Memory and its sources
Memory retains its original sources and subsequent corrections, so recalled information can be checked.
01Research project
Unified mind, memories and autonomy for agents
ColonyMind is a harness for agent memory, autonomy and model orchestration, built for Hermes.
Open source · MIT · In development
Request illustration
ColonyMind, Hermes & models
Hermes runs the agent, ColonyMind stores memory and shared work, and selected models perform inference.
ColonyMind / illustrative request
Memory across conversations
A request reaches Hermes
A message arrives through a configured conversation channel.
Inspect the architecture
Aevonix ResearchArchitecture
01 / Research lab
ColonyMind architecture
Ongoing research toward Proto-AGI.
Messages and user direction
Messages arrive through Hermes channels. A person can provide context, correct the agent’s understanding and steer work across conversations.
Conversations → Hermes runtimeHermes runs the agent
Hermes handles conversations, model requests, tools, workers and schedules. ColonyMind connects through plugins, a memory provider and request hooks.
Hermes ↔ ColonyMind · Hermes → models and toolsTool and worker execution
Hermes executes tools, workers and scheduled tasks. ColonyMind keeps shared work state available across conversations, so users can inspect or redirect it within the deployment’s permissions.
Hermes → tools, workers and schedulesPersistent memory and work state
ColonyMind retains original evidence and attributable corrections, recalls relevant context, and keeps identity and shared work across sessions. The deployment stores these records independently of the selected model.
Hermes ↔ memory and work context · ColonyMind → selected modelsLocal and replaceable models
Hermes and ColonyMind each use their configured models. Local inference supports normal operation without a cloud model; changing a model does not replace the agent’s stored memory or identity.
Hermes + ColonyMind → model inferenceIn development and validation.
ColonyMind architectureSelect a module for details
Selected module
Messages and user direction
Messages arrive through Hermes channels. A person can provide context, correct the agent’s understanding and steer work across conversations.
Conversations → Hermes runtimeHermes runs the agent
Hermes handles conversations, model requests, tools, workers and schedules. ColonyMind connects through plugins, a memory provider and request hooks.
Hermes ↔ ColonyMind · Hermes → models and toolsTool and worker execution
Hermes executes tools, workers and scheduled tasks. ColonyMind keeps shared work state available across conversations, so users can inspect or redirect it within the deployment’s permissions.
Hermes → tools, workers and schedulesPersistent memory and work state
ColonyMind retains original evidence and attributable corrections, recalls relevant context, and keeps identity and shared work across sessions. The deployment stores these records independently of the selected model.
Hermes ↔ memory and work context · ColonyMind → selected modelsLocal and replaceable models
Hermes and ColonyMind each use their configured models. Local inference supports normal operation without a cloud model; changing a model does not replace the agent’s stored memory or identity.
Hermes + ColonyMind → model inferenceIn development and validation.
Project
Development areas
Models and local operation
Different models can handle conversation, reasoning and vision, including local inference. Memory persists when models change.
Identity and shared work
Identity and shared tasks persist across sessions. Connected conversations can inspect, redirect or stop work.
Current development
Memory integration and shared task controls are implemented. Autonomous initiative and improvement remain under development.
Source