MemoryRouter

ai.memoryrouterPublished by memoryrouter.aiOfficial publisherv1.1.0Updated Oct 24, 2026

Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.

VerifiedStreamable HTTPWeb executableKnowledge & MemoryAI & ML

Overview

AI-generated overview

MemoryRouter is a remote MCP memory vault that lets an assistant search, store, consolidate and delete durable memories shared across Claude, ChatGPT and coding

What you can do
Search and recall memories semantically, or by date window, from the connected vault, with tiered results from raw memories to high-level reflections and a legacy search alias for clients that request it. Store a concise, user-approved fact as a new memory. Check connection status, the active vault binding and memory or token counts. Consolidate unconsolidated memories under a lease and commit written reflections with importance ratings. Inspect why the assistant believes something by tracing reflections back to source memories. Delete specific memories by id, or permanently wipe the whole vault after an exact confirmation phrase.
When to use it
Useful when you want an assistant to remember decisions, preferences, people and projects across sessions and across different clients, and to reflect on and organize those memories over time. Best for users who want explicit, user-approved memory rather than silent conversation capture.
Requirements
A remote streamable HTTP endpoint; no local package or runtime is needed. Access is scoped to an OAuth-selected vault, so an OAuth connection and a MemoryRouter vault are required. No environment variables or headers are declared.
Before you install
The server can permanently delete data: delete_memories removes specific memories, and forget_all_memories wipes the entire vault and cannot be undone. Deleting a raw memory does not delete reflections built from it. Stored memories persist and are shared across connected clients, so review what is saved. Consolidation checks out memories under a 15 minute lease.

Installation

In SourceWeft

  1. Open MemoryRouter in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/ai.memoryrouter%2Fmemoryrouter/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "memoryrouter": {
      "type": "http",
      "url": "https://mcp.memoryrouter.ai/mcp"
    }
  }
}

Tools

10

search_memories

Search memories

Use this when the user asks about a prior decision, preference, person, project, or other fact that may be in their connected MemoryRouter vault. Semantically searches only the OAuth-selected vault and does not modify it. Tier guide: tier 3 holds the highest-level consolidated reflections (identity, principles, what matters most), tier 2 holds reflections of specific meaningful events, tier 1 holds raw verbatim memories. Match filters to the question. Broad 'most important things we have done' questions: tiers [3], importance 8 or higher, large limit such as 250. Big-picture overviews like 'what do you know about this area or period': tiers [3] with no importance filter. Specific lookups like 'find the decision about X': omit tiers to search all tiers blended and leave importance unset; a high importance filter hides the specific detail you are looking for. Specific meaningful events without raw noise: tiers [2] or [2, 3]. Exact verbatim detail: if the detail is not already in context, drill down by lineage: search tiers [3] to find the relevant thread, then use inspect_memory on the result to see the tier 2 reflections it consolidated, then inspect those to reach the raw memories underneath. Direct tiers [1] search works when you know exact wording to match. Mixing tiers is legitimate; adjust the importance threshold to the question rather than always setting it high. Only reflections (tiers 2 and 3) carry importance ratings. For time-window questions like 'what happened last week' or 'lately' use date_search_memories instead.

Parameterslimitqueryrequiredtiersimportance

date_search_memories

Search memories by date

Use this to retrieve memories from a specific time window, for questions like 'what happened last week' or 'most important things this month or lately'. Tier guide: tier 3 holds the highest-level consolidated reflections (identity, principles, what matters most), tier 2 holds reflections of specific meaningful events, tier 1 holds raw verbatim memories. Match filters to the question. Time-bounded 'most important things last month' questions: tiers [3], importance 8 or higher, generous max_tokens. Big-picture overviews of a period like 'what do you know about July': tiers [3] with no importance filter. Specific lookups in a window like 'find the decision about X': omit tiers to include all tiers and leave importance unset; a high importance filter hides the specific detail you are looking for. Specific meaningful events without raw noise: tiers [2] or [2, 3]. Exact verbatim detail: if the detail is not already in context, drill down by lineage: search tiers [3] to find the relevant thread, then use inspect_memory on the result to see the tier 2 reflections it consolidated, then inspect those to reach the raw memories underneath. Direct tiers [1] search works when you know exact wording to match. Mixing tiers is legitimate; adjust the importance threshold to the question rather than always setting it high. Only reflections (tiers 2 and 3) carry importance ratings. Omit query for a chronological review; include query to rank by relevance within the window. Resolve relative phrases like 'lately' to concrete ISO dates before calling. Reads only the OAuth-selected vault and does not modify it.

Parameterstofromrequiredquerytiersimportancemax_tokens

store_memory

Remember something

Use this only when the user explicitly asks to remember something or confirms a proposed durable fact. Saves concise user-approved content to the OAuth-selected vault; do not use it for silent conversation capture.

Parameterstagscontentrequired

memory_status

Check memory status

Use this when the user asks whether MemoryRouter is connected, which opaque vault binding is active, or how many memories/tokens the connected vault contains. Does not reveal the memory key.

Takes no parameters.

forget_all_memories

Delete all memories in connected vault

Use this only after the user explicitly requests a full-vault deletion, receives a permanent whole-vault warning, and provides the exact confirmation phrase in a later turn. Permanently deletes every memory in the OAuth-selected vault. This cannot delete one memory; use delete_memories for specific items. Requires separate memories:delete authorization and destructive host confirmation.

Parametersconfirmationrequired

consolidate_memories

Consolidate memories (checkout)

Use this when the user asks to consolidate, reflect on, or organize their memories, or when memory_status or a search result reports that consolidation is available. Checks out a batch of unconsolidated memories from the connected vault under a 15 minute lease and returns their texts plus server instructions for writing reflection entries. Follow the returned instructions to write the entries, then call commit_reflections with the batch_id. Nothing is modified until commit.

Parameterstiermax_tokens

commit_reflections

Commit reflections

Use this to complete a consolidation started with consolidate_memories. Submits the reflection entries you wrote for the checked-out batch. Each entry is standalone prose with an importance rating from 1 to 10. The server stores the reflections, links them to their source memories, and marks the sources consolidated. Safe to retry: committing the same batch twice returns the original result.

Parametersentriesrequiredbatch_idrequired

inspect_memory

Inspect memory lineage

Use this when the user asks why the assistant believes something, or wants to see the source memories behind a reflection. Given a memory id from a search result, returns the lower-tier memories that reflection was built from, or, for a raw memory, which reflections it was consolidated into. Use repeatedly to drill down from a high-level reflection to the tier 2 reflections and raw memories beneath it. Read only.

Parametersmemory_idrequired

delete_memories

Delete specific memories

Use this only when the user explicitly asks to delete specific memories and confirms after seeing what will be deleted. Permanently deletes the identified memories from the connected vault by their ids from search or inspect results. Deleting a raw memory does not delete reflections built from it. This cannot be undone. For deleting everything, forget_all_memories is the separate whole-vault tool.

Parametersidsrequiredconfirmationrequired

Version history

1
  1. v1.1.0LatestSep 16, 2026

Score

83% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts

    Declare a GitHub repository with a README, or serve resource_documentation in the OAuth protected-resource metadata.

  • LicenseNot applicable

    A hosted service is used under its provider's terms, so no license is expected here. This check is left out of the percentage.