Shared Memory for AI Agents: 4 Options for a Team Move

Engineers moving laptop memory into a team service need to decide which records the team actually needs. Separate personal preferences and private conversations from reviewed project facts before choosing a shared destination. This guide uses the selected records and user-access tests to bound the move.

Key takeaways

Should you copy all laptop memory into a shared service?

No. Share the information that another engineer needs to do the work, not every item your agent has learned about you. A preference such as “show me concise diffs” belongs to your own assistant context; a reviewed decision such as “the worker retries this queue three times” belongs with the project record.

Under NIST’s least-privilege definition, users and processes receive only the access needed for their assigned tasks. In practice, label a record as personal or project-shared when it is written, identify which teammates need it, and grant access to that group rather than using one shared account for every engineer.

Keep the record’s source and status with it. For a team decision, that might mean storing the decision text, its date, the project name, and a link or reference to the approved design. This gives another engineer context to judge whether the note still applies, instead of treating an old laptop memory as current project truth.

Which four destinations fit a laptop-to-team move?

Choose first by who will own the memory workflow: the managed API, your application, or the team curating organizational knowledge for MCP access.

For a useful decision, compare all four destinations using the same selected records, queries, and identities, and assess retrieval quality, latency, and operating effort in your intended deployment.

When does a managed memory API fit?

Choose Mem0 Platform when your agents turn conversations into reusable memories and your team prefers an API-led service over designing every memory operation. In Mem0’s Organizations and Projects documentation, organization and project scope resolve automatically from the API key. The application can use a different key for each project boundary.

Mem0’s Add Memories API extracts memories from conversation turns and requires at least one entity identifier, such as a user, agent, run, or app. That is a useful distinction for a three-person team: keep per-engineer preferences under individual identifiers, and use project membership and a project-specific key to target shared project data.

These identifiers and project keys define memory and project scope, but do not by themselves decide what an individual end user may read or write in your application. Authorize each read and write at the application level and protect the keys.

The request also accepts custom key/value metadata and an optional expiration date. Mem0’s documentation says expired memories remain stored and are hidden from search and get-all by default; expiration is not deletion.

When should you build around Qdrant Cloud?

Choose Qdrant Cloud when the team wants to own the application layer that converts project activity into searchable records. In Qdrant Cloud’s database-key documentation, API keys can be restricted to selected collections and set to read-only or read/write.

This gives you a place to define collections such as project-decisions or service-runbooks, then decide which application components may search or update each collection. A database key controls access to Qdrant data; your application still needs to identify the person or agent making a request, decide whether that identity can see the record, and determine which conversation material becomes durable memory.

The same Qdrant documentation states that removing a key’s creator from the account does not revoke the database key. Revoke the relevant keys as part of offboarding and verify both application-user access and service-key access. Choose the narrowest key permissions that suit each client, and keep key handling, project membership, record updates, and deletion behavior in the application plan. This route suits developers who want to tune retrieval and record shape; it adds implementation work for memory extraction and team policy.

When is PostgreSQL with pgvector the better fit?

Choose PostgreSQL with pgvector when your project knowledge already belongs in relational records and you want vector search beside those records. The pgvector v0.8.5 README documents exact and approximate nearest-neighbor search. That lets one application store structured facts and search their vector representations through PostgreSQL.

By default, pgvector’s exact nearest-neighbor search returns the exact nearest neighbors under the chosen distance and filter definition (“perfect recall” in that retrieval sense); this does not mean perfect semantic relevance, complete evidence, or answer accuracy. Embedding, corpus, and query quality still matter.

Approximate search trades some recall for speed. An index on a filter column can provide fast, exact nearest-neighbor search in many cases, and the pgvector README recommends it as a starting point. For a small engineering knowledge set, begin with the exact path if it meets your test needs, then measure the queries that matter before adding approximate indexing.

PostgreSQL with pgvector keeps the database foundation familiar when your team already operates PostgreSQL, but your application still defines what qualifies as a memory, how to extract it, how to combine structured filters and similarity search, and who can read or update it. This path suits teams prepared to own those decisions as code and database policy. It is a database-backed application project rather than a ready-made personal-memory API.

When does AKB fit instead of a personal-memory service?

Choose AKB when the shared records are durable project knowledge that multiple agents should find, read, and update. AKB provides organizational knowledge and memory to AI agents through MCP, with documents, tables, files, document revision history, hybrid semantic and keyword retrieval, and explicit relations.

The current AKB README, checked October 14, 2026, describes PostgreSQL-native document revisions for new installations and continued support for explicit legacy Bare Git deployments. Operating footprint depends on the selected deployment; evaluate its backup, worker, file-store, and embedding dependencies.

For example, a team can keep an approved service decision, its supporting project document, and a related runbook in a shared knowledge space. An agent can retrieve that maintained project material when a teammate asks how a service is configured. This is a knowledge-management model: the team curates durable records as part of its working information.

AKB core is a read/write knowledge store. It does not itself include a knowledge gardener, so an ingestion or curation workflow that collects and updates material is a separate component. Add automation only when your process needs it, and treat updates to original sources as a separate operating task from adding another department or workflow to the shared store.

The current AKB LICENSE identifies Business Source License 1.1. Its conditional production-use grant applies below 100 Named Seats aggregated across the related deployments and organizations defined in the license.

A separate commercial license is required at or above that aggregate threshold and for any third-party hosted, embedded, or rebranded offering, regardless of seat count.

Each version has its own Apache License 2.0 change date four years after its first public distribution. Retain the selected release/commit and its LICENSE; the MIT proxy license is separate from the core license.

What should you test before moving records?

Run a small project pilot with one shared item and one private item. Use the pilot to test identity, retrieval, update, and removal behavior in the exact clients and deployment you intend to use.

1. Keep one personal example private: Choose a harmless preference or work note that should remain visible only to its owner, and record which user identity or private space should contain it.
2. Move one approved project fact: Put a reviewed configuration decision or runbook excerpt into the destination’s shared scope, along with its project label and source reference.
3. Test intended access: Query the shared item as each teammate and confirm the private example stays out of those results. Then remove one test user’s access and confirm a new request is denied.
4. Test the memory lifecycle: Update the project fact, retrieve the current version, and remove the test record. Confirm that your app or knowledge workflow applies those changes to the shared record, not just to a local copy.
5. Choose the operating model: Select Mem0 for extracting and storing conversation memories, Qdrant Cloud for collection-scoped API-key controls, PostgreSQL with pgvector for exact or approximate nearest-neighbor search, or AKB for versioned organizational knowledge through MCP.

This keeps the move small enough to check the records and access rules before making the team’s project knowledge available to more agent workflows.

Keep a move inventory

For each selected record, note its source, sensitivity, intended audience, reviewer, destination identity, and update/removal rule. Leave personal context outside the team move unless its approved purpose and access scope call for sharing it. Apply that rule to copied text and generated summaries as well as source links.

A project API key selects a service scope; the application still needs to authorize the end user. Test a removed teammate against search, snippets, citations, and retained derived records, then document the observed propagation time.

Related background: Expanding Agent Knowledge to a New Team Without Rebuilding

Related reading

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Tracing an Agent Answer Back to a Source Version

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