemented as Plugins, ensuring secondary features never bloat the primary engine.
  3. Agentic by Design: Native adherence to the Agentic Design Patterns (Memory, Reflection, Tool Use, etc.) is baked into the orchestrator.

Architecture Overview

graph TD
    subgraph "Sacred Core (Agnostic Engine)"
        A["Core Orchestrator"]
        M["Memory Hierarchy (STM/MTM/LTM)"]
        S["Storage Layer (DB/Vector)"]
        R["Plugin Registry"]
    end

    R --> C["Custom Agent Plugins"]
    R --> D["Capability Extensions"]
    
    A --> M
    M --> S
    A --> F["Flow Handlers"]
    
    R -.->|Inject Handlers| A
    R -.->|Inject Routers| G["API Gateway"]
    
    A --> H["LLM Layer (Anthropic, OpenAI, Ollama, HF)"]
    F --> H

Key Capabilities

Cognitive Orchestration

We manage the complexity of agentic reasoning so you can focus on domain value.

  • Strategic Optimization: Native Monte Carlo Tree Search (MCTS) and Tree of Thoughts for advanced decision-making and "What-If" simulations.
  • Swarm Intelligence: Decentralized Auction Protocols for optimal task allocation and resource efficiency across agent collectives.
  • Multilayered Memory: Research-grade memory hierarchy (STM → MTM → LTM) with intelligent context consolidation.
  • Interoperability: Built with native Model Context Protocol (MCP) support for seamless tool and data integration.

Quick Start

1. Prerequisites

  • Python: 3.10+
  • Docker: For Redis, Qdrant, and PostgreSQL infrastructure.
  • Vector/Relational Storage: Managed via Docker Compose.

2. Installation

Install the core engine via pip:

pip install baselith-core

Or clone for extension development:

git clone https://github.com/baselithcore/baselithcore.git
cd baselith-core
docker compose up -d

3. Verification

baselith doctor  # Validate environment and configuration

Resources

Resource Description
Official Website The core landing page for the BaselithCore framework.
Official Documentation The official docs for the BaselithCore framework.
Architecture Deep dive into the "Sacred Core" and design choices.
Plugin Guide How to extend BaselithCore using the plugin system.
Agentic Patterns Implementation of Agentic Design Patterns.
Deployment Production-ready deployment strategies.

Contributing & License

We welcome contributions that adhere to our code standards. Please review CONTRIBUTING.md.

BaselithCore is licensed under the GNU Affero General Public License v3.0 (AGPL v3). See LICENSE for full details.

Copyright © 2026 BaselithCore Team.

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  • License: GNU Affero General Public License v3 (AGPL-3.0)
  • Author: Gippo
  • Tags ai , agents , framework
  • Requires: Python <3.13, >=3.10
  • Provides-Extra: adapters , test
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