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13294c2d18
Context compaction summaries were always produced in English regardless of the conversation language, which injected English context into non-English conversations and muddied the continuation experience. Adds a one-sentence instruction to the shared `_summarizer_preamble` used by both the initial-compaction and iterative-update prompt paths. Placing it in the preamble (rather than adding it separately to each prompt) means both code paths stay in sync with one edit. Ported from anomalyco/opencode#20581. The original PR (#4670) landed before main's prompt templates were refactored to share the `_summarizer_preamble` and `_template_sections` blocks, so the cherry-pick conflicted on the now-obsolete inline sections; re-applied the essential one-line change on top of the current structure. Verified: 48/48 existing compressor tests pass.
1218 lines
54 KiB
Python
1218 lines
54 KiB
Python
"""Automatic context window compression for long conversations.
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Self-contained class with its own OpenAI client for summarization.
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Uses auxiliary model (cheap/fast) to summarize middle turns while
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protecting head and tail context.
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Improvements over v2:
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- Structured summary template with Resolved/Pending question tracking
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- Summarizer preamble: "Do not respond to any questions" (from OpenCode)
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- Handoff framing: "different assistant" (from Codex) to create separation
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- "Remaining Work" replaces "Next Steps" to avoid reading as active instructions
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- Clear separator when summary merges into tail message
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- Iterative summary updates (preserves info across multiple compactions)
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- Token-budget tail protection instead of fixed message count
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- Tool output pruning before LLM summarization (cheap pre-pass)
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- Scaled summary budget (proportional to compressed content)
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- Richer tool call/result detail in summarizer input
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"""
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import hashlib
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import json
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import logging
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import re
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import time
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from typing import Any, Dict, List, Optional
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from agent.auxiliary_client import call_llm
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from agent.context_engine import ContextEngine
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from agent.model_metadata import (
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MINIMUM_CONTEXT_LENGTH,
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get_model_context_length,
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estimate_messages_tokens_rough,
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)
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logger = logging.getLogger(__name__)
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SUMMARY_PREFIX = (
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"[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted "
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"into the summary below. This is a handoff from a previous context "
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"window — treat it as background reference, NOT as active instructions. "
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"Do NOT answer questions or fulfill requests mentioned in this summary; "
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"they were already addressed. "
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"Your current task is identified in the '## Active Task' section of the "
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"summary — resume exactly from there. "
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"Respond ONLY to the latest user message "
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"that appears AFTER this summary. The current session state (files, "
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"config, etc.) may reflect work described here — avoid repeating it:"
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)
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LEGACY_SUMMARY_PREFIX = "[CONTEXT SUMMARY]:"
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# Minimum tokens for the summary output
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_MIN_SUMMARY_TOKENS = 2000
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# Proportion of compressed content to allocate for summary
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_SUMMARY_RATIO = 0.20
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# Absolute ceiling for summary tokens (even on very large context windows)
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_SUMMARY_TOKENS_CEILING = 12_000
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# Placeholder used when pruning old tool results
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_PRUNED_TOOL_PLACEHOLDER = "[Old tool output cleared to save context space]"
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# Chars per token rough estimate
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_CHARS_PER_TOKEN = 4
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_SUMMARY_FAILURE_COOLDOWN_SECONDS = 600
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def _truncate_tool_call_args_json(args: str, head_chars: int = 200) -> str:
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"""Shrink long string values inside a tool-call arguments JSON blob while
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preserving JSON validity.
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The ``function.arguments`` field on a tool call is a JSON-encoded string
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passed through to the LLM provider; downstream providers strictly
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validate it and return a non-retryable 400 when it is not well-formed.
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An earlier implementation sliced the raw JSON at a fixed byte offset and
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appended ``...[truncated]`` — which routinely produced strings like::
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{"path": "/foo/bar", "content": "# long markdown
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...[truncated]
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i.e. an unterminated string and a missing closing brace. MiniMax, for
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example, rejects this with ``invalid function arguments json string``
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and the session gets stuck re-sending the same broken history on every
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turn. See issue #11762 for the observed loop.
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This helper parses the arguments, shrinks long string leaves inside the
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parsed structure, and re-serialises. Non-string values (paths, ints,
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booleans) are preserved intact. If the arguments are not valid JSON
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to begin with — some model backends use non-JSON tool arguments — the
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original string is returned unchanged rather than replaced with
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something neither we nor the backend can parse.
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"""
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try:
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parsed = json.loads(args)
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except (ValueError, TypeError):
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return args
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def _shrink(obj: Any) -> Any:
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if isinstance(obj, str):
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if len(obj) > head_chars:
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return obj[:head_chars] + "...[truncated]"
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return obj
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if isinstance(obj, dict):
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return {k: _shrink(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [_shrink(v) for v in obj]
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return obj
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shrunken = _shrink(parsed)
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# ensure_ascii=False preserves CJK/emoji instead of bloating with \uXXXX
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return json.dumps(shrunken, ensure_ascii=False)
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def _summarize_tool_result(tool_name: str, tool_args: str, tool_content: str) -> str:
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"""Create an informative 1-line summary of a tool call + result.
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Used during the pre-compression pruning pass to replace large tool
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outputs with a short but useful description of what the tool did,
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rather than a generic placeholder that carries zero information.
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Returns strings like::
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[terminal] ran `npm test` -> exit 0, 47 lines output
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[read_file] read config.py from line 1 (1,200 chars)
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[search_files] content search for 'compress' in agent/ -> 12 matches
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"""
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try:
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args = json.loads(tool_args) if tool_args else {}
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except (json.JSONDecodeError, TypeError):
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args = {}
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content = tool_content or ""
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content_len = len(content)
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line_count = content.count("\n") + 1 if content.strip() else 0
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if tool_name == "terminal":
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cmd = args.get("command", "")
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if len(cmd) > 80:
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cmd = cmd[:77] + "..."
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exit_match = re.search(r'"exit_code"\s*:\s*(-?\d+)', content)
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exit_code = exit_match.group(1) if exit_match else "?"
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return f"[terminal] ran `{cmd}` -> exit {exit_code}, {line_count} lines output"
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if tool_name == "read_file":
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path = args.get("path", "?")
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offset = args.get("offset", 1)
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return f"[read_file] read {path} from line {offset} ({content_len:,} chars)"
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if tool_name == "write_file":
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path = args.get("path", "?")
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written_lines = args.get("content", "").count("\n") + 1 if args.get("content") else "?"
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return f"[write_file] wrote to {path} ({written_lines} lines)"
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if tool_name == "search_files":
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pattern = args.get("pattern", "?")
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path = args.get("path", ".")
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target = args.get("target", "content")
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match_count = re.search(r'"total_count"\s*:\s*(\d+)', content)
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count = match_count.group(1) if match_count else "?"
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return f"[search_files] {target} search for '{pattern}' in {path} -> {count} matches"
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if tool_name == "patch":
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path = args.get("path", "?")
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mode = args.get("mode", "replace")
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return f"[patch] {mode} in {path} ({content_len:,} chars result)"
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if tool_name in ("browser_navigate", "browser_click", "browser_snapshot",
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"browser_type", "browser_scroll", "browser_vision"):
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url = args.get("url", "")
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ref = args.get("ref", "")
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detail = f" {url}" if url else (f" ref={ref}" if ref else "")
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return f"[{tool_name}]{detail} ({content_len:,} chars)"
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if tool_name == "web_search":
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query = args.get("query", "?")
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return f"[web_search] query='{query}' ({content_len:,} chars result)"
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if tool_name == "web_extract":
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urls = args.get("urls", [])
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url_desc = urls[0] if isinstance(urls, list) and urls else "?"
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if isinstance(urls, list) and len(urls) > 1:
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url_desc += f" (+{len(urls) - 1} more)"
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return f"[web_extract] {url_desc} ({content_len:,} chars)"
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if tool_name == "delegate_task":
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goal = args.get("goal", "")
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if len(goal) > 60:
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goal = goal[:57] + "..."
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return f"[delegate_task] '{goal}' ({content_len:,} chars result)"
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if tool_name == "execute_code":
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code_preview = (args.get("code") or "")[:60].replace("\n", " ")
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if len(args.get("code", "")) > 60:
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code_preview += "..."
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return f"[execute_code] `{code_preview}` ({line_count} lines output)"
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if tool_name in ("skill_view", "skills_list", "skill_manage"):
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name = args.get("name", "?")
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return f"[{tool_name}] name={name} ({content_len:,} chars)"
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if tool_name == "vision_analyze":
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question = args.get("question", "")[:50]
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return f"[vision_analyze] '{question}' ({content_len:,} chars)"
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if tool_name == "memory":
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action = args.get("action", "?")
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target = args.get("target", "?")
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return f"[memory] {action} on {target}"
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if tool_name == "todo":
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return "[todo] updated task list"
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if tool_name == "clarify":
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return "[clarify] asked user a question"
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if tool_name == "text_to_speech":
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return f"[text_to_speech] generated audio ({content_len:,} chars)"
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if tool_name == "cronjob":
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action = args.get("action", "?")
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return f"[cronjob] {action}"
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if tool_name == "process":
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action = args.get("action", "?")
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sid = args.get("session_id", "?")
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return f"[process] {action} session={sid}"
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# Generic fallback
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first_arg = ""
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for k, v in list(args.items())[:2]:
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sv = str(v)[:40]
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first_arg += f" {k}={sv}"
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return f"[{tool_name}]{first_arg} ({content_len:,} chars result)"
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class ContextCompressor(ContextEngine):
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"""Default context engine — compresses conversation context via lossy summarization.
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Algorithm:
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1. Prune old tool results (cheap, no LLM call)
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2. Protect head messages (system prompt + first exchange)
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3. Protect tail messages by token budget (most recent ~20K tokens)
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4. Summarize middle turns with structured LLM prompt
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5. On subsequent compactions, iteratively update the previous summary
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"""
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@property
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def name(self) -> str:
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return "compressor"
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def on_session_reset(self) -> None:
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"""Reset all per-session state for /new or /reset."""
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super().on_session_reset()
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self._context_probed = False
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self._context_probe_persistable = False
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self._previous_summary = None
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self._last_compression_savings_pct = 100.0
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self._ineffective_compression_count = 0
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def update_model(
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self,
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model: str,
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context_length: int,
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base_url: str = "",
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api_key: str = "",
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provider: str = "",
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api_mode: str = "",
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) -> None:
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"""Update model info after a model switch or fallback activation."""
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self.model = model
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self.base_url = base_url
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self.api_key = api_key
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self.provider = provider
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self.api_mode = api_mode
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self.context_length = context_length
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self.threshold_tokens = max(
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int(context_length * self.threshold_percent),
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MINIMUM_CONTEXT_LENGTH,
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)
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def __init__(
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self,
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model: str,
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threshold_percent: float = 0.50,
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protect_first_n: int = 3,
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protect_last_n: int = 20,
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summary_target_ratio: float = 0.20,
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quiet_mode: bool = False,
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summary_model_override: str = None,
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base_url: str = "",
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api_key: str = "",
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config_context_length: int | None = None,
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provider: str = "",
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api_mode: str = "",
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):
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self.model = model
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self.base_url = base_url
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self.api_key = api_key
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self.provider = provider
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self.api_mode = api_mode
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self.threshold_percent = threshold_percent
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self.protect_first_n = protect_first_n
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self.protect_last_n = protect_last_n
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self.summary_target_ratio = max(0.10, min(summary_target_ratio, 0.80))
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self.quiet_mode = quiet_mode
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self.context_length = get_model_context_length(
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model, base_url=base_url, api_key=api_key,
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config_context_length=config_context_length,
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provider=provider,
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)
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# Floor: never compress below MINIMUM_CONTEXT_LENGTH tokens even if
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# the percentage would suggest a lower value. This prevents premature
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# compression on large-context models at 50% while keeping the % sane
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# for models right at the minimum.
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self.threshold_tokens = max(
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int(self.context_length * threshold_percent),
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MINIMUM_CONTEXT_LENGTH,
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)
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self.compression_count = 0
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# Derive token budgets: ratio is relative to the threshold, not total context
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target_tokens = int(self.threshold_tokens * self.summary_target_ratio)
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self.tail_token_budget = target_tokens
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self.max_summary_tokens = min(
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int(self.context_length * 0.05), _SUMMARY_TOKENS_CEILING,
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)
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if not quiet_mode:
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logger.info(
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"Context compressor initialized: model=%s context_length=%d "
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"threshold=%d (%.0f%%) target_ratio=%.0f%% tail_budget=%d "
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"provider=%s base_url=%s",
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model, self.context_length, self.threshold_tokens,
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threshold_percent * 100, self.summary_target_ratio * 100,
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self.tail_token_budget,
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provider or "none", base_url or "none",
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)
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self._context_probed = False # True after a step-down from context error
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self.last_prompt_tokens = 0
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self.last_completion_tokens = 0
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self.summary_model = summary_model_override or ""
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# Stores the previous compaction summary for iterative updates
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self._previous_summary: Optional[str] = None
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# Anti-thrashing: track whether last compression was effective
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self._last_compression_savings_pct: float = 100.0
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self._ineffective_compression_count: int = 0
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self._summary_failure_cooldown_until: float = 0.0
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def update_from_response(self, usage: Dict[str, Any]):
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"""Update tracked token usage from API response."""
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self.last_prompt_tokens = usage.get("prompt_tokens", 0)
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self.last_completion_tokens = usage.get("completion_tokens", 0)
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def should_compress(self, prompt_tokens: int = None) -> bool:
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"""Check if context exceeds the compression threshold.
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Includes anti-thrashing protection: if the last two compressions
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each saved less than 10%, skip compression to avoid infinite loops
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where each pass removes only 1-2 messages.
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"""
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tokens = prompt_tokens if prompt_tokens is not None else self.last_prompt_tokens
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if tokens < self.threshold_tokens:
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return False
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# Anti-thrashing: back off if recent compressions were ineffective
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if self._ineffective_compression_count >= 2:
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if not self.quiet_mode:
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logger.warning(
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"Compression skipped — last %d compressions saved <10%% each. "
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"Consider /new to start a fresh session, or /compress <topic> "
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"for focused compression.",
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self._ineffective_compression_count,
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)
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return False
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return True
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# ------------------------------------------------------------------
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# Tool output pruning (cheap pre-pass, no LLM call)
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# ------------------------------------------------------------------
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def _prune_old_tool_results(
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self, messages: List[Dict[str, Any]], protect_tail_count: int,
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protect_tail_tokens: int | None = None,
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) -> tuple[List[Dict[str, Any]], int]:
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"""Replace old tool result contents with informative 1-line summaries.
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Instead of a generic placeholder, generates a summary like::
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[terminal] ran `npm test` -> exit 0, 47 lines output
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[read_file] read config.py from line 1 (3,400 chars)
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Also deduplicates identical tool results (e.g. reading the same file
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5x keeps only the newest full copy) and truncates large tool_call
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arguments in assistant messages outside the protected tail.
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Walks backward from the end, protecting the most recent messages that
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fall within ``protect_tail_tokens`` (when provided) OR the last
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``protect_tail_count`` messages (backward-compatible default).
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When both are given, the token budget takes priority and the message
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count acts as a hard minimum floor.
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Returns (pruned_messages, pruned_count).
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"""
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if not messages:
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return messages, 0
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result = [m.copy() for m in messages]
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pruned = 0
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# Build index: tool_call_id -> (tool_name, arguments_json)
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call_id_to_tool: Dict[str, tuple] = {}
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for msg in result:
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if msg.get("role") == "assistant":
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for tc in msg.get("tool_calls") or []:
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if isinstance(tc, dict):
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cid = tc.get("id", "")
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fn = tc.get("function", {})
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call_id_to_tool[cid] = (fn.get("name", "unknown"), fn.get("arguments", ""))
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else:
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cid = getattr(tc, "id", "") or ""
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fn = getattr(tc, "function", None)
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name = getattr(fn, "name", "unknown") if fn else "unknown"
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args_str = getattr(fn, "arguments", "") if fn else ""
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call_id_to_tool[cid] = (name, args_str)
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# Determine the prune boundary
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if protect_tail_tokens is not None and protect_tail_tokens > 0:
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# Token-budget approach: walk backward accumulating tokens
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accumulated = 0
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boundary = len(result)
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min_protect = min(protect_tail_count, len(result) - 1)
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for i in range(len(result) - 1, -1, -1):
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msg = result[i]
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raw_content = msg.get("content") or ""
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content_len = sum(len(p.get("text", "")) for p in raw_content) if isinstance(raw_content, list) else len(raw_content)
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msg_tokens = content_len // _CHARS_PER_TOKEN + 10
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for tc in msg.get("tool_calls") or []:
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if isinstance(tc, dict):
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args = tc.get("function", {}).get("arguments", "")
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msg_tokens += len(args) // _CHARS_PER_TOKEN
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if accumulated + msg_tokens > protect_tail_tokens and (len(result) - i) >= min_protect:
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boundary = i
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break
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accumulated += msg_tokens
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boundary = i
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prune_boundary = max(boundary, len(result) - min_protect)
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else:
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prune_boundary = len(result) - protect_tail_count
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# Pass 1: Deduplicate identical tool results.
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# When the same file is read multiple times, keep only the most recent
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# full copy and replace older duplicates with a back-reference.
|
|
content_hashes: dict = {} # hash -> (index, tool_call_id)
|
|
for i in range(len(result) - 1, -1, -1):
|
|
msg = result[i]
|
|
if msg.get("role") != "tool":
|
|
continue
|
|
content = msg.get("content") or ""
|
|
# Skip multimodal content (list of content blocks)
|
|
if isinstance(content, list):
|
|
continue
|
|
if len(content) < 200:
|
|
continue
|
|
h = hashlib.md5(content.encode("utf-8", errors="replace")).hexdigest()[:12]
|
|
if h in content_hashes:
|
|
# This is an older duplicate — replace with back-reference
|
|
result[i] = {**msg, "content": "[Duplicate tool output — same content as a more recent call]"}
|
|
pruned += 1
|
|
else:
|
|
content_hashes[h] = (i, msg.get("tool_call_id", "?"))
|
|
|
|
# Pass 2: Replace old tool results with informative summaries
|
|
for i in range(prune_boundary):
|
|
msg = result[i]
|
|
if msg.get("role") != "tool":
|
|
continue
|
|
content = msg.get("content", "")
|
|
# Skip multimodal content (list of content blocks)
|
|
if isinstance(content, list):
|
|
continue
|
|
if not content or content == _PRUNED_TOOL_PLACEHOLDER:
|
|
continue
|
|
# Skip already-deduplicated or previously-summarized results
|
|
if content.startswith("[Duplicate tool output"):
|
|
continue
|
|
# Only prune if the content is substantial (>200 chars)
|
|
if len(content) > 200:
|
|
call_id = msg.get("tool_call_id", "")
|
|
tool_name, tool_args = call_id_to_tool.get(call_id, ("unknown", ""))
|
|
summary = _summarize_tool_result(tool_name, tool_args, content)
|
|
result[i] = {**msg, "content": summary}
|
|
pruned += 1
|
|
|
|
# Pass 3: Truncate large tool_call arguments in assistant messages
|
|
# outside the protected tail. write_file with 50KB content, for
|
|
# example, survives pruning entirely without this.
|
|
#
|
|
# The shrinking is done inside the parsed JSON structure so the
|
|
# result remains valid JSON — otherwise downstream providers 400
|
|
# on every subsequent turn until the broken call falls out of
|
|
# the window. See ``_truncate_tool_call_args_json`` docstring.
|
|
for i in range(prune_boundary):
|
|
msg = result[i]
|
|
if msg.get("role") != "assistant" or not msg.get("tool_calls"):
|
|
continue
|
|
new_tcs = []
|
|
modified = False
|
|
for tc in msg["tool_calls"]:
|
|
if isinstance(tc, dict):
|
|
args = tc.get("function", {}).get("arguments", "")
|
|
if len(args) > 500:
|
|
new_args = _truncate_tool_call_args_json(args)
|
|
if new_args != args:
|
|
tc = {**tc, "function": {**tc["function"], "arguments": new_args}}
|
|
modified = True
|
|
new_tcs.append(tc)
|
|
if modified:
|
|
result[i] = {**msg, "tool_calls": new_tcs}
|
|
|
|
return result, pruned
|
|
|
|
# ------------------------------------------------------------------
|
|
# Summarization
|
|
# ------------------------------------------------------------------
|
|
|
|
def _compute_summary_budget(self, turns_to_summarize: List[Dict[str, Any]]) -> int:
|
|
"""Scale summary token budget with the amount of content being compressed.
|
|
|
|
The maximum scales with the model's context window (5% of context,
|
|
capped at ``_SUMMARY_TOKENS_CEILING``) so large-context models get
|
|
richer summaries instead of being hard-capped at 8K tokens.
|
|
"""
|
|
content_tokens = estimate_messages_tokens_rough(turns_to_summarize)
|
|
budget = int(content_tokens * _SUMMARY_RATIO)
|
|
return max(_MIN_SUMMARY_TOKENS, min(budget, self.max_summary_tokens))
|
|
|
|
# Truncation limits for the summarizer input. These bound how much of
|
|
# each message the summary model sees — the budget is the *summary*
|
|
# model's context window, not the main model's.
|
|
_CONTENT_MAX = 6000 # total chars per message body
|
|
_CONTENT_HEAD = 4000 # chars kept from the start
|
|
_CONTENT_TAIL = 1500 # chars kept from the end
|
|
_TOOL_ARGS_MAX = 1500 # tool call argument chars
|
|
_TOOL_ARGS_HEAD = 1200 # kept from the start of tool args
|
|
|
|
def _serialize_for_summary(self, turns: List[Dict[str, Any]]) -> str:
|
|
"""Serialize conversation turns into labeled text for the summarizer.
|
|
|
|
Includes tool call arguments and result content (up to
|
|
``_CONTENT_MAX`` chars per message) so the summarizer can preserve
|
|
specific details like file paths, commands, and outputs.
|
|
"""
|
|
parts = []
|
|
for msg in turns:
|
|
role = msg.get("role", "unknown")
|
|
content = msg.get("content") or ""
|
|
|
|
# Tool results: keep enough content for the summarizer
|
|
if role == "tool":
|
|
tool_id = msg.get("tool_call_id", "")
|
|
if len(content) > self._CONTENT_MAX:
|
|
content = content[:self._CONTENT_HEAD] + "\n...[truncated]...\n" + content[-self._CONTENT_TAIL:]
|
|
parts.append(f"[TOOL RESULT {tool_id}]: {content}")
|
|
continue
|
|
|
|
# Assistant messages: include tool call names AND arguments
|
|
if role == "assistant":
|
|
if len(content) > self._CONTENT_MAX:
|
|
content = content[:self._CONTENT_HEAD] + "\n...[truncated]...\n" + content[-self._CONTENT_TAIL:]
|
|
tool_calls = msg.get("tool_calls", [])
|
|
if tool_calls:
|
|
tc_parts = []
|
|
for tc in tool_calls:
|
|
if isinstance(tc, dict):
|
|
fn = tc.get("function", {})
|
|
name = fn.get("name", "?")
|
|
args = fn.get("arguments", "")
|
|
# Truncate long arguments but keep enough for context
|
|
if len(args) > self._TOOL_ARGS_MAX:
|
|
args = args[:self._TOOL_ARGS_HEAD] + "..."
|
|
tc_parts.append(f" {name}({args})")
|
|
else:
|
|
fn = getattr(tc, "function", None)
|
|
name = getattr(fn, "name", "?") if fn else "?"
|
|
tc_parts.append(f" {name}(...)")
|
|
content += "\n[Tool calls:\n" + "\n".join(tc_parts) + "\n]"
|
|
parts.append(f"[ASSISTANT]: {content}")
|
|
continue
|
|
|
|
# User and other roles
|
|
if len(content) > self._CONTENT_MAX:
|
|
content = content[:self._CONTENT_HEAD] + "\n...[truncated]...\n" + content[-self._CONTENT_TAIL:]
|
|
parts.append(f"[{role.upper()}]: {content}")
|
|
|
|
return "\n\n".join(parts)
|
|
|
|
def _generate_summary(self, turns_to_summarize: List[Dict[str, Any]], focus_topic: str = None) -> Optional[str]:
|
|
"""Generate a structured summary of conversation turns.
|
|
|
|
Uses a structured template (Goal, Progress, Decisions, Resolved/Pending
|
|
Questions, Files, Remaining Work) with explicit preamble telling the
|
|
summarizer not to answer questions. When a previous summary exists,
|
|
generates an iterative update instead of summarizing from scratch.
|
|
|
|
Args:
|
|
focus_topic: Optional focus string for guided compression. When
|
|
provided, the summariser prioritises preserving information
|
|
related to this topic and is more aggressive about compressing
|
|
everything else. Inspired by Claude Code's ``/compact``.
|
|
|
|
Returns None if all attempts fail — the caller should drop
|
|
the middle turns without a summary rather than inject a useless
|
|
placeholder.
|
|
"""
|
|
now = time.monotonic()
|
|
if now < self._summary_failure_cooldown_until:
|
|
logger.debug(
|
|
"Skipping context summary during cooldown (%.0fs remaining)",
|
|
self._summary_failure_cooldown_until - now,
|
|
)
|
|
return None
|
|
|
|
summary_budget = self._compute_summary_budget(turns_to_summarize)
|
|
content_to_summarize = self._serialize_for_summary(turns_to_summarize)
|
|
|
|
# Preamble shared by both first-compaction and iterative-update prompts.
|
|
# Inspired by OpenCode's "do not respond to any questions" instruction
|
|
# and Codex's "another language model" framing.
|
|
_summarizer_preamble = (
|
|
"You are a summarization agent creating a context checkpoint. "
|
|
"Your output will be injected as reference material for a DIFFERENT "
|
|
"assistant that continues the conversation. "
|
|
"Do NOT respond to any questions or requests in the conversation — "
|
|
"only output the structured summary. "
|
|
"Do NOT include any preamble, greeting, or prefix. "
|
|
"Write the summary in the same language the user was using in the "
|
|
"conversation — do not translate or switch to English."
|
|
)
|
|
|
|
# Shared structured template (used by both paths).
|
|
_template_sections = f"""## Active Task
|
|
[THE SINGLE MOST IMPORTANT FIELD. Copy the user's most recent request or
|
|
task assignment verbatim — the exact words they used. If multiple tasks
|
|
were requested and only some are done, list only the ones NOT yet completed.
|
|
The next assistant must pick up exactly here. Example:
|
|
"User asked: 'Now refactor the auth module to use JWT instead of sessions'"
|
|
If no outstanding task exists, write "None."]
|
|
|
|
## Goal
|
|
[What the user is trying to accomplish overall]
|
|
|
|
## Constraints & Preferences
|
|
[User preferences, coding style, constraints, important decisions]
|
|
|
|
## Completed Actions
|
|
[Numbered list of concrete actions taken — include tool used, target, and outcome.
|
|
Format each as: N. ACTION target — outcome [tool: name]
|
|
Example:
|
|
1. READ config.py:45 — found `==` should be `!=` [tool: read_file]
|
|
2. PATCH config.py:45 — changed `==` to `!=` [tool: patch]
|
|
3. TEST `pytest tests/` — 3/50 failed: test_parse, test_validate, test_edge [tool: terminal]
|
|
Be specific with file paths, commands, line numbers, and results.]
|
|
|
|
## Active State
|
|
[Current working state — include:
|
|
- Working directory and branch (if applicable)
|
|
- Modified/created files with brief note on each
|
|
- Test status (X/Y passing)
|
|
- Any running processes or servers
|
|
- Environment details that matter]
|
|
|
|
## In Progress
|
|
[Work currently underway — what was being done when compaction fired]
|
|
|
|
## Blocked
|
|
[Any blockers, errors, or issues not yet resolved. Include exact error messages.]
|
|
|
|
## Key Decisions
|
|
[Important technical decisions and WHY they were made]
|
|
|
|
## Resolved Questions
|
|
[Questions the user asked that were ALREADY answered — include the answer so the next assistant does not re-answer them]
|
|
|
|
## Pending User Asks
|
|
[Questions or requests from the user that have NOT yet been answered or fulfilled. If none, write "None."]
|
|
|
|
## Relevant Files
|
|
[Files read, modified, or created — with brief note on each]
|
|
|
|
## Remaining Work
|
|
[What remains to be done — framed as context, not instructions]
|
|
|
|
## Critical Context
|
|
[Any specific values, error messages, configuration details, or data that would be lost without explicit preservation]
|
|
|
|
Target ~{summary_budget} tokens. Be CONCRETE — include file paths, command outputs, error messages, line numbers, and specific values. Avoid vague descriptions like "made some changes" — say exactly what changed.
|
|
|
|
Write only the summary body. Do not include any preamble or prefix."""
|
|
|
|
if self._previous_summary:
|
|
# Iterative update: preserve existing info, add new progress
|
|
prompt = f"""{_summarizer_preamble}
|
|
|
|
You are updating a context compaction summary. A previous compaction produced the summary below. New conversation turns have occurred since then and need to be incorporated.
|
|
|
|
PREVIOUS SUMMARY:
|
|
{self._previous_summary}
|
|
|
|
NEW TURNS TO INCORPORATE:
|
|
{content_to_summarize}
|
|
|
|
Update the summary using this exact structure. PRESERVE all existing information that is still relevant. ADD new completed actions to the numbered list (continue numbering). Move items from "In Progress" to "Completed Actions" when done. Move answered questions to "Resolved Questions". Update "Active State" to reflect current state. Remove information only if it is clearly obsolete. CRITICAL: Update "## Active Task" to reflect the user's most recent unfulfilled request — this is the most important field for task continuity.
|
|
|
|
{_template_sections}"""
|
|
else:
|
|
# First compaction: summarize from scratch
|
|
prompt = f"""{_summarizer_preamble}
|
|
|
|
Create a structured handoff summary for a different assistant that will continue this conversation after earlier turns are compacted. The next assistant should be able to understand what happened without re-reading the original turns.
|
|
|
|
TURNS TO SUMMARIZE:
|
|
{content_to_summarize}
|
|
|
|
Use this exact structure:
|
|
|
|
{_template_sections}"""
|
|
|
|
# Inject focus topic guidance when the user provides one via /compress <focus>.
|
|
# This goes at the end of the prompt so it takes precedence.
|
|
if focus_topic:
|
|
prompt += f"""
|
|
|
|
FOCUS TOPIC: "{focus_topic}"
|
|
The user has requested that this compaction PRIORITISE preserving all information related to the focus topic above. For content related to "{focus_topic}", include full detail — exact values, file paths, command outputs, error messages, and decisions. For content NOT related to the focus topic, summarise more aggressively (brief one-liners or omit if truly irrelevant). The focus topic sections should receive roughly 60-70% of the summary token budget."""
|
|
|
|
try:
|
|
call_kwargs = {
|
|
"task": "compression",
|
|
"main_runtime": {
|
|
"model": self.model,
|
|
"provider": self.provider,
|
|
"base_url": self.base_url,
|
|
"api_key": self.api_key,
|
|
"api_mode": self.api_mode,
|
|
},
|
|
"messages": [{"role": "user", "content": prompt}],
|
|
"max_tokens": int(summary_budget * 1.3),
|
|
# timeout resolved from auxiliary.compression.timeout config by call_llm
|
|
}
|
|
if self.summary_model:
|
|
call_kwargs["model"] = self.summary_model
|
|
response = call_llm(**call_kwargs)
|
|
content = response.choices[0].message.content
|
|
# Handle cases where content is not a string (e.g., dict from llama.cpp)
|
|
if not isinstance(content, str):
|
|
content = str(content) if content else ""
|
|
summary = content.strip()
|
|
# Store for iterative updates on next compaction
|
|
self._previous_summary = summary
|
|
self._summary_failure_cooldown_until = 0.0
|
|
self._summary_model_fallen_back = False
|
|
return self._with_summary_prefix(summary)
|
|
except RuntimeError:
|
|
# No provider configured — long cooldown, unlikely to self-resolve
|
|
self._summary_failure_cooldown_until = time.monotonic() + _SUMMARY_FAILURE_COOLDOWN_SECONDS
|
|
logging.warning("Context compression: no provider available for "
|
|
"summary. Middle turns will be dropped without summary "
|
|
"for %d seconds.",
|
|
_SUMMARY_FAILURE_COOLDOWN_SECONDS)
|
|
return None
|
|
except Exception as e:
|
|
# If the summary model is different from the main model and the
|
|
# error looks permanent (model not found, 503, 404), fall back to
|
|
# using the main model instead of entering cooldown that leaves
|
|
# context growing unbounded. (#8620 sub-issue 4)
|
|
_status = getattr(e, "status_code", None) or getattr(getattr(e, "response", None), "status_code", None)
|
|
_err_str = str(e).lower()
|
|
_is_model_not_found = (
|
|
_status in (404, 503)
|
|
or "model_not_found" in _err_str
|
|
or "does not exist" in _err_str
|
|
or "no available channel" in _err_str
|
|
)
|
|
if (
|
|
_is_model_not_found
|
|
and self.summary_model
|
|
and self.summary_model != self.model
|
|
and not getattr(self, "_summary_model_fallen_back", False)
|
|
):
|
|
self._summary_model_fallen_back = True
|
|
logging.warning(
|
|
"Summary model '%s' not available (%s). "
|
|
"Falling back to main model '%s' for compression.",
|
|
self.summary_model, e, self.model,
|
|
)
|
|
self.summary_model = "" # empty = use main model
|
|
self._summary_failure_cooldown_until = 0.0 # no cooldown
|
|
return self._generate_summary(messages, summary_budget) # retry immediately
|
|
|
|
# Transient errors (timeout, rate limit, network) — shorter cooldown
|
|
_transient_cooldown = 60
|
|
self._summary_failure_cooldown_until = time.monotonic() + _transient_cooldown
|
|
logging.warning(
|
|
"Failed to generate context summary: %s. "
|
|
"Further summary attempts paused for %d seconds.",
|
|
e,
|
|
_transient_cooldown,
|
|
)
|
|
return None
|
|
|
|
@staticmethod
|
|
def _with_summary_prefix(summary: str) -> str:
|
|
"""Normalize summary text to the current compaction handoff format."""
|
|
text = (summary or "").strip()
|
|
for prefix in (LEGACY_SUMMARY_PREFIX, SUMMARY_PREFIX):
|
|
if text.startswith(prefix):
|
|
text = text[len(prefix):].lstrip()
|
|
break
|
|
return f"{SUMMARY_PREFIX}\n{text}" if text else SUMMARY_PREFIX
|
|
|
|
# ------------------------------------------------------------------
|
|
# Tool-call / tool-result pair integrity helpers
|
|
# ------------------------------------------------------------------
|
|
|
|
@staticmethod
|
|
def _get_tool_call_id(tc) -> str:
|
|
"""Extract the call ID from a tool_call entry (dict or SimpleNamespace)."""
|
|
if isinstance(tc, dict):
|
|
return tc.get("id", "")
|
|
return getattr(tc, "id", "") or ""
|
|
|
|
def _sanitize_tool_pairs(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
"""Fix orphaned tool_call / tool_result pairs after compression.
|
|
|
|
Two failure modes:
|
|
1. A tool *result* references a call_id whose assistant tool_call was
|
|
removed (summarized/truncated). The API rejects this with
|
|
"No tool call found for function call output with call_id ...".
|
|
2. An assistant message has tool_calls whose results were dropped.
|
|
The API rejects this because every tool_call must be followed by
|
|
a tool result with the matching call_id.
|
|
|
|
This method removes orphaned results and inserts stub results for
|
|
orphaned calls so the message list is always well-formed.
|
|
"""
|
|
surviving_call_ids: set = set()
|
|
for msg in messages:
|
|
if msg.get("role") == "assistant":
|
|
for tc in msg.get("tool_calls") or []:
|
|
cid = self._get_tool_call_id(tc)
|
|
if cid:
|
|
surviving_call_ids.add(cid)
|
|
|
|
result_call_ids: set = set()
|
|
for msg in messages:
|
|
if msg.get("role") == "tool":
|
|
cid = msg.get("tool_call_id")
|
|
if cid:
|
|
result_call_ids.add(cid)
|
|
|
|
# 1. Remove tool results whose call_id has no matching assistant tool_call
|
|
orphaned_results = result_call_ids - surviving_call_ids
|
|
if orphaned_results:
|
|
messages = [
|
|
m for m in messages
|
|
if not (m.get("role") == "tool" and m.get("tool_call_id") in orphaned_results)
|
|
]
|
|
if not self.quiet_mode:
|
|
logger.info("Compression sanitizer: removed %d orphaned tool result(s)", len(orphaned_results))
|
|
|
|
# 2. Add stub results for assistant tool_calls whose results were dropped
|
|
missing_results = surviving_call_ids - result_call_ids
|
|
if missing_results:
|
|
patched: List[Dict[str, Any]] = []
|
|
for msg in messages:
|
|
patched.append(msg)
|
|
if msg.get("role") == "assistant":
|
|
for tc in msg.get("tool_calls") or []:
|
|
cid = self._get_tool_call_id(tc)
|
|
if cid in missing_results:
|
|
patched.append({
|
|
"role": "tool",
|
|
"content": "[Result from earlier conversation — see context summary above]",
|
|
"tool_call_id": cid,
|
|
})
|
|
messages = patched
|
|
if not self.quiet_mode:
|
|
logger.info("Compression sanitizer: added %d stub tool result(s)", len(missing_results))
|
|
|
|
return messages
|
|
|
|
def _align_boundary_forward(self, messages: List[Dict[str, Any]], idx: int) -> int:
|
|
"""Push a compress-start boundary forward past any orphan tool results.
|
|
|
|
If ``messages[idx]`` is a tool result, slide forward until we hit a
|
|
non-tool message so we don't start the summarised region mid-group.
|
|
"""
|
|
while idx < len(messages) and messages[idx].get("role") == "tool":
|
|
idx += 1
|
|
return idx
|
|
|
|
def _align_boundary_backward(self, messages: List[Dict[str, Any]], idx: int) -> int:
|
|
"""Pull a compress-end boundary backward to avoid splitting a
|
|
tool_call / result group.
|
|
|
|
If the boundary falls in the middle of a tool-result group (i.e.
|
|
there are consecutive tool messages before ``idx``), walk backward
|
|
past all of them to find the parent assistant message. If found,
|
|
move the boundary before the assistant so the entire
|
|
assistant + tool_results group is included in the summarised region
|
|
rather than being split (which causes silent data loss when
|
|
``_sanitize_tool_pairs`` removes the orphaned tail results).
|
|
"""
|
|
if idx <= 0 or idx >= len(messages):
|
|
return idx
|
|
# Walk backward past consecutive tool results
|
|
check = idx - 1
|
|
while check >= 0 and messages[check].get("role") == "tool":
|
|
check -= 1
|
|
# If we landed on the parent assistant with tool_calls, pull the
|
|
# boundary before it so the whole group gets summarised together.
|
|
if check >= 0 and messages[check].get("role") == "assistant" and messages[check].get("tool_calls"):
|
|
idx = check
|
|
return idx
|
|
|
|
# ------------------------------------------------------------------
|
|
# Tail protection by token budget
|
|
# ------------------------------------------------------------------
|
|
|
|
def _find_last_user_message_idx(
|
|
self, messages: List[Dict[str, Any]], head_end: int
|
|
) -> int:
|
|
"""Return the index of the last user-role message at or after *head_end*, or -1."""
|
|
for i in range(len(messages) - 1, head_end - 1, -1):
|
|
if messages[i].get("role") == "user":
|
|
return i
|
|
return -1
|
|
|
|
def _ensure_last_user_message_in_tail(
|
|
self,
|
|
messages: List[Dict[str, Any]],
|
|
cut_idx: int,
|
|
head_end: int,
|
|
) -> int:
|
|
"""Guarantee the most recent user message is in the protected tail.
|
|
|
|
Context compressor bug (#10896): ``_align_boundary_backward`` can pull
|
|
``cut_idx`` past a user message when it tries to keep tool_call/result
|
|
groups together. If the last user message ends up in the *compressed*
|
|
middle region the LLM summariser writes it into "Pending User Asks",
|
|
but ``SUMMARY_PREFIX`` tells the next model to respond only to user
|
|
messages *after* the summary — so the task effectively disappears from
|
|
the active context, causing the agent to stall, repeat completed work,
|
|
or silently drop the user's latest request.
|
|
|
|
Fix: if the last user-role message is not already in the tail
|
|
(``messages[cut_idx:]``), walk ``cut_idx`` back to include it. We
|
|
then re-align backward one more time to avoid splitting any
|
|
tool_call/result group that immediately precedes the user message.
|
|
"""
|
|
last_user_idx = self._find_last_user_message_idx(messages, head_end)
|
|
if last_user_idx < 0:
|
|
# No user message found beyond head — nothing to anchor.
|
|
return cut_idx
|
|
|
|
if last_user_idx >= cut_idx:
|
|
# Already in the tail; nothing to do.
|
|
return cut_idx
|
|
|
|
# The last user message is in the middle (compressed) region.
|
|
# Pull cut_idx back to it directly — a user message is already a
|
|
# clean boundary (no tool_call/result splitting risk), so there is no
|
|
# need to call _align_boundary_backward here; doing so would
|
|
# unnecessarily pull the cut further back into the preceding
|
|
# assistant + tool_calls group.
|
|
if not self.quiet_mode:
|
|
logger.debug(
|
|
"Anchoring tail cut to last user message at index %d "
|
|
"(was %d) to prevent active-task loss after compression",
|
|
last_user_idx,
|
|
cut_idx,
|
|
)
|
|
# Safety: never go back into the head region.
|
|
return max(last_user_idx, head_end + 1)
|
|
|
|
def _find_tail_cut_by_tokens(
|
|
self, messages: List[Dict[str, Any]], head_end: int,
|
|
token_budget: int | None = None,
|
|
) -> int:
|
|
"""Walk backward from the end of messages, accumulating tokens until
|
|
the budget is reached. Returns the index where the tail starts.
|
|
|
|
``token_budget`` defaults to ``self.tail_token_budget`` which is
|
|
derived from ``summary_target_ratio * context_length``, so it
|
|
scales automatically with the model's context window.
|
|
|
|
Token budget is the primary criterion. A hard minimum of 3 messages
|
|
is always protected, but the budget is allowed to exceed by up to
|
|
1.5x to avoid cutting inside an oversized message (tool output, file
|
|
read, etc.). If even the minimum 3 messages exceed 1.5x the budget
|
|
the cut is placed right after the head so compression still runs.
|
|
|
|
Never cuts inside a tool_call/result group. Always ensures the most
|
|
recent user message is in the tail (see ``_ensure_last_user_message_in_tail``).
|
|
"""
|
|
if token_budget is None:
|
|
token_budget = self.tail_token_budget
|
|
n = len(messages)
|
|
# Hard minimum: always keep at least 3 messages in the tail
|
|
min_tail = min(3, n - head_end - 1) if n - head_end > 1 else 0
|
|
soft_ceiling = int(token_budget * 1.5)
|
|
accumulated = 0
|
|
cut_idx = n # start from beyond the end
|
|
|
|
for i in range(n - 1, head_end - 1, -1):
|
|
msg = messages[i]
|
|
content = msg.get("content") or ""
|
|
msg_tokens = len(content) // _CHARS_PER_TOKEN + 10 # +10 for role/metadata
|
|
# Include tool call arguments in estimate
|
|
for tc in msg.get("tool_calls") or []:
|
|
if isinstance(tc, dict):
|
|
args = tc.get("function", {}).get("arguments", "")
|
|
msg_tokens += len(args) // _CHARS_PER_TOKEN
|
|
# Stop once we exceed the soft ceiling (unless we haven't hit min_tail yet)
|
|
if accumulated + msg_tokens > soft_ceiling and (n - i) >= min_tail:
|
|
break
|
|
accumulated += msg_tokens
|
|
cut_idx = i
|
|
|
|
# Ensure we protect at least min_tail messages
|
|
fallback_cut = n - min_tail
|
|
if cut_idx > fallback_cut:
|
|
cut_idx = fallback_cut
|
|
|
|
# If the token budget would protect everything (small conversations),
|
|
# force a cut after the head so compression can still remove middle turns.
|
|
if cut_idx <= head_end:
|
|
cut_idx = max(fallback_cut, head_end + 1)
|
|
|
|
# Align to avoid splitting tool groups
|
|
cut_idx = self._align_boundary_backward(messages, cut_idx)
|
|
|
|
# Ensure the most recent user message is always in the tail so the
|
|
# active task is never lost to compression (fixes #10896).
|
|
cut_idx = self._ensure_last_user_message_in_tail(messages, cut_idx, head_end)
|
|
|
|
return max(cut_idx, head_end + 1)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Main compression entry point
|
|
# ------------------------------------------------------------------
|
|
|
|
def compress(self, messages: List[Dict[str, Any]], current_tokens: int = None, focus_topic: str = None) -> List[Dict[str, Any]]:
|
|
"""Compress conversation messages by summarizing middle turns.
|
|
|
|
Algorithm:
|
|
1. Prune old tool results (cheap pre-pass, no LLM call)
|
|
2. Protect head messages (system prompt + first exchange)
|
|
3. Find tail boundary by token budget (~20K tokens of recent context)
|
|
4. Summarize middle turns with structured LLM prompt
|
|
5. On re-compression, iteratively update the previous summary
|
|
|
|
After compression, orphaned tool_call / tool_result pairs are cleaned
|
|
up so the API never receives mismatched IDs.
|
|
|
|
Args:
|
|
focus_topic: Optional focus string for guided compression. When
|
|
provided, the summariser will prioritise preserving information
|
|
related to this topic and be more aggressive about compressing
|
|
everything else. Inspired by Claude Code's ``/compact``.
|
|
"""
|
|
n_messages = len(messages)
|
|
# Only need head + 3 tail messages minimum (token budget decides the real tail size)
|
|
_min_for_compress = self.protect_first_n + 3 + 1
|
|
if n_messages <= _min_for_compress:
|
|
if not self.quiet_mode:
|
|
logger.warning(
|
|
"Cannot compress: only %d messages (need > %d)",
|
|
n_messages, _min_for_compress,
|
|
)
|
|
return messages
|
|
|
|
display_tokens = current_tokens if current_tokens else self.last_prompt_tokens or estimate_messages_tokens_rough(messages)
|
|
|
|
# Phase 1: Prune old tool results (cheap, no LLM call)
|
|
messages, pruned_count = self._prune_old_tool_results(
|
|
messages, protect_tail_count=self.protect_last_n,
|
|
protect_tail_tokens=self.tail_token_budget,
|
|
)
|
|
if pruned_count and not self.quiet_mode:
|
|
logger.info("Pre-compression: pruned %d old tool result(s)", pruned_count)
|
|
|
|
# Phase 2: Determine boundaries
|
|
compress_start = self.protect_first_n
|
|
compress_start = self._align_boundary_forward(messages, compress_start)
|
|
|
|
# Use token-budget tail protection instead of fixed message count
|
|
compress_end = self._find_tail_cut_by_tokens(messages, compress_start)
|
|
|
|
if compress_start >= compress_end:
|
|
return messages
|
|
|
|
turns_to_summarize = messages[compress_start:compress_end]
|
|
|
|
if not self.quiet_mode:
|
|
logger.info(
|
|
"Context compression triggered (%d tokens >= %d threshold)",
|
|
display_tokens,
|
|
self.threshold_tokens,
|
|
)
|
|
logger.info(
|
|
"Model context limit: %d tokens (%.0f%% = %d)",
|
|
self.context_length,
|
|
self.threshold_percent * 100,
|
|
self.threshold_tokens,
|
|
)
|
|
tail_msgs = n_messages - compress_end
|
|
logger.info(
|
|
"Summarizing turns %d-%d (%d turns), protecting %d head + %d tail messages",
|
|
compress_start + 1,
|
|
compress_end,
|
|
len(turns_to_summarize),
|
|
compress_start,
|
|
tail_msgs,
|
|
)
|
|
|
|
# Phase 3: Generate structured summary
|
|
summary = self._generate_summary(turns_to_summarize, focus_topic=focus_topic)
|
|
|
|
# Phase 4: Assemble compressed message list
|
|
compressed = []
|
|
for i in range(compress_start):
|
|
msg = messages[i].copy()
|
|
if i == 0 and msg.get("role") == "system":
|
|
existing = msg.get("content") or ""
|
|
_compression_note = "[Note: Some earlier conversation turns have been compacted into a handoff summary to preserve context space. The current session state may still reflect earlier work, so build on that summary and state rather than re-doing work.]"
|
|
if _compression_note not in existing:
|
|
msg["content"] = existing + "\n\n" + _compression_note
|
|
compressed.append(msg)
|
|
|
|
# If LLM summary failed, insert a static fallback so the model
|
|
# knows context was lost rather than silently dropping everything.
|
|
if not summary:
|
|
if not self.quiet_mode:
|
|
logger.warning("Summary generation failed — inserting static fallback context marker")
|
|
n_dropped = compress_end - compress_start
|
|
summary = (
|
|
f"{SUMMARY_PREFIX}\n"
|
|
f"Summary generation was unavailable. {n_dropped} conversation turns were "
|
|
f"removed to free context space but could not be summarized. The removed "
|
|
f"turns contained earlier work in this session. Continue based on the "
|
|
f"recent messages below and the current state of any files or resources."
|
|
)
|
|
|
|
_merge_summary_into_tail = False
|
|
last_head_role = messages[compress_start - 1].get("role", "user") if compress_start > 0 else "user"
|
|
first_tail_role = messages[compress_end].get("role", "user") if compress_end < n_messages else "user"
|
|
# Pick a role that avoids consecutive same-role with both neighbors.
|
|
# Priority: avoid colliding with head (already committed), then tail.
|
|
if last_head_role in ("assistant", "tool"):
|
|
summary_role = "user"
|
|
else:
|
|
summary_role = "assistant"
|
|
# If the chosen role collides with the tail AND flipping wouldn't
|
|
# collide with the head, flip it.
|
|
if summary_role == first_tail_role:
|
|
flipped = "assistant" if summary_role == "user" else "user"
|
|
if flipped != last_head_role:
|
|
summary_role = flipped
|
|
else:
|
|
# Both roles would create consecutive same-role messages
|
|
# (e.g. head=assistant, tail=user — neither role works).
|
|
# Merge the summary into the first tail message instead
|
|
# of inserting a standalone message that breaks alternation.
|
|
_merge_summary_into_tail = True
|
|
if not _merge_summary_into_tail:
|
|
compressed.append({"role": summary_role, "content": summary})
|
|
|
|
for i in range(compress_end, n_messages):
|
|
msg = messages[i].copy()
|
|
if _merge_summary_into_tail and i == compress_end:
|
|
original = msg.get("content") or ""
|
|
msg["content"] = (
|
|
summary
|
|
+ "\n\n--- END OF CONTEXT SUMMARY — "
|
|
"respond to the message below, not the summary above ---\n\n"
|
|
+ original
|
|
)
|
|
_merge_summary_into_tail = False
|
|
compressed.append(msg)
|
|
|
|
self.compression_count += 1
|
|
|
|
compressed = self._sanitize_tool_pairs(compressed)
|
|
|
|
new_estimate = estimate_messages_tokens_rough(compressed)
|
|
saved_estimate = display_tokens - new_estimate
|
|
|
|
# Anti-thrashing: track compression effectiveness
|
|
savings_pct = (saved_estimate / display_tokens * 100) if display_tokens > 0 else 0
|
|
self._last_compression_savings_pct = savings_pct
|
|
if savings_pct < 10:
|
|
self._ineffective_compression_count += 1
|
|
else:
|
|
self._ineffective_compression_count = 0
|
|
|
|
if not self.quiet_mode:
|
|
logger.info(
|
|
"Compressed: %d -> %d messages (~%d tokens saved, %.0f%%)",
|
|
n_messages,
|
|
len(compressed),
|
|
saved_estimate,
|
|
savings_pct,
|
|
)
|
|
logger.info("Compression #%d complete", self.compression_count)
|
|
|
|
return compressed
|