For bigger tasks, Forge (the main agent) dispatches sub-agents in parallel. Each one does a slice of the work, then reports back.Documentation Index
Fetch the complete documentation index at: https://soulforge.proxysoul.com/llms.txt
Use this file to discover all available pages before exploring further.
Two tiers
| Agent | Role | Model (router slot) |
|---|---|---|
| Spark | Read-only research: navigate, read, analyze | spark |
| Ember | File edits, refactors | ember |
spark, strong models to ember in the task router. That is where most of the cost savings come from.
What they share
- I/O cache. When multiple agents run concurrently and one has already fetched a file, the others get the cached bytes instead of touching disk again. This is a speed win, not a token win: every agent still reads the content into its own context window and pays tokens for it.
- Edit serialization. Concurrent writes to the same file are queued, not raced.
- Findings channel. One agent’s discovery reaches the others at their next step.
How the cost savings actually work
Savings do not come from shared context. They come from:- Model mix. Spark agents run on a cheap model (Haiku, Flash) while Ember runs on a strong one. The task router decides per task.
- Symbol-level access. Agents use LSP go-to-definition and surgical symbol reads, not
grep + caton whole files. See code intelligence. - Parallelism hides latency. Three agents finishing in parallel beats one agent doing three things serially.