4 Commits

Include renames

Author	SHA1	Message	Date
dongarandClaude Opus 5	ed2f5dfc7f	Choose the LLM route by model, and add a capability matrix ... The arrival of non-Anthropic models made a latent bug observable: the delegate's client was hardcoded to the Anthropic passthrough, so pointing NISSE_ROLE_DELEGATE at gpt-6-astra or a local model would have sent Anthropic-shaped /v1/messages bodies to something that does not speak them. A5 promises the frontier tier is swappable "as a configuration change, not a refactor", and it was not. select_client now picks the route from the model: the vendor-native adapter where one exists, LiteLLM's normalization otherwise — which is exactly what A13 prescribes for a vendor whose renderer adapter has not been written, pending validation against the A16 eval set. Also adds the capability matrix Part H asks for. It says the thresholds that depend on local capability — A12's failure count, A14's consult budget, A15's manifest size — must be tuned "on the real hardware", and that needs measurement rather than reputation. The harness probes each model for the things that actually break: whether it calls a tool at all, whether the plan it submits has substance (llama3.2 emitted "{"), whether it can consume its own tool result, and whether prompt-level JSON holds. Opt in with NISSE_CAPABILITY_MODELS; it spends real tokens across several models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-22 23:20:38 +00:00
dongarandClaude Opus 5	af9bdaac8d	Stop CI running two suites at once, and retry rate limits ... CI failed on the delegation branch with a 429 from the proxy: token limit 200000, 17399 remaining. Not a defect — the live delegation tests are token-hungry, and every push to a PR branch fired two identical suites concurrently, since the push and pull_request events both matched. - Push now only triggers on main. A PR branch is already covered by the pull_request event, so this halves both the wall time and the tokens spent per branch - A 429 is retried once before being surfaced. Losing a ten-turn delegation to a transient limit wastes far more than waiting out the window, and retrying forever would hide a real ceiling. The sleep is injectable, so the existing 429 test no longer waits 20 seconds for real Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-08 00:18:32 +00:00
dongarandClaude Opus 5	cf85648719	Add LLM-backed planning agent (A4/A11/A13) — step 5a ... - planner.py: LLMPlanningAgent offers exactly two tools, submit_plan and ask_human, so gating stays structural at the tool layer; no proceed tool exists. System prompt requires explicit, low-inference plans because a weaker model executes them (A13) - Model output is appended to the canonical log as it arrives, before being interpreted, so a crash mid-planning leaves a truthful transcript - A bad turn (prose instead of a tool call, malformed arguments, or a failed call) retries once, then raises PlanningError; the orchestrator gates to AWAIT_INPUT rather than inventing a plan - Orchestrator: appends every operator message, plan_submitted, and gate_opened to the transcript, so re-planning sees rejection feedback as conversation rather than out-of-band parameters; pins the A11 role mapping at task start - llm.py: non-JSON or non-object tool arguments now raise LLMError instead of escaping as JSONDecodeError — observed from llama3.2, which emits truncated argument strings - Live planning test is opt-in on NISSE_LIVE_PLANNER_MODEL: planning escalates unconditionally (A12) and the local model does not hold the tool-call contract, so it awaits a frontier route Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-07 14:47:12 +00:00
dongarandClaude Fable 5	0c2021fced	Add LLM client interface with LiteLLM adapter (A5/A11) ... PR 2 of the steps-3/4 plan. - llm.py: LLMClient protocol — complete(events, tools, model) -> canonical events — present from the first real LLM call. LiteLLMClient renders requests via the A10 renderer, targets the proxy's OpenAI-compatible route, and maps provider tool-call IDs to locally-minted canonical IDs at the boundary - RoleMap (A11): executor/planner/delegate/consultant resolved from env; pinned() returns the mapping to record at task start - Config strictly from env: NISSE_LLM_BASE_URL, NISSE_LLM_TOKEN; envfile.py is a minimal .env loader where real env wins; .env now gitignored with a committed .env.example documenting all variables (Matrix ones included for the upcoming transport PR) - Live smoke test against the proxy is integration-marked and skips without credentials — CI stays credential-free per operator decision; the test runs locally once .env exists - httpx added as nisse-dev's HTTP client Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-09-06 20:57:51 +00:00