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feat(memory): integrate memory system with context engine (#97)

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## Changes

### New Context Type
- Add MEMORY to ContextType enum for agent memory context
- Create MemoryContext class with subtypes (working, episodic, semantic, procedural)
- Factory methods: from_working_memory, from_episodic_memory, from_semantic_memory, from_procedural_memory

### Memory Context Source
- MemoryContextSource service fetches relevant memories for context assembly
- Configurable fetch limits per memory type
- Parallel fetching from all memory types

### Agent Lifecycle Hooks
- AgentLifecycleManager handles spawn, pause, resume, terminate events
- spawn: Initialize working memory with optional initial state
- pause: Create checkpoint of working memory
- resume: Restore from checkpoint
- terminate: Consolidate working memory to episodic memory
- LifecycleHooks for custom extension points

### Context Engine Integration
- Add memory_query parameter to assemble_context()
- Add session_id and agent_type_id for memory scoping
- Memory budget allocation (15% by default)
- set_memory_source() for runtime configuration

### Tests
- 48 new tests for MemoryContext, MemoryContextSource, and lifecycle hooks
- All 108 memory-related tests passing
- mypy and ruff checks passing

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Felipe Cardoso
2026-01-05 03:49:22 +01:00
parent 0b24d4c6cc
commit 30e5c68304
13 changed files with 2509 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
backend/app/services/context/__init__.py
View File

@@ -114,6 +114,8 @@ from .types import (
ContextType,
ConversationContext,
KnowledgeContext,
MemoryContext,
MemorySubtype,
MessageRole,
SystemContext,
TaskComplexity,
@@ -149,6 +151,8 @@ __all__ = [
"FormattingError",
"InvalidContextError",
"KnowledgeContext",
"MemoryContext",
"MemorySubtype",
"MessageRole",
"ModelAdapter",
"OpenAIAdapter",

12
backend/app/services/context/budget/allocator.py
View File

@@ -30,6 +30,7 @@ class TokenBudget:
knowledge: int = 0
conversation: int = 0
tools: int = 0
memory: int = 0 # Agent memory (working, episodic, semantic, procedural)
response_reserve: int = 0
buffer: int = 0
@@ -60,6 +61,7 @@ class TokenBudget:
"knowledge": self.knowledge,
"conversation": self.conversation,
"tool": self.tools,
"memory": self.memory,
}
return allocation_map.get(context_type, 0)
@@ -211,6 +213,7 @@ class TokenBudget:
"knowledge": self.knowledge,
"conversation": self.conversation,
"tools": self.tools,
"memory": self.memory,
"response_reserve": self.response_reserve,
"buffer": self.buffer,
},
@@ -264,9 +267,10 @@ class BudgetAllocator:
total=total_tokens,
system=int(total_tokens * alloc.get("system", 0.05)),
task=int(total_tokens * alloc.get("task", 0.10)),
knowledge=int(total_tokens * alloc.get("knowledge", 0.40)),
conversation=int(total_tokens * alloc.get("conversation", 0.20)),
knowledge=int(total_tokens * alloc.get("knowledge", 0.30)),
conversation=int(total_tokens * alloc.get("conversation", 0.15)),
tools=int(total_tokens * alloc.get("tools", 0.05)),
memory=int(total_tokens * alloc.get("memory", 0.15)),
response_reserve=int(total_tokens * alloc.get("response", 0.15)),
buffer=int(total_tokens * alloc.get("buffer", 0.05)),
)
@@ -317,6 +321,8 @@ class BudgetAllocator:
budget.conversation = max(0, budget.conversation + actual_adjustment)
elif context_type == "tool":
budget.tools = max(0, budget.tools + actual_adjustment)
elif context_type == "memory":
budget.memory = max(0, budget.memory + actual_adjustment)
return budget
@@ -338,7 +344,7 @@ class BudgetAllocator:
Rebalanced budget
"""
if prioritize is None:
prioritize = [ContextType.KNOWLEDGE, ContextType.TASK, ContextType.SYSTEM]
prioritize = [ContextType.KNOWLEDGE, ContextType.MEMORY, ContextType.TASK, ContextType.SYSTEM]
# Calculate unused tokens per type
unused: dict[str, int] = {}

103
backend/app/services/context/engine.py
View File

@@ -7,6 +7,7 @@ Provides a high-level API for assembling optimized context for LLM requests.
import logging
from typing import TYPE_CHECKING, Any
from uuid import UUID
from .assembly import ContextPipeline
from .budget import BudgetAllocator, TokenBudget, TokenCalculator
@@ -20,6 +21,7 @@ from .types import (
BaseContext,
ConversationContext,
KnowledgeContext,
MemoryContext,
MessageRole,
SystemContext,
TaskContext,
@@ -30,6 +32,7 @@ if TYPE_CHECKING:
from redis.asyncio import Redis
from app.services.mcp.client_manager import MCPClientManager
from app.services.memory.integration import MemoryContextSource
logger = logging.getLogger(__name__)
@@ -64,6 +67,7 @@ class ContextEngine:
mcp_manager: "MCPClientManager | None" = None,
redis: "Redis | None" = None,
settings: ContextSettings | None = None,
memory_source: "MemoryContextSource | None" = None,
) -> None:
"""
Initialize the context engine.
@@ -72,9 +76,11 @@ class ContextEngine:
mcp_manager: MCP client manager for LLM Gateway/Knowledge Base
redis: Redis connection for caching
settings: Context settings
memory_source: Optional memory context source for agent memory
"""
self._mcp = mcp_manager
self._settings = settings or get_context_settings()
self._memory_source = memory_source
# Initialize components
self._calculator = TokenCalculator(mcp_manager=mcp_manager)
@@ -115,6 +121,15 @@ class ContextEngine:
"""
self._cache.set_redis(redis)
def set_memory_source(self, memory_source: "MemoryContextSource") -> None:
"""
Set memory context source for agent memory integration.
Args:
memory_source: Memory context source
"""
self._memory_source = memory_source
async def assemble_context(
self,
project_id: str,
@@ -126,6 +141,10 @@ class ContextEngine:
task_description: str | None = None,
knowledge_query: str | None = None,
knowledge_limit: int = 10,
memory_query: str | None = None,
memory_limit: int = 20,
session_id: str | None = None,
agent_type_id: str | None = None,
conversation_history: list[dict[str, str]] | None = None,
tool_results: list[dict[str, Any]] | None = None,
custom_contexts: list[BaseContext] | None = None,
@@ -151,6 +170,10 @@ class ContextEngine:
task_description: Current task description
knowledge_query: Query for knowledge base search
knowledge_limit: Max number of knowledge results
memory_query: Query for agent memory search
memory_limit: Max number of memory results
session_id: Session ID for working memory access
agent_type_id: Agent type ID for procedural memory
conversation_history: List of {"role": str, "content": str}
tool_results: List of tool results to include
custom_contexts: Additional custom contexts
@@ -197,15 +220,27 @@ class ContextEngine:
)
contexts.extend(knowledge_contexts)
# 4. Conversation history
# 4. Memory context from Agent Memory System
if memory_query and self._memory_source:
memory_contexts = await self._fetch_memory(
project_id=project_id,
agent_id=agent_id,
query=memory_query,
limit=memory_limit,
session_id=session_id,
agent_type_id=agent_type_id,
)
contexts.extend(memory_contexts)
# 5. Conversation history
if conversation_history:
contexts.extend(self._convert_conversation(conversation_history))
# 5. Tool results
# 6. Tool results
if tool_results:
contexts.extend(self._convert_tool_results(tool_results))
# 6. Custom contexts
# 7. Custom contexts
if custom_contexts:
contexts.extend(custom_contexts)
@@ -308,6 +343,65 @@ class ContextEngine:
logger.warning(f"Failed to fetch knowledge: {e}")
return []
async def _fetch_memory(
self,
project_id: str,
agent_id: str,
query: str,
limit: int = 20,
session_id: str | None = None,
agent_type_id: str | None = None,
) -> list[MemoryContext]:
"""
Fetch relevant memories from Agent Memory System.
Args:
project_id: Project identifier
agent_id: Agent identifier
query: Search query
limit: Maximum results
session_id: Session ID for working memory
agent_type_id: Agent type ID for procedural memory
Returns:
List of MemoryContext instances
"""
if not self._memory_source:
return []
try:
# Import here to avoid circular imports
# Configure fetch limits
from app.services.memory.integration.context_source import MemoryFetchConfig
config = MemoryFetchConfig(
working_limit=min(limit // 4, 5),
episodic_limit=min(limit // 2, 10),
semantic_limit=min(limit // 2, 10),
procedural_limit=min(limit // 4, 5),
include_working=session_id is not None,
)
result = await self._memory_source.fetch_context(
query=query,
project_id=UUID(project_id),
agent_instance_id=UUID(agent_id) if agent_id else None,
agent_type_id=UUID(agent_type_id) if agent_type_id else None,
session_id=session_id,
config=config,
)
logger.debug(
f"Fetched {len(result.contexts)} memory contexts for query: {query}, "
f"by_type: {result.by_type}"
)
return result.contexts[:limit]
except Exception as e:
logger.warning(f"Failed to fetch memory: {e}")
return []
def _convert_conversation(
self,
history: list[dict[str, str]],
@@ -466,6 +560,7 @@ def create_context_engine(
mcp_manager: "MCPClientManager | None" = None,
redis: "Redis | None" = None,
settings: ContextSettings | None = None,
memory_source: "MemoryContextSource | None" = None,
) -> ContextEngine:
"""
Create a context engine instance.
@@ -474,6 +569,7 @@ def create_context_engine(
mcp_manager: MCP client manager
redis: Redis connection
settings: Context settings
memory_source: Optional memory context source
Returns:
Configured ContextEngine instance
@@ -482,4 +578,5 @@ def create_context_engine(
mcp_manager=mcp_manager,
redis=redis,
settings=settings,
memory_source=memory_source,
)

6
backend/app/services/context/types/__init__.py
View File

@@ -15,6 +15,10 @@ from .conversation import (
MessageRole,
)
from .knowledge import KnowledgeContext
from .memory import (
MemoryContext,
MemorySubtype,
)
from .system import SystemContext
from .task import (
TaskComplexity,
@@ -33,6 +37,8 @@ __all__ = [
"ContextType",
"ConversationContext",
"KnowledgeContext",
"MemoryContext",
"MemorySubtype",
"MessageRole",
"SystemContext",
"TaskComplexity",

1
backend/app/services/context/types/base.py
View File

@@ -26,6 +26,7 @@ class ContextType(str, Enum):
KNOWLEDGE = "knowledge"
CONVERSATION = "conversation"
TOOL = "tool"
MEMORY = "memory" # Agent memory (working, episodic, semantic, procedural)
@classmethod
def from_string(cls, value: str) -> "ContextType":

282
backend/app/services/context/types/memory.py Normal file
View File

@@ -0,0 +1,282 @@
"""
Memory Context Type.
Represents agent memory as context for LLM requests.
Includes working, episodic, semantic, and procedural memories.
"""
from dataclasses import dataclass, field
from datetime import UTC, datetime
from enum import Enum
from typing import Any
from .base import BaseContext, ContextPriority, ContextType
class MemorySubtype(str, Enum):
"""Types of agent memory."""
WORKING = "working" # Session-scoped temporary data
EPISODIC = "episodic" # Task history and outcomes
SEMANTIC = "semantic" # Facts and knowledge
PROCEDURAL = "procedural" # Learned procedures
@dataclass(eq=False)
class MemoryContext(BaseContext):
"""
Context from agent memory system.
Memory context represents data retrieved from the agent
memory system, including:
- Working memory: Current session state
- Episodic memory: Past task experiences
- Semantic memory: Learned facts and knowledge
- Procedural memory: Known procedures and workflows
Each memory item includes relevance scoring from search.
"""
# Memory-specific fields
memory_subtype: MemorySubtype = field(default=MemorySubtype.EPISODIC)
memory_id: str | None = field(default=None)
relevance_score: float = field(default=0.0)
importance: float = field(default=0.5)
search_query: str = field(default="")
# Type-specific fields (populated based on memory_subtype)
key: str | None = field(default=None) # For working memory
task_type: str | None = field(default=None) # For episodic
outcome: str | None = field(default=None) # For episodic
subject: str | None = field(default=None) # For semantic
predicate: str | None = field(default=None) # For semantic
object_value: str | None = field(default=None) # For semantic
trigger: str | None = field(default=None) # For procedural
success_rate: float | None = field(default=None) # For procedural
def get_type(self) -> ContextType:
"""Return MEMORY context type."""
return ContextType.MEMORY
def to_dict(self) -> dict[str, Any]:
"""Convert to dictionary with memory-specific fields."""
base = super().to_dict()
base.update(
{
"memory_subtype": self.memory_subtype.value,
"memory_id": self.memory_id,
"relevance_score": self.relevance_score,
"importance": self.importance,
"search_query": self.search_query,
"key": self.key,
"task_type": self.task_type,
"outcome": self.outcome,
"subject": self.subject,
"predicate": self.predicate,
"object_value": self.object_value,
"trigger": self.trigger,
"success_rate": self.success_rate,
}
)
return base
@classmethod
def from_dict(cls, data: dict[str, Any]) -> "MemoryContext":
"""Create MemoryContext from dictionary."""
return cls(
id=data.get("id", ""),
content=data["content"],
source=data["source"],
timestamp=datetime.fromisoformat(data["timestamp"])
if isinstance(data.get("timestamp"), str)
else data.get("timestamp", datetime.now(UTC)),
priority=data.get("priority", ContextPriority.NORMAL.value),
metadata=data.get("metadata", {}),
memory_subtype=MemorySubtype(data.get("memory_subtype", "episodic")),
memory_id=data.get("memory_id"),
relevance_score=data.get("relevance_score", 0.0),
importance=data.get("importance", 0.5),
search_query=data.get("search_query", ""),
key=data.get("key"),
task_type=data.get("task_type"),
outcome=data.get("outcome"),
subject=data.get("subject"),
predicate=data.get("predicate"),
object_value=data.get("object_value"),
trigger=data.get("trigger"),
success_rate=data.get("success_rate"),
)
@classmethod
def from_working_memory(
cls,
key: str,
value: Any,
source: str = "working_memory",
query: str = "",
) -> "MemoryContext":
"""
Create MemoryContext from working memory entry.
Args:
key: Working memory key
value: Value stored at key
source: Source identifier
query: Search query used
Returns:
MemoryContext instance
"""
return cls(
content=str(value),
source=source,
memory_subtype=MemorySubtype.WORKING,
key=key,
relevance_score=1.0, # Working memory is always relevant
importance=0.8, # Higher importance for current session state
search_query=query,
priority=ContextPriority.HIGH.value,
)
@classmethod
def from_episodic_memory(
cls,
episode: Any,
query: str = "",
) -> "MemoryContext":
"""
Create MemoryContext from episodic memory episode.
Args:
episode: Episode object from episodic memory
query: Search query used
Returns:
MemoryContext instance
"""
outcome_val = None
if hasattr(episode, "outcome") and episode.outcome:
outcome_val = (
episode.outcome.value
if hasattr(episode.outcome, "value")
else str(episode.outcome)
)
return cls(
content=episode.task_description,
source=f"episodic:{episode.id}",
memory_subtype=MemorySubtype.EPISODIC,
memory_id=str(episode.id),
relevance_score=getattr(episode, "importance_score", 0.5),
importance=getattr(episode, "importance_score", 0.5),
search_query=query,
task_type=getattr(episode, "task_type", None),
outcome=outcome_val,
metadata={
"session_id": getattr(episode, "session_id", None),
"occurred_at": episode.occurred_at.isoformat()
if hasattr(episode, "occurred_at") and episode.occurred_at
else None,
"lessons_learned": getattr(episode, "lessons_learned", []),
},
)
@classmethod
def from_semantic_memory(
cls,
fact: Any,
query: str = "",
) -> "MemoryContext":
"""
Create MemoryContext from semantic memory fact.
Args:
fact: Fact object from semantic memory
query: Search query used
Returns:
MemoryContext instance
"""
triple = f"{fact.subject} {fact.predicate} {fact.object}"
return cls(
content=triple,
source=f"semantic:{fact.id}",
memory_subtype=MemorySubtype.SEMANTIC,
memory_id=str(fact.id),
relevance_score=getattr(fact, "confidence", 0.5),
importance=getattr(fact, "confidence", 0.5),
search_query=query,
subject=fact.subject,
predicate=fact.predicate,
object_value=fact.object,
priority=ContextPriority.NORMAL.value,
)
@classmethod
def from_procedural_memory(
cls,
procedure: Any,
query: str = "",
) -> "MemoryContext":
"""
Create MemoryContext from procedural memory procedure.
Args:
procedure: Procedure object from procedural memory
query: Search query used
Returns:
MemoryContext instance
"""
# Format steps as content
steps = getattr(procedure, "steps", [])
steps_content = "\n".join(
f" {i + 1}. {step.get('action', step) if isinstance(step, dict) else step}"
for i, step in enumerate(steps)
)
content = f"Procedure: {procedure.name}\nTrigger: {procedure.trigger_pattern}\nSteps:\n{steps_content}"
return cls(
content=content,
source=f"procedural:{procedure.id}",
memory_subtype=MemorySubtype.PROCEDURAL,
memory_id=str(procedure.id),
relevance_score=getattr(procedure, "success_rate", 0.5),
importance=0.7, # Procedures are moderately important
search_query=query,
trigger=procedure.trigger_pattern,
success_rate=getattr(procedure, "success_rate", None),
metadata={
"steps_count": len(steps),
"execution_count": getattr(procedure, "success_count", 0)
+ getattr(procedure, "failure_count", 0),
},
)
def is_working_memory(self) -> bool:
"""Check if this is working memory."""
return self.memory_subtype == MemorySubtype.WORKING
def is_episodic_memory(self) -> bool:
"""Check if this is episodic memory."""
return self.memory_subtype == MemorySubtype.EPISODIC
def is_semantic_memory(self) -> bool:
"""Check if this is semantic memory."""
return self.memory_subtype == MemorySubtype.SEMANTIC
def is_procedural_memory(self) -> bool:
"""Check if this is procedural memory."""
return self.memory_subtype == MemorySubtype.PROCEDURAL
def get_formatted_source(self) -> str:
"""
Get a formatted source string for display.
Returns:
Formatted source string
"""
parts = [f"[{self.memory_subtype.value}]", self.source]
if self.memory_id:
parts.append(f"({self.memory_id[:8]}...)")
return " ".join(parts)