Cognitive AI Memory vs Telemem
A side-by-side look at the Cognitive AI Memory and Telemem MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Cognitive AI Memory for knowledge & memory work as a local process; choose Telemem for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Cognitive AI Memory when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
Choose Telemem when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
add_memory, search_memories, get_memories.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 44/100 · Fair | 64/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 270 | 494 |
| Directory activity | 3 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | Not listed | 8 |
| Full listing | View Cognitive AI Memory | View Telemem |
Tools & capabilities
Cognitive AI Memory tools
No tool names declared yet. Check the project README from the listing page.
Telemem tools 8
add_memoryStore a fact, preference, or conversation in TeleMem long-term memory. Provide `text` for a single statement or `messages` for conversation turns. Scoped by user_id/agent_id/run_id; defaults to the server's default user.search_memoriesSemantic search over TeleMem memories. Returns the matching memories as one consolidated text passage (TeleMem fuses related memories). Shared event memories (pseudo-user "events") are searched automatically.get_memoriesList stored memories for a user/agent/run with their memory_ids. Use user_id "events" to list shared conversation-event memories.get_memoryFetch a single memory by its memory_id.update_memoryOverwrite the text of an existing memory by memory_id.delete_memoryDelete a single memory by memory_id.delete_all_memoriesDelete every memory in the given scope. Destructive: requires an explicit user_id, agent_id, or run_id — the default user is never assumed.memory_historyShow the change history (ADD/UPDATE/DELETE events) of a memory.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Cognitive AI Memory
{
"mcpServers": {
"cognitive-ai-memory": {
"command": "uvx",
"args": [
"yourmemory"
]
}
}
}
Telemem
{
"mcpServers": {
"telemem": {
"command": "uvx",
"args": [
"telemem"
]
}
}
}
Frequently asked questions
Cognitive AI Memory is categorized under Knowledge & Memory and uses a local stdio subprocess. Telemem belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Cognitive AI Memory for knowledge & memory work and Telemem for knowledge & memory.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Cognitive AI Memory has no declared pricing — check the project's own site. Telemem has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add cognitive-ai-memory and telemem as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Cognitive AI Memory has a quality signal of 44/100 (Fair) with 3 views, 0 installs and 270 GitHub stars. Telemem has 64/100 (Good) with 0 views, 0 installs and 494 GitHub stars.