How do multiple AI agents share memory?

Either they pass context to each other directly, or they read and write a shared store. A shared memory layer such as Membase gives every agent its own key to one long-term memory, so what one agent saves, the others can search.

Can agents built with different frameworks share one memory?

Yes, if the memory sits outside the frameworks. Membase is reached over a REST API, Python and TypeScript SDKs, and MCP, so a LangChain agent, a script and Claude Code can all use the same Memory.

How do I stop one agent from changing what the others rely on?

Give it a Read key. Only agents with Read & write keys can add to a Memory, and only Full access keys can delete, and only when the call passes an explicit confirmation.

Is shared agent memory real-time?

No. Membase accepts new material at once and learns it in the background. Use direct messages for second-by-second coordination, and memory for what should last.

Can I run shared agent memory without a cloud service?

Yes. Membase's local mode runs on your own machine with pip install 'unibaseio-membase[local]'.

What does Membase cost?

Membase is free during the beta. Paid plans are coming soon.

How to Share Memory Between Multiple AI Agents (2026 Guide)

When several AI agents work on the same job, each one usually starts with its own blank context. The planner decides something, and the coder never hears about it. The reviewer flags a problem, and next week the same mistake comes back.

There are three common ways to give agents a shared memory:

Pass context in the handoff. Each agent hands a summary to the next one.
Use your framework’s shared store. Agents inside one framework read and write the same store.
Use a shared memory layer. Every agent and AI tool reads one long-term memory through an API or MCP, whatever framework it runs in.

This guide explains when each one works, then shows the third option step by step with Membase.

Option 1: Pass context in the handoff

The simplest pattern: when agent A finishes, it writes a summary into the message that starts agent B.

Good for: short pipelines that run once, where every agent sees the whole job in one session.

Where it breaks:

Nothing survives the run. Tomorrow’s run starts from zero.
Summaries lose detail at every hop. By the third agent, the reason behind a decision is often gone.
Context windows fill up as the pipeline grows.

Option 2: Your framework’s shared store

Most agent frameworks have some form of memory or store that agents in the same app can share.

Good for: a system where every agent lives in one framework and one codebase.

Where it breaks:

The memory stays inside that framework. A coding assistant like Claude Code or Cursor, or an agent built with a different framework, cannot read it.
You own the plumbing: what to save, how to retrieve it, and who may write.

Option 3: One shared memory layer

A memory layer sits outside your agents. Each agent connects to it with its own credential, saves what the others should know, and searches it before it acts.

Good for: agents that run on different days, in different frameworks or tools, and need to build on each other’s work.

For giving a single agent long-term memory first, see How to Add Long-Term Memory to an AI Agent.

The rest of this guide sets this up with Membase. Membase is the memory layer for AI agents: one long-term memory that your code, Claude Code, Cursor, Codex and ChatGPT read through the SDK or MCP.

Step 1: Create a Memory for the shared work

In the Membase app, create a Memory for the project the agents share, for example “Ledger project”. A Memory is one topic in your account, and it learns what you and your agents add to it.

Step 2: Give each agent its own key

Under Connect › Developer keys, create one key per agent. Each key carries three settings:

Access level: Read, Read & write, or Full access.
Reach: which Memories the key may use.
Expiry: never, 30 days, 90 days or a year.

A typical split:

Planner agent: Read & write, reaching only “Ledger project”. It records decisions.
Coder and reviewer agents: Read, reaching “Ledger project”. They search before they act.

Writes from a key land in its write target, the one text Memory it reaches. If a key reaches several text Memories, Membase refuses the write instead of guessing where it should go. So a writer agent’s key should reach exactly one Memory.

Separate keys also mean you can see and stop each agent on its own. Revoking a key ends that agent’s access on its next call, and the other agents keep working.

Step 3: Save what the other agents should know

From the planner, using the Python SDK:

from membase import Membase

planner = Membase(api_key="mbk_...")  # Read & write key, reaching "Ledger project"
planner.memories.add("Decision: the ledger service uses Postgres. Reason: we need row-level security.")

The call returns at once with a receipt. Membase then learns the note in the background, so it may take a short while before a search can find it.

Step 4: Search before acting

From the coder, or any other agent:

from membase import Membase

coder = Membase(api_key="mbk_...")  # Read key, reaching "Ledger project"
hits = coder.search("Which database does the ledger service use, and why?", limit=5)
print(hits)

A search covers every Memory the key reaches, and each result names the Memory it came from, so an agent can tell which project a passage belongs to.

Step 5: Connect your AI coding tools to the same memory

Coding assistants can read the same memory over MCP. For Claude Code, with a developer key:

claude mcp add --transport http membase https://api.app.membase.com/mcp-http \
  --header "Authorization: Bearer $MEMBASE_API_KEY"

Without the header, Claude Code signs you in instead, and that connection is read-only. Setup for Cursor, Codex, ChatGPT and other clients is in Connect your AI. To give ChatGPT and Claude the same memory, see How to Share Memory Between ChatGPT and Claude.

Now a decision your planner agent saved on Monday is there when you open Claude Code on Tuesday.

Running everything locally

Membase also runs fully on your own machine:

pip install 'unibaseio-membase[local]'

Local mode has agent memory as well: it learns from an agent’s traces and keeps that agent’s cases and skills under its own id.

membase --local agent ingest trace.json --agent coder
membase --local agent search "fix flaky deploy" --agent coder

See the README for local requirements.

What a shared memory layer does not do

It is not a message bus. New material is learned in the background, so it is not instant. For agents that must coordinate within seconds, pass the message directly and save the outcome to memory afterwards.
It does not isolate your customers from each other. A Memory is one of your own topics, not a tenant. If your product serves many people, each person needs their own Membase account.
Searches cannot filter on metadata. Put what matters, such as the project, the date or the owner, into the text you save.

Which option should you use?

One short run, all in one session: pass context in the handoff.
Every agent in one framework, and no outside tools need the memory: use the framework’s store.
Agents in different frameworks or tools, or work that spans days: use a shared memory layer like Membase.

Many teams combine them: handoffs for the current run, and a shared memory for what should outlast it.