> ## Documentation Index
> Fetch the complete documentation index at: https://docs.moss.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Agent Context
> Persist and recall agent context as documents in a Moss index.
Persist agent context as documents in a Moss index. On each turn the agent queries the
index for relevant context, grounds its response in the results, then writes new context
back with `addDocs`. The pattern is: store documents, query per turn, write new context back.
Two kinds of context live in the same index and surface through the same semantic query:
* **Working context** - unstructured insights drawn from conversations: preferences, tone,
recurring topics, and decisions. Updated after each interaction.
* **Durable context** - stable facts about the user: profile, account tier, identifiers,
long-standing preferences. Changes rarely.
## Capture and recall loop
On each turn the agent queries the index for the most relevant context, grounds its
response in it, then writes new context back with `addDocs`. Subsequent turns query the
updated index.
## Example (JavaScript)
```ts theme={null}
import { MossClient } from '@moss-dev/moss'
const client = new MossClient(process.env.MOSS_PROJECT_ID!, process.env.MOSS_PROJECT_KEY!)
const index = 'agent-context'
await client.createIndex(index, [
{ id: 'user_profile', text: 'User prefers concise answers.' }
], { modelId: 'moss-minilm' })
// Load before querying.
await client.loadIndex(index)
// Recall: pull relevant context for this turn.
const context = await client.query(index, 'preferences for responses', { topK: 3 })
// Capture: write new insights back so the next turn can use them.
await client.addDocs(index, [
{ id: 'pref_format', text: 'User asked for bullet-point summaries.' }
], { upsert: true })
```
## Related
Short-term + long-term context during a call.
Carry context across agents and channels.
