Implement 3 integration kwargs that were silently dropped

These kwargs are documented on the website but never wired up in the
runtime integrations. Users passing them got a variety of broken
behaviour — none of which raised, so the bugs were invisible.

LangChainMemory
- session_id: previously accepted via **kwargs and ignored. Multiple
  LangChainMemory(agent_id=X, session_id='A') and session_id='B' shared
  a single history (verified live). Now each session_id namespaces
  message storage under a separate key prefix — sessions are isolated.
- return_messages: previously accepted via **kwargs and ignored.
  load_memory_variables always returned a concatenated 'Human: ... /
  AI: ...' string regardless. Now when True, returns a list of
  langchain_core.messages.HumanMessage/AIMessage objects suitable for
  chat models. Defaults to False (prior behaviour preserved).

CrewAIMemory
- crew_name: previously raised TypeError
  ('unexpected keyword argument crew_name') on construction. Now
  accepted; stored as self.crew_name (falls back to crew_id).

Verified: 28/28 tests pass (7 new targeted tests + 15 README examples
in local mode + 6 live framework integrations with real LLM calls and
live MCP stdio). No back-compat regressions — existing callers with no
new kwargs behave identically.

Author: RyjoxTechnologies

synrix_runtime/integrations/crewai_memory.py

@@ -46,8 +46,9 @@ class SynrixCrewMemory:
         finding = crew_memory.get_finding("market_size")
     """
 
-    def __init__(self, crew_id: str, backend=None):
+    def __init__(self, crew_id: str, backend=None, crew_name: Optional[str] = None, **kwargs):
         self.crew_id = crew_id
+        self.crew_name = crew_name or crew_id
 
         if backend is not None:
             from synrix_runtime.integrations._local_adapter import _LocalAgentAdapter

synrix_runtime/integrations/langchain_memory.py

@@ -60,9 +60,12 @@ class SynrixMemory(BaseMemory):
     """
 
     def __init__(self, agent_id: str = "langchain_default", memory_key: str = "history",
+                 session_id: Optional[str] = None, return_messages: bool = False,
                  backend=None, **kwargs):
         self.agent_id = agent_id
         self.memory_key = memory_key
+        self.session_id = session_id
+        self.return_messages = return_messages
         self._memory_variables = [memory_key]
         self._message_count = 0
 
@@ -75,6 +78,12 @@ def __init__(self, agent_id: str = "langchain_default", memory_key: str = "histo
             self._agent = client.agent(agent_id, metadata={"type": "langchain"})
             self.backend = None
 
+    def _messages_prefix(self) -> str:
+        """Key prefix for this session's messages. Isolates sessions when session_id is set."""
+        if self.session_id:
+            return f"langchain:{self.agent_id}:sessions:{self.session_id}:messages:"
+        return f"langchain:{self.agent_id}:messages:"
+
     @property
     def memory_variables(self) -> List[str]:
         return self._memory_variables
@@ -91,7 +100,7 @@ def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         }
 
         self._agent.write(
-            f"langchain:{self.agent_id}:messages:{int(time.time() * 1000000)}",
+            f"{self._messages_prefix()}{int(time.time() * 1000000)}",
             entry,
             tags=["langchain_message", self.agent_id],
         )
@@ -103,8 +112,13 @@ def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         )
 
     def load_memory_variables(self, inputs: Dict[str, Any] = None) -> Dict[str, Any]:
-        """Load conversation history from Octopoda Cloud."""
-        results = self._agent.keys(prefix=f"langchain:{self.agent_id}:messages:", limit=100)
+        """Load conversation history from Octopoda Cloud.
+
+        If return_messages=True was passed at construction, returns a list of
+        LangChain message objects (HumanMessage/AIMessage) suitable for chat models.
+        Otherwise returns a concatenated string.
+        """
+        results = self._agent.keys(prefix=self._messages_prefix(), limit=100)
 
         messages = []
         for r in results:
@@ -114,6 +128,21 @@ def load_memory_variables(self, inputs: Dict[str, Any] = None) -> Dict[str, Any]
 
         messages.sort(key=lambda x: x.get("turn", 0))
 
+        if self.return_messages:
+            try:
+                from langchain_core.messages import HumanMessage, AIMessage
+                msg_objects = []
+                for msg in messages:
+                    inp = msg.get("inputs", {})
+                    out = msg.get("outputs", {})
+                    human_input = inp.get("input", inp.get("human_input", str(inp)))
+                    ai_output = out.get("output", out.get("response", str(out)))
+                    msg_objects.append(HumanMessage(content=human_input))
+                    msg_objects.append(AIMessage(content=ai_output))
+                return {self.memory_key: msg_objects}
+            except ImportError:
+                pass  # langchain_core not installed — fall through to string format
+
         history_parts = []
         for msg in messages:
             inp = msg.get("inputs", {})
@@ -130,8 +159,8 @@ def clear(self) -> None:
         pass
 
     def get_full_history(self) -> List[dict]:
-        """Get complete conversation history."""
-        results = self._agent.keys(prefix=f"langchain:{self.agent_id}:messages:", limit=500)
+        """Get complete conversation history (scoped to the current session_id if set)."""
+        results = self._agent.keys(prefix=self._messages_prefix(), limit=500)
         messages = []
         for r in results:
             val = r.get("value", r)