Fixes to deduplication, publish to GHCR, prompt agent example

Author: abrookins

.github/workflows/python-tests.yml

@@ -81,11 +81,12 @@ jobs:
     - name: Set up Docker Buildx
       uses: docker/setup-buildx-action@v3
 
-    - name: Log in to Docker Hub
+    - name: Log in to GitHub Container Registry
       uses: docker/login-action@v3
       with:
-        username: ${{ secrets.DOCKER_USERNAME }}
-        password: ${{ secrets.DOCKER_TOKEN }}
+        registry: ghcr.io
+        username: ${{ github.actor }}
+        password: ${{ secrets.GITHUB_TOKEN }}
 
     - name: Extract version from __init__.py
       id: version
@@ -102,7 +103,7 @@ jobs:
         platforms: linux/amd64,linux/arm64
         push: true
         tags: |
-          andrewbrookins510/agent-memory-server:latest
-          andrewbrookins510/agent-memory-server:${{ steps.version.outputs.version }}
+          ghcr.io/${{ github.repository }}:latest
+          ghcr.io/${{ github.repository }}:${{ steps.version.outputs.version }}
         cache-from: type=gha
         cache-to: type=gha,mode=max

agent-memory-client/agent_memory_client/client.py

@@ -676,7 +676,7 @@ async def search_long_term_memory(
 
         try:
             response = await self._client.post(
-                "/v1/memory/search",
+                "/v1/long-term-memory/search",
                 json=payload,
             )
             response.raise_for_status()
@@ -2114,14 +2114,17 @@ async def append_messages_to_working_memory(
         final_messages = existing_messages + converted_messages
 
         # Create updated working memory
-        working_memory = WorkingMemory(
-            session_id=session_id,
-            namespace=namespace or self.config.default_namespace,
-            messages=final_messages,
-            memories=existing_memory.memories if existing_memory else [],
-            data=existing_memory.data if existing_memory else {},
-            context=existing_memory.context if existing_memory else None,
-            user_id=existing_memory.user_id if existing_memory else None,
+        working_memory = (
+            existing_memory.model_copy(
+                update={"messages": final_messages},
+            )
+            if existing_memory
+            else WorkingMemory(
+                session_id=session_id,
+                namespace=namespace or self.config.default_namespace,
+                messages=final_messages,
+                user_id=user_id or None,
+            )
         )
 
         return await self.put_working_memory(
@@ -2216,7 +2219,6 @@ async def memory_prompt(
             payload["long_term_search"] = long_term_search
 
         try:
-            print("Payload: ", payload)
             response = await self._client.post(
                 "/v1/memory/prompt",
                 json=payload,

agent-memory-client/agent_memory_client/models.py

@@ -61,6 +61,10 @@ class MemoryMessage(BaseModel):
         default=None,
         description="Server-assigned timestamp when message was persisted to long-term storage",
     )
+    discrete_memory_extracted: Literal["t", "f"] = Field(
+        default="f",
+        description="Whether memory extraction has run for this message",
+    )
 
 
 class MemoryRecord(BaseModel):

agent_memory_server/api.py

@@ -7,6 +7,7 @@
 from agent_memory_server.auth import UserInfo, get_current_user
 from agent_memory_server.config import settings
 from agent_memory_server.dependencies import get_background_tasks
+from agent_memory_server.filters import SessionId, UserId
 from agent_memory_server.llms import get_model_client, get_model_config
 from agent_memory_server.logging import get_logger
 from agent_memory_server.models import (
@@ -234,7 +235,6 @@ async def get_working_memory(
     """
     redis = await get_redis_conn()
 
-    # Get unified working memory
     working_mem = await working_memory.get_working_memory(
         session_id=session_id,
         namespace=namespace,
@@ -243,7 +243,7 @@ async def get_working_memory(
     )
 
     if not working_mem:
-        # Return empty working memory if none exists
+        # Create empty working memory if none exists
         working_mem = WorkingMemory(
             messages=[],
             memories=[],
@@ -267,6 +267,8 @@ async def get_working_memory(
                     break
             working_mem.messages = truncated_messages
 
+    logger.debug(f"Working mem: {working_mem}")
+
     return working_mem
 
 
@@ -314,6 +316,14 @@ async def put_working_memory(
                 detail="All memory records in working memory must have an ID",
             )
 
+    # Validate that all messages have non-empty content
+    for msg in memory.messages:
+        if not msg.content or not msg.content.strip():
+            raise HTTPException(
+                status_code=400,
+                detail=f"Message content cannot be empty (message ID: {msg.id})",
+            )
+
     # Handle summarization if needed (before storing) - now token-based
     updated_memory = memory
     if memory.messages:
@@ -333,8 +343,9 @@ async def put_working_memory(
         # Promote structured memories from working memory to long-term storage
         await background_tasks.add_task(
             long_term_memory.promote_working_memory_to_long_term,
-            session_id,
-            updated_memory.namespace,
+            session_id=session_id,
+            user_id=updated_memory.user_id,
+            namespace=updated_memory.namespace,
         )
 
     return updated_memory
@@ -431,7 +442,7 @@ async def search_long_term_memory(
     # Extract filter objects from the payload
     filters = payload.get_filters()
 
-    print("Long-term search filters: ", filters)
+    logger.debug(f"Long-term search filters: {filters}")
 
     kwargs = {
         "distance_threshold": payload.distance_threshold,
@@ -440,10 +451,10 @@ async def search_long_term_memory(
         **filters,
     }
 
-    print("Kwargs: ", kwargs)
-
     kwargs["text"] = payload.text or ""
 
+    logger.debug(f"Long-term search kwargs: {kwargs}")
+
     # Pass text and filter objects to the search function (no redis needed for vectorstore adapter)
     return await long_term_memory.search_long_term_memories(**kwargs)
 
@@ -466,53 +477,6 @@ async def delete_long_term_memory(
     return AckResponse(status=f"ok, deleted {count} memories")
 
 
-@router.post("/v1/memory/search", response_model=MemoryRecordResultsResponse)
-async def search_memory(
-    payload: SearchRequest,
-    current_user: UserInfo = Depends(get_current_user),
-):
-    """
-    Run a search across all memory types (working memory and long-term memory).
-
-    This endpoint searches both working memory (ephemeral, session-scoped) and
-    long-term memory (persistent, indexed) to provide comprehensive results.
-
-    For working memory:
-    - Uses simple text matching
-    - Searches across all sessions (unless session_id filter is provided)
-    - Returns memories that haven't been promoted to long-term storage
-
-    For long-term memory:
-    - Uses semantic vector search
-    - Includes promoted memories from working memory
-    - Supports advanced filtering by topics, entities, etc.
-
-    Args:
-        payload: Search payload with filter objects for precise queries
-
-    Returns:
-        Search results from both memory types, sorted by relevance
-    """
-    redis = await get_redis_conn()
-
-    # Extract filter objects from the payload
-    filters = payload.get_filters()
-
-    kwargs = {
-        "redis": redis,
-        "distance_threshold": payload.distance_threshold,
-        "limit": payload.limit,
-        "offset": payload.offset,
-        **filters,
-    }
-
-    if payload.text:
-        kwargs["text"] = payload.text
-
-    # Use the search function
-    return await long_term_memory.search_memories(**kwargs)
-
-
 @router.post("/v1/memory/prompt", response_model=MemoryPromptResponse)
 async def memory_prompt(
     params: MemoryPromptRequest,
@@ -546,7 +510,7 @@ async def memory_prompt(
     redis = await get_redis_conn()
     _messages = []
 
-    print("Received params: ", params)
+    logger.debug(f"Memory prompt params: {params}")
 
     if params.session:
         # Use token limit for memory prompt, fallback to message count for backward compatibility
@@ -569,6 +533,8 @@ async def memory_prompt(
             redis_client=redis,
         )
 
+        logger.debug(f"Found working memory: {working_mem}")
+
         if working_mem:
             if working_mem.context:
                 # TODO: Weird to use MCP types here?
@@ -580,7 +546,7 @@ async def memory_prompt(
                         ),
                     )
                 )
-            # Apply token-based or message-based truncation
+            # Apply token-based truncation if model info is provided
             if params.session.model_name or params.session.context_window_max:
                 # Token-based truncation
                 if (
@@ -598,39 +564,55 @@ async def memory_prompt(
                             break
                 else:
                     recent_messages = working_mem.messages
+
+                for msg in recent_messages:
+                    if msg.role == "user":
+                        msg_class = base.UserMessage
+                    else:
+                        msg_class = base.AssistantMessage
+                    _messages.append(
+                        msg_class(
+                            content=TextContent(type="text", text=msg.content),
+                        )
+                    )
             else:
-                # Message-based truncation (backward compatibility)
-                recent_messages = (
-                    working_mem.messages[-effective_window_size:]
-                    if len(working_mem.messages) > effective_window_size
-                    else working_mem.messages
-                )
-            for msg in recent_messages:
-                if msg.role == "user":
-                    msg_class = base.UserMessage
-                else:
-                    msg_class = base.AssistantMessage
-                _messages.append(
-                    msg_class(
-                        content=TextContent(type="text", text=msg.content),
+                # No token-based truncation - use all messages
+                for msg in working_mem.messages:
+                    if msg.role == "user":
+                        msg_class = base.UserMessage
+                    else:
+                        msg_class = base.AssistantMessage
+                    _messages.append(
+                        msg_class(
+                            content=TextContent(type="text", text=msg.content),
+                        )
                     )
-                )
 
     if params.long_term_search:
-        # TODO: Exclude session messages if we already included them from session memory
-
-        # If no text is provided in long_term_search, use the user's query
-        if not params.long_term_search.text:
-            # Create a new SearchRequest with the query as text
-            search_payload = params.long_term_search.model_copy()
-            search_payload.text = params.query
+        logger.debug(
+            f"[memory_prompt] Long-term search args: {params.long_term_search}"
+        )
+        if isinstance(params.long_term_search, bool):
+            search_kwargs = {}
+            if params.session:
+                # Exclude memories from the current session because we already included them
+                search_kwargs["session_id"] = SessionId(ne=params.session.session_id)
+            if params.session and params.session.user_id:
+                search_kwargs["user_id"] = UserId(eq=params.session.user_id)
+            search_payload = SearchRequest(**search_kwargs, limit=20, offset=0)
         else:
-            search_payload = params.long_term_search
+            search_payload = params.long_term_search.model_copy()
+            # Merge session user_id into the search request if not already specified
+            if params.session and params.session.user_id and not search_payload.user_id:
+                search_payload.user_id = UserId(eq=params.session.user_id)
 
+        logger.debug(f"[memory_prompt] Search payload: {search_payload}")
         long_term_memories = await search_long_term_memory(
             search_payload,
         )
 
+        logger.debug(f"[memory_prompt] Long-term memories: {long_term_memories}")
+
         if long_term_memories.total > 0:
             long_term_memories_text = "\n".join(
                 [f"- {m.text}" for m in long_term_memories.memories]

agent_memory_server/config.py

@@ -87,6 +87,7 @@ class Settings(BaseSettings):
     # Used for extracting entities from text
     ner_model: str = "dbmdz/bert-large-cased-finetuned-conll03-english"
     enable_ner: bool = True
+    index_all_messages_in_long_term_memory: bool = False
 
     # RedisVL Settings
     # TODO: Adapt to vector store settings

agent_memory_server/filters.py

@@ -245,7 +245,7 @@ class MemoryHash(TagFilter):
 
 
 class Id(TagFilter):
-    field: str = "id_"
+    field: str = "id"
 
 
 class DiscreteMemoryExtracted(TagFilter):