Feature request: Add structured memory primitives for better context engineering

[mchockal]

feat(memory): Add Session, WorkingContext, and Processor Pipeline primitives

Summary

This PR introduces structured memory primitives for building context-aware agents, based on the tiered memory architecture principles from Google's ADK whitepaper.

Why?

Currently, agent developers must manually manage conversation history, handle context window limits, and implement their own compaction strategies. This leads to:

• Duplicated effort across agent implementations
• Inconsistent approaches to memory management
• Tight coupling between conversation state and model-specific formats
• Complex compaction logic scattered throughout application code

These primitives provide a model-agnostic foundation that separates the ground truth (Session) from the computed view (WorkingContext), enabling clean abstractions for context management and compaction.

---

What's Added

1. Session - The Ground Truth

A durable, structured log of agent interactions. Sessions are model-agnostic and serve as the source of truth.

import { Session, EventAction, generateEventId } from 'agents/memory';

// Create a new session
const session = new Session('my-agent');

// Add events
session.addEvent({
  id: generateEventId(),
  action: EventAction.USER_MESSAGE,
  timestamp: Date.now(),
  content: 'What is the weather in San Francisco?'
});

// Get conversation turns (user message + agent response + tool calls)
const turns = session.getConversationTurns();

// Serialize for storage
const serialized = session.serialize();
const restored = Session.deserialize(serialized);

2. WorkingContext - The Computed View

An ephemeral, computed view sent to the LLM. Rebuilt for each invocation.

import { WorkingContext } from 'agents/memory';

const context = new WorkingContext('session-123');

// Add system instructions
context.addSystemInstruction('You are a helpful assistant.');

// Add conversation content
context.addContent({ role: 'user', content: 'Hello!' });
context.addContent({ role: 'assistant', content: 'Hi there!' });

// Convert to model format (currently supports workers-ai)
const modelInput = context.toModelFormat('workers-ai', {
  format: 'chat_completions'
});
// { messages: [{ role: 'system', content: '...' }, ...] }

3. Processor Pipeline - Modular Context Building

A pipeline of processors that transform Session state into WorkingContext, inspired by Google ADK's processor pattern.

import {
  ProcessorPipeline,
  basicRequestProcessor,
  instructionsRequestProcessor,
  compactionFilterRequestProcessor
} from 'agents/memory';

// Create pipeline with processors
const pipeline = new ProcessorPipeline();
pipeline.addRequestProcessor('basic', basicRequestProcessor);
pipeline.addRequestProcessor('instructions', instructionsRequestProcessor, {
  instructions: ['You are a helpful assistant.']
});
pipeline.addRequestProcessor('compaction-filter', compactionFilterRequestProcessor, {
  keepCompactionSummaries: true
});

// Execute pipeline to build WorkingContext from Session
const workingContext = await pipeline.executeRequestPipeline(session);

Built-in Request Processors:

Processor	Description
basicRequestProcessor	Initializes context with session metadata
instructionsRequestProcessor	Adds system instructions
identityRequestProcessor	Adds agent identity
contentsRequestProcessor	Transforms events to content
slidingWindowRequestProcessor	Keeps recent conversation turns
compactionFilterRequestProcessor	Filters compacted events, includes summaries
contextCacheRequestProcessor	Marks stable prefixes for caching
tokenLimitRequestProcessor	Truncates to fit token limits

Built-in Response Processors:

Processor	Description
statisticsResponseProcessor	Updates session statistics

---

Bonus: Compaction Without Model Coupling

The primitives enable context compaction without worrying about underlying model structure:

import { Session, EventAction, generateEventId, type CompactionEvent } from 'agents/memory';

// Session tracks compaction configuration
session.updateCompactionConfig({
  enabled: true,
  windowSize: 5,        // Keep last 5 turns
  strategy: 'sliding_window'
});

// When context exceeds limits, create a compaction event
const compactionEvent: CompactionEvent = {
  id: generateEventId(),
  action: EventAction.COMPACTION,
  timestamp: Date.now(),
  summary: 'User asked about weather in SF, NY, and London. All responses provided.',
  compactedEventIds: ['event-1', 'event-2', 'event-3'],
  compactionStrategy: 'sliding_window',
  originalTokenCount: 2000,
  compactedTokenCount: 150
};

// Add compaction event and remove compacted events
session.addEvent(compactionEvent);

// The compactionFilterRequestProcessor automatically:
// 1. Filters out events that were compacted
// 2. Includes the compaction summary in the context
// 3. Preserves recent turns for continuity

Why this matters:

• Compaction logic is decoupled from model format - the Session stores structured events, not raw messages
• The Processor Pipeline handles the transformation - compaction summaries are injected at the right place
• Developers can implement custom summarization (e.g., using LLM) while the SDK handles the plumbing
• Future model support requires only adding new toModelFormat() adapters, not changing compaction logic

---

Event Types

Strongly-typed events for all agent interactions:

Event Type	Description
USER_MESSAGE	User input
AGENT_MESSAGE	Agent response
TOOL_CALL	Tool invocation
TOOL_RESULT	Tool execution result
COMPACTION	Context summarization
ERROR	Error occurrence
CONTROL_SIGNAL	Control flow signals
AGENT_TRANSFER	Multi-agent handoff
SYSTEM_INSTRUCTION	Dynamic instruction updates

---

Current Limitations

⚠️ Note: For now, this only supports Workers AI chat completions format via toModelFormat('workers-ai'). Support for additional native model providers (OpenAI, Anthropic, etc.) , integration with vercel ai sdk will have to shortly follow.

---

Files Added

packages/agents/src/memory/
├── index.ts           # Module exports
├── events.ts          # Event types and EventAction enum
├── session.ts         # Session class
├── working-context.ts # WorkingContext class
└── processors.ts      # ProcessorPipeline and built-in processors

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[deathbyknowledge]

This is not a final review yet but I'm struggling to see the bigger picture with this, it seems quite opinionated and not sure if the benefits outweigh the negatives.

Could you update one or two of the existing examples (or make a new one) to use this approach and see how if feels?

[deathbyknowledge]

This relies on estimateTokenCount which just guesses tokens. Since there's quite some variation in tokenizers depending on the model at use, I'd prefer if we remove this one and let users provide their own.

Maybe we can add "character" truncation instead?

[deathbyknowledge]

Do we want to use the DO sqlite to handle storage here? Or perhaps storing events directly, kind of like an event store?