ROOTDNA

Instead of optimizing isolated tasks, RootDna focuses on long-term intelligence. The goal is not short-term performance, but survival, adaptation, and growth.

WHY ROOTDNA

Agents

Evolving

Agents

Evolving

Agents

Evolving

Agents

Github

Stars

Github

Stars

Github

Stars

Autonomous

Operation

Autonomous

Operation

Autonomous

Operation

CORE CAPABILITIES

Building agents that learn, adapt, and evolve across environments.

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Modular

Composable

Scalable

MODULAR INTELLIGENCE

RootDna builds agents from flexible decision modules that can be combined and evolved over time.

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002

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Feedback

Adaptation

Iteration

CONTINUOUS LEARNING

Agents improve through real-world feedback instead of relying on fixed models.

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Memory

Continuity

Stability

PERSISTENT IDENTITY

Agents accumulate memory and experience, developing stable behavior across time.

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Mutation

Exploration

Selection

CONTROLLED EVOLUTION

Strategies can be mutated and recombined to explore new forms of intelligence.

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003

{

Memory

Continuity

Stability

PERSISTENT IDENTITY

Agents accumulate memory and experience, developing stable behavior across time.

}

004

{

Mutation

Exploration

Selection

CONTROLLED EVOLUTION

Strategies can be mutated and recombined to explore new forms of intelligence.

ROOTDNA WORKFLOW

A new workflow for creating adaptive and evolving agents.

Define the Agent

Design the agent’s initial structure, including its goals, strategy modules, and core parameters.

Define the Agent

Design the agent’s initial structure, including its goals, strategy modules, and core parameters.

Deploy in Real Environments

Run the agent in dynamic environments such as markets, simulations, or digital systems.

Deploy in Real Environments

Run the agent in dynamic environments such as markets, simulations, or digital systems.

Learn and Adapt

The agent continuously updates its behavior based on feedback and real-world outcomes.

Learn and Adapt

The agent continuously updates its behavior based on feedback and real-world outcomes.

Evolve and Inherit

Successful strategies are refined, inherited, and recombined to create the next generation of intelligence.

Evolve and Inherit

Successful strategies are refined, inherited, and recombined to create the next generation of intelligence.

FAQ

Answers to common questions about RootDna and evolving intelligence.

What is RootDna?

RootDna is a system for building autonomous agents that can learn, adapt, and evolve over time. Instead of relying on fixed models, agents continuously improve through real-world feedback and inherited decision structures.

How is RootDna different from traditional AI?

Most AI systems are static and task-specific. RootDna focuses on long-term intelligence, where agents develop identity, accumulate experience, and improve across generations rather than resetting after each training cycle.

What kinds of agents can be built with RootDna?

RootDna supports a wide range of autonomous systems, including trading agents, research agents, simulation agents, and digital environments where multiple agents interact and evolve.

Is RootDna focused only on financial markets?

No. Financial environments are just one application because they provide fast feedback and real-world constraints. The same framework can be applied to simulations, robotics, and adaptive digital systems.

How do agents improve over time?

Agents learn from interaction, adjust their strategies, and refine their decision-making. Effective structures can be inherited and recombined, allowing intelligence to evolve rather than restart.

Is RootDna open source?

RootDna is being developed with an open and modular approach. Core components, agent examples, and research experiments will be gradually released to support collaboration and community-driven evolution.