proposal(environments): SupportsPersistence Protocol for multi-turn sessions

rlm/core/rlm.py

@@ -13,7 +13,7 @@
     RLMIteration,
     RLMMetadata,
 )
-from rlm.environments import BaseEnv, get_environment
+from rlm.environments import BaseEnv, SupportsPersistence, get_environment
 from rlm.logger import RLMLogger, VerbosePrinter
 from rlm.utils.parsing import (
     find_code_blocks,
@@ -51,6 +51,7 @@ def __init__(
         other_backend_kwargs: list[dict[str, Any]] | None = None,
         logger: RLMLogger | None = None,
         verbose: bool = False,
+        persistent: bool = False,
     ):
         """
         Args:
@@ -66,6 +67,7 @@ def __init__(
             other_backend_kwargs: The kwargs to pass to the other client backends (ordered to match other_backends).
             logger: The logger to use for the RLM.
             verbose: Whether to print verbose output in rich to console.
+            persistent: If True, reuse the environment across completion() calls for multi-turn conversations.
         """
         # Store config for spawning per-completion
         self.backend = backend
@@ -84,6 +86,14 @@ def __init__(
         self.logger = logger
         self.verbose = VerbosePrinter(enabled=verbose)
 
+        # Persistence support
+        self.persistent = persistent
+        self._persistent_env: SupportsPersistence | None = None
+
+        # Validate persistence support at initialization
+        if self.persistent:
+            self._validate_persistent_environment_support()
+
         # Log metadata if logger is provided
         if self.logger or verbose:
             metadata = RLMMetadata(
@@ -108,7 +118,9 @@ def __init__(
     def _spawn_completion_context(self, prompt: str | dict[str, Any]):
         """
         Spawn an LM handler and environment for a single completion call.
-        Cleans up both when the context exits.
+
+        When persistent=True, the environment is reused across calls.
+        When persistent=False (default), creates fresh environment each call.
         """
         # Create client and wrap in handler
         client: BaseLM = get_client(self.backend, self.backend_kwargs)
@@ -122,20 +134,32 @@ def _spawn_completion_context(self, prompt: str | dict[str, Any]):
 
         lm_handler.start()
 
-        # Pass handler address to environment so it can make llm_query() calls
-        env_kwargs = self.environment_kwargs.copy()
-        env_kwargs["lm_handler_address"] = (lm_handler.host, lm_handler.port)
-        env_kwargs["context_payload"] = prompt
+        # Environment: reuse if persistent, otherwise create fresh
+        if self.persistent and self._persistent_env is not None:
+            environment = self._persistent_env
+            # Defensive check: ensure environment supports persistence methods
+            if not self._env_supports_persistence(environment):
+                raise RuntimeError(
+                    f"Persistent environment of type '{type(environment).__name__}' does not "
+                    f"implement required methods (update_handler_address, add_context, get_context_count). "
+                    f"This should have been caught at initialization."
+                )
+            environment.update_handler_address((lm_handler.host, lm_handler.port))
+            environment.add_context(prompt)
+        else:
+            env_kwargs = self.environment_kwargs.copy()
+            env_kwargs["lm_handler_address"] = (lm_handler.host, lm_handler.port)
+            env_kwargs["context_payload"] = prompt
+            environment: BaseEnv = get_environment(self.environment_type, env_kwargs)
 
-        # Initialize the environment
-        environment: BaseEnv = get_environment(self.environment_type, env_kwargs)
+            if self.persistent:
+                self._persistent_env = environment
 
         try:
             yield lm_handler, environment
         finally:
-            # Cleanup
             lm_handler.stop()
-            if hasattr(environment, "cleanup"):
+            if not self.persistent and hasattr(environment, "cleanup"):
                 environment.cleanup()
 
     def _setup_prompt(self, prompt: str | dict[str, Any]) -> list[dict[str, Any]]:
@@ -177,7 +201,19 @@ def completion(
 
             for i in range(self.max_iterations):
                 # Current prompt = message history + additional prompt suffix
-                current_prompt = message_history + [build_user_prompt(root_prompt, i)]
+                context_count = (
+                    environment.get_context_count()
+                    if isinstance(environment, SupportsPersistence)
+                    else 1
+                )
+                history_count = (
+                    environment.get_history_count()
+                    if isinstance(environment, SupportsPersistence)
+                    else 0
+                )
+                current_prompt = message_history + [
+                    build_user_prompt(root_prompt, i, context_count, history_count)
+                ]
 
                 iteration: RLMIteration = self._completion_turn(
                     prompt=current_prompt,
@@ -201,6 +237,11 @@ def completion(
                     usage = lm_handler.get_usage_summary()
                     self.verbose.print_final_answer(final_answer)
                     self.verbose.print_summary(i + 1, time_end - time_start, usage.to_dict())
+
+                    # Store message history in persistent environment
+                    if self.persistent and isinstance(environment, SupportsPersistence):
+                        environment.add_history(message_history)
+
                     return RLMChatCompletion(
                         root_model=self.backend_kwargs.get("model_name", "unknown")
                         if self.backend_kwargs
@@ -223,6 +264,11 @@ def completion(
             usage = lm_handler.get_usage_summary()
             self.verbose.print_final_answer(final_answer)
             self.verbose.print_summary(self.max_iterations, time_end - time_start, usage.to_dict())
+
+            # Store message history in persistent environment
+            if self.persistent and isinstance(environment, SupportsPersistence):
+                environment.add_history(message_history)
+
             return RLMChatCompletion(
                 root_model=self.backend_kwargs.get("model_name", "unknown")
                 if self.backend_kwargs
@@ -292,3 +338,47 @@ def _fallback_answer(self, message: str | dict[str, Any]) -> str:
         client: BaseLM = get_client(self.backend, self.backend_kwargs)
         response = client.completion(message)
         return response
+
+    def _validate_persistent_environment_support(self) -> None:
+        """
+        Validate that the configured environment type supports persistent mode.
+
+        Persistent mode requires environments to implement:
+        - update_handler_address(address): Update LM handler address between calls
+        - add_context(payload, index): Add new context for multi-turn conversations
+        - get_context_count(): Return the number of loaded contexts
+
+        Currently only 'local' (LocalREPL) supports these methods.
+
+        Raises:
+            ValueError: If the environment type does not support persistent mode.
+        """
+        # Known environments that support persistence
+        persistent_supported_environments = {"local"}
+
+        if self.environment_type not in persistent_supported_environments:
+            raise ValueError(
+                f"persistent=True is not supported for environment type '{self.environment_type}'. "
+                f"Persistent mode requires environments that implement update_handler_address(), "
+                f"add_context(), and get_context_count(). "
+                f"Supported environments: {sorted(persistent_supported_environments)}"
+            )
+
+    @staticmethod
+    def _env_supports_persistence(env: BaseEnv) -> bool:
+        """Check if an environment instance supports persistent mode methods."""
+        return isinstance(env, SupportsPersistence)
+
+    def close(self) -> None:
+        """Clean up persistent environment. Call when done with multi-turn conversations."""
+        if self._persistent_env is not None:
+            if hasattr(self._persistent_env, "cleanup"):
+                self._persistent_env.cleanup()
+            self._persistent_env = None
+
+    def __enter__(self) -> "RLM":
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb) -> bool:
+        self.close()
+        return False

rlm/environments/__init__.py

@@ -1,8 +1,10 @@
 from typing import Any, Literal
 
-from rlm.environments.base_env import BaseEnv
+from rlm.environments.base_env import BaseEnv, SupportsPersistence
 from rlm.environments.local_repl import LocalREPL
 
+__all__ = ["BaseEnv", "LocalREPL", "SupportsPersistence", "get_environment"]
+
 
 def get_environment(
     environment: Literal["local", "modal", "docker"],

rlm/environments/base_env.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from typing import Any, Protocol, runtime_checkable
 
 from rlm.core.types import REPLResult
 
@@ -9,7 +10,8 @@ class BaseEnv(ABC):
     where isolated environments are on a separate machine from the LM.
     """
 
-    def __init__(self, **kwargs):
+    def __init__(self, persistent: bool = False, **kwargs):
+        self.persistent = persistent
         self.kwargs = kwargs
 
     @abstractmethod
@@ -31,8 +33,8 @@ class IsolatedEnv(BaseEnv, ABC):
     guaranteeing complete isolation from the LM process.
     """
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+    def __init__(self, persistent: bool = False, **kwargs):
+        super().__init__(persistent=persistent, **kwargs)
 
     @abstractmethod
     def setup(self):
@@ -54,8 +56,8 @@ class NonIsolatedEnv(BaseEnv, ABC):
     as a subprocess.
     """
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+    def __init__(self, persistent: bool = False, **kwargs):
+        super().__init__(persistent=persistent, **kwargs)
 
     @abstractmethod
     def setup(self):
@@ -68,3 +70,112 @@ def load_context(self, context_payload: dict | list | str):
     @abstractmethod
     def execute_code(self, code: str) -> REPLResult:
         raise NotImplementedError
+
+
+@runtime_checkable
+class SupportsPersistence(Protocol):
+    """Protocol for environments that support persistent multi-turn sessions.
+
+    CHECKING SUPPORT:
+        Use isinstance(env, SupportsPersistence) to check if an environment
+        supports persistence capabilities.
+
+    IMPLEMENTING THIS PROTOCOL:
+        To add persistence to your environment, implement these 5 methods.
+        See tests/test_local_repl_persistent.py for expected behavior.
+
+    VERSIONING BEHAVIOR:
+        Contexts and histories are versioned with numeric suffixes:
+        - First context  -> context_0, context_1, context_2, ...
+        - First history  -> history_0, history_1, history_2, ...
+
+    ALIASING BEHAVIOR:
+        The unversioned names always point to index 0:
+        - context  -> context_0 (first context)
+        - history  -> history_0 (first history)
+
+    EXAMPLE IMPLEMENTATION:
+        See rlm/environments/local_repl.py for a complete reference.
+
+    TESTS:
+        - Unit tests: tests/test_local_repl_persistent.py
+        - Integration tests: tests/test_multi_turn_integration.py
+
+        Run: uv run pytest tests/test_local_repl_persistent.py -v
+    """
+
+    def update_handler_address(self, address: tuple[str, int]) -> None:
+        """Update the LM handler address for nested LLM calls.
+
+        Called by RLM when the handler address changes between completions.
+        Store the address so llm_query() calls from executed code can reach
+        the LM handler.
+
+        Args:
+            address: (host, port) tuple for the LM handler server.
+        """
+        ...
+
+    def add_context(
+        self, context_payload: dict | list | str, context_index: int | None = None
+    ) -> int:
+        """Add a context payload, making it available as context_N in code.
+
+        Versioning:
+            - context_index=None: auto-increment (0, 1, 2, ...)
+            - context_index=N: use specific index N
+
+        Storage:
+            Must store so executed code can access:
+            - context_0, context_1, etc. (versioned)
+            - context (alias to context_0)
+
+        Args:
+            context_payload: The context data (string, dict, or list).
+            context_index: Optional specific index, or None to auto-increment.
+
+        Returns:
+            The index used (for auto-increment, returns the assigned index).
+        """
+        ...
+
+    def get_context_count(self) -> int:
+        """Return the number of contexts added so far.
+
+        Used by RLM to inform the model how many contexts are available.
+        """
+        ...
+
+    def add_history(
+        self, message_history: list[dict[str, Any]], history_index: int | None = None
+    ) -> int:
+        """Add a message history, making it available as history_N in code.
+
+        Versioning:
+            - history_index=None: auto-increment (0, 1, 2, ...)
+            - history_index=N: use specific index N
+
+        Storage:
+            Must store so executed code can access:
+            - history_0, history_1, etc. (versioned)
+            - history (alias to history_0)
+
+        IMPORTANT: Store a deep copy, not a reference. The caller may
+        modify the list after calling this method.
+
+        Args:
+            message_history: List of message dicts (role, content).
+            history_index: Optional specific index, or None to auto-increment.
+
+        Returns:
+            The index used.
+        """
+        ...
+
+    def get_history_count(self) -> int:
+        """Return the number of histories added so far.
+
+        Used by RLM to inform the model how many conversation histories
+        are available.
+        """
+        ...

rlm/environments/docker_repl.py

@@ -180,9 +180,14 @@ def __init__(
         lm_handler_address: tuple[str, int] | None = None,
         context_payload: dict | list | str | None = None,
         setup_code: str | None = None,
+        persistent: bool = False,
         **kwargs,
     ):
-        super().__init__(**kwargs)
+        if persistent:
+            raise NotImplementedError(
+                "Persistent REPLs are currently not supported for environment: DockerREPL"
+            )
+        super().__init__(persistent=persistent, **kwargs)
 
         self.image = image
         self.lm_handler_address = lm_handler_address
@@ -292,13 +297,13 @@ def execute_code(self, code: str) -> REPLResult:
             )
 
     def cleanup(self):
-        if self.container_id:
+        if hasattr(self, "container_id") and self.container_id:
             subprocess.run(["docker", "stop", self.container_id], capture_output=True)
             self.container_id = None
-        if self.proxy_server:
+        if hasattr(self, "proxy_server") and self.proxy_server:
             self.proxy_server.shutdown()
             self.proxy_server = None
-        if os.path.exists(self.temp_dir):
+        if hasattr(self, "temp_dir") and os.path.exists(self.temp_dir):
             import shutil
 
             shutil.rmtree(self.temp_dir, ignore_errors=True)