Context and Memory

Documentation Index

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Context management drives your agent’s behavior. What the agent knows at any moment determines how it responds—so understanding how context works helps you design more effective agents.

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Global context

Some information is always available to your agent, no matter what it’s doing. Whether responding to a chat message, processing an incoming email, or waking up for a scheduled task, the agent has access to:

• Instructions — The agent’s mission, guidelines, and core knowledge
• Agent documents — Files, databases, and reference materials (depending on visibility settings—by default the agent knows which documents it has and can read them when needed)

These are the foundation for everything the agent knows across interactions.

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Local context

Each interaction has its own isolated context that the agent sees alongside the global context:

• Chat conversations — When you’re in a chat, the agent knows all previous messages in that conversation. But it doesn’t know about other chats—each conversation is independent.
• Triggers — When responding to an email, scheduled task, or webhook, the agent sees the trigger details (email content, task instructions, etc.) but not any chat conversations.

The platform optimizes what context to include for each trigger type. For example, when triggered by a Slack message, the agent receives the 5 previous messages in that channel for context. If needed, the agent can actively search for more information.

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What is memory?

Memory is the ability to remember information across different contexts. For example, the agent might remember which company you work for, your communication preferences, or decisions made in previous conversations.

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Instructions and documents work as memory

Since agents can read and write their own instructions and documents, these already serve as memory. The agent can store facts, preferences, and accumulated knowledge—and access it in any future context. You can guide this behavior in your agent’s instructions:

• “Create a database to store personal preferences for everyone in my workspace. Whenever someone expresses a preference, store it. When communicating with someone, check their preferences first.”
• “Keep a running log of all decisions we make in our conversations”
• “When you learn something important about a client, add it to the client notes document”

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When to use what

For use cases like accumulating facts over time or deduplicating recurring information (e.g. “remember news items you’ve already sent so similar stories aren’t repeated”), use an agent database with semantic search. Databases find entries by meaning, not exact text, so the agent can recognize when two items are essentially the same even if worded differently.

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The Memory capability

If your agent has the Memory capability enabled, it has a dedicated memory store with semantic search. Saved memories are converted to vector embeddings so they can be retrieved by meaning rather than exact text match. To view and manage memories, open the Memory capability card and click View memories. You can search semantically, add new memories, or remove existing ones.

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