Skip to main content
Glama

add_memory

Store facts, preferences, or conversation turns into long-term memory for AI agents. Accepts text or message arrays with user/agent/run scoping.

Instructions

Store 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoA single statement to remember. Use this or `messages`.
inferNoExtract salient facts with the LLM before storing (TeleMem's default pipeline). Set false to store the raw text as-is.
run_idNoOptional run/session scope.
user_idNoUser the memory belongs to.
agent_idNoOptional agent scope.
messagesNoConversation turns as [{"role": "user"|"assistant", "content": "..."}]. Takes precedence over `text`.
metadataNoArbitrary metadata to attach to the stored memories.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must convey behavior. It describes the inference pipeline and scoping defaults, but does not mention return values, auth requirements, or concurrency effects. Adequate but not detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The first sentence states the purpose, the second details inputs and scoping. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although no annotations exist, the description covers the two input modes, scoping, and inference behavior. Output schema exists so missing return values are acceptable. Could mention that it adds rather than overwrites, but it's implied.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (baseline 3). The description adds value by explaining the mutual exclusivity of text and messages, and that messages take precedence, going beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb (store) and resource (TeleMem long-term memory), and clearly distinguishes from sibling tools that delete, get, search, or update memories.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides guidance on when to use text vs messages, and mentions scoping by user/agent/run. Does not explicitly exclude alternative tools, but the purpose is clear enough to guide the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/TeleAI-UAGI/telemem'

If you have feedback or need assistance with the MCP directory API, please join our Discord server