final changes from successful run

- hasattr() returns bool, not None — changed 'is not None' to proper check
- Fixed variable name typo: assistant_msg -> assistant_message
- Trajectory format: use 'in' dict check instead of hasattr on dicts

environments/agent_loop.py

@@ -193,6 +193,10 @@ class HermesAgentLoop:
 
         import time as _time
 
+        prompt_token_ids = None
+        generation_token_ids = None
+        generation_log_probs = None
+
         for turn in range(self.max_turns):
             turn_start = _time.monotonic()
 
@@ -246,6 +250,12 @@ class HermesAgentLoop:
                 )
 
             assistant_msg = response.choices[0].message
+            if hasattr(assistant_msg, "prompt_token_ids"):
+                prompt_token_ids = assistant_msg.prompt_token_ids
+            if hasattr(assistant_msg, "generation_token_ids"):
+                generation_token_ids = assistant_msg.generation_token_ids
+            if hasattr(assistant_msg, "generation_log_probs"):
+                generation_log_probs = assistant_msg.generation_log_probs
 
             # Extract reasoning content from the response (all provider formats)
             reasoning = _extract_reasoning_from_message(assistant_msg)
@@ -308,7 +318,10 @@ class HermesAgentLoop:
                     "content": assistant_msg.content or "",
                     "tool_calls": [_tc_to_dict(tc) for tc in assistant_msg.tool_calls],
                 }
-
+                if prompt_token_ids is not None:
+                    msg_dict["prompt_token_ids"] = prompt_token_ids
+                    msg_dict["generation_token_ids"] = generation_token_ids
+                    msg_dict["generation_log_probs"] = generation_log_probs
                 # Preserve reasoning_content for multi-turn chat template handling
                 # (e.g., Kimi-K2's template renders <think> blocks differently
                 # for history vs. the latest turn based on this field)
@@ -471,6 +484,10 @@ class HermesAgentLoop:
                 }
                 if reasoning:
                     msg_dict["reasoning_content"] = reasoning
+                if prompt_token_ids is not None:
+                    msg_dict["prompt_token_ids"] = prompt_token_ids
+                    msg_dict["generation_token_ids"] = generation_token_ids
+                    msg_dict["generation_log_probs"] = generation_log_probs
                 messages.append(msg_dict)
 
                 turn_elapsed = _time.monotonic() - turn_start

environments/check_gym_compat.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""
+Quick compatibility check: connect to a local OpenAI-compatible endpoint
+and run a single agent turn via HermesAgentLoop with all standard tools.
+
+Usage:
+    python environments/check_gym_compat.py                    # auto-detect model
+    python environments/check_gym_compat.py --model my-model   # explicit model
+    python environments/check_gym_compat.py --base-url http://... --model ...
+"""
+
+import asyncio
+import argparse
+import json
+import logging
+import sys
+from pathlib import Path
+
+# Ensure repo root is on sys.path when run as a standalone script
+_repo_root = str(Path(__file__).resolve().parent.parent)
+if _repo_root not in sys.path:
+    sys.path.insert(0, _repo_root)
+
+import requests
+from openai import AsyncOpenAI
+
+from environments.agent_loop import HermesAgentLoop, AgentResult
+from model_tools import get_tool_definitions
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Thin server wrapper — gives HermesAgentLoop the chat_completion() it wants
+# ---------------------------------------------------------------------------
+
+class OpenAIServer:
+    """Minimal async server wrapping an OpenAI-compatible endpoint."""
+
+    def __init__(self, base_url: str, model: str, api_key: str = "dummy"):
+        self.model = model
+        self.client = AsyncOpenAI(base_url=base_url, api_key=api_key)
+
+    async def chat_completion(self, **kwargs):
+        kwargs.setdefault("model", self.model)
+        return await self.client.chat.completions.create(**kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def detect_model(base_url: str) -> str:
+    try:
+        resp = requests.get(f"{base_url}/models", timeout=10)
+        resp.raise_for_status()
+        models = resp.json().get("data", [])
+        if not models:
+            print("WARNING: /v1/models returned no models")
+            return "default"
+        model_id = models[0]["id"]
+        print(f"Auto-detected model: {model_id}")
+        return model_id
+    except Exception as e:
+        print(f"Could not auto-detect model ({e}), falling back to 'default'")
+        return "default"
+
+
+async def run_check(base_url: str, model: str, message: str) -> AgentResult:
+    server = OpenAIServer(base_url=base_url, model=model)
+
+    # Get all default hermes tools
+    tool_schemas = get_tool_definitions(quiet_mode=False)
+    valid_names = {t["function"]["name"] for t in tool_schemas}
+
+    agent = HermesAgentLoop(
+        server=server,
+        tool_schemas=tool_schemas,
+        valid_tool_names=valid_names,
+        max_turns=5,
+    )
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant with access to tools."},
+        {"role": "user", "content": message},
+    ]
+
+    return await agent.run(messages)
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="Check gym endpoint compatibility")
+    parser.add_argument("--base-url", default="http://127.0.0.1:11746/v1")
+    parser.add_argument("--model", default=None)
+    parser.add_argument("--message", default="Hello! What's the current directory you're in?")
+    args = parser.parse_args()
+
+    model = args.model or detect_model(args.base_url)
+
+    print(f"\n{'='*60}")
+    print(f"Endpoint:  {args.base_url}")
+    print(f"Model:     {model}")
+    print(f"Message:   {args.message}")
+    print(f"{'='*60}\n")
+
+    try:
+        result = asyncio.run(run_check(args.base_url, model, args.message))
+
+        print(f"\n{'='*60}")
+        print(f"Turns used:         {result.turns_used}")
+        print(f"Finished naturally: {result.finished_naturally}")
+        print(f"Tool errors:        {len(result.tool_errors)}")
+        print(f"{'='*60}")
+
+        # Print the final assistant response
+        for msg in reversed(result.messages):
+            # if msg.get("role") == "assistant" and msg.get("content"):
+            #     print("\nRESPONSE:")
+            #     print(msg["content"])
+            #     break
+            print(msg)
+
+        if result.tool_errors:
+            print("\nTOOL ERRORS:")
+            for err in result.tool_errors:
+                print(f"  turn {err.turn}: {err.tool_name} — {err.error}")
+
+        status = "✅ passed" if result.finished_naturally else "⚠️  hit max turns"
+        print(f"\nGym compatibility check {status}")
+
+    except Exception as e:
+        print(f"\n❌ Gym compatibility check failed: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

run_agent.py

@@ -516,6 +516,9 @@ class AIAgent:
         checkpoint_max_snapshots: int = 50,
         pass_session_id: bool = False,
         persist_session: bool = True,
+        use_streaming: bool = True,
+        temperature: float = None,
+        insert_reasoning: bool = True,
     ):
         """
         Initialize the AI Agent.
@@ -559,11 +562,17 @@ class AIAgent:
                 When provided and Honcho is enabled in config, enables persistent cross-session user modeling.
             honcho_manager: Optional shared HonchoSessionManager owned by the caller.
             honcho_config: Optional HonchoClientConfig corresponding to honcho_manager.
+            use_streaming (bool): Whether to use streaming for API calls (default: True)
+            temperature (float): Temperature for model responses (optional, uses model default if not set)
+            insert_reasoning (bool): Whether to insert reasoning into the API response (default: True)
         """
         _install_safe_stdio()
 
         self.model = model
         self.max_iterations = max_iterations
+        self.use_streaming = use_streaming
+        self.temperature = temperature
+        self.insert_reasoning = insert_reasoning
         # Shared iteration budget — parent creates, children inherit.
         # Consumed by every LLM turn across parent + all subagents.
         self.iteration_budget = iteration_budget or IterationBudget(max_iterations)
@@ -1917,6 +1926,10 @@ class AIAgent:
                         "value": content.rstrip()
                     })
 
+                    if "prompt_token_ids" in msg:
+                        trajectory[-1]["prompt_token_ids"] = msg["prompt_token_ids"]
+                        trajectory[-1]["generation_token_ids"] = msg["generation_token_ids"]
+                        trajectory[-1]["generation_log_probs"] = msg["generation_log_probs"]
                     # Collect all subsequent tool responses
                     tool_responses = []
                     j = i + 1
@@ -1978,6 +1991,10 @@ class AIAgent:
                         "from": "gpt",
                         "value": content.strip()
                     })
+                    if "prompt_token_ids" in msg:
+                        trajectory[-1]["prompt_token_ids"] = msg["prompt_token_ids"]
+                        trajectory[-1]["generation_token_ids"] = msg["generation_token_ids"]
+                        trajectory[-1]["generation_log_probs"] = msg["generation_log_probs"]
             
             elif msg["role"] == "user":
                 trajectory.append({
@@ -5055,6 +5072,8 @@ class AIAgent:
             "messages": sanitized_messages,
             "timeout": float(os.getenv("HERMES_API_TIMEOUT", 1800.0)),
         }
+        if self.temperature is not None:
+            api_kwargs["temperature"] = self.temperature
         if self.tools:
             api_kwargs["tools"] = self.tools
 
@@ -5230,6 +5249,11 @@ class AIAgent:
             "finish_reason": finish_reason,
         }
         
+        if hasattr(assistant_message, "prompt_token_ids") and assistant_message.prompt_token_ids is not None:
+            msg["prompt_token_ids"] = assistant_message.prompt_token_ids
+            msg["generation_token_ids"] = assistant_message.generation_token_ids
+            msg["generation_log_probs"] = assistant_message.generation_log_probs
+
         if hasattr(assistant_message, 'reasoning_details') and assistant_message.reasoning_details:
             # Pass reasoning_details back unmodified so providers (OpenRouter,
             # Anthropic, OpenAI) can maintain reasoning continuity across turns.
@@ -5377,7 +5401,7 @@ class AIAgent:
                 api_msg = msg.copy()
                 if msg.get("role") == "assistant":
                     reasoning = msg.get("reasoning")
-                    if reasoning:
+                    if reasoning and self.insert_reasoning:
                         api_msg["reasoning_content"] = reasoning
                 api_msg.pop("reasoning", None)
                 api_msg.pop("finish_reason", None)
@@ -6374,6 +6398,7 @@ class AIAgent:
         stream_callback: Optional[callable] = None,
         persist_user_message: Optional[str] = None,
         sync_honcho: bool = True,
+        dont_review: bool = False,
     ) -> Dict[str, Any]:
         """
         Run a complete conversation with tool calling until completion.
@@ -6391,7 +6416,7 @@ class AIAgent:
                 synthetic prefixes.
             sync_honcho: When False, skip writing the final synthetic turn back
                 to Honcho or queuing follow-up prefetch work.
-
+            dont_review: When True, skip reviewing memory and skills.
         Returns:
             Dict: Complete conversation result with final response and message history
         """
@@ -6728,7 +6753,7 @@ class AIAgent:
                 # This ensures multi-turn reasoning context is preserved
                 if msg.get("role") == "assistant":
                     reasoning_text = msg.get("reasoning")
-                    if reasoning_text:
+                    if reasoning_text and self.insert_reasoning:
                         # Add reasoning_content for API compatibility (Moonshot AI, Novita, OpenRouter)
                         api_msg["reasoning_content"] = reasoning_text
 
@@ -6856,7 +6881,7 @@ class AIAgent:
                         if self.thinking_callback:
                             self.thinking_callback("")
 
-                    _use_streaming = True
+                    _use_streaming = self.use_streaming
                     if not self._has_stream_consumers():
                         # No display/TTS consumer. Still prefer streaming for
                         # health checking, but skip for Mock clients in tests
@@ -7034,6 +7059,15 @@ class AIAgent:
                         finish_reason = response.choices[0].finish_reason
 
                     if finish_reason == "length":
+                        if not self.compression_enabled:
+                            return {
+                                "final_response": None,
+                                "messages": messages,
+                                "api_calls": api_call_count,
+                                "completed": False,
+                                "partial": True,
+                                "error": "Response truncated due to output length limit",
+                            }
                         self._vprint(f"{self.log_prefix}⚠️  Response truncated (finish_reason='length') - model hit max output tokens", force=True)
 
                         # ── Detect thinking-budget exhaustion ──────────────
@@ -7433,7 +7467,7 @@ class AIAgent:
                         or 'error code: 413' in error_msg
                     )
 
-                    if is_payload_too_large:
+                    if is_payload_too_large and self.compression_enabled:
                         compression_attempts += 1
                         if compression_attempts > max_compression_attempts:
                             self._vprint(f"{self.log_prefix}❌ Max compression attempts ({max_compression_attempts}) reached for payload-too-large error.", force=True)
@@ -7448,23 +7482,7 @@ class AIAgent:
                                 "partial": True
                             }
                         self._emit_status(f"⚠️  Request payload too large (413) — compression attempt {compression_attempts}/{max_compression_attempts}...")
-
+                    elif is_payload_too_large and not self.compression_enabled:
-                        original_len = len(messages)
-                        messages, active_system_prompt = self._compress_context(
-                            messages, system_message, approx_tokens=approx_tokens,
-                            task_id=effective_task_id,
-                        )
-
-                        if len(messages) < original_len:
-                            self._emit_status(f"🗜️ Compressed {original_len} → {len(messages)} messages, retrying...")
-                            time.sleep(2)  # Brief pause between compression retries
-                            restart_with_compressed_messages = True
-                            break
-                        else:
-                            self._vprint(f"{self.log_prefix}❌ Payload too large and cannot compress further.", force=True)
-                            self._vprint(f"{self.log_prefix}   💡 Try /new to start a fresh conversation, or /compress to retry compression.", force=True)
-                            logging.error(f"{self.log_prefix}413 payload too large. Cannot compress further.")
-                            self._persist_session(messages, conversation_history)
                         return {
                             "messages": messages,
                             "completed": False,
@@ -7507,7 +7525,7 @@ class AIAgent:
                                 force=True,
                             )
                     
-                    if is_context_length_error:
+                    if is_context_length_error and self.compression_enabled:
                         compressor = self.context_compressor
                         old_ctx = compressor.context_length
 
@@ -7576,6 +7594,14 @@ class AIAgent:
                                 "error": f"Context length exceeded ({approx_tokens:,} tokens). Cannot compress further.",
                                 "partial": True
                             }
+                    elif is_context_length_error and not self.compression_enabled:
+                        return {
+                            "messages": messages,
+                            "completed": False,
+                            "api_calls": api_call_count,
+                            "error": f"Context length exceeded ({approx_tokens:,} tokens). Cannot compress further.",
+                            "partial": True
+                        }
 
                     # Check for non-retryable client errors (4xx HTTP status codes).
                     # These indicate a problem with the request itself (bad model ID,
@@ -7789,6 +7815,9 @@ class AIAgent:
                 break
 
             try:
+                prompt_token_ids = None
+                generation_token_ids = None
+                generation_log_probs = None
                 if self.api_mode == "codex_responses":
                     assistant_message, finish_reason = self._normalize_codex_response(response)
                 elif self.api_mode == "anthropic_messages":
@@ -7798,6 +7827,12 @@ class AIAgent:
                     )
                 else:
                     assistant_message = response.choices[0].message
+                    if hasattr(assistant_message, "prompt_token_ids") and assistant_message.prompt_token_ids is not None:
+                        prompt_token_ids = assistant_message.prompt_token_ids
+                    if hasattr(assistant_message, "generation_token_ids") and assistant_message.generation_token_ids is not None:
+                        generation_token_ids = assistant_message.generation_token_ids
+                    if hasattr(assistant_message, "generation_log_probs") and assistant_message.generation_log_probs is not None:
+                        generation_log_probs = assistant_message.generation_log_probs
                 
                 # Normalize content to string — some OpenAI-compatible servers
                 # (llama-server, etc.) return content as a dict or list instead
@@ -8240,28 +8275,34 @@ class AIAgent:
                                 self._response_was_previewed = True
                                 break
                             
-                            # No fallback -- if reasoning_text exists, the model put its
+                            # No fallback -- the model kept emitting <think>...</think>
-                            # entire response inside <think> tags; use that as the content.
+                            # with empty content for 3 retries.  Preserve token IDs from
+                            # the last API attempt (reasoning-only generation) so RL can
+                            # train on this trajectory instead of dropping it entirely.
+                            # Using _build_assistant_message ensures prompt_token_ids,
+                            # generation_token_ids, and generation_log_probs are attached
+                            # when present on the assistant_message object.
                             if reasoning_text:
                                 self._vprint(f"{self.log_prefix}Using reasoning as response content (model wrapped entire response in think tags).", force=True)
                                 final_response = reasoning_text
-                                empty_msg = {
-                                    "role": "assistant",
-                                    "content": final_response,
-                                    "reasoning": reasoning_text,
-                                    "finish_reason": finish_reason,
-                                }
-                                messages.append(empty_msg)
-                                break
 
-                            # Truly empty -- no reasoning and no content
+                            # Preserve token IDs from the last API attempt by building the
-                            empty_msg = {
+                            # assistant message from the live API response object.  This
+                            # avoids the all-empty-output-items ValueError in NeMo RL's
+                            # nemo_gym postprocessor when every turn was reasoning-only.
+                            try:
+                                _last_msg = self._build_assistant_message(assistant_message, finish_reason)
+                                messages.append(_last_msg)
+                            except Exception:
+                                # If assistant_message is out of scope or _build fails,
+                                # fall back to a message without token IDs (matches
+                                # original behavior).
+                                messages.append({
                                     "role": "assistant",
                                     "content": final_response,
                                     "reasoning": reasoning_text,
                                     "finish_reason": finish_reason,
-                            }
+                                })
-                            messages.append(empty_msg)
 
                             self._cleanup_task_resources(effective_task_id)
                             self._persist_session(messages, conversation_history)
@@ -8471,7 +8512,9 @@ class AIAgent:
                 and "skill_manage" in self.valid_tool_names):
             _should_review_skills = True
             self._iters_since_skill = 0
-
+        if dont_review:
+            _should_review_memory = False
+            _should_review_skills = False
         # Background memory/skill review — runs AFTER the response is delivered
         # so it never competes with the user's task for model attention.
         if final_response and not interrupted and (_should_review_memory or _should_review_skills):