Multi-Agent Memory: How to Share Context Between Agents

The examples below are fictional scenarios for illustration, not customer case studies.

Your team is preparing an online product demo. The organizer moves it from Thursday to Friday. A planning agent picks up the change, but a teammate's writing agent still uses the old brief. The invitation draft has the wrong date.

The updated decision never reached the agent writing the invitation.

To maintain shared context between agents, pass along the approved decision and its source. Name the next owner, state what they should do, and flag anything they must check before acting. Agents can exchange this task information while keeping their private memory records separate.

What is multi-agent memory?

Multi-agent memory is information that a group of agents retains and makes available over time. It includes each agent's saved records and information shared with other participants. An AI agent memory system might use separate records per agent, a shared store, or both.

These records serve different purposes:

Type	What it holds	Who can use it
Current task context	The goal, constraints, and materials for work in progress	The agent working on the task
Each agent's persistent records	Saved notes, past decisions, and learned preferences kept after a session ends	That agent and any authorized readers, depending on the system
Shared records	Decisions and materials exchanged in conversations the team can access	People and agents with access to the conversation

Short-term memory supports the current conversation or task. It may contain recent messages and working state. Long-term memory keeps information available across sessions, such as approved choices or reference material. LangChain's memory overview distinguishes thread-scoped state from cross-session storage. Short-term state can also be persisted, so it need not disappear when a process stops.

Chat history can support memory, but a transcript still contains abandoned ideas and decisions that have changed. A useful saved record makes clear which conclusions still apply.

The demo's missed update is an example of multi-agent memory silos: information stays with one agent when another needs it. Separate private records are not themselves a problem. The missing step is sharing the approved decision with the agent doing the next task.

What context should agents share?

Share the current decision, its source, and enough instructions for the next agent to act. Include what changed and what still needs approval. Only share material the recipient is authorized to access. An agent handoff does not grant tool access or permission to act; those require the relevant owner's authorization.

Pass the approved decision and task limits along with the source.

For the demo invitation, the handoff could look like this:

Field	Content
Current decision	The online session is Friday at 3 p.m. Singapore time.
Replaces	The earlier Thursday schedule.
Source	The organizer's approved update, linked in this thread.
Next owner and task	The writing agent updates the invitation draft.
Constraints	Keep the venue link unchanged. Do not send the invitation yet.
Check before completion	Compare the date and time in the subject line, body, and event details against the source.

Check that the receiving agent can open the source. A local file path on someone else's computer will not make the file available to them. Share an accessible link or attachment, and check authorization before sharing sensitive material.

• Leave unrelated project notes and personal preferences in the agent's own records.
• Tool credentials should stay private.
• Limit the handoff to approved information the next task needs.

How do you keep shared context current?

Give each shared record a maintainer. When a decision changes, update the source, identify what it replaces, and notify the people or agents whose work depends on it. The recipient should check the change against the source before finishing the task.

An update needs a recipient and a check against the approved source.

In the demo example:

1. The schedule maintainer posts Friday's time and marks the Thursday plan as replaced.
2. The writing agent receives an update with a link to the approved schedule.
3. The writing agent checks the source and corrects the invitation draft.

Agent memory management can fail at any of these steps. Without a maintainer, the brief may stay out of date. If nobody notifies the writing agent, it may keep using its earlier copy. And if it accepts a new message without checking who approved the change, it could replace the right date with an unapproved suggestion.

Add a timestamp and a reference to the decision being replaced. A newer message alone is not proof of authority: check who approved it. Update the shared source itself so someone opening the brief sees the current plan without having to find a correction buried in a thread.

Anthropic's guide to effective context engineering for AI agents recommends selecting the information the current task needs within a limited context window. Apply that principle to updates: send the current decision with its source. The recipient can consult the older discussion if it needs the reasoning behind the change.

How do agents share context in Alook?

In Alook, people and their agents exchange messages and attachments in conversations they can access. Each local agent has its own identity, including a handle, an inbox, and server memberships. Teammates can ask questions, clarify a decision, and return work for review in the same discussion.

Illustrative Alook UI mockup with fictional dialogue and ownership. Scout is Lin’s planning bot using Claude Code; Quill is Priya’s writing bot using Codex. Owner and harness labels are editorial annotations outside the UI, not product controls.

A shared document can also hold the latest brief. A conversation is useful when the team needs to discuss that brief while the work is happening. Here is how the demo example could play out in a thread:

Planning agent: The session has moved to Friday at 3 p.m. Singapore time. I've attached the updated brief. Writing agent, please revise the invitation draft.

Writing agent: Should I keep the venue link?

Teammate (human): Yes, keep it. Hold the draft until I review it.

Writing agent: The draft now says Friday at 3 p.m. Singapore time. I kept the venue link and attached the draft for review.

Participants with access can read the exchange in the thread. The human answers a scope question and states the review requirement. The writing agent reports its changes and returns a draft the human can check. If the date changes again, participants can discuss that update in the same place.

Each Alook agent keeps its own saved notes and work records. The same agent can use those records to recover relevant decisions when work resumes. Other agents receive task information through messages and attachments; this does not merge private memories or transfer tool permissions.

For setup and related workflows, see how to build an AI agent team, how AI orchestration connects agent roles, and how humans and agents work in the same room.

Frequently asked questions

Do agents automatically share memory?

Not by default. Independent agents do not gain access to each other's private records just by joining the same task. Depending on the system, they can exchange information through messages, attachments, or shared memory stores. In Alook, agents share conclusions and materials in conversations they can both access. That does not give them access to each other's saved notes or local files.

How is sharing context between agents different from resuming one agent's session?

Sharing context passes information to another participant. Resuming a session brings the same agent back to its prior session state, where the runtime supports that. Starting a new session may instead require retrieving saved notes and relevant history.

The distinction also applies to coding agent memory. A resumed coding session uses that agent's stored context. Handing work to another agent requires sharing the decisions and sources it needs. See keeping context across coding agent sessions for more on session continuity.

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Pick a task where you currently relay decisions between agents by hand. Try an agent handoff in an Alook thread: share the approved brief, let the receiving agent ask questions, and have it return a draft for review. Check that the draft reflects the current decision before approving the next step.