Shared Memory for AI Agents That Work from the Same Context
Different agents. A continuing foundation.
Build shared memory for AI agents with EverOS. Give connected workflows access to relevant user context and retained experience, so a handoff does not have to become a fresh start. Keep ownership and scope explicit as information moves between tasks, sessions, and agent hosts.
Keep the handoff from losing the context
One agent develops the plan. Another writes the code. A third reviews the result. The task can move between them while the reasons behind it stay trapped in the first conversation.
Passing a final instruction helps, but it may omit the user's constraints, the earlier decision, or the approach that has already been tested. Shared memory for AI agents gives your system a durable place to retrieve that context when another agent needs it.
The goal is not to let every agent read everything. It is to make relevant context available to the right workflow, with the correct owner, application, and project boundaries. A shared foundation should improve continuity without turning distinct users and tasks into one undifferentiated memory pool.
Share understanding, not just messages
An agent handoff can contain a transcript, an instruction, or a link to a result. Those artifacts describe part of the work, but they are not necessarily organized for the next agent's question.
With shared memory for AI agents, a new workflow can retrieve relevant episodes, facts, and profiles instead of relying only on the material manually pasted into its prompt. Agent experience can remain separately represented as Cases and Skills.
That separation matters. Understanding a person's preference is not the same as reusing an agent's procedure. Both can help the next task, but they should not be mixed together without a clear reason.
What shared memory for AI agents should preserve
Start from the context your system needs to carry forward. Some information belongs to the user. Some belongs to an agent's experience. Some is meaningful only inside a particular application or project.
User context across connected workflows
User profiles capture persistent attributes and preferences. Episodes preserve meaningful interactions and the facts extracted from them. Multiple connected workflows can use that context when configured to retrieve it for the appropriate user.
For shared memory for AI agents, consistent identity is the foundation. A planning agent and a coding agent can work from the same person's context only if the application deliberately connects them to the correct memory owner and scope.
Experience from an agent's work
Agent Cases capture execution experience; Agent Skills represent reusable approaches distilled from that experience. We keep these separate from personal profiles and episodes.
A team may want to reuse a useful procedure, but not every previous run is a team-wide instruction. Your application should decide which experience belongs in the intended workflow and whether its conditions match the new task. Shared memory for AI agents should make useful experience accessible, not make all experience universally applicable.
The boundaries around the information
EverOS provides memory scoping through owner and application, project, and session identifiers. These dimensions help your system retrieve context from the intended part of its memory foundation.
They are not interchangeable. The owner answers whose memory you are retrieving. The application and project identify the operating context. The session can narrow the interaction history. Design these boundaries before expanding shared memory for AI agents across a larger set of tools.
Multi-agent memory with explicit identity and scope
The practical difference between coherent sharing and accidental mixing is often a small configuration decision. Make that decision explicit rather than relying on whichever default identity an integration uses.
Choose the owner track
User memory and agent memory are separate first-class tracks. In the current API, get and search use exactly one of user_id or agent_id, with the memory type matching the owner track.
User-owned episodes and profiles should be retrieved for the user. Agent-owned Cases and Skills should be retrieved for the agent experience you intend to use. Shared memory for AI agents does not mean passing both identities into a request and expecting the service to merge them.
Keep application and project boundaries stable
The application and project identifiers partition memory operations. Queries do not cross those scopes automatically. Keep the identifiers consistent across the integrations that should work within the same context.
A connected agent using a different project may correctly receive no results even though relevant information exists elsewhere. A shared default scope can also be too broad for your needs. Treat scope design as part of building shared memory for AI agents, not as an implementation detail to fix after rollout.
Narrow to a session when needed
A session identifies a particular interaction stream. Use consistent session identifiers when related messages belong together, and narrow retrieval when the task needs context from a particular conversation.
Session-specific retrieval can also expose the most recent unprocessed messages in the supported search flow. This helps distinguish recent interaction context from information that has already been extracted into durable episodes.
Keep authorization in the application
Memory scoping is a retrieval boundary, not a complete access-control policy for your users and tools. Your application still decides which callers may access a user's context and which actions a connected agent may perform.
Shared memory for AI agents should be paired with those application-level decisions. A stored plan is not permission to deploy it. A retained preference is not permission to disclose it to another user. Keep the scope of knowledge separate from the authority to act.
A practical cross-agent handoff
Build the first handoff around a simple task with a clear owner and project. Then verify that the receiving agent gets the intended context without inheriting unrelated information.
Capture the useful interaction
The first agent records the interaction through the memory loop. Identify the sender and session consistently, and preserve the information that will matter after the immediate task ends.
For example, a user might specify the target audience, a technical constraint, and a decision that was already made. Those details can form useful context for shared memory for AI agents. The full tool log may not be necessary for the next agent to understand the task.
Allow the write lifecycle to complete
Cloud writes are asynchronous by default. Recent messages may remain buffered until a subsequent add, a flush, or idle session closure. Vector indexing can also lag extraction.
Account for that lifecycle before concluding that a handoff failed. Shared memory for AI agents is not a promise of instantaneous synchronization after every message. If the next step requires immediate context, follow the supported write, extraction, and retrieval flow rather than treating a queued acknowledgement as completion.
Retrieve for the receiving task
The receiving agent searches within the intended identity and scope, then uses relevant context in its prompt. The query should reflect what that agent needs to accomplish now, not request every stored detail by default.
A coding agent may need the technical decision and constraints. A writer may need the audience and tone. Shared memory for AI agents is more useful when retrieval is task-specific, even when both agents use the same foundation.
Return new context to the foundation
When the second agent completes its work, record the new interaction and retain useful execution experience in the appropriate track. The result becomes available for future work through the same lifecycle.
This closes the loop without requiring every agent to duplicate the full memory history locally. You still need to decide what belongs to the user, what belongs to the agent, and what remains specific to the project.
Where cross-agent memory helps
Start with workflows where repeated explanation is already a problem. The following are illustrative patterns for connected systems, not claims that every integration automatically performs these tasks.
Planning to implementation
A planning workflow establishes requirements and trade-offs. An implementation workflow needs those decisions to produce the right result.
Shared memory for AI agents can preserve the context behind the plan so the receiving agent can retrieve it alongside the current instruction. It still needs access to the relevant tools and should verify implementation details in the actual environment.
Research to writing
A research agent gathers and evaluates sources. A writing agent needs the approved direction, the important findings, and the distinction between a confirmed fact and an open question.
Keep that distinction in the handoff. Shared memory for AI agents can retain useful context, but a remembered claim is not automatically current evidence. The writing workflow should still check sources when the task requires up-to-date information.
Personal context across agent hosts
You may use different assistants for planning, coding, and other work. Repeating your background in each tool can create conflicting versions of the same preference.
EverOS provides a portable memory layer across supported integrations. For a personal memory experience, EverMe also lets supported agents work from a shared foundation about you. Choose the product and setup that match whether you are building an application or connecting your own assistants.
Keep shared memory for AI agents current
A shared mistake can spread across several workflows. Review the information that is repeatedly reused, and provide a correction path when a detail is outdated or attributed incorrectly.
EverOS Cloud includes profile editing, episode correction, deletion, and feedback operations. Our memory console lets you inspect profiles, episodes, Cases, and Skills by owner and type. These controls help you address the underlying context rather than rewriting the same incorrect output in every agent.
A rating is not a correction
Feedback records a signal about a memory. It does not immediately rewrite that memory. Use the correction or deletion operation when the underlying information needs to change.
For shared memory for AI agents, this is especially important: a negative rating should not be treated as confirmation that every connected workflow now sees a corrected fact. Verify the resulting memory through the supported read surface.
Keep deployment controls aligned
Cloud and open-source EverOS share core memory operations, while some lifecycle endpoints differ in availability. The self-hosted runtime provides readable Markdown and local indexing; several API correction and feedback operations are Cloud-specific.
Design the control path for the deployment you actually use. Self-hosting also requires deliberate provider configuration. Running the runtime locally does not by itself prove that no data is processed by an external model service.
Shared context is not task orchestration
Memory and orchestration complement each other. Orchestration assigns work, coordinates dependencies, and decides what happens next. Memory retains and retrieves information that can help each step.
Use shared memory for AI agents as the context foundation beneath your workflow. Keep task routing, tool permissions, approvals, and output checks in the appropriate application or orchestration layer. A memory service does not become an execution scheduler simply because multiple agents use it.
That separation lets you improve continuity without changing every part of the stack at once. Begin with one handoff and one well-defined scope; add more connected workflows after the boundary and lifecycle checks pass.
Connect the memory layer you need
Our ecosystem includes Claude Code, Codex, OpenClaw, Hermes, and MCP integrations, alongside supported API and SDK access for your own applications.
Choose Cloud when you want managed memory infrastructure. Choose the open-source runtime when you want to configure and operate the memory stack yourself. The local stack uses Markdown, SQLite, and LanceDB, with optional providers enabling additional retrieval and memory-evolution capabilities.
Before expanding shared memory for AI agents, verify:
- The receiving agent uses the intended owner, application, and project.
- Another user or project does not inherit the same personal context.
- A new session can retrieve the information that should persist.
- A recent write is handled correctly during extraction and index delay.
- A corrected memory is read back through the appropriate control surface.
- Current task instructions and tool permissions remain authoritative.
Frequently asked questions
What is shared memory for AI agents?
It is a memory foundation that connected agents can use to retrieve relevant context across workflows. EverOS separates user profiles and episodes from agent Cases and Skills and provides owner, application, project, and session scoping. Sharing requires deliberate configuration rather than global access to all memories.
Can shared memory for AI agents keep user data separate?
We provide user and agent owner tracks plus application and project scope boundaries. Your application must choose those identities consistently and enforce the appropriate access policy. Retrieval scoping helps separate context, but it is not a substitute for application-level authorization.
Is multi-agent memory the same as passing a transcript?
No. A transcript passes a sequence of messages. Structured memory gives the receiving agent a way to retrieve relevant profiles, episodes, facts, or execution experience for its own task. You can still pass immediate instructions alongside the selected memory.
Can cross-agent memory work across coding assistants?
Our ecosystem includes Claude Code, Codex, OpenClaw, Hermes, and MCP integrations. Configure the hosts that should connect to your memory foundation, then verify their identities and scopes. Different installation paths and defaults should not be assumed to create the same shared context automatically.
Does shared memory for AI agents synchronize instantly?
Not necessarily. Cloud writes are asynchronous by default, recent messages may be buffered, and vector indexing can lag extraction. Follow the supported add, flush, and retrieval lifecycle when another agent needs recently written context.
Does cross-agent memory assign tasks or authorize actions?
No. Memory provides context. Your application or orchestration layer assigns tasks and manages tool access, approvals, and execution. Retrieving a past plan does not grant permission to carry it out or to disclose its contents to another user.
Let the next agent continue with context
Start with one handoff that currently loses useful information. Define the owner and scope, capture the interaction, and verify retrieval from the receiving workflow. Build shared memory for AI agents around continuity you can test and boundaries you can explain.
