| name | verify |
| description | Build, launch, and drive a local Honcho stack to verify a change at its runtime surface (the /v3 HTTP API and the deriver queue). Use when verifying a diff or confirming a change works in the running app. |
Verifying changes in Honcho
Prerequisites
Docker Compose is the preferred way to run the stack — docker-compose.yml 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 .env 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 .env nor node_modules. Copy
.env from the main checkout (that's where the provider keys live), and run
bun install in sdks/typescript if you'll run the full test suite —
otherwise the pre-push gate fails on a phantom Cannot find package 'zod'.
Launch
First check whether the stack is already running via Docker Compose:
docker compose ps
docker compose up -d
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
uv run python -m src.deriver
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 uv run fastapi dev src/main.py).
When reading server logs, note that with the main .env the telemetry emitter
spams connection warnings at an unreachable endpoint — grep -v telemetry.emitter before looking for the real error.
Drive it
Prefer driving through the honcho-cli skill; fall back to the SDKs
(sdks/python, sdks/typescript), then raw REST against /v3, in that order
when a method isn't available at the higher level. The honcho-integration
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
minimal reasoning level: send a couple of messages, then hit
/v3/.../peers/{peer_id}/chat and observe the response.
Configuration
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 > .env > config.toml > 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.
[dialectic.levels.minimal.model_config.overrides.provider_params]) are
miserable as env vars — drop a partial config.toml in the repo root instead.
And for load-time config behavior, uv run python -c "import src.config; ..."
is faster than booting the server.
Test suites
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:
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