You've built a crew of specialized agents. But when Agent B takes over from Agent A, it has no idea what Agent A learned. Memory solves the handoff problem.
Gathering market data...
Processing research findings...
Drafting report...
Final review complete
Multi-agent architectures are powerful in theory. You have a research agent that gathers information, an analysis agent that processes it, and a writing agent that creates the final output. Each agent is specialized and good at its job.
But in practice, they fall apart at the handoffs:
When Agent A hands off to Agent B, you have to manually pass context. What do you include? Everything? A summary? You end up either losing important details or hitting token limits.
Each agent operates in isolation. The writing agent doesn't know that the research agent found conflicting data, or that the analysis agent had low confidence in certain conclusions.
When something goes wrong, you can't trace what each agent knew at each step. Was the error in research, analysis, or writing? Without a shared memory layer, you're guessing.
MemoryStack gives your agents a shared brain. Every agent can store what it learns and retrieve what others have stored. No manual context passing. No token limit juggling. Just seamless collaboration.
When your research agent stores a finding, your analysis agent can retrieve it instantly. No explicit handoff code required — just search for what you need.
Agents don't need to know exactly what to ask for. Search by meaning, not keywords. "What did we learn about competitor pricing?" returns relevant memories even if they weren't tagged that way.
Every memory tracks which agent created it and when. Debug issues by tracing the exact path information took through your system.
A research agent gathers data from multiple sources. An analysis agent identifies patterns and insights. A writing agent produces the final report. Each agent builds on what came before.
Used by: Consulting firms, market research teams, due diligence workflows
A triage agent categorizes incoming tickets. A specialist agent handles domain-specific questions. A resolution agent confirms the fix. Full context flows through every step.
Used by: SaaS companies, e-commerce platforms, technical support teams
An ideation agent generates topics. A research agent gathers supporting data. A writing agent creates drafts. An editing agent polishes the final piece. Consistent voice throughout.
Used by: Marketing teams, content agencies, media companies
MemoryStack integrates with CrewAI, LangGraph, AutoGen, and any custom agent framework. Add shared memory without rewriting your agents.
from memorystack import MemoryStack
client = MemoryStack(api_key="your-api-key")
# Research agent stores findings
await client.add(
content="Market analysis: AI adoption up 40% in enterprise",
agent_id="research-agent",
metadata={"type": "research", "topic": "market-trends"}
)
# Analysis agent retrieves research automatically
research = await client.search(
query="market trends AI adoption",
agent_id="analysis-agent" # Gets research agent's findings
)
# Writing agent gets full context from both agents
context = await client.search(
query="report context market analysis",
agent_id="writing-agent" # Gets research + analysis
)
# Every agent sees what came before
# No manual context passing requiredStart with 1,000 free memories. Build agent crews that actually collaborate.