Docker Compose is the preferred way to run the stack — at the repo root brings up Postgres (pgvector), Redis, the API server, and the deriver worker together. If Docker Compose isn't available, the stack can also run directly on the host (see Launch below); you'll need Postgres with pgvector and Redis reachable, plus a with connection strings and an LLM provider key for any flow that hits a model (deriver, dialectic, dreamer).

Working in a worktree? It carries neither nor . Copy from the main checkout (that's where the provider keys live), and run in if you'll run the full test suite — otherwise the pre-push gate fails on a phantom .

First check whether the stack is already running via Docker Compose:

docker compose ps          # look for api / deriver / database / redis
docker compose up -d       # start it if not

A running stack is not the same as your branch's code running — check the CREATED column; the images may be weeks old. For verifying a diff, the cheap path is to reuse the stack's Postgres/Redis containers but run the branch's API as a host process on a spare port:

uv run uvicorn src.main:app --port 8901   # branch code, stack's DB/Redis
uv run python -m src.deriver              # if the diff touches the worker

This avoids both an image rebuild and the port/project-name conflicts a second compose stack in a worktree would cause. Without Docker at all, run the two processes the same way against host Postgres + Redis (API default port 8000 via ).

When reading server logs, note that with the main the telemetry emitter spams connection warnings at an unreachable endpoint — before looking for the real error.

Prefer driving through the skill; fall back to the SDKs (, ), then raw REST against , in that order when a method isn't available at the higher level. The skill covers how to use the SDKs.

When a change updates endpoints or makes schema changes, verify across all three surfaces — CLI, SDKs, and REST — since they can drift independently.

For LLM-path changes, the fastest synchronous surface is dialectic at the reasoning level: send a couple of messages, then hit and observe the response.

Configuration is central to both driving the app and running tests: it's how API keys reach the server and how a config-related change gets exercised at all. Settings come from environment variables or files, with precedence env > > > defaults. To verify a configuration change, set the relevant option through one of these layers, restart the affected process, and observe the behavioral difference at the surface — the same mechanism lets you point provider base URLs, model choices, and timeouts at credentials and proxies you actually have.

Two concrete levers: deep-nested settings (e.g. ) are miserable as env vars — drop a partial in the repo root instead. And for load-time config behavior, is faster than booting the server.

Three test types matter here. All run in CI, but they're also runnable locally with whatever API keys and configuration you have — Honcho's config surface is large, so options like per-agent timeouts and provider base URLs can be pointed at your own keys/proxies to exercise a change:

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uv run pytest tests/

uv run python -m tests.unified.run uv run python -m tests.unified.run --test-dir tests/unified/test_cases

export LLM_ANTHROPIC_API_KEY=... export LIVE_LLM_ANTHROPIC_45_PLUS_MODELS=claude-sonnet-4-5 uv run pytest tests/live_llm -n 0 --live-llm --no-header -q

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