3 Commits

Include renames

Author	SHA1	Message	Date
dongarandClaude Opus 5.5	d49435d4e5	Render one assistant message per turn, and make the request timeout a setting ... Two setup failures found while calibrating the new benchmark tasks, both of which would also fail real milestones. Devstral's chat template (Mistral) rejected any turn in which the model wrote some text before a tool call: the OpenAI renderer made one message per event, so the turn became a text message and a tool-call message in a row, and llama.cpp answered 500. The renderer now folds consecutive user or assistant messages into one, which is the OpenAI format for a turn anyway. Checked against the live server: the captured failing request no longer occurs, over six real runs. LiteLLMClient gave up on any request after a fixed 120 s. A dense 27B model writing a whole module in one turn takes longer, so the executor escalated work that was going well. llm.request_timeout_s now sets it for both clients, default 600. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>	2026-09-27 22:15:30 +00:00
dongarandClaude Opus 5	0fd0b2e4a5	Add executor tool loop (A12/A15) — step 5b ... - tools.py: four flat tools (read_file, write_file, list_dir, run_command) per A15's reduced manifest. Deliberately unsandboxed: the VM is the trust boundary (B3) and tool use is auto-approved inside an approved unit of work (A4), so path confinement would add failure modes without adding safety. What is enforced is operational — timeout with process-group kill so a backgrounded server cannot outlive its command, output truncation so a build log cannot eat the context window, closed stdin so prompts fail fast, and a scrubbed environment so project code never sees the agent's credentials - executor.py: renders the log, calls the executor role with the reduced manifest, dispatches tool calls, and checkpoints every result (A13). Escalation is observable, never self-assessed (A12): consecutive command failures and a hard turn cap. Failed reads do not count — looking around is not failing - Until delegation exists (step 7), escalation gates to the human with the failures attached - control.py: start milestone now runs the executor; a stopped milestone gates to AWAIT_INPUT Two bugs found by running it live against real models: - A turn-ending gate tool (submit_plan) left a tool_call with no tool_result, which providers reject outright, so every planning turn after the first failed with a 400. The planner now records the gate as the call's outcome, which is also the truthful transcript - The renderer now emits a placeholder for any tool_call lacking a result, so a crash between call and result (A3) cannot poison the transcript permanently Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-07 16:35:39 +00:00
dongarandClaude Fable 5	ac81dd6234	Add canonical event log and renderer skeleton (A10) ... Build-order step 4, sequenced before the LLM client (step 3) because the client interface consumes the canonical log. - canonical.py: typed events for the full A10 table (user_message through compaction); canonical tool-call IDs minted locally, provider IDs never a join key; strict payload validation on load - StateStore: canonical_events table via schema v2 — the first real migration, exercised in a test against a hand-built v1 database — with append_event/read_events on the same fsync-per-commit path - render.py: request bodies rendered from the log at call time; first adapter targets the OpenAI-compatible shape of LiteLLM's normalized route; compaction events replace their id range at render time while staying in the durable log - Crash integration test now also verifies transcript appends survive process death Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-09-06 19:51:58 +00:00