4 Commits

Include renames

Author	SHA1	Message	Date
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	f4a94281ef	Add the Anthropic passthrough renderer and client (A10/A13) — 7a ... The second renderer adapter, and the concrete case for A13's decision to normalise in the renderer rather than on the wire. The two vendors disagree structurally, not cosmetically: the system prompt is a top-level field rather than a message, a tool call is a tool_use content block rather than an array, a tool result is a block inside a user message rather than a "tool" role, and tool schemas use input_schema rather than parameters. A middlebox flattening those would be lossy exactly where a long-running delegation lives. - render_anthropic.py builds vendor-native /v1/messages bodies from the canonical log, coalescing adjacent same-role blocks so a tool_result lands directly after its tool_use - Tool manifests are declared once in OpenAI shape and converted per vendor, so adding a tool does not mean editing two places - AnthropicPassthroughClient implements the same LLMClient protocol, so callers pick a route rather than a different kind of client, and maps provider tool ids to canonical ones at the boundary - Dangling tool calls get a placeholder result here too; this API rejects them more strictly than the OpenAI shape - Vendor-opaque reasoning blocks are dropped for now with the reason recorded: A10 wants them stored and never replayed across sessions, and the canonical schema has no event for that yet Live tests prove the body is accepted as sent, that a tool_result turn round-trips, and — as A5 asks — that prompt cache accounting survives the passthrough rather than being flattened by the proxy. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-07 23:13:22 +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