AI Agent Context Management

Mastra observational memory cuts AI agent costs 10x. Google ADK tiered context. Agentic RAG. Multi-agent context management for production systems.

Mastra 10x Cost Cut Google ADK

Observational memory cuts AI agent costs 10x and outscores RAG on long-context benchmarks.

The Multi-Agent Context Management Problem

Production AI agents don't just need memory — they need the right memory at the right time. When multiple agents share context, context management becomes a systems engineering problem: tiered storage, selective retrieval, cost-efficient memory, and graceful degradation.

Mastra: Observational Memory (February 10, 2026)

"Observational memory cuts AI agent costs 10x and outscores RAG on long-context benchmarks. The agent can respond while remembering the full context, without requiring the user to re-explain preferences or previous decisions. The system shipped." — VentureBeat, February 10, 2026

Mastra: Pause and Resume (1 week ago)

"Mastra uses storage to remember execution state, so you can pause indefinitely and resume where you left off. Context management — Give your agents the ability to store and retrieve information across interactions." — mastra-ai/mastra on GitHub, 1 week ago

Mem0 + Mastra Integration (4 days ago)

"It exposes memory as two tools (Mem0-memorize and Mem0-remember) that Mastra agents use through standard tool-calling, with memories saved asynchronously." — Mem0.ai: State of AI Agent Memory 2026, 4 days ago

Google ADK: Tiered Context for Production (December 4, 2025)

"ADK's Context Engineering scales production AI agents. Architect tiered context for efficiency, reliability, and multi-agent context scoping." — Google Developers Blog, December 4, 2025

Agentic RAG: Survey (4 days ago)

"Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG. Embedding autonomous AI agents into RAG systems to transcend traditional retrieval limitations." — arXiv:2501.09136, 4 days ago

Multi-Agent Frameworks Comparison

Framework	Context Type	Pause/Resume	Cost Reduction	License
★ agent-memory	TTL + Encrypted	Yes	Auto-prune stale	MIT
Mastra	Observational	Yes	10x cut	MIT
Google ADK	Tiered	—	Scoped	—
Agentic RAG	Retrieval-aug	—	Long-context	Open

How agent-memory Solves Multi-Agent Context Management

TTL auto-expiration — stale context auto-deleted, matching Mastra's cost-efficient memory
AES-256 encryption — sensitive context encrypted at rest
Redis backend — shared context across multiple agents in production
Pause and resume — agent-memory persists state, supports indefinite pauses
MIT license — deploy commercially without restrictions

# agent-memory: multi-agent context management
pip install agent-memory

# Run with Redis for shared multi-agent context
python -m agent_memory.mcp_server \
  --storage redis \
  --host localhost \
  --port 6379 \
  --ttl 604800 \
  --path ./context-management

# Your agents now:
# - Share context across sessions (Redis backend)
# - Auto-expire stale context (TTL)
# - Encrypted at rest (AES-256)
# - Survive restarts (pause and resume)

agent-memory on GitHub VentureBeat: Observational Memory