dongarandClaude Opus 5.5 15d69799bf
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Make the config file the only home for settings
With no production instance yet, there is no reason to carry two ways
of configuring the agent. Every environment variable that duplicated a
config key is gone; the environment now supplies only the secrets the
file references by ${…}, plus NISSE_CONFIG and NISSE_ENV to find the
files.

- AgentConfig loads from a file (load, from_file) or a parsed tree
  (from_tree, for tests); a missing file is an error naming the example
- The from_env constructors on LiteLLMClient, AnthropicPassthroughClient,
  GiteaClient, MatrixConfig and RoleMap are removed, with the env
  reading for max output tokens; clients take their settings from config
- The live integration tests share a committed agent config,
  tests/integration/nisse-test.yaml, aimed at test resources, and one
  conftest that loads it and builds clients. NISSE_LIVE_PLANNER_MODEL
  becomes its planner role; NISSE_CAPABILITY_MODELS stays as a per-run
  switch
- A missing secret now fails the live tests instead of skipping them
- CI passes only the three tokens
- .env.example is secrets only; spec A5 says config comes from the file

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-09-23 23:25:58 +00:00

Nisse

Two self-hosted AI agents sharing a common library, named for the Nordic farm spirit that keeps the place running overnight and expects to be treated well in return.

Package Role
nisse-core/ Shared library both agents sit on (Part A)
nisse-dev/ Dev Agent — plans and implements software projects (Part B)
nisse-ops/ Sysadmin Agent — operates infrastructure (Part C)

One repository, three packages. The library does not exist as code yet: it is extracted from nisse-dev once that agent works (build-order step 10), as a package split within this tree.

Architecture

The system-level specification lives at docs/Architecture-Build-Spec.md. It covers the shared library, both agents, the supporting services, the build order, and the remaining open decisions.

S
Description
LLM Agents for specialized tasks.
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