Set healtcheck to run every 60s

pallas/health.py

@@ -34,6 +34,7 @@ import json
 import logging
 import os
 import re
+import time
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any
@@ -66,9 +67,16 @@ _ANTHROPIC_DEFAULT_API = "https://api.anthropic.com/v1"
 _OPENAI_DEFAULT_API = "https://api.openai.com/v1"
 _GENERIC_DEFAULT_API = "http://localhost:11434/v1"
 
-# Populated by validate_llm_providers() at startup, read by get_health()
+# Populated by validate_llm_providers() at startup, refreshed on a TTL by
+# _refresh_llm_status_if_stale() from inside the get_health MCP tool. The
+# TTL re-probe is what makes the agent self-heal when an upstream LLM was
+# briefly unreachable at boot — without it the cached error sticks until
+# the process restarts. See docs/pallas.md § Health System.
 _llm_status: dict[str, dict] = {}
 _active_provider: str = ""
+_llm_status_ts: float = 0.0  # time.monotonic() of last successful probe; 0 = never
+_llm_refresh_lock = asyncio.Lock()
+_LLM_HEALTH_TTL_S = float(os.environ.get("PALLAS_LLM_HEALTH_TTL", "60"))
 
 
 # ── Config loading ───────────────────────────────────────────────────────────
@@ -342,6 +350,92 @@ def _preflight_bedrock(config: dict, secrets: dict, active_model: str) -> dict:
     return {"status": "error", "model": active_model, "message": msg}
 
 
+async def _probe_active_provider(
+    client: httpx.AsyncClient,
+    config: dict,
+    secrets: dict,
+    active_provider: str,
+    active_model: str,
+) -> dict:
+    """Run the preflight probe for a single provider and return its result dict.
+
+    Shared by the startup path (:func:`validate_llm_providers`) and the
+    runtime TTL refresh path (:func:`_refresh_llm_status_if_stale`). Keeps
+    the dispatch matrix from being duplicated.
+    """
+    if active_provider == "anthropic":
+        return await _preflight_anthropic(client, config, secrets, active_model)
+    if active_provider == "openai":
+        return await _preflight_openai(client, config, secrets, active_model)
+    if active_provider == "generic":
+        return await _preflight_generic(client, config, secrets, active_model)
+    if active_provider == "bedrock":
+        return _preflight_bedrock(config, secrets, active_model)
+
+    # Known to fast-agent? Surface that gap explicitly rather than silently
+    # reporting "error" from an empty dict lookup later.
+    try:
+        from fast_agent.llm.provider_types import Provider
+
+        Provider(active_provider)  # raises ValueError if unknown
+        msg = (
+            f"preflight for provider '{active_provider}' is not "
+            "implemented in pallas.health; LLM health will be "
+            "validated on first inference call"
+        )
+        logger.info("%s: %s", active_provider, msg)
+        return {"status": "ok", "model": active_model, "message": msg}
+    except ValueError:
+        msg = f"unknown provider '{active_provider}' in default_model"
+        logger.warning(msg)
+        return {"status": "error", "message": msg}
+
+
+async def _refresh_llm_status_if_stale(timeout: float = 5.0) -> None:
+    """Re-probe the active LLM provider when the cached status is older than
+    ``PALLAS_LLM_HEALTH_TTL`` seconds. No-op on a fresh cache or when no
+    active provider has been resolved yet.
+
+    On any unexpected exception, the previous cache entry is left intact —
+    a real probe result is better signal than our own internal failure.
+    """
+    global _llm_status_ts
+
+    if not _active_provider:
+        return
+    if time.monotonic() - _llm_status_ts < _LLM_HEALTH_TTL_S:
+        return
+
+    async with _llm_refresh_lock:
+        # Re-check under the lock — another coroutine may have refreshed
+        # while we were waiting.
+        if time.monotonic() - _llm_status_ts < _LLM_HEALTH_TTL_S:
+            return
+        try:
+            _load_dotenv()
+            config, secrets = _load_config()
+            default_model = config.get("default_model", "") or ""
+            if "." not in default_model:
+                return
+            active_provider, active_model = default_model.split(".", 1)
+            if active_provider != _active_provider:
+                # default_model was edited under us — defer to the next
+                # full validate_llm_providers() rather than guessing.
+                return
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                result = await _probe_active_provider(
+                    client, config, secrets, active_provider, active_model
+                )
+            _llm_status[_active_provider] = result
+            _llm_status_ts = time.monotonic()
+        except Exception as exc:
+            logger.warning(
+                "llm health re-probe failed (%s); keeping previous cache: %s",
+                _active_provider,
+                exc,
+            )
+
+
 async def validate_llm_providers(timeout: float = 5.0) -> dict[str, dict]:
     """
     Validate the configured LLM provider and populate the module-level cache
@@ -361,7 +455,7 @@ async def validate_llm_providers(timeout: float = 5.0) -> dict[str, dict]:
         {"openai":    {"status": "error", "message": "API request failed (401)"}}
         {"unknown":   {"status": "error", "message": "unknown provider 'foo'"}}
     """
-    global _active_provider
+    global _active_provider, _llm_status_ts
 
     _load_dotenv()
     config, secrets = _load_config()
@@ -377,52 +471,21 @@ async def validate_llm_providers(timeout: float = 5.0) -> dict[str, dict]:
         _llm_status.clear()
         _llm_status.update(results)
         _active_provider = "unknown"
+        _llm_status_ts = time.monotonic()
         return results
 
     active_provider, active_model = default_model.split(".", 1)
 
-    results: dict[str, dict] = {}
     async with httpx.AsyncClient(timeout=timeout) as client:
-        if active_provider == "anthropic":
+        result = await _probe_active_provider(
-            results["anthropic"] = await _preflight_anthropic(
+            client, config, secrets, active_provider, active_model
-                client, config, secrets, active_model
         )
-        elif active_provider == "openai":
+    results: dict[str, dict] = {active_provider: result}
-            results["openai"] = await _preflight_openai(
-                client, config, secrets, active_model
-            )
-        elif active_provider == "generic":
-            results["generic"] = await _preflight_generic(
-                client, config, secrets, active_model
-            )
-        elif active_provider == "bedrock":
-            results["bedrock"] = _preflight_bedrock(config, secrets, active_model)
-        else:
-            # Known to fast-agent? Surface that gap explicitly rather than
-            # silently reporting "error" from an empty dict lookup later.
-            try:
-                from fast_agent.llm.provider_types import Provider
-
-                Provider(active_provider)  # raises ValueError if unknown
-                msg = (
-                    f"preflight for provider '{active_provider}' is not "
-                    "implemented in pallas.health; LLM health will be "
-                    "validated on first inference call"
-                )
-                logger.info("%s: %s", active_provider, msg)
-                results[active_provider] = {
-                    "status": "ok",
-                    "model": active_model,
-                    "message": msg,
-                }
-            except ValueError:
-                msg = f"unknown provider '{active_provider}' in default_model"
-                logger.warning(msg)
-                results[active_provider] = {"status": "error", "message": msg}
 
     _llm_status.clear()
     _llm_status.update(results)
     _active_provider = active_provider
+    _llm_status_ts = time.monotonic()
     return results
 
 
@@ -521,6 +584,7 @@ def register_health_tool(mcp_server, servers: dict[str, dict]) -> None:
         description="Returns the health status of this agent and its downstream dependencies.",
     )
     async def get_health() -> str:
+        await _refresh_llm_status_if_stale()
         result = await check_downstream_health(servers)
         # Include LLM provider status from startup preflight (active provider only)
         if _active_provider: