[email protected]andClaude Opus 4.6 d2d93f98fe feat(engine): persist variables across code steps via state dict ...

Monty creates a fresh runtime per code step, so variables are lost
between steps. This caused the model to re-paste tool results from
system messages, wasting tokens.

Fix: maintain a `persisted_state` JSON dict in the ExecutionLoop that
accumulates across steps:
- Tool results stored by tool name: state["web_search"] = {results...}
- Return values stored: state["last_return"], state["step_0_return"]
- Injected as a `state` Python variable in each new MontyRun

Now the model can do:
  Step 1: results = web_search(query="...")  # tool result saved in state
  Step 2: data = state["web_search"]         # access previous result
          summary = llm_query("summarize", str(data))
          FINAL(summary)

System prompt updated to document the `state` variable.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

2026-03-22 23:16:41 -07:00

..

src

feat(engine): persist variables across code steps via state dict

2026-03-22 23:16:41 -07:00

Cargo.toml

feat(engine): Phase 3 — Monty Python executor with RLM pattern

2026-03-21 21:32:52 -07:00

CLAUDE.md

feat(engine): Phase 3 — Monty Python executor with RLM pattern

2026-03-21 21:32:52 -07:00