Mental model · 5 minutes

# The lattice does
the routing.

Every other agentic framework puts a scheduler at the center: "now run agent B." Axocoatl puts a coordination fabric at the center and lets the agents decide for themselves when they're ready. Here's what that means.

1 · The event lattice

## A bulletin board for events.

Agents publish events when they finish work — or fail, or get interrupted. Every other agent watches for events it cares about. When an agent's threshold is crossed (usually one event from each dependency), it wakes up and runs.

There's no orchestrator deciding the order. The order is whatever the dependency graph implies. The lattice is what makes the ordering work.

2 · Agents are actors

## Supervised, checkpointed, budgeted.

Each agent is a ractor actor: an isolated unit of computation with its own mailbox, memory tier, token budget, and supervised lifecycle. Crashes get restarted from the last checkpoint. State survives the process.

Agents talk to LLMs through a provider trait. Ollama, OpenAI, Anthropic, Mistral, Gemini, OpenRouter — all swappable per agent. Local model for one agent, frontier model for another, same workflow.

3 · Skills are lattice-aware

## Capabilities that route themselves.

A Skill declares the events it emits and the events it reacts to. The lattice wires it automatically: any agent that holds the right Skill can be the one that fires when the matching event lands.

You don't wire Skills to agents. You declare what each Skill listens for, and the lattice does the routing.

4 · Sessions are directories

## Build with your agents.

A Session is an agent — or a lattice of agents — running in a single directory. The session sandbox is a container (podman) bind-mounted to your folder. The agent reads, writes, and runs commands inside. You see every action stream into the dashboard.

Close the laptop. Open it tomorrow. The session is still there. Hot-restart from the last checkpoint. The agent picks up where it left off.

5 · Memory has tiers

## Hot to cold, local to neural.

Four tiers, plus checkpointing. Session is the live window. A daily log records every exchange, readable back by date. Core memory is a set of small, agent-editable blocks the agent curates as it learns and renders into its context every turn (the MemGPT model). And semantic recall is a neural tier: an embedded BERT model through Candle (pure Rust, no ONNX, no C++ runtime), so retrieval never hits an API.

Checkpointing snapshots state for crash recovery, and while the agent is idle a background pass consolidates durable facts up into the core blocks. All of it local-first. Nothing calls out unless you tell it to.

## Read the full docs.

Every concept above has a full reference in the docs, with embedded code and a live lattice demo on the page.

Open the docs → See workflows