ARCHITECTURE

Multi-Agent Memory: How Teams of AI Agents Share Context

March 30, 2026 · 10 min read

The era of single-agent AI is ending. Modern applications deploy teams of specialized agents — a researcher, a coder, a reviewer, a coordinator — each with distinct capabilities working toward a common goal. CrewAI, AutoGen, LangGraph, and OpenClaw have made multi-agent orchestration accessible. But they all share one critical gap: memory.

How does Agent A know what Agent B discovered? How does a handoff between a sales agent and a support agent preserve context? How do you prevent Agent C from contradicting what Agent D already told the user?

0Latency was designed from the ground up for multi-agent memory. Every API call is agent-scoped. Memory can be private, shared, or selectively visible. Session handoffs preserve full context. And it works with every major orchestration framework out of the box.

The Multi-Agent Memory Problem

Without shared memory, multi-agent systems suffer from three failure modes:

1. Context Amnesia

Agent A gathers user requirements through a conversation. Agent B is supposed to execute on those requirements. But Agent B has no memory of Agent A's conversation. The user has to repeat themselves, or worse, Agent B makes assumptions based on incomplete information.

2. Contradictory Responses

Agent A tells the user "Your order will arrive Tuesday." Agent B, handling a follow-up, says "Delivery is expected Thursday." Both agents accessed different data sources at different times. Without shared memory of what was communicated, they contradict each other.

3. Duplicated Work

Agent A researches a topic and produces findings. Agent B gets a related question and performs the same research from scratch. Without shared memory, agents can't benefit from each other's work.

These aren't edge cases. They're the default behavior of every multi-agent system that relies on passing context through function calls or message queues. The moment the context window resets, everything is lost.

Agent-Scoped vs Shared Memory

0Latency provides two memory visibility models that can be combined freely:

Agent-Scoped Memory (Default)

Every memory is owned by the agent that created it. Agent A's memories are invisible to Agent B by default. This is important for:

• Preventing context pollution between unrelated agents
• Maintaining clear ownership of information
• Security — some agents handle sensitive data

Shared Memory Pools

Agents can be organized into groups that share a common memory pool. Any agent in the group can read and write to the shared pool while maintaining their own private memories.

import requests

API_KEY = "your-api-key"
BASE = "https://api.0latency.ai"

# Create a shared memory group
requests.post(f"{BASE}/groups", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "group_id": "customer-support-team",
    "agents": ["triage-agent", "billing-agent", "technical-agent"],
    "shared_memory": True,
    "share_mode": "read_write"  # or "read_only" for some agents
})

# Triage agent stores a memory — visible to the whole group
requests.post(f"{BASE}/memories/extract", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "agent_id": "triage-agent",
    "content": "Customer reported billing discrepancy on invoice #4521. They were charged $299 instead of $99 (Scale plan pricing).",
    "metadata": {"user_id": "customer-456", "ticket_id": "T-7890"},
    "visibility": "group"  # Shared with the group
})

# Billing agent searches — finds triage agent's memory automatically
results = requests.post(f"{BASE}/memories/search", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "agent_id": "billing-agent",
    "query": "invoice issues for customer-456",
    "user_id": "customer-456",
    "include_shared": True  # Search own memories + group shared
})

JavaScript

// Create agent group
await fetch(`${BASE}/groups`, {
  method: "POST",
  headers: {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    group_id: "research-team",
    agents: ["web-researcher", "data-analyst", "report-writer"],
    shared_memory: true,
    share_mode: "read_write"
  })
});

// Web researcher stores findings — shared with the team
await fetch(`${BASE}/memory`, {
  method: "POST",
  headers: {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    agent_id: "web-researcher",
    content: "Found 3 competitor products launching in Q2: AlphaAI ($49/mo), BetaBot ($79/mo), GammaGen (freemium).",
    metadata: { topic: "competitive-analysis", confidence: 0.9 },
    visibility: "group"
  })
});

// Report writer can access researcher's findings
const results = await fetch(`${BASE}/memories/search`, {
  method: "POST",
  headers: {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    agent_id: "report-writer",
    query: "competitor landscape Q2",
    include_shared: true
  })
});

Session Handoff

One of the most common multi-agent patterns is session handoff: Agent A handles a conversation, then transfers to Agent B. The handoff needs to preserve full context without dumping the entire conversation history into Agent B's prompt.

0Latency's handoff API creates a compact context summary and transfers relevant memories:

# Agent A initiates handoff to Agent B
handoff = requests.post(f"{BASE}/sessions/handoff", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "from_agent": "triage-agent",
    "to_agent": "billing-agent",
    "session_id": "session-abc-123",
    "user_id": "customer-456",
    "handoff_context": "Customer needs billing adjustment for invoice #4521. Overcharged $210. Verified account ownership.",
    "transfer_memories": True,     # Copy relevant session memories
    "memory_filter": {
        "min_relevance": 0.5,      # Only transfer relevant memories
        "categories": ["billing", "account"]  # Filter by category
    }
})

# Agent B now has full context
print(handoff.json())
# {
#   "handoff_id": "ho-789",
#   "memories_transferred": 4,
#   "context_tokens": 850,
#   "summary": "Customer needs billing adjustment for invoice #4521..."
# }

Framework Integrations

0Latency integrates natively with the most popular multi-agent frameworks. Here's how to add persistent shared memory to each:

CrewAI

from crewai import Agent, Task, Crew
from zerolat import ZeroLatencyMemory

# Initialize 0Latency memory backend
memory = ZeroLatencyMemory(
    api_key="your-api-key",
    group_id="research-crew"
)

# Create agents with shared memory
researcher = Agent(
    role="Senior Researcher",
    goal="Find comprehensive market data",
    memory=memory.for_agent("researcher"),
    verbose=True
)

analyst = Agent(
    role="Data Analyst",
    goal="Analyze research findings and identify trends",
    memory=memory.for_agent("analyst"),
    verbose=True
)

writer = Agent(
    role="Report Writer",
    goal="Produce clear, actionable reports",
    memory=memory.for_agent("writer"),
    verbose=True
)

# Memories persist across crew runs
# Analyst can access what researcher found in previous sessions
# Writer can access both researcher and analyst findings
crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, writing_task],
    memory=memory  # Shared memory backend
)

AutoGen

import autogen
from zerolat import ZeroLatencyMemory

memory = ZeroLatencyMemory(
    api_key="your-api-key",
    group_id="autogen-team"
)

# AutoGen agents with persistent memory
assistant = autogen.AssistantAgent(
    name="assistant",
    system_message="You are a helpful AI assistant.",
    llm_config=llm_config
)

# Wrap with 0Latency memory
assistant = memory.wrap_autogen_agent(assistant, agent_id="assistant")

coder = autogen.AssistantAgent(
    name="coder",
    system_message="You write Python code to solve problems.",
    llm_config=llm_config
)
coder = memory.wrap_autogen_agent(coder, agent_id="coder")

# Memories from assistant's conversations are available to coder
# and persist across sessions

OpenClaw

# OpenClaw has native 0Latency integration
# In your AGENTS.md or skill configuration:

# Store memory
/root/.openclaw/workspace/skills/0latency-memory/store.sh \
  "Research finding: competitor AlphaAI launching Q2 at $49/mo" \
  "Web research by researcher agent, March 2026" \
  "research-crew"

# Recall across agents
/root/.openclaw/workspace/skills/0latency-memory/recall.sh \
  "competitor pricing Q2"
# Returns memories from ALL agents in the group

Memory Visibility Patterns

Different multi-agent architectures need different memory visibility patterns. Here are the three most common:

# Hub-and-spoke: coordinator sees everything, workers see own + coordinator
requests.post(f"{BASE}/groups", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "group_id": "support-hub",
    "agents": [
        {"id": "coordinator", "role": "hub", "visibility": "all"},
        {"id": "billing-agent", "role": "spoke", "visibility": "own+hub"},
        {"id": "technical-agent", "role": "spoke", "visibility": "own+hub"},
        {"id": "escalation-agent", "role": "spoke", "visibility": "own+hub"}
    ]
})

Conflict Resolution

When multiple agents write to shared memory, conflicts can arise. Agent A stores "Customer prefers email contact" while Agent B stores "Customer prefers phone contact" (based on a more recent conversation). 0Latency handles this through:

• Temporal scoring: More recent memories naturally rank higher in retrieval
• Source attribution: Every memory records which agent created it, when, and in what context
• Confidence scoring: Agents can attach confidence levels; higher confidence wins ties
• Deduplication: Semantically identical memories are merged, preserving the most recent metadata

Scaling Multi-Agent Memory

0Latency's pricing is designed for multi-agent workloads:

Shared memory pools don't count against individual agent limits — the memory limit is per account, not per agent. A 5-agent crew on the Pro plan shares the full 100,000 memory allocation.

Real-World Architecture: Customer Support System

Here's a complete multi-agent architecture using 0Latency for a customer support platform:

# Architecture: 4-agent support system with shared memory

# 1. Triage Agent — classifies and routes tickets
#    Memories: customer history, routing rules, escalation patterns
#    Visibility: reads all shared, writes to shared

# 2. Billing Agent — handles payment and subscription issues
#    Memories: billing history, plan details, refund policies
#    Visibility: reads own + triage + coordinator shared

# 3. Technical Agent — handles product/technical issues
#    Memories: bug reports, workarounds, product documentation
#    Visibility: reads own + triage + coordinator shared

# 4. Coordinator Agent — monitors quality, handles escalations
#    Memories: all agent interactions, quality metrics, escalation logs
#    Visibility: reads ALL agent memories

# Setup
import requests

API_KEY = "your-api-key"
BASE = "https://api.0latency.ai"

# Create the support team
requests.post(f"{BASE}/groups", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "group_id": "support-team",
    "agents": [
        {"id": "triage", "role": "spoke", "visibility": "all"},
        {"id": "billing", "role": "spoke", "visibility": "own+hub"},
        {"id": "technical", "role": "spoke", "visibility": "own+hub"},
        {"id": "coordinator", "role": "hub", "visibility": "all"}
    ]
})

# When triage hands off to billing:
requests.post(f"{BASE}/sessions/handoff", headers={
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}, json={
    "from_agent": "triage",
    "to_agent": "billing",
    "session_id": "ticket-T-7890",
    "user_id": "customer-456",
    "handoff_context": "Billing discrepancy on invoice #4521. Customer verified. Needs $210 credit.",
    "transfer_memories": True
})

# Billing agent immediately has full context — no user repetition needed

This architecture handles hundreds of concurrent support sessions with full context continuity across agent handoffs. Every interaction is persisted, every handoff is seamless, and the coordinator has complete visibility for quality assurance.

Getting Started

Multi-agent memory works out of the box. The basic flow:

1. Create agents — each agent gets a unique agent_id
2. Create a group — assign agents and define visibility rules
3. Store memories — set visibility: "group" for shared memories
4. Search across agents — use include_shared: true
5. Hand off sessions — use the /sessions/handoff endpoint

Check the documentation for detailed API reference, and explore our integration guides for your preferred framework.