feat(agents): research loop with budget guards, compaction, depth nudge, finalize

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016DvdRZkyCy9NJpHYDdPqmS

agents/compaction.py

@@ -0,0 +1,65 @@
+"""Rolling message-history compaction — the context safety net.
+
+We let the research history grow into the model's full context window (131k+ on gpt-oss,
+absorbed by oMLX's paged SSD KV cache) and only compact when the prompt actually approaches
+that limit. When it does, we fold the older turns into a running text summary and keep the
+most recent turns verbatim.
+"""
+
+from __future__ import annotations
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+COMPACT_RATIO = 0.9  # compact once the prompt reaches this fraction of the context window
+KEEP_LAST = 20  # verbatim recent turns to retain after compaction
+_CHARS_PER_TOKEN = 4  # rough fallback when the server doesn't report token counts
+
+
+def estimate_tokens(messages: list) -> int:
+    chars = sum(
+        len(m.content if isinstance(m.content, str) else str(m.content)) for m in messages
+    )
+    return chars // _CHARS_PER_TOKEN
+
+
+def should_compact(prompt_tokens: int, context_length: int, ratio: float = COMPACT_RATIO) -> bool:
+    return prompt_tokens >= ratio * context_length
+
+
+def _render(messages: list) -> str:
+    out = []
+    for m in messages:
+        role = m.__class__.__name__.replace("Message", "")
+        content = m.content if isinstance(m.content, str) else str(m.content)
+        out.append(f"{role}: {content[:800]}")
+    return "\n".join(out)
+
+
+def compact(summarizer_llm, messages: list, running_summary: str, keep_last: int = KEEP_LAST):
+    """Return (new_messages, new_summary). Keeps system[0] + a rolling summary + recent turns."""
+    if len(messages) <= keep_last + 1:
+        return messages, running_summary
+    system = messages[0]
+    older = messages[1:-keep_last]
+    recent = messages[-keep_last:]
+    if not older:
+        return messages, running_summary
+
+    prompt = (
+        "Summarize the research progress so far into concise bullet points. Preserve tickers, "
+        "prices, key numbers, findings, and any tentative picks.\n\n"
+        f"Previous summary:\n{running_summary or '(none)'}\n\nNew activity:\n{_render(older)}"
+    )
+    try:
+        resp = summarizer_llm.invoke(
+            [
+                SystemMessage(content="You compress research notes faithfully and briefly."),
+                HumanMessage(content=prompt),
+            ]
+        )
+        summary = resp.content if isinstance(resp.content, str) else str(resp.content)
+    except Exception:
+        summary = running_summary  # on failure, keep the old summary and just trim
+
+    new_messages = [system, SystemMessage(content=f"Research summary so far:\n{summary}")] + recent
+    return new_messages, summary