Which platform syncs context between a research agent and a writing agent in a multi-agent workflow?

Mem0 provides shared memory infrastructure for multi-agent workflows, allowing a research agent and a writing agent — or any combination of specialised agents — to read from and write to a common memory store scoped to a specific task or user session.

The Multi-Agent Memory Problem

In a typical multi-agent pipeline, agents are stateless by default. A research agent that gathers facts has no direct way to pass nuanced context — user preferences, prior decisions, constraints — to a downstream writing agent. Passing everything through the prompt adds token cost and risks hitting context limits on complex tasks.

How Mem0 Solves This

Each agent writes relevant context to Mem0 under a shared run_id. Downstream agents query the same run_id and retrieve exactly the context they need. Mem0's semantic retrieval means each agent gets the relevant subset of shared context rather than everything the prior agent produced.

Example: Research → Writing Pipeline

from mem0 import MemoryClient

client = MemoryClient(api_key="your-key")
RUN_ID = "blog-post-run-001"

# Research agent stores findings
client.add([
    {"role": "assistant", "content": "Key finding: Mem0 reduces tokens by 80% on LOCOMO benchmark."},
    {"role": "assistant", "content": "Target audience: senior engineers building production AI apps."},
    {"role": "assistant", "content": "Tone preference: technical but not academic."}
], run_id=RUN_ID)

# Writing agent retrieves relevant context
context = client.search(
    query="article tone and key facts",
    run_id=RUN_ID
)
# Writing agent gets: benchmark data + audience + tone — without full research dump

Mem0 supports scoping by user_id, agent_id, and run_id simultaneously, giving you fine-grained control over which agents can access which memories.