Timothy
1226 lines
47 KiB
Python
1226 lines
47 KiB
Python
"""NodeConversation: Message history management for graph nodes."""
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from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Literal, Protocol, runtime_checkable
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LEGACY_RUN_ID = "__legacy_run__"
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def is_legacy_run_id(run_id: str | None) -> bool:
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"""True when run_id represents pre-migration (no run boundary) data."""
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return run_id is None or run_id == LEGACY_RUN_ID
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@dataclass
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class Message:
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"""A single message in a conversation.
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Attributes:
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seq: Monotonic sequence number.
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role: One of "user", "assistant", or "tool".
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content: Message text.
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tool_use_id: Internal tool-use identifier (output as ``tool_call_id`` in LLM dicts).
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tool_calls: OpenAI-format tool call list for assistant messages.
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is_error: When True and role is "tool", ``to_llm_dict`` prepends "ERROR: " to content.
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"""
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seq: int
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role: Literal["user", "assistant", "tool"]
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content: str
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tool_use_id: str | None = None
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tool_calls: list[dict[str, Any]] | None = None
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is_error: bool = False
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# Phase-aware compaction metadata (continuous mode)
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phase_id: str | None = None
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is_transition_marker: bool = False
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# True when this message is real human input (from /chat), not a system prompt
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is_client_input: bool = False
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# Optional image content blocks (e.g. from browser_screenshot)
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image_content: list[dict[str, Any]] | None = None
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# True when message contains an activated skill body (AS-10: never prune)
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is_skill_content: bool = False
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# Logical worker run identifier for shared-session persistence
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run_id: str | None = None
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def to_llm_dict(self) -> dict[str, Any]:
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"""Convert to OpenAI-format message dict."""
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if self.role == "user":
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if self.image_content:
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blocks: list[dict[str, Any]] = []
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if self.content:
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blocks.append({"type": "text", "text": self.content})
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blocks.extend(self.image_content)
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return {"role": "user", "content": blocks}
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return {"role": "user", "content": self.content}
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if self.role == "assistant":
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d: dict[str, Any] = {"role": "assistant", "content": self.content}
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if self.tool_calls:
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d["tool_calls"] = self.tool_calls
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return d
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# role == "tool"
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content = f"ERROR: {self.content}" if self.is_error else self.content
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if self.image_content:
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# Multimodal tool result: text + image content blocks
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blocks: list[dict[str, Any]] = [{"type": "text", "text": content}]
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blocks.extend(self.image_content)
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return {
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"role": "tool",
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"tool_call_id": self.tool_use_id,
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"content": blocks,
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}
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return {
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"role": "tool",
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"tool_call_id": self.tool_use_id,
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"content": content,
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}
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def to_storage_dict(self) -> dict[str, Any]:
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"""Serialize all fields for persistence. Omits None/default-False fields."""
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d: dict[str, Any] = {
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"seq": self.seq,
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"role": self.role,
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"content": self.content,
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}
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if self.tool_use_id is not None:
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d["tool_use_id"] = self.tool_use_id
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if self.tool_calls is not None:
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d["tool_calls"] = self.tool_calls
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if self.is_error:
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d["is_error"] = self.is_error
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if self.phase_id is not None:
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d["phase_id"] = self.phase_id
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if self.is_transition_marker:
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d["is_transition_marker"] = self.is_transition_marker
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if self.is_client_input:
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d["is_client_input"] = self.is_client_input
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if self.image_content is not None:
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d["image_content"] = self.image_content
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if self.run_id is not None:
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d["run_id"] = self.run_id
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return d
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@classmethod
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def from_storage_dict(cls, data: dict[str, Any]) -> Message:
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"""Deserialize from a storage dict."""
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return cls(
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seq=data["seq"],
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role=data["role"],
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content=data["content"],
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tool_use_id=data.get("tool_use_id"),
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tool_calls=data.get("tool_calls"),
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is_error=data.get("is_error", False),
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phase_id=data.get("phase_id"),
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is_transition_marker=data.get("is_transition_marker", False),
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is_client_input=data.get("is_client_input", False),
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image_content=data.get("image_content"),
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run_id=data.get("run_id"),
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)
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def _normalize_cursor(cursor: dict[str, Any] | None) -> dict[str, Any]:
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"""Normalize legacy and run-scoped cursor formats into one flat shape."""
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return dict(cursor) if cursor else {}
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def get_cursor_next_seq(cursor: dict[str, Any] | None) -> int | None:
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next_seq = (cursor or {}).get("next_seq")
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return next_seq if isinstance(next_seq, int) else None
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def update_cursor_next_seq(cursor: dict[str, Any] | None, next_seq: int) -> dict[str, Any]:
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updated = dict(cursor or {})
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updated["next_seq"] = next_seq
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return updated
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def get_run_cursor(cursor: dict[str, Any] | None, run_id: str | None) -> dict[str, Any] | None:
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return dict(cursor) if cursor else None
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def update_run_cursor(
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cursor: dict[str, Any] | None,
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run_id: str | None,
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values: dict[str, Any],
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) -> dict[str, Any]:
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updated = dict(cursor or {})
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updated.update(values)
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return updated
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def _extract_spillover_filename(content: str) -> str | None:
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"""Extract spillover filename from a tool result annotation.
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Matches patterns produced by EventLoopNode._truncate_tool_result():
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- Large result: "saved to 'web_search_1.txt'"
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- Small result: "[Saved to 'web_search_1.txt']"
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"""
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match = re.search(r"[Ss]aved to '([^']+)'", content)
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return match.group(1) if match else None
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_TC_ARG_LIMIT = 200 # max chars per tool_call argument after compaction
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def _compact_tool_calls(tool_calls: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Truncate tool_call arguments to save context tokens during compaction.
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Preserves ``id``, ``type``, and ``function.name`` exactly. When arguments
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exceed ``_TC_ARG_LIMIT``, replaces the full JSON string with a compact
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**valid** JSON summary. The Anthropic API parses tool_call arguments and
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rejects requests with malformed JSON (e.g. unterminated strings), so we
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must never produce broken JSON here.
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"""
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compact = []
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for tc in tool_calls:
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func = tc.get("function", {})
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args = func.get("arguments", "")
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if len(args) > _TC_ARG_LIMIT:
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# Build a valid JSON summary instead of slicing mid-string.
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# Try to extract top-level keys for a meaningful preview.
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try:
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parsed = json.loads(args)
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if isinstance(parsed, dict):
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# Preserve key names, truncate values
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summary_parts = []
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for k, v in parsed.items():
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v_str = str(v)
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if len(v_str) > 60:
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v_str = v_str[:60] + "..."
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summary_parts.append(f"{k}={v_str}")
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summary = ", ".join(summary_parts)
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if len(summary) > _TC_ARG_LIMIT:
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summary = summary[:_TC_ARG_LIMIT] + "..."
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args = json.dumps({"_compacted": summary})
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else:
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args = json.dumps({"_compacted": str(parsed)[:_TC_ARG_LIMIT]})
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except (json.JSONDecodeError, TypeError):
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# Args were already invalid JSON — wrap the preview safely
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args = json.dumps({"_compacted": args[:_TC_ARG_LIMIT]})
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compact.append(
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{
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"id": tc.get("id", ""),
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"type": tc.get("type", "function"),
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"function": {
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"name": func.get("name", ""),
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"arguments": args,
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},
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}
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)
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return compact
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def extract_tool_call_history(messages: list[Message], max_entries: int = 30) -> str:
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"""Build a compact tool call history from a list of messages.
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Used in compaction summaries to prevent the LLM from re-calling
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tools it already called. Extracts tool call details, files saved,
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outputs set, and errors encountered.
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"""
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tool_calls_detail: dict[str, list[str]] = {}
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files_saved: list[str] = []
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outputs_set: list[str] = []
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errors: list[str] = []
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def _summarize_input(name: str, args: dict) -> str:
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if name == "web_search":
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return args.get("query", "")
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if name == "web_scrape":
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return args.get("url", "")
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if name in ("load_data", "save_data"):
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return args.get("filename", "")
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return ""
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for msg in messages:
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if msg.role == "assistant" and msg.tool_calls:
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for tc in msg.tool_calls:
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func = tc.get("function", {})
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name = func.get("name", "unknown")
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try:
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args = json.loads(func.get("arguments", "{}"))
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except (json.JSONDecodeError, TypeError):
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args = {}
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summary = _summarize_input(name, args)
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tool_calls_detail.setdefault(name, []).append(summary)
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if name == "save_data" and args.get("filename"):
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files_saved.append(args["filename"])
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if name == "set_output" and args.get("key"):
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outputs_set.append(args["key"])
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if msg.role == "tool" and msg.is_error:
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preview = msg.content[:120].replace("\n", " ")
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errors.append(preview)
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parts: list[str] = []
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if tool_calls_detail:
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lines: list[str] = []
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for name, inputs in list(tool_calls_detail.items())[:max_entries]:
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count = len(inputs)
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non_empty = [s for s in inputs if s]
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if non_empty:
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detail_lines = [f" - {s[:120]}" for s in non_empty[:8]]
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lines.append(f" {name} ({count}x):\n" + "\n".join(detail_lines))
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else:
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lines.append(f" {name} ({count}x)")
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parts.append("TOOLS ALREADY CALLED:\n" + "\n".join(lines))
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if files_saved:
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unique = list(dict.fromkeys(files_saved))
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parts.append("FILES SAVED: " + ", ".join(unique))
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if outputs_set:
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unique = list(dict.fromkeys(outputs_set))
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parts.append("OUTPUTS SET: " + ", ".join(unique))
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if errors:
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parts.append("ERRORS (do NOT retry these):\n" + "\n".join(f" - {e}" for e in errors[:10]))
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return "\n\n".join(parts)
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# ---------------------------------------------------------------------------
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# ConversationStore protocol (Phase 2)
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# ---------------------------------------------------------------------------
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@runtime_checkable
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class ConversationStore(Protocol):
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"""Protocol for conversation persistence backends."""
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async def write_part(self, seq: int, data: dict[str, Any]) -> None: ...
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async def read_parts(self) -> list[dict[str, Any]]: ...
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async def write_meta(self, data: dict[str, Any]) -> None: ...
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async def read_meta(self) -> dict[str, Any] | None: ...
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async def write_cursor(self, data: dict[str, Any]) -> None: ...
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async def read_cursor(self) -> dict[str, Any] | None: ...
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async def delete_parts_before(self, seq: int, run_id: str | None = None) -> None: ...
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async def close(self) -> None: ...
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async def destroy(self) -> None: ...
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# ---------------------------------------------------------------------------
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# NodeConversation
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# ---------------------------------------------------------------------------
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def _try_extract_key(content: str, key: str) -> str | None:
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"""Try 4 strategies to extract a *key*'s value from message content.
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Strategies (in order):
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1. Whole message is JSON — ``json.loads``, check for key.
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2. Embedded JSON via ``find_json_object`` helper.
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3. Colon format: ``key: value``.
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4. Equals format: ``key = value``.
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"""
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from framework.orchestrator.node import find_json_object
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# 1. Whole message is JSON
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try:
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parsed = json.loads(content)
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if isinstance(parsed, dict) and key in parsed:
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val = parsed[key]
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return json.dumps(val) if not isinstance(val, str) else val
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except (json.JSONDecodeError, TypeError):
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pass
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# 2. Embedded JSON via find_json_object
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json_str = find_json_object(content)
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if json_str:
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try:
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parsed = json.loads(json_str)
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if isinstance(parsed, dict) and key in parsed:
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val = parsed[key]
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return json.dumps(val) if not isinstance(val, str) else val
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except (json.JSONDecodeError, TypeError):
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pass
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# 3. Colon format: key: value
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match = re.search(rf"\b{re.escape(key)}\s*:\s*(.+)", content)
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if match:
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return match.group(1).strip()
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# 4. Equals format: key = value
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match = re.search(rf"\b{re.escape(key)}\s*=\s*(.+)", content)
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if match:
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return match.group(1).strip()
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return None
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class NodeConversation:
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"""Message history for a graph node with optional write-through persistence.
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When *store* is ``None`` the conversation works purely in-memory.
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When a :class:`ConversationStore` is supplied every mutation is
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persisted via write-through (meta is lazily written on the first
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``_persist`` call).
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"""
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def __init__(
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self,
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system_prompt: str = "",
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max_context_tokens: int = 32000,
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compaction_threshold: float = 0.8,
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output_keys: list[str] | None = None,
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store: ConversationStore | None = None,
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run_id: str | None = None,
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) -> None:
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self._system_prompt = system_prompt
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self._max_context_tokens = max_context_tokens
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self._compaction_threshold = compaction_threshold
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self._output_keys = output_keys
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self._store = store
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self._messages: list[Message] = []
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self._next_seq: int = 0
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self._meta_persisted: bool = False
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self._last_api_input_tokens: int | None = None
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self._current_phase: str | None = None
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self._run_id: str | None = run_id
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# --- Properties --------------------------------------------------------
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@property
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def system_prompt(self) -> str:
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return self._system_prompt
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def update_system_prompt(self, new_prompt: str) -> None:
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"""Update the system prompt.
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Used in continuous conversation mode at phase transitions to swap
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Layer 3 (focus) while preserving the conversation history.
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"""
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self._system_prompt = new_prompt
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self._meta_persisted = False # re-persist with new prompt
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def set_current_phase(self, phase_id: str) -> None:
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"""Set the current phase ID. Subsequent messages will be stamped with it."""
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self._current_phase = phase_id
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@property
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def current_phase(self) -> str | None:
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return self._current_phase
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@property
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def messages(self) -> list[Message]:
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"""Return a defensive copy of the message list."""
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return list(self._messages)
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@property
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def turn_count(self) -> int:
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"""Number of conversational turns (one turn = one user message)."""
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return sum(1 for m in self._messages if m.role == "user")
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@property
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def message_count(self) -> int:
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"""Total number of messages (all roles)."""
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return len(self._messages)
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@property
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def next_seq(self) -> int:
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return self._next_seq
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# --- Add messages ------------------------------------------------------
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async def add_user_message(
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self,
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content: str,
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*,
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is_transition_marker: bool = False,
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is_client_input: bool = False,
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image_content: list[dict[str, Any]] | None = None,
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) -> Message:
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msg = Message(
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seq=self._next_seq,
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role="user",
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content=content,
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phase_id=self._current_phase,
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run_id=self._run_id,
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is_transition_marker=is_transition_marker,
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is_client_input=is_client_input,
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image_content=image_content,
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)
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self._messages.append(msg)
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self._next_seq += 1
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# Invalidate stale API token count so estimate_tokens() uses
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# the char-based heuristic which reflects the new message.
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self._last_api_input_tokens = None
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await self._persist(msg)
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return msg
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async def add_assistant_message(
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self,
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content: str,
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tool_calls: list[dict[str, Any]] | None = None,
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) -> Message:
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msg = Message(
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seq=self._next_seq,
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role="assistant",
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content=content,
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tool_calls=tool_calls,
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phase_id=self._current_phase,
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run_id=self._run_id,
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)
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self._messages.append(msg)
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self._next_seq += 1
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self._last_api_input_tokens = None
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await self._persist(msg)
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return msg
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async def add_tool_result(
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self,
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tool_use_id: str,
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content: str,
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is_error: bool = False,
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image_content: list[dict[str, Any]] | None = None,
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is_skill_content: bool = False,
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) -> Message:
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msg = Message(
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seq=self._next_seq,
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role="tool",
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content=content,
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tool_use_id=tool_use_id,
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is_error=is_error,
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phase_id=self._current_phase,
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image_content=image_content,
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is_skill_content=is_skill_content,
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run_id=self._run_id,
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)
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self._messages.append(msg)
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|
self._next_seq += 1
|
|
self._last_api_input_tokens = None
|
|
await self._persist(msg)
|
|
return msg
|
|
|
|
# --- Query -------------------------------------------------------------
|
|
|
|
def to_llm_messages(self) -> list[dict[str, Any]]:
|
|
"""Return messages as OpenAI-format dicts (system prompt excluded).
|
|
|
|
Automatically repairs orphaned tool_use blocks (assistant messages
|
|
with tool_calls that lack corresponding tool-result messages). This
|
|
can happen when a loop is cancelled mid-tool-execution.
|
|
"""
|
|
msgs = [m.to_llm_dict() for m in self._messages]
|
|
return self._repair_orphaned_tool_calls(msgs)
|
|
|
|
@staticmethod
|
|
def _repair_orphaned_tool_calls(
|
|
msgs: list[dict[str, Any]],
|
|
) -> list[dict[str, Any]]:
|
|
"""Ensure tool_call / tool_result pairs are consistent.
|
|
|
|
1. **Orphaned tool results** (tool_result with no preceding tool_use)
|
|
are dropped. This happens when compaction removes an assistant
|
|
message but leaves its tool-result messages behind.
|
|
2. **Orphaned tool calls** (tool_use with no following tool_result)
|
|
get a synthetic error result appended. This happens when a loop
|
|
is cancelled mid-tool-execution.
|
|
"""
|
|
# Pass 1: collect all tool_call IDs from assistant messages so we
|
|
# can identify orphaned tool-result messages.
|
|
all_tool_call_ids: set[str] = set()
|
|
for m in msgs:
|
|
if m.get("role") == "assistant":
|
|
for tc in m.get("tool_calls") or []:
|
|
tc_id = tc.get("id")
|
|
if tc_id:
|
|
all_tool_call_ids.add(tc_id)
|
|
|
|
# Pass 2: build repaired list — drop orphaned tool results, patch
|
|
# missing tool results.
|
|
repaired: list[dict[str, Any]] = []
|
|
for i, m in enumerate(msgs):
|
|
# Drop tool-result messages whose tool_call_id has no matching
|
|
# tool_use in any assistant message (orphaned by compaction).
|
|
if m.get("role") == "tool":
|
|
tid = m.get("tool_call_id")
|
|
if tid and tid not in all_tool_call_ids:
|
|
continue # skip orphaned result
|
|
|
|
repaired.append(m)
|
|
tool_calls = m.get("tool_calls")
|
|
if m.get("role") != "assistant" or not tool_calls:
|
|
continue
|
|
# Collect IDs of tool results that follow this assistant message
|
|
answered: set[str] = set()
|
|
for j in range(i + 1, len(msgs)):
|
|
if msgs[j].get("role") == "tool":
|
|
tid = msgs[j].get("tool_call_id")
|
|
if tid:
|
|
answered.add(tid)
|
|
else:
|
|
break # stop at first non-tool message
|
|
# Patch any missing results
|
|
for tc in tool_calls:
|
|
tc_id = tc.get("id")
|
|
if tc_id and tc_id not in answered:
|
|
repaired.append(
|
|
{
|
|
"role": "tool",
|
|
"tool_call_id": tc_id,
|
|
"content": "ERROR: Tool execution was interrupted.",
|
|
}
|
|
)
|
|
return repaired
|
|
|
|
def estimate_tokens(self) -> int:
|
|
"""Best available token estimate.
|
|
|
|
Uses actual API input token count when available (set via
|
|
:meth:`update_token_count`), otherwise falls back to a
|
|
character-based heuristic that includes message content, tool_call
|
|
arguments, and image blocks. The heuristic applies a 4/3 safety
|
|
margin to avoid under-counting (inspired by Claude Code's compact
|
|
service).
|
|
"""
|
|
if self._last_api_input_tokens is not None:
|
|
return self._last_api_input_tokens
|
|
total_chars = 0
|
|
image_tokens = 0
|
|
for m in self._messages:
|
|
total_chars += len(m.content)
|
|
if m.tool_calls:
|
|
for tc in m.tool_calls:
|
|
func = tc.get("function", {})
|
|
total_chars += len(func.get("arguments", ""))
|
|
total_chars += len(func.get("name", ""))
|
|
if m.image_content:
|
|
# Images/documents have a fixed token cost per block
|
|
image_tokens += len(m.image_content) * 2000
|
|
# Apply 4/3 safety margin to character-based estimate
|
|
return (total_chars * 4) // (3 * 4) + image_tokens
|
|
|
|
def update_token_count(self, actual_input_tokens: int) -> None:
|
|
"""Store actual API input token count for more accurate compaction.
|
|
|
|
Called by EventLoopNode after each LLM call with the ``input_tokens``
|
|
value from the API response. This value includes system prompt and
|
|
tool definitions, so it may be higher than a message-only estimate.
|
|
"""
|
|
self._last_api_input_tokens = actual_input_tokens
|
|
|
|
def usage_ratio(self) -> float:
|
|
"""Current token usage as a fraction of *max_context_tokens*.
|
|
|
|
Returns 0.0 when ``max_context_tokens`` is zero (unlimited).
|
|
"""
|
|
if self._max_context_tokens <= 0:
|
|
return 0.0
|
|
return self.estimate_tokens() / self._max_context_tokens
|
|
|
|
def needs_compaction(self) -> bool:
|
|
return self.estimate_tokens() >= self._max_context_tokens * self._compaction_threshold
|
|
|
|
# --- Output-key extraction ---------------------------------------------
|
|
|
|
def _extract_protected_values(self, messages: list[Message]) -> dict[str, str]:
|
|
"""Scan assistant messages for output_key values before compaction.
|
|
|
|
Iterates most-recent-first. Once a key is found, it's skipped for
|
|
older messages (latest value wins).
|
|
"""
|
|
if not self._output_keys:
|
|
return {}
|
|
|
|
found: dict[str, str] = {}
|
|
remaining_keys = set(self._output_keys)
|
|
|
|
for msg in reversed(messages):
|
|
if msg.role != "assistant" or not remaining_keys:
|
|
continue
|
|
|
|
for key in list(remaining_keys):
|
|
value = self._try_extract_key(msg.content, key)
|
|
if value is not None:
|
|
found[key] = value
|
|
remaining_keys.discard(key)
|
|
|
|
return found
|
|
|
|
def _try_extract_key(self, content: str, key: str) -> str | None:
|
|
"""Try 4 strategies to extract a key's value from message content."""
|
|
return _try_extract_key(content, key)
|
|
|
|
# --- Lifecycle ---------------------------------------------------------
|
|
|
|
async def prune_old_tool_results(
|
|
self,
|
|
protect_tokens: int = 5000,
|
|
min_prune_tokens: int = 2000,
|
|
) -> int:
|
|
"""Replace old tool result content with compact placeholders.
|
|
|
|
Walks backward through messages. Recent tool results (within
|
|
*protect_tokens*) are kept intact. Older tool results have their
|
|
content replaced with a ~100-char placeholder that preserves the
|
|
spillover filename reference (if any). Message structure (role,
|
|
seq, tool_use_id) stays valid for the LLM API.
|
|
|
|
Phase-aware behavior (continuous mode): when messages have ``phase_id``
|
|
metadata, all messages in the current phase are protected regardless of
|
|
token budget. Transition markers are never pruned. Older phases' tool
|
|
results are pruned more aggressively.
|
|
|
|
Error tool results are never pruned — they prevent re-calling
|
|
failing tools.
|
|
|
|
Returns the number of messages pruned (0 if nothing was pruned).
|
|
"""
|
|
if not self._messages:
|
|
return 0
|
|
|
|
# Walk backward, classify tool results as protected vs pruneable
|
|
protected_tokens = 0
|
|
pruneable: list[int] = [] # indices into self._messages
|
|
pruneable_tokens = 0
|
|
|
|
for i in range(len(self._messages) - 1, -1, -1):
|
|
msg = self._messages[i]
|
|
|
|
# Transition markers are never pruned (any role)
|
|
if msg.is_transition_marker:
|
|
continue
|
|
|
|
if msg.role != "tool":
|
|
continue
|
|
if msg.is_error:
|
|
continue # never prune errors
|
|
if msg.is_skill_content:
|
|
continue # never prune activated skill instructions (AS-10)
|
|
if msg.content.startswith("[Pruned tool result"):
|
|
continue # already pruned
|
|
# Tiny results (set_output acks, confirmations) — pruning
|
|
# saves negligible space but makes the LLM think the call
|
|
# failed, causing costly retries.
|
|
if len(msg.content) < 100:
|
|
continue
|
|
|
|
# Phase-aware: protect current phase messages
|
|
if self._current_phase and msg.phase_id == self._current_phase:
|
|
continue
|
|
|
|
est = len(msg.content) // 4
|
|
if protected_tokens < protect_tokens:
|
|
protected_tokens += est
|
|
else:
|
|
pruneable.append(i)
|
|
pruneable_tokens += est
|
|
|
|
# Only prune if enough to be worthwhile
|
|
if pruneable_tokens < min_prune_tokens:
|
|
return 0
|
|
|
|
# Replace content with compact placeholder
|
|
count = 0
|
|
for i in pruneable:
|
|
msg = self._messages[i]
|
|
orig_len = len(msg.content)
|
|
spillover = _extract_spillover_filename(msg.content)
|
|
|
|
if spillover:
|
|
placeholder = (
|
|
f"[Pruned tool result: {orig_len} chars. "
|
|
f"Full data in '{spillover}'. "
|
|
f"Use load_data('{spillover}') to retrieve.]"
|
|
)
|
|
else:
|
|
placeholder = f"[Pruned tool result: {orig_len} chars cleared from context.]"
|
|
|
|
self._messages[i] = Message(
|
|
seq=msg.seq,
|
|
role=msg.role,
|
|
content=placeholder,
|
|
tool_use_id=msg.tool_use_id,
|
|
tool_calls=msg.tool_calls,
|
|
is_error=msg.is_error,
|
|
phase_id=msg.phase_id,
|
|
is_transition_marker=msg.is_transition_marker,
|
|
run_id=msg.run_id,
|
|
)
|
|
count += 1
|
|
|
|
if self._store:
|
|
await self._store.write_part(msg.seq, self._messages[i].to_storage_dict())
|
|
|
|
# Reset token estimate — content lengths changed
|
|
self._last_api_input_tokens = None
|
|
return count
|
|
|
|
async def compact(
|
|
self,
|
|
summary: str,
|
|
keep_recent: int = 2,
|
|
phase_graduated: bool = False,
|
|
) -> None:
|
|
"""Replace old messages with a summary, optionally keeping recent ones.
|
|
|
|
Args:
|
|
summary: Caller-provided summary text.
|
|
keep_recent: Number of recent messages to preserve (default 2).
|
|
Clamped to [0, len(messages) - 1].
|
|
phase_graduated: When True and messages have phase_id metadata,
|
|
split at phase boundaries instead of using keep_recent.
|
|
Keeps current + previous phase intact; compacts older phases.
|
|
"""
|
|
if not self._messages:
|
|
return
|
|
|
|
total = len(self._messages)
|
|
|
|
# Phase-graduated: find the split point based on phase boundaries.
|
|
# Keeps current phase + previous phase intact, compacts older phases.
|
|
if phase_graduated and self._current_phase:
|
|
split = self._find_phase_graduated_split()
|
|
else:
|
|
split = None
|
|
|
|
if split is None:
|
|
# Fallback: use keep_recent (non-phase or single-phase conversation)
|
|
keep_recent = max(0, min(keep_recent, total - 1))
|
|
split = total - keep_recent if keep_recent > 0 else total
|
|
|
|
# Advance split past orphaned tool results at the boundary.
|
|
# Tool-role messages reference a tool_use from the preceding
|
|
# assistant message; if that assistant message falls into the
|
|
# compacted (old) portion the tool_result becomes invalid.
|
|
while split < total and self._messages[split].role == "tool":
|
|
split += 1
|
|
|
|
# Nothing to compact
|
|
if split == 0:
|
|
return
|
|
|
|
old_messages = list(self._messages[:split])
|
|
recent_messages = list(self._messages[split:])
|
|
|
|
# Extract protected values from messages being discarded
|
|
if self._output_keys:
|
|
protected = self._extract_protected_values(old_messages)
|
|
if protected:
|
|
lines = ["PRESERVED VALUES (do not lose these):"]
|
|
for k, v in protected.items():
|
|
lines.append(f"- {k}: {v}")
|
|
lines.append("")
|
|
lines.append("CONVERSATION SUMMARY:")
|
|
lines.append(summary)
|
|
summary = "\n".join(lines)
|
|
|
|
# Determine summary seq
|
|
if recent_messages:
|
|
summary_seq = recent_messages[0].seq - 1
|
|
else:
|
|
summary_seq = self._next_seq
|
|
self._next_seq += 1
|
|
|
|
summary_msg = Message(seq=summary_seq, role="user", content=summary, run_id=self._run_id)
|
|
|
|
# Persist
|
|
if self._store:
|
|
delete_before = recent_messages[0].seq if recent_messages else self._next_seq
|
|
await self._store.delete_parts_before(delete_before)
|
|
await self._store.write_part(summary_msg.seq, summary_msg.to_storage_dict())
|
|
await self._write_next_seq()
|
|
|
|
self._messages = [summary_msg] + recent_messages
|
|
self._last_api_input_tokens = None # reset; next LLM call will recalibrate
|
|
|
|
async def compact_preserving_structure(
|
|
self,
|
|
spillover_dir: str,
|
|
keep_recent: int = 4,
|
|
phase_graduated: bool = False,
|
|
aggressive: bool = False,
|
|
) -> None:
|
|
"""Structure-preserving compaction: save freeform text to file, keep tool messages.
|
|
|
|
Unlike ``compact()`` which replaces ALL old messages with a single LLM
|
|
summary, this method preserves the tool call structure (assistant
|
|
messages with tool_calls + tool result messages) that are already tiny
|
|
after pruning. Only freeform text exchanges (user messages,
|
|
text-only assistant messages) are saved to a file and removed.
|
|
|
|
When *aggressive* is True, non-essential tool call pairs are also
|
|
collapsed into a compact summary instead of being kept individually.
|
|
Only ``set_output`` calls and error results are preserved; all other
|
|
old tool pairs are replaced by a tool-call history summary.
|
|
|
|
The result: the agent retains exact knowledge of what tools it called,
|
|
where each result is stored, and can load the conversation text if
|
|
needed. No LLM summary call. No heuristics. Nothing lost.
|
|
"""
|
|
if not self._messages:
|
|
return
|
|
|
|
total = len(self._messages)
|
|
|
|
# Determine split point (same logic as compact)
|
|
if phase_graduated and self._current_phase:
|
|
split = self._find_phase_graduated_split()
|
|
else:
|
|
split = None
|
|
|
|
if split is None:
|
|
keep_recent = max(0, min(keep_recent, total - 1))
|
|
split = total - keep_recent if keep_recent > 0 else total
|
|
|
|
# Advance split past orphaned tool results at the boundary
|
|
while split < total and self._messages[split].role == "tool":
|
|
split += 1
|
|
|
|
if split == 0:
|
|
return
|
|
|
|
old_messages = self._messages[:split]
|
|
|
|
# Classify old messages: structural (keep) vs freeform (save to file)
|
|
kept_structural: list[Message] = []
|
|
freeform_lines: list[str] = []
|
|
collapsed_msgs: list[Message] = []
|
|
|
|
# Collect all tool_use IDs present in old messages so we can detect
|
|
# orphaned tool results whose parent assistant message was already
|
|
# compacted away (API invariant protection).
|
|
old_tc_ids: set[str] = set()
|
|
for msg in old_messages:
|
|
if msg.tool_calls:
|
|
for tc in msg.tool_calls:
|
|
old_tc_ids.add(tc.get("id", ""))
|
|
|
|
if aggressive:
|
|
# Aggressive: only keep set_output tool pairs and error results.
|
|
# Everything else is collapsed into a tool-call history summary.
|
|
# We need to track tool_call IDs to pair assistant messages with
|
|
# their tool results.
|
|
protected_tc_ids: set[str] = set()
|
|
collapsible_tc_ids: set[str] = set()
|
|
|
|
# First pass: classify assistant messages
|
|
for msg in old_messages:
|
|
if msg.role != "assistant" or not msg.tool_calls:
|
|
continue
|
|
has_protected = any(
|
|
tc.get("function", {}).get("name") == "set_output" for tc in msg.tool_calls
|
|
)
|
|
tc_ids = {tc.get("id", "") for tc in msg.tool_calls}
|
|
if has_protected:
|
|
protected_tc_ids |= tc_ids
|
|
else:
|
|
collapsible_tc_ids |= tc_ids
|
|
|
|
# Skill content and transition markers are always protected
|
|
for msg in old_messages:
|
|
if msg.role == "tool" and msg.is_skill_content and msg.tool_use_id:
|
|
protected_tc_ids.add(msg.tool_use_id)
|
|
|
|
# Second pass: classify all messages
|
|
for msg in old_messages:
|
|
if msg.is_transition_marker:
|
|
# Transition markers are always kept (phase boundaries)
|
|
kept_structural.append(msg)
|
|
elif msg.role == "tool":
|
|
tc_id = msg.tool_use_id or ""
|
|
if tc_id in protected_tc_ids:
|
|
kept_structural.append(msg)
|
|
elif msg.is_error:
|
|
# Error results are always protected
|
|
kept_structural.append(msg)
|
|
# Protect the parent assistant message too
|
|
protected_tc_ids.add(tc_id)
|
|
elif msg.is_skill_content:
|
|
kept_structural.append(msg)
|
|
elif tc_id and tc_id not in old_tc_ids:
|
|
# Orphaned tool result — parent tool_use not in old msgs.
|
|
# Keep it to maintain API invariants.
|
|
kept_structural.append(msg)
|
|
else:
|
|
collapsed_msgs.append(msg)
|
|
elif msg.role == "assistant" and msg.tool_calls:
|
|
tc_ids = {tc.get("id", "") for tc in msg.tool_calls}
|
|
if tc_ids & protected_tc_ids:
|
|
# Has at least one protected tool call — keep entire msg
|
|
compact_tcs = _compact_tool_calls(msg.tool_calls)
|
|
kept_structural.append(
|
|
Message(
|
|
seq=msg.seq,
|
|
role=msg.role,
|
|
content="",
|
|
tool_calls=compact_tcs,
|
|
is_error=msg.is_error,
|
|
phase_id=msg.phase_id,
|
|
is_transition_marker=msg.is_transition_marker,
|
|
run_id=msg.run_id,
|
|
)
|
|
)
|
|
else:
|
|
collapsed_msgs.append(msg)
|
|
else:
|
|
# Freeform text — save to file
|
|
role_label = msg.role
|
|
text = msg.content
|
|
if len(text) > 2000:
|
|
text = text[:2000] + "…"
|
|
freeform_lines.append(f"[{role_label}] (seq={msg.seq}): {text}")
|
|
else:
|
|
# Standard mode: keep all tool call pairs as structural
|
|
for msg in old_messages:
|
|
if msg.is_transition_marker:
|
|
# Transition markers are always kept (phase boundaries)
|
|
kept_structural.append(msg)
|
|
elif msg.role == "tool":
|
|
kept_structural.append(msg)
|
|
elif msg.role == "assistant" and msg.tool_calls:
|
|
compact_tcs = _compact_tool_calls(msg.tool_calls)
|
|
kept_structural.append(
|
|
Message(
|
|
seq=msg.seq,
|
|
role=msg.role,
|
|
content="",
|
|
tool_calls=compact_tcs,
|
|
is_error=msg.is_error,
|
|
phase_id=msg.phase_id,
|
|
is_transition_marker=msg.is_transition_marker,
|
|
run_id=msg.run_id,
|
|
)
|
|
)
|
|
else:
|
|
role_label = msg.role
|
|
text = msg.content
|
|
if len(text) > 2000:
|
|
text = text[:2000] + "…"
|
|
freeform_lines.append(f"[{role_label}] (seq={msg.seq}): {text}")
|
|
|
|
# Write freeform text to a numbered conversation file
|
|
spill_path = Path(spillover_dir)
|
|
spill_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Find next conversation file number
|
|
existing = sorted(spill_path.glob("conversation_*.md"))
|
|
next_n = len(existing) + 1
|
|
conv_filename = f"conversation_{next_n}.md"
|
|
|
|
if freeform_lines:
|
|
header = f"## Compacted conversation (messages 1-{split})\n\n"
|
|
conv_text = header + "\n\n".join(freeform_lines)
|
|
(spill_path / conv_filename).write_text(conv_text, encoding="utf-8")
|
|
else:
|
|
# Nothing to save — skip file creation
|
|
conv_filename = ""
|
|
|
|
# Build reference message
|
|
ref_parts: list[str] = []
|
|
if conv_filename:
|
|
full_path = str((spill_path / conv_filename).resolve())
|
|
ref_parts.append(
|
|
f"[Previous conversation saved to '{full_path}'. "
|
|
f"Use load_data('{conv_filename}') to review if needed.]"
|
|
)
|
|
elif not collapsed_msgs:
|
|
ref_parts.append("[Previous freeform messages compacted.]")
|
|
|
|
# Aggressive: add collapsed tool-call history to the reference
|
|
if collapsed_msgs:
|
|
tool_history = extract_tool_call_history(collapsed_msgs)
|
|
if tool_history:
|
|
ref_parts.append(tool_history)
|
|
elif not ref_parts:
|
|
ref_parts.append("[Previous tool calls compacted.]")
|
|
|
|
ref_content = "\n\n".join(ref_parts)
|
|
|
|
# Use a seq just before the first kept message
|
|
recent_messages = list(self._messages[split:])
|
|
if kept_structural:
|
|
ref_seq = kept_structural[0].seq - 1
|
|
elif recent_messages:
|
|
ref_seq = recent_messages[0].seq - 1
|
|
else:
|
|
ref_seq = self._next_seq
|
|
self._next_seq += 1
|
|
|
|
ref_msg = Message(seq=ref_seq, role="user", content=ref_content, run_id=self._run_id)
|
|
|
|
# Persist: delete old messages from store, write reference + kept structural.
|
|
# In aggressive mode, collapsed messages may be interspersed with kept
|
|
# messages, so we delete everything before the recent boundary and
|
|
# rewrite only what we want to keep.
|
|
if self._store:
|
|
recent_boundary = recent_messages[0].seq if recent_messages else self._next_seq
|
|
await self._store.delete_parts_before(recent_boundary)
|
|
# Write the reference message
|
|
await self._store.write_part(ref_msg.seq, ref_msg.to_storage_dict())
|
|
# Write kept structural messages (they may have been modified)
|
|
for msg in kept_structural:
|
|
await self._store.write_part(msg.seq, msg.to_storage_dict())
|
|
await self._write_next_seq()
|
|
|
|
# Reassemble: reference + kept structural (in original order) + recent
|
|
self._messages = [ref_msg] + kept_structural + recent_messages
|
|
self._last_api_input_tokens = None
|
|
|
|
def _find_phase_graduated_split(self) -> int | None:
|
|
"""Find split point that preserves current + previous phase.
|
|
|
|
Returns the index of the first message in the protected set,
|
|
or None if phase graduation doesn't apply (< 3 phases).
|
|
"""
|
|
# Collect distinct phases in order of first appearance
|
|
phases_seen: list[str] = []
|
|
for msg in self._messages:
|
|
if msg.phase_id and msg.phase_id not in phases_seen:
|
|
phases_seen.append(msg.phase_id)
|
|
|
|
# Need at least 3 phases for graduation to be meaningful
|
|
# (current + previous are protected, older get compacted)
|
|
if len(phases_seen) < 3:
|
|
return None
|
|
|
|
# Protect: current phase + previous phase
|
|
protected_phases = {phases_seen[-1], phases_seen[-2]}
|
|
|
|
# Find split: first message belonging to a protected phase
|
|
for i, msg in enumerate(self._messages):
|
|
if msg.phase_id in protected_phases:
|
|
return i
|
|
|
|
return None
|
|
|
|
async def clear(self) -> None:
|
|
"""Remove all messages, keep system prompt, preserve ``_next_seq``."""
|
|
if self._store:
|
|
await self._store.delete_parts_before(self._next_seq)
|
|
await self._write_next_seq()
|
|
self._messages.clear()
|
|
self._last_api_input_tokens = None
|
|
|
|
def export_summary(self) -> str:
|
|
"""Structured summary with [STATS], [CONFIG], [RECENT_MESSAGES] sections."""
|
|
prompt_preview = (
|
|
self._system_prompt[:80] + "..."
|
|
if len(self._system_prompt) > 80
|
|
else self._system_prompt
|
|
)
|
|
|
|
lines = [
|
|
"[STATS]",
|
|
f"turns: {self.turn_count}",
|
|
f"messages: {self.message_count}",
|
|
f"estimated_tokens: {self.estimate_tokens()}",
|
|
"",
|
|
"[CONFIG]",
|
|
f"system_prompt: {prompt_preview!r}",
|
|
]
|
|
|
|
if self._output_keys:
|
|
lines.append(f"output_keys: {', '.join(self._output_keys)}")
|
|
|
|
lines.append("")
|
|
lines.append("[RECENT_MESSAGES]")
|
|
for m in self._messages[-5:]:
|
|
preview = m.content[:60] + "..." if len(m.content) > 60 else m.content
|
|
lines.append(f" [{m.role}] {preview}")
|
|
|
|
return "\n".join(lines)
|
|
|
|
# --- Persistence internals ---------------------------------------------
|
|
|
|
async def _persist(self, message: Message) -> None:
|
|
"""Write-through a single message. No-op when store is None."""
|
|
if self._store is None:
|
|
return
|
|
if not self._meta_persisted:
|
|
await self._persist_meta()
|
|
await self._store.write_part(message.seq, message.to_storage_dict())
|
|
await self._write_next_seq()
|
|
|
|
async def _persist_meta(self) -> None:
|
|
"""Lazily write conversation metadata to the store (called once).
|
|
|
|
When ``self._run_id`` is set, metadata is written flat for backward
|
|
compatibility (run-scoped isolation has been reverted).
|
|
"""
|
|
if self._store is None:
|
|
return
|
|
run_meta = {
|
|
"system_prompt": self._system_prompt,
|
|
"max_context_tokens": self._max_context_tokens,
|
|
"compaction_threshold": self._compaction_threshold,
|
|
"output_keys": self._output_keys,
|
|
}
|
|
await self._store.write_meta(run_meta)
|
|
self._meta_persisted = True
|
|
|
|
async def _write_next_seq(self) -> None:
|
|
if self._store is None:
|
|
return
|
|
cursor = await self._store.read_cursor() or {}
|
|
cursor["next_seq"] = self._next_seq
|
|
await self._store.write_cursor(cursor)
|
|
|
|
# --- Restore -----------------------------------------------------------
|
|
|
|
@classmethod
|
|
async def restore(
|
|
cls,
|
|
store: ConversationStore,
|
|
phase_id: str | None = None,
|
|
run_id: str | None = None,
|
|
) -> NodeConversation | None:
|
|
"""Reconstruct a NodeConversation from a store.
|
|
|
|
Args:
|
|
store: The conversation store to read from.
|
|
phase_id: If set, only load parts matching this phase_id.
|
|
Used in isolated mode so a node only sees its own
|
|
messages in the shared flat store. In continuous mode
|
|
pass ``None`` to load all parts.
|
|
run_id: If set, only load parts matching this run_id.
|
|
Ensures intentional restarts (new run_id) start fresh
|
|
while crash recovery (same run_id) resumes correctly.
|
|
|
|
Returns ``None`` if the store contains no metadata (i.e. the
|
|
conversation was never persisted).
|
|
"""
|
|
meta = await store.read_meta()
|
|
if meta is None:
|
|
return None
|
|
|
|
conv = cls(
|
|
system_prompt=meta.get("system_prompt", ""),
|
|
max_context_tokens=meta.get("max_context_tokens", 32000),
|
|
compaction_threshold=meta.get("compaction_threshold", 0.8),
|
|
output_keys=meta.get("output_keys"),
|
|
store=store,
|
|
run_id=run_id,
|
|
)
|
|
conv._meta_persisted = True
|
|
|
|
parts = await store.read_parts()
|
|
if phase_id:
|
|
parts = [p for p in parts if p.get("phase_id") == phase_id]
|
|
# Filter by run_id so intentional restarts (new run_id) start fresh
|
|
# while crash recovery (same run_id) loads prior parts.
|
|
if run_id and not is_legacy_run_id(run_id):
|
|
parts = [p for p in parts if p.get("run_id") == run_id]
|
|
conv._messages = [Message.from_storage_dict(p) for p in parts]
|
|
|
|
cursor = await store.read_cursor()
|
|
next_seq = get_cursor_next_seq(cursor)
|
|
if next_seq is not None:
|
|
conv._next_seq = next_seq
|
|
elif conv._messages:
|
|
conv._next_seq = conv._messages[-1].seq + 1
|
|
|
|
return conv
|