"""RLM Reference Orchestrator — Illustrative Only.

This module is NOT imported by the hook system.  It demonstrates the
map→reduce→supersede→propose flow using FAVA Trails MCP tools.  Copy and
adapt it for your orchestrator agent.

Literature:
  MIT RLM arXiv:2512.24601 "Recursive Language Models" (Zhang, Kraska, Khattab)
  Reference: https://alexzhang13.github.io/rlm/

MapReduce Flow
--------------

  1. Orchestrator (root LLM) generates N decomposed sub-tasks
  2. Mapper agents run in parallel, each saving a thought with:
       tags: ["rlm-mapper"]
       metadata.extra: {mapper_id: "mapper-N", batch_id: "batch-uuid"}
  3. after_save hook tracks progress; signals "REDUCE READY" when all N done
  4. Orchestrator reduces via recall(tags=["rlm-mapper"], batch_id="...")
     - on_recall hook sorts results by (mapper_id, created_at) deterministically
  5. Orchestrator saves the synthesis:
       tags: ["rlm-reducer"]
       metadata.extra: {batch_id: "...", source_mapper_ids: [...]}
  6. Optionally supersede intermediate mapper drafts (mark as resolved)
  7. propose_truth() on the reducer thought to promote to permanent namespace

Pseudocode shown as comments — replace with your actual MCP client calls.
"""

from __future__ import annotations

# --- Illustrative pseudocode (not executable) ---
#
# async def run_rlm_mapreduce(
#     question: str,
#     documents: list[str],
#     trail_name: str,
#     batch_id: str,
# ) -> str:
#     """Run a MapReduce pass over documents using FAVA Trails as state store."""
#     import asyncio
#     import uuid
#
#     # Phase 1: MAP — spawn parallel mapper agents
#     #   Each mapper extracts relevant information from one chunk.
#     #   Real implementation: spawn N Claude Sonnet agents concurrently.
#
#     mapper_ids = [f"mapper-{i}" for i in range(len(documents))]
#
#     async def run_mapper(doc: str, mapper_id: str) -> str:
#         """Mapper agent: extract relevant information from one document chunk."""
#         extraction = await call_llm(
#             prompt=f"Extract information relevant to: {question}\n\nDocument:\n{doc}"
#         )
#         # Save mapper output to FAVA Trails
#         # MCP call: save_thought(
#         #   trail_name=trail_name,
#         #   content=extraction,
#         #   source_type="observation",
#         #   tags=["rlm-mapper"],
#         #   metadata={"mapper_id": mapper_id, "batch_id": batch_id},
#         # )
#         return mapper_id
#
#     await asyncio.gather(*[
#         run_mapper(doc, mid)
#         for doc, mid in zip(documents, mapper_ids)
#     ])
#
#     # Phase 2: WAIT — after_save hook signals REDUCE READY
#     #   In practice: poll or listen for the advisory. Here we proceed directly
#     #   since we know all mappers have run.
#
#     # Phase 3: REDUCE — recall all mapper outputs sorted deterministically
#     #   MCP call: recall(
#     #     trail_name=trail_name,
#     #     query=question,
#     #     scope={"tags": ["rlm-mapper"]},  # triggers on_recall sort hook
#     #   )
#     #   on_recall hook returns results sorted by (mapper_id, created_at)
#
#     mapper_outputs = []  # populated from recall results
#
#     synthesis = await call_llm(
#         prompt=(
#             f"Synthesize the following extractions to answer: {question}\n\n"
#             + "\n\n---\n\n".join(mapper_outputs)
#         )
#     )
#
#     # Phase 4: SAVE REDUCER — save synthesis thought
#     #   MCP call: save_thought(
#     #     trail_name=trail_name,
#     #     content=synthesis,
#     #     source_type="inference",
#     #     tags=["rlm-reducer"],
#     #     metadata={"batch_id": batch_id, "source_mapper_ids": mapper_ids},
#     #   )
#
#     # Phase 5: PROMOTE — propose_truth on the reducer to make it permanent
#     #   MCP call: propose_truth(trail_name=trail_name, thought_id=reducer_ulid)
#
#     # Phase 6: SUPERSEDE dead-end mapper drafts (optional)
#     #   For each mapper_ulid that is fully subsumed by the reducer:
#     #   MCP call: supersede(
#     #     trail_name=trail_name,
#     #     thought_id=mapper_ulid,
#     #     reason="Subsumed by reducer",
#     #   )
#
#     return synthesis
