fix: use ManagedServer for vLLM in TBLite eval + local_vllm config

TBLite eval was bypassing ManagedServer and calling ServerManager
directly, which uses /v1/chat/completions — not available on the
atropos vllm_api_server (/generate only).

Now uses _use_managed_server() to detect vLLM/SGLang backends and
route through ManagedServer (Phase 2) with proper tool_parser and
/generate endpoint. Falls back to Phase 1 for OpenAI endpoints.

Also adds local_vllm.yaml config for running against a local vLLM
server with Docker sandboxes.

environments/benchmarks/tblite/local_vllm.yaml

@@ -0,0 +1,39 @@
+# OpenThoughts-TBLite Evaluation -- Local vLLM Backend
+#
+# Runs against a local vLLM server with Docker sandboxes.
+#
+# Start the vLLM server from the atropos directory:
+#   python -m example_trainer.vllm_api_server \
+#       --model Qwen/Qwen3-4B-Thinking-2507 \
+#       --port 9001 \
+#       --gpu-memory-utilization 0.8 \
+#       --max-model-len=32000
+#
+# Then run:
+#   python environments/benchmarks/tblite/tblite_env.py evaluate \
+#       --config environments/benchmarks/tblite/local_vllm.yaml
+
+env:
+  enabled_toolsets: ["terminal", "file"]
+  max_agent_turns: 60
+  max_token_length: 16000
+  agent_temperature: 0.6
+  terminal_backend: "docker"
+  terminal_timeout: 300
+  tool_pool_size: 16
+  dataset_name: "NousResearch/openthoughts-tblite"
+  test_timeout: 600
+  task_timeout: 1200
+  eval_concurrency: 8
+  tool_call_parser: "hermes"
+  tokenizer_name: "Qwen/Qwen3-4B-Thinking-2507"
+  use_wandb: false
+  wandb_name: "tblite-qwen3-4b-thinking"
+  ensure_scores_are_not_same: false
+  data_dir_to_save_evals: "environments/benchmarks/evals/tblite-qwen3-4b-local"
+
+openai:
+  base_url: "http://localhost:9001"
+  model_name: "Qwen/Qwen3-4B-Thinking-2507"
+  server_type: "vllm"
+  health_check: false

environments/benchmarks/terminalbench_2/terminalbench2_env.py

@@ -468,17 +468,37 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
             messages.append({"role": "user", "content": self.format_prompt(eval_item)})
 
             # --- 4. Run agent loop ---
-            agent = HermesAgentLoop(
-                server=self.server,
-                tool_schemas=tools,
-                valid_tool_names=valid_names,
-                max_turns=self.config.max_agent_turns,
-                task_id=task_id,
-                temperature=self.config.agent_temperature,
-                max_tokens=self.c...gth,
-                extra_body=self.config.extra_body,
-            )
-            result = await agent.run(messages)
+            # Use ManagedServer (Phase 2) for vLLM/SGLang backends to get
+            # token-level tracking via /generate. Falls back to direct
+            # ServerManager (Phase 1) for OpenAI endpoints.
+            if self._use_managed_server():
+                async with self.server.managed_server(
+                    tokenizer=self.tokenizer,
+                    preserve_think_blocks=bool(self.config.thinking_mode),
+                ) as managed:
+                    agent = HermesAgentLoop(
+                        server=managed,
+                        tool_schemas=tools,
+                        valid_tool_names=valid_names,
+                        max_turns=self.config.max_agent_turns,
+                        task_id=task_id,
+                        temperature=self.config.agent_temperature,
+                        max_tokens=self.config.max_token_length,
+                        extra_body=self.config.extra_body,
+                    )
+                    result = await agent.run(messages)
+            else:
+                agent = HermesAgentLoop(
+                    server=self.server,
+                    tool_schemas=tools,
+                    valid_tool_names=valid_names,
+                    max_turns=self.config.max_agent_turns,
+                    task_id=task_id,
+                    temperature=self.config.agent_temperature,
+                    max_tokens=self.config.max_token_length,
+                    extra_body=self.config.extra_body,
+                )
+                result = await agent.run(messages)
 
             # --- 5. Verify -- run test suite in the agent's sandbox ---
             # Skip verification if the agent produced no meaningful output