"""
Pattern 098: Shared Context/Workspace
Description:
Shared Context/Workspace provides a common space where multiple agents can read and write
shared information, coordinate activities, and collaborate on tasks. This pattern enables
agents to maintain shared state, exchange data without direct messaging, and build upon
each other's work. The shared workspace acts as a collaborative knowledge base and
coordination medium for multi-agent systems.
Shared workspace features:
- Common data structures accessible to all agents
- Read/write operations with concurrency control
- Version tracking and conflict resolution
- Notifications on changes
- Access control and permissions
- Transaction support
Components:
1. Workspace
- Shared data store
- Key-value storage
- Document/object storage
- Access control lists
- Change notifications
- Version history
2. Context Manager
- Manages workspace state
- Handles concurrent access
- Resolves conflicts
- Maintains consistency
- Provides transactions
- Enforces policies
3. Agent Interface
- Read operations
- Write operations
- Subscribe to changes
- Lock/unlock resources
- Query workspace
- Batch operations
4. Synchronization
- Optimistic locking
- Pessimistic locking
- Version control
- Merge strategies
- Conflict detection
- Resolution policies
Use Cases:
1. Collaborative Problem Solving
- Shared scratchpad
- Intermediate results
- Partial solutions
- Knowledge accumulation
- Iterative refinement
- Collective intelligence
2. Multi-Agent Coordination
- Task assignments
- Status tracking
- Resource allocation
- Progress monitoring
- Goal management
- Constraint sharing
3. Knowledge Sharing
- Common knowledge base
- Learned patterns
- Best practices
- Historical data
- Shared context
- Collective memory
4. Workflow Management
- Process state
- Task dependencies
- Input/output data
- Checkpoint saving
- Error recovery
- Audit trails
LangChain Implementation:
LangChain supports shared context through:
- Memory objects shared across agents
- Vector stores for shared knowledge
- Custom state management
- LangGraph for workflow state
- Integration with databases
Key Features:
1. Concurrent Access
- Multiple readers
- Controlled writers
- Lock management
- Deadlock prevention
- Fair scheduling
- Priority access
2. Consistency Models
- Strong consistency
- Eventual consistency
- Causal consistency
- Read-your-writes
- Monotonic reads
- Application-specific
3. Change Management
- Version tracking
- Change logs
- Rollback capability
- Diff computation
- Merge operations
- Conflict resolution
4. Scalability
- Partitioning
- Replication
- Caching
- Lazy loading
- Efficient indexing
- Distributed storage
Best Practices:
1. Data Organization
- Clear naming conventions
- Logical structure
- Minimize shared state
- Immutable where possible
- Document schemas
- Version data formats
2. Concurrency Control
- Use appropriate locking
- Keep critical sections small
- Avoid long-held locks
- Handle deadlocks
- Implement timeouts
- Test concurrent scenarios
3. Performance
- Cache frequently read data
- Batch operations
- Async notifications
- Index for queries
- Minimize contention
- Monitor bottlenecks
4. Reliability
- Persist important data
- Backup regularly
- Handle failures gracefully
- Validate writes
- Log operations
- Support recovery
Trade-offs:
Advantages:
- Simple coordination
- Shared visibility
- Flexible collaboration
- Natural data sharing
- Easy debugging
- Common patterns
Disadvantages:
- Potential bottleneck
- Concurrency complexity
- Consistency challenges
- Scalability limits
- Testing difficulty
- State management overhead
Production Considerations:
1. Storage Backend
- Redis: Fast, in-memory
- MongoDB: Document store
- PostgreSQL: Relational, ACID
- DynamoDB: Managed, scalable
- Etcd: Distributed coordination
- Memcached: Simple caching
2. Consistency Strategy
- ACID transactions for critical data
- Eventual consistency for non-critical
- CRDTs for conflict-free updates
- Last-write-wins for simple cases
- Application-level resolution
- Version vectors for causality
3. Performance Optimization
- Read-through caching
- Write-behind buffering
- Optimistic locking
- Sharding by key
- Denormalization
- Materialized views
4. Monitoring
- Access patterns
- Lock contention
- Query performance
- Storage usage
- Conflict rate
- Error frequency
5. Security
- Authentication
- Authorization (ACLs)
- Encryption at rest
- Audit logging
- Input validation
- Rate limiting
"""
import os
import json
import time
import threading
from typing import List, Dict, Any, Optional, Callable, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
from copy import deepcopy
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
load_dotenv()
class AccessMode(Enum):
"""Access modes for workspace resources"""
READ = "read"
WRITE = "write"
READ_WRITE = "read_write"
@dataclass
class WorkspaceEntry:
"""Entry in the shared workspace"""
key: str
value: Any
version: int
created_at: datetime
updated_at: datetime
created_by: str
updated_by: str
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class WorkspaceEvent:
"""Event notification for workspace changes"""
event_type: str # created, updated, deleted
key: str
value: Any
agent_id: str
timestamp: datetime
class SharedWorkspace:
"""
Shared workspace for multi-agent collaboration.
Provides concurrent access with locking, versioning, and change notifications.
"""
def __init__(self):
"""Initialize shared workspace"""
self.data: Dict[str, WorkspaceEntry] = {}
self.locks: Dict[str, str] = {} # key -> agent_id holding lock
self.subscribers: Dict[str, Set[Callable]] = defaultdict(set) # key -> callbacks
self.history: List[WorkspaceEvent] = []
self.lock = threading.RLock() # Reentrant lock for thread safety
def read(self, key: str, default: Any = None) -> Any:
"""
Read value from workspace.
Args:
key: Key to read
default: Default value if key doesn't exist
Returns:
Value or default
"""
with self.lock:
entry = self.data.get(key)
if entry:
return deepcopy(entry.value)
return default
def write(self, key: str, value: Any, agent_id: str, metadata: Optional[Dict[str, Any]] = None) -> bool:
"""
Write value to workspace.
Args:
key: Key to write
value: Value to write
agent_id: Agent performing write
metadata: Optional metadata
Returns:
True if successful
"""
with self.lock:
now = datetime.now()
if key in self.data:
# Update existing
entry = self.data[key]
entry.value = value
entry.version += 1
entry.updated_at = now
entry.updated_by = agent_id
if metadata:
entry.metadata.update(metadata)
event_type = "updated"
else:
# Create new
entry = WorkspaceEntry(
key=key,
value=value,
version=1,
created_at=now,
updated_at=now,
created_by=agent_id,
updated_by=agent_id,
metadata=metadata or {}
)
self.data[key] = entry
event_type = "created"
# Record event
event = WorkspaceEvent(
event_type=event_type,
key=key,
value=value,
agent_id=agent_id,
timestamp=now
)
self.history.append(event)
# Notify subscribers
self._notify_subscribers(key, event)
return True
def delete(self, key: str, agent_id: str) -> bool:
"""
Delete value from workspace.
Args:
key: Key to delete
agent_id: Agent performing deletion
Returns:
True if deleted, False if not found
"""
with self.lock:
if key in self.data:
value = self.data[key].value
del self.data[key]
# Record event
event = WorkspaceEvent(
event_type="deleted",
key=key,
value=value,
agent_id=agent_id,
timestamp=datetime.now()
)
self.history.append(event)
# Notify subscribers
self._notify_subscribers(key, event)
return True
return False
def update(self, key: str, updater: Callable[[Any], Any], agent_id: str) -> bool:
"""
Atomically update value using updater function.
Args:
key: Key to update
updater: Function that takes current value and returns new value
agent_id: Agent performing update
Returns:
True if successful
"""
with self.lock:
current = self.read(key)
new_value = updater(current)
return self.write(key, new_value, agent_id)
def lock_key(self, key: str, agent_id: str, timeout: float = 5.0) -> bool:
"""
Acquire exclusive lock on key.
Args:
key: Key to lock
agent_id: Agent requesting lock
timeout: Maximum time to wait for lock
Returns:
True if lock acquired
"""
start_time = time.time()
while time.time() - start_time bool:
"""
Release lock on key.
Args:
key: Key to unlock
agent_id: Agent releasing lock
Returns:
True if unlocked
"""
with self.lock:
if key in self.locks and self.locks[key] == agent_id:
del self.locks[key]
return True
return False
def subscribe(self, key: str, callback: Callable[[WorkspaceEvent], None]):
"""
Subscribe to changes on a key.
Args:
key: Key to watch
callback: Function to call on changes
"""
with self.lock:
self.subscribers[key].add(callback)
def unsubscribe(self, key: str, callback: Callable[[WorkspaceEvent], None]):
"""
Unsubscribe from changes on a key.
Args:
key: Key to stop watching
callback: Callback to remove
"""
with self.lock:
if key in self.subscribers:
self.subscribers[key].discard(callback)
def _notify_subscribers(self, key: str, event: WorkspaceEvent):
"""
Notify subscribers of change.
Args:
key: Key that changed
event: Event details
"""
# Notify exact key subscribers
for callback in self.subscribers.get(key, []):
try:
callback(event)
except Exception as e:
print(f"Error in subscriber callback: {e}")
# Notify wildcard subscribers
for callback in self.subscribers.get("*", []):
try:
callback(event)
except Exception as e:
print(f"Error in wildcard subscriber callback: {e}")
def get_all_keys(self) -> List[str]:
"""Get all keys in workspace"""
with self.lock:
return list(self.data.keys())
def get_info(self, key: str) -> Optional[Dict[str, Any]]:
"""
Get metadata about a key.
Args:
key: Key to get info for
Returns:
Info dictionary or None
"""
with self.lock:
if key in self.data:
entry = self.data[key]
return {
'key': entry.key,
'version': entry.version,
'created_at': entry.created_at.isoformat(),
'updated_at': entry.updated_at.isoformat(),
'created_by': entry.created_by,
'updated_by': entry.updated_by,
'metadata': entry.metadata,
'is_locked': key in self.locks,
'locked_by': self.locks.get(key)
}
return None
def get_history(self, key: Optional[str] = None, limit: int = 10) -> List[WorkspaceEvent]:
"""
Get history of changes.
Args:
key: Filter by key (None for all)
limit: Maximum events to return
Returns:
List of events
"""
with self.lock:
if key:
events = [e for e in self.history if e.key == key]
else:
events = self.history
return events[-limit:]
def clear(self):
"""Clear all data from workspace"""
with self.lock:
self.data.clear()
self.locks.clear()
self.history.clear()
class CollaborativeAgent:
"""
Agent that works with shared workspace.
Demonstrates collaborative problem solving.
"""
def __init__(self, agent_id: str, workspace: SharedWorkspace):
"""
Initialize collaborative agent.
Args:
agent_id: Unique agent identifier
workspace: Shared workspace
"""
self.agent_id = agent_id
self.workspace = workspace
self.llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)
def contribute(self, key: str, contribution: Any):
"""
Add contribution to workspace.
Args:
key: Key to write to
contribution: Data to contribute
"""
print(f"[{self.agent_id}] Contributing to '{key}'")
self.workspace.write(key, contribution, self.agent_id)
def read_contribution(self, key: str) -> Any:
"""
Read contribution from workspace.
Args:
key: Key to read
Returns:
Value or None
"""
value = self.workspace.read(key)
if value is not None:
print(f"[{self.agent_id}] Read '{key}': {value}")
return value
def append_to_list(self, key: str, item: Any):
"""
Append item to a list in workspace.
Args:
key: Key of list
item: Item to append
"""
print(f"[{self.agent_id}] Appending to '{key}'")
def updater(current):
if current is None:
return [item]
elif isinstance(current, list):
return current + [item]
else:
return [current, item]
self.workspace.update(key, updater, self.agent_id)
def increment_counter(self, key: str, amount: int = 1):
"""
Atomically increment a counter.
Args:
key: Key of counter
amount: Amount to increment
"""
print(f"[{self.agent_id}] Incrementing '{key}' by {amount}")
def updater(current):
if current is None:
return amount
return current + amount
self.workspace.update(key, updater, self.agent_id)
def locked_operation(self, key: str, operation: Callable):
"""
Perform operation with exclusive lock.
Args:
key: Key to lock
operation: Operation to perform
"""
if self.workspace.lock_key(key, self.agent_id):
try:
print(f"[{self.agent_id}] Acquired lock on '{key}'")
operation()
finally:
self.workspace.unlock_key(key, self.agent_id)
print(f"[{self.agent_id}] Released lock on '{key}'")
else:
print(f"[{self.agent_id}] Failed to acquire lock on '{key}'")
def demonstrate_shared_workspace():
"""Demonstrate shared context/workspace pattern"""
print("=" * 80)
print("SHARED CONTEXT/WORKSPACE DEMONSTRATION")
print("=" * 80)
workspace = SharedWorkspace()
# Example 1: Basic Read/Write
print("\n" + "=" * 80)
print("EXAMPLE 1: Basic Read/Write Operations")
print("=" * 80)
agent1 = CollaborativeAgent("agent1", workspace)
agent2 = CollaborativeAgent("agent2", workspace)
print("\nAgent1 writes, Agent2 reads:")
agent1.contribute("shared_data", {"status": "initialized", "count": 0})
data = agent2.read_contribution("shared_data")
# Example 2: Collaborative List Building
print("\n" + "=" * 80)
print("EXAMPLE 2: Collaborative List Building")
print("=" * 80)
agent3 = CollaborativeAgent("agent3", workspace)
print("\nMultiple agents appending to shared list:")
agent1.append_to_list("tasks", "Analyze data")
agent2.append_to_list("tasks", "Generate report")
agent3.append_to_list("tasks", "Send notifications")
tasks = workspace.read("tasks")
print(f"\nFinal task list: {tasks}")
# Example 3: Atomic Counter Updates
print("\n" + "=" * 80)
print("EXAMPLE 3: Atomic Counter Updates")
print("=" * 80)
print("\nMultiple agents incrementing shared counter:")
workspace.write("counter", 0, "system")
agent1.increment_counter("counter", 5)
agent2.increment_counter("counter", 3)
agent3.increment_counter("counter", 7)
final_count = workspace.read("counter")
print(f"\nFinal counter value: {final_count}")
# Example 4: Locking for Critical Sections
print("\n" + "=" * 80)
print("EXAMPLE 4: Exclusive Locking")
print("=" * 80)
workspace.write("resource", {"balance": 100}, "system")
def withdraw_funds():
resource = workspace.read("resource")
if resource["balance"] >= 30:
resource["balance"] -= 30
workspace.write("resource", resource, agent1.agent_id)
print(f"[{agent1.agent_id}] Withdrew 30, new balance: {resource['balance']}")
print("\nAgent1 performing locked operation:")
agent1.locked_operation("resource", withdraw_funds)
# Example 5: Change Notifications
print("\n" + "=" * 80)
print("EXAMPLE 5: Change Notifications (Subscriptions)")
print("=" * 80)
def on_status_change(event: WorkspaceEvent):
print(f"[Subscriber] '{event.key}' {event.event_type} by {event.agent_id}: {event.value}")
print("\nSubscribing to 'system_status' changes:")
workspace.subscribe("system_status", on_status_change)
agent1.contribute("system_status", "starting")
time.sleep(0.1)
agent2.contribute("system_status", "running")
time.sleep(0.1)
agent3.contribute("system_status", "completed")
time.sleep(0.1)
# Example 6: Versioning and Metadata
print("\n" + "=" * 80)
print("EXAMPLE 6: Versioning and Metadata")
print("=" * 80)
print("\nTracking versions of shared document:")
workspace.write("document", "Version 1 content", "agent1")
print(f"Info: {workspace.get_info('document')}")
time.sleep(0.1)
workspace.write("document", "Version 2 content", "agent2", {"reviewed": True})
print(f"Info: {workspace.get_info('document')}")
time.sleep(0.1)
workspace.write("document", "Version 3 content", "agent3")
print(f"Info: {workspace.get_info('document')}")
# Example 7: History Tracking
print("\n" + "=" * 80)
print("EXAMPLE 7: Change History")
print("=" * 80)
history = workspace.get_history("document")
print(f"\nHistory for 'document' ({len(history)} events):")
for event in history:
print(f" {event.timestamp.strftime('%H:%M:%S')} - {event.event_type} by {event.agent_id}")
# Example 8: Workspace Overview
print("\n" + "=" * 80)
print("EXAMPLE 8: Workspace Overview")
print("=" * 80)
all_keys = workspace.get_all_keys()
print(f"\nWorkspace contains {len(all_keys)} keys:")
for key in all_keys:
info = workspace.get_info(key)
print(f" - {key}: v{info['version']}, updated by {info['updated_by']}")
# Summary
print("\n" + "=" * 80)
print("SHARED CONTEXT/WORKSPACE SUMMARY")
print("=" * 80)
print("""
Shared Workspace Benefits:
1. Centralized State: Single source of truth
2. Easy Coordination: Natural collaboration point
3. Flexible Access: Read/write as needed
4. Transparency: All agents see same data
5. Simple Model: Familiar data structure
6. Change Tracking: Version history
Key Components:
1. Workspace
- Key-value storage
- Thread-safe operations
- Version tracking
- Change history
- Notification system
- Lock management
2. Operations
- read(key): Get value
- write(key, value): Set value
- update(key, updater): Atomic update
- delete(key): Remove value
- lock/unlock: Exclusive access
- subscribe: Watch for changes
3. Concurrency Control
- Thread-safe operations
- Optimistic updates (default)
- Pessimistic locking (when needed)
- Atomic operations
- Version tracking
- Conflict detection
Use Cases:
1. Collaborative Problem Solving
- Shared scratchpad
- Incremental solutions
- Knowledge accumulation
- Team coordination
2. Workflow State
- Task tracking
- Progress monitoring
- Status updates
- Result sharing
3. Knowledge Base
- Shared facts
- Learned patterns
- Best practices
- Historical data
4. Resource Management
- Resource allocation
- Capacity tracking
- Scheduling
- Conflict resolution
Design Patterns:
1. Shared Scratchpad
- Agents contribute ideas
- Build on each other's work
- Iterative refinement
- Collective intelligence
2. Blackboard Pattern
- Problem-solving workspace
- Multiple experts contribute
- Control component coordinates
- Solution emerges
3. Task Board
- Todo/In Progress/Done
- Agents claim tasks
- Update status
- Track completion
4. Data Pipeline
- Input → Transform → Output
- Each agent processes
- Results in workspace
- Next agent consumes
Concurrency Strategies:
1. No Locking (Simple)
- For non-critical data
- Last write wins
- Fast, simple
- Risk of conflicts
2. Optimistic Locking
- Read freely
- Check version on write
- Retry on conflict
- Good for low contention
3. Pessimistic Locking
- Lock before read/write
- Hold during operation
- Unlock after
- Good for critical sections
4. Atomic Operations
- Single operation
- All or nothing
- No intermediate state
- Best for counters, lists
Best Practices:
1. Data Organization
✓ Logical key structure
✓ Minimize shared state
✓ Clear ownership
✓ Document schemas
✓ Version formats
✓ Clean up old data
2. Concurrency
✓ Keep locks short
✓ Use atomic operations
✓ Handle conflicts
✓ Avoid deadlocks
✓ Test concurrent access
✓ Monitor contention
3. Performance
✓ Cache frequently read
✓ Batch updates
✓ Index for queries
✓ Lazy loading
✓ Minimize writes
✓ Async notifications
4. Reliability
✓ Persist critical data
✓ Backup regularly
✓ Validate writes
✓ Handle failures
✓ Log operations
✓ Support rollback
Common Pitfalls:
❌ Too much shared state (tight coupling)
✓ Share only necessary data
❌ Long-held locks (deadlocks)
✓ Minimize critical sections
❌ No conflict handling
✓ Implement retry logic
❌ Missing validation
✓ Validate all writes
❌ No access control
✓ Implement permissions
Production Considerations:
1. Storage Backend
- Redis: Fast, in-memory
- MongoDB: Document store
- PostgreSQL: ACID compliance
- Memcached: Simple cache
- DynamoDB: Scalable cloud
- Etcd: Distributed config
2. Consistency Model
- Strong: All see same value
- Eventual: Converges over time
- Causal: Cause before effect
- Read-your-writes: See own changes
- Choose based on needs
3. Scalability
- Partition by key
- Replicate for reads
- Cache hot data
- Shard large datasets
- Horizontal scaling
- Load balancing
4. Monitoring
- Access patterns
- Lock contention
- Read/write ratios
- Storage usage
- Conflict rate
- Performance metrics
When to Use:
✓ Multi-agent collaboration
✓ Shared state needed
✓ Flexible data sharing
✓ Simple coordination
✓ Transparent operations
✓ Natural fit for problem
✗ No shared state needed
✗ Message passing sufficient
✗ Ultra-high performance critical
✗ Complex distributed system
Alternatives:
- Message Passing: Decoupled, async
- Event Sourcing: Immutable events
- Tuple Spaces: Linda-style coordination
- Distributed Cache: Geographically distributed
- Blockchain: Immutable, consensus
ROI Analysis:
- Simplicity: High (natural pattern)
- Performance: Medium (can be bottleneck)
- Scalability: Medium (single point)
- Reliability: High (with proper backend)
- Maintainability: High (centralized state)
- Cost: Low (simple infrastructure)
""")
print("\n" + "=" * 80)
print("Pattern 098 (Shared Context/Workspace) demonstration complete!")
print("=" * 80)
if __name__ == "__main__":
demonstrate_shared_workspace()
Agentic Design Pattern: Shared Context
Pattern 098: Shared Context – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Shared Context pattern implementation.
Pattern Description
Pattern 098: Shared Context/Workspace
Description: Shared Context/Workspace provides a common space where multiple agents can read and write shared information, coordinate activities, and collaborate on tasks. This pattern enables agents to maintain shared state, exchange data without direct messaging, and build upon each other’s work. The shared workspace acts as a collaborative knowledge base and coordination medium for multi-agent systems.
Shared workspace features:
- Common data structures accessible to all agents
- Read/write operations with concurrency control
- Version tracking and conflict resolution
- Notifications on changes
- Access control and permissions
- Transaction support
Components: 1. Workspace – Shared data store – Key-value storage – Document/object storage – Access control lists – Change notifications – Version history
2. Context Manager
- Manages workspace state
- Handles concurrent access
- Resolves conflicts
- Maintains consistency
- Provides transactions
- Enforces policies
3. Agent Interface
- Read operations
- Write operations
- Subscribe to changes
- Lock/unlock resources
- Query workspace
- Batch operations
4. Synchronization
- Optimistic locking
- Pessimistic locking
- Version control
- Merge strategies
- Conflict detection
- Resolution policies
Use Cases: 1. Collaborative Problem Solving – Shared scratchpad – Intermediate results – Partial solutions – Knowledge accumulation – Iterative refinement – Collective intelligence
2. Multi-Agent Coordination
- Task assignments
- Status tracking
- Resource allocation
- Progress monitoring
- Goal management
- Constraint sharing
3. Knowledge Sharing
- Common knowledge base
- Learned patterns
- Best practices
- Historical data
- Shared context
- Collective memory
4. Workflow Management
- Process state
- Task dependencies
- Input/output data
- Checkpoint saving
- Error recovery
- Audit trails
LangChain Implementation: LangChain supports shared context through: – Memory objects shared across agents – Vector stores for shared knowledge – Custom state management – LangGraph for workflow state – Integration with databases
Key Features: 1. Concurrent Access – Multiple readers – Controlled writers – Lock management – Deadlock prevention – Fair scheduling – Priority access
2. Consistency Models
- Strong consistency
- Eventual consistency
- Causal consistency
- Read-your-writes
- Monotonic reads
- Application-specific
3. Change Management
- Version tracking
- Change logs
- Rollback capability
- Diff computation
- Merge operations
- Conflict resolution
4. Scalability
- Partitioning
- Replication
- Caching
- Lazy loading
- Efficient indexing
- Distributed storage
Best Practices: 1. Data Organization – Clear naming conventions – Logical structure – Minimize shared state – Immutable where possible – Document schemas – Version data formats
2. Concurrency Control
- Use appropriate locking
- Keep critical sections small
- Avoid long-held locks
- Handle deadlocks
- Implement timeouts
- Test concurrent scenarios
3. Performance
- Cache frequently read data
- Batch operations
- Async notifications
- Index for queries
- Minimize contention
- Monitor bottlenecks
4. Reliability
- Persist important data
- Backup regularly
- Handle failures gracefully
- Validate writes
- Log operations
- Support recovery
Trade-offs: Advantages: – Simple coordination – Shared visibility – Flexible collaboration – Natural data sharing – Easy debugging – Common patterns
Disadvantages:
- Potential bottleneck
- Concurrency complexity
- Consistency challenges
- Scalability limits
- Testing difficulty
- State management overhead
Production Considerations: 1. Storage Backend – Redis: Fast, in-memory – MongoDB: Document store – PostgreSQL: Relational, ACID – DynamoDB: Managed, scalable – Etcd: Distributed coordination – Memcached: Simple caching
2. Consistency Strategy
- ACID transactions for critical data
- Eventual consistency for non-critical
- CRDTs for conflict-free updates
- Last-write-wins for simple cases
- Application-level resolution
- Version vectors for causality
3. Performance Optimization
- Read-through caching
- Write-behind buffering
- Optimistic locking
- Sharding by key
- Denormalization
- Materialized views
4. Monitoring
- Access patterns
- Lock contention
- Query performance
- Storage usage
- Conflict rate
- Error frequency
5. Security
- Authentication
- Authorization (ACLs)
- Encryption at rest
- Audit logging
- Input validation
- Rate limiting
File Statistics
- Total Lines: 999
- Classes: 5
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 098: Shared Context/Workspace
Description:
Shared Context/Workspace provides a common space where multiple agents can read and write
shared information, coordinate activities, and collaborate on tasks. This pattern enables
agents to maintain shared state, exchange data without direct messaging, and build upon
each other's work. The shared workspace acts as a collaborative knowledge base and
coordination medium for multi-agent systems.
Shared workspace features:
- Common data structures accessible to all agents
- Read/write operations with concurrency control
- Version tracking and conflict resolution
- Notifications on changes
- Access control and permissions
- Transaction support
Components:
1. Workspace
- Shared data store
- Key-value storage
- Document/object storage
- Access control lists
- Change notifications
- Version history
2. Context Manager
- Manages workspace state
- Handles concurrent access
- Resolves conflicts
- Maintains consistency
- Provides transactions
- Enforces policies
3. Agent Interface
- Read operations
- Write operations
- Subscribe to changes
- Lock/unlock resources
- Query workspace
- Batch operations
4. Synchronization
- Optimistic locking
- Pessimistic locking
- Version control
- Merge strategies
- Conflict detection
- Resolution policies
Use Cases:
1. Collaborative Problem Solving
- Shared scratchpad
- Intermediate results
- Partial solutions
- Knowledge accumulation
- Iterative refinement
- Collective intelligence
2. Multi-Agent Coordination
- Task assignments
- Status tracking
- Resource allocation
- Progress monitoring
- Goal management
- Constraint sharing
3. Knowledge Sharing
- Common knowledge base
- Learned patterns
- Best practices
- Historical data
- Shared context
- Collective memory
4. Workflow Management
- Process state
- Task dependencies
- Input/output data
- Checkpoint saving
- Error recovery
- Audit trails
LangChain Implementation:
LangChain supports shared context through:
- Memory objects shared across agents
- Vector stores for shared knowledge
- Custom state management
- LangGraph for workflow state
- Integration with databases
Key Features:
1. Concurrent Access
- Multiple readers
- Controlled writers
- Lock management
- Deadlock prevention
- Fair scheduling
- Priority access
2. Consistency Models
- Strong consistency
- Eventual consistency
- Causal consistency
- Read-your-writes
- Monotonic reads
- Application-specific
3. Change Management
- Version tracking
- Change logs
- Rollback capability
- Diff computation
- Merge operations
- Conflict resolution
4. Scalability
- Partitioning
- Replication
- Caching
- Lazy loading
- Efficient indexing
- Distributed storage
Best Practices:
1. Data Organization
- Clear naming conventions
- Logical structure
- Minimize shared state
- Immutable where possible
- Document schemas
- Version data formats
2. Concurrency Control
- Use appropriate locking
- Keep critical sections small
- Avoid long-held locks
- Handle deadlocks
- Implement timeouts
- Test concurrent scenarios
3. Performance
- Cache frequently read data
- Batch operations
- Async notifications
- Index for queries
- Minimize contention
- Monitor bottlenecks
4. Reliability
- Persist important data
- Backup regularly
- Handle failures gracefully
- Validate writes
- Log operations
- Support recovery
Trade-offs:
Advantages:
- Simple coordination
- Shared visibility
- Flexible collaboration
- Natural data sharing
- Easy debugging
- Common patterns
Disadvantages:
- Potential bottleneck
- Concurrency complexity
- Consistency challenges
- Scalability limits
- Testing difficulty
- State management overhead
Production Considerations:
1. Storage Backend
- Redis: Fast, in-memory
- MongoDB: Document store
- PostgreSQL: Relational, ACID
- DynamoDB: Managed, scalable
- Etcd: Distributed coordination
- Memcached: Simple caching
2. Consistency Strategy
- ACID transactions for critical data
- Eventual consistency for non-critical
- CRDTs for conflict-free updates
- Last-write-wins for simple cases
- Application-level resolution
- Version vectors for causality
3. Performance Optimization
- Read-through caching
- Write-behind buffering
- Optimistic locking
- Sharding by key
- Denormalization
- Materialized views
4. Monitoring
- Access patterns
- Lock contention
- Query performance
- Storage usage
- Conflict rate
- Error frequency
5. Security
- Authentication
- Authorization (ACLs)
- Encryption at rest
- Audit logging
- Input validation
- Rate limiting
"""
Purpose: This module implements the Shared Context pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import collections.defaultdict
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import json
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import threading
import time
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional
import typing.Set
Purpose: Core Python functionality:
– collections.defaultdict: Specialized container datatypes
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– json: JSON serialization/deserialization
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– threading: Regular expression operations
– time: Time-related functions
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Set: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from copy.deepcopy
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: AccessMode
Lines 237-241
Inherits from: Enum
Purpose:
Access modes for workspace resources
Class: WorkspaceEntry
Lines 245-254
Purpose:
Entry in the shared workspace
Class: WorkspaceEvent
Lines 258-264
Purpose:
Event notification for workspace changes
Class: SharedWorkspace
Lines 267-543
Purpose:
Shared workspace for multi-agent collaboration.
Provides concurrent access with locking, versioning, and change notifications.
Methods:
__init__(self)
Lines 274-280
Purpose:
Initialize shared workspace
Parameters:
read(self, key, default)
Lines 282-297
Purpose:
Read value from workspace.
Args:
key: Key to read
default: Default value if key doesn't exist
Returns:
Value or default
Parameters:
– key: Key
– default: Default
Returns: Any
write(self, key, value, agent_id, metadata)
Lines 299-353
Purpose:
Write value to workspace.
Args:
key: Key to write
value: Value to write
agent_id: Agent performing write
metadata: Optional metadata
Returns:
True if successful
Parameters:
– key: Key
– value: Value
– agent_id: Agent id
– metadata: Metadata
Returns: bool
delete(self, key, agent_id)
Lines 355-385
Purpose:
Delete value from workspace.
Args:
key: Key to delete
agent_id: Agent performing deletion
Returns:
True if deleted, False if not found
Parameters:
– key: Key
– agent_id: Agent id
Returns: bool
update(self, key, updater, agent_id)
Lines 387-402
Purpose:
Atomically update value using updater function.
Args:
key: Key to update
updater: Function that takes current value and returns new value
agent_id: Agent performing update
Returns:
True if successful
Parameters:
– key: Key
– updater: Updater
– agent_id: Agent id
Returns: bool
lock_key(self, key, agent_id, timeout)
Lines 404-425
Purpose:
Acquire exclusive lock on key.
Args:
key: Key to lock
agent_id: Agent requesting lock
timeout: Maximum time to wait for lock
Returns:
True if lock acquired
Parameters:
– key: Key
– agent_id: Agent id
– timeout: Timeout
Returns: bool
unlock_key(self, key, agent_id)
Lines 427-442
Purpose:
Release lock on key.
Args:
key: Key to unlock
agent_id: Agent releasing lock
Returns:
True if unlocked
Parameters:
– key: Key
– agent_id: Agent id
Returns: bool
subscribe(self, key, callback)
Lines 444-453
Purpose:
Subscribe to changes on a key.
Args:
key: Key to watch
callback: Function to call on changes
Parameters:
– key: Key
– callback: Callback
unsubscribe(self, key, callback)
Lines 455-465
Purpose:
Unsubscribe from changes on a key.
Args:
key: Key to stop watching
callback: Callback to remove
Parameters:
– key: Key
– callback: Callback
_notify_subscribers(self, key, event)
Lines 467-487
Purpose:
Notify subscribers of change.
Args:
key: Key that changed
event: Event details
Parameters:
– key: Key
– event: Event
get_all_keys(self)
Lines 489-492
Purpose:
Get all keys in workspace
Parameters:
Returns: <ast.Subscript object at 0x000001A06D674890>
get_info(self, key)
Lines 494-518
Purpose:
Get metadata about a key.
Args:
key: Key to get info for
Returns:
Info dictionary or None
Parameters:
– key: Key
Returns: <ast.Subscript object at 0x000001A06D88BDD0>
get_history(self, key, limit)
Lines 520-536
Purpose:
Get history of changes.
Args:
key: Filter by key (None for all)
limit: Maximum events to return
Returns:
List of events
Parameters:
– key: Key
– limit: Limit
Returns: <ast.Subscript object at 0x000001A06D8886D0>
clear(self)
Lines 538-543
Purpose:
Clear all data from workspace
Parameters:
Class: CollaborativeAgent
Lines 546-644
Purpose:
Agent that works with shared workspace.
Demonstrates collaborative problem solving.
Methods:
__init__(self, agent_id, workspace)
Lines 553-563
Purpose:
Initialize collaborative agent.
Args:
agent_id: Unique agent identifier
workspace: Shared workspace
Parameters:
– agent_id: Agent id
– workspace: Workspace
contribute(self, key, contribution)
Lines 565-574
Purpose:
Add contribution to workspace.
Args:
key: Key to write to
contribution: Data to contribute
Parameters:
– key: Key
– contribution: Contribution
read_contribution(self, key)
Lines 576-589
Purpose:
Read contribution from workspace.
Args:
key: Key to read
Returns:
Value or None
Parameters:
– key: Key
Returns: Any
append_to_list(self, key, item)
Lines 591-609
Purpose:
Append item to a list in workspace.
Args:
key: Key of list
item: Item to append
Parameters:
– key: Key
– item: Item
increment_counter(self, key, amount)
Lines 611-626
Purpose:
Atomically increment a counter.
Args:
key: Key of counter
amount: Amount to increment
Parameters:
– key: Key
– amount: Amount
locked_operation(self, key, operation)
Lines 628-644
Purpose:
Perform operation with exclusive lock.
Args:
key: Key to lock
operation: Operation to perform
Parameters:
– key: Key
– operation: Operation
Functions
demonstrate_shared_workspace()
Lines 647-994
Purpose:
Demonstrate shared context/workspace pattern
Key Concepts Summary
Pattern Overview
The Shared Context pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 097
- Pattern 099
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Shared Context pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 098_shared_context.py
Total Lines: 999
Pattern Category: AI Agent Design Pattern #098
Generated from actual implementation code
For educational and reference purposes
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