Harnesses & Providers

Cogni-Code is two independent layers, and each one is swappable. You are never locked into a single vendor.

	The coding agent	The memory worker
What it is	The CLI you chat with	The LLM that runs the background daemon
Where it runs	In front, your session	Behind, the pipeline (scribe/auditor/librarian/dreamer)
Options	Claude Code, Codex CLI, OpenCode, pi	codex, claude, opencode, pi
You change it with	Install flags	--worker / /memory-switch-harness

Your memory lives in one shared graph on disk. Switching either layer does not touch the graph — it just changes which agent reads it or which LLM maintains it.

The coding agent (front)

This is the agent you actually talk to. Cogni-Code injects context into its session and exposes the graph_memory tool to it. Install into whichever harness you run:

cogni-code install --claude      # Claude Code only
cogni-code install --codex       # Codex CLI only
cogni-code install --opencode    # OpenCode only
cogni-code install --all         # every detected harness

With no flags, the installer auto-detects every installed harness and wires them all. Install into several at once and they share the same ~/.graph-memory/ graph — start a session in any of them and you pick up the same memory.

The memory worker (back)

This is the LLM the daemon uses to run the background pipeline (scribe, auditor, librarian, dreamer, observer). It is a separate concern from your coding agent — you can chat in Claude Code while the daemon works via Codex, or any other combination.

The valid worker providers are codex, claude, opencode, and pi.

At install time

cogni-code install --docker --worker codex
# or: --worker claude / opencode / pi

Anytime, from a session

Switch the worker without reinstalling:

/memory-switch-harness

Via the tool

graph_memory(
  action="configure_runtime",
  runtimeMode="docker",
  workerProvider="codex",
  workerModel="gpt-5.2"
)

Resilience: a fallback worker

If the primary worker hits a limit or times out, the daemon retries on the fallback. Configure both:

graph_memory(
  action="configure_runtime",
  runtimeMode="docker",
  workerProvider="claude",
  workerModel="sonnet",
  fallbackProvider="codex",
  fallbackModel="o3"
)

Manual mode (no daemon)

runtimeMode: "manual" runs the tool surface only — no background pipeline, no worker needed. Useful for lightweight local testing. Switch back to "docker" to re-enable the full pipeline.

graph_memory(action="configure_runtime", runtimeMode="manual")

Summary

• Front layer (coding agent): swappable per session, install with --claude / --codex / --opencode / --all.
• Back layer (memory worker): the daemon’s LLM, set with --worker, /memory-switch-harness, or configure_runtime.
• The graph is shared and untouched by either change.