> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Conversation Context
export const ModelBadges = ({models}) => {
return
{models.map(model =>
{model}
)}
;
};
Improve voice agent outcomes by giving Universal-3.5 Pro the full conversational context. Pass your voice agent's spoken replies into the session via the `agent_context` parameter and the model knows both sides of the dialog when transcribing the next user turn.
With the agent's side of the conversation in context, Universal-3.5 Pro can anticipate the kind of answer to expect, sharpen entity recognition, and disambiguate words that sound similar. For example, after your agent asks `"What's your email address?"` the model might transcribe the reply as `"user at assemblyai dot com"`. With that question passed in via `agent_context`, the model knows an email is coming and produces `"user@assemblyai.com"`.
The user side comes along for free: Universal-3.5 Pro Streaming automatically carries prior STT-finalized turns forward as context, so you don't need to configure anything for the user half of the conversation. `agent_context` is what fills in the agent half.
**Set `agent_context` at connection time and after each agent reply**
Pass `agent_context` as a connection-time query parameter to seed the model with your agent's opening greeting, then send `UpdateConfiguration` mid-stream after each subsequent agent reply. See [Passing your agent's reply as context](#passing-your-agents-reply-as-context) for the full pattern.
## How it works
During a streaming session, Universal-3.5 Pro Streaming keeps a short memory of the conversation. Two sources feed that memory: `agent_context` values you push in (the agent half) and prior finalized user turns (the user half, carried forward automatically). The model uses both when transcribing the next user turn.
This means:
* Context is **per-session**. Closing the WebSocket clears it, and a new session starts fresh.
* Only `agent_context` values and **finalized** user turns (`end_of_turn: true`) are carried forward, not partials.
### Defaults
| Behavior | Default |
| ------------------------------- | --------------- |
| Auto-carry of prior user turns | Enabled |
| Number of prior entries carried | 3 |
| Maximum context size | 1750 characters |
Older entries are dropped first as new ones come in, so the most recent conversation is always preserved.
## Passing your agent's reply as context
Pass your voice agent's spoken reply (what your TTS just said) via the `agent_context` parameter. There are two ways to set it:
* **At connection time:** pass `agent_context` as a query parameter on the WebSocket URL. Use this to seed the model with your agent's opening greeting before the user has said anything.
* **Mid-stream:** send an `UpdateConfiguration` message with the `agent_context` field after each subsequent agent reply.
Both forms let the model know the question the user is about to answer, which is especially important for short replies (`"yes"`, `"7pm"`, `"that's all"`).
The user side is handled for you: prior STT-finalized turns are automatically carried forward as context, so you only need to manage the agent half explicitly.
### Setting an opening greeting at connection time
When you open the WebSocket, pass `agent_context` alongside your other connection parameters. The first user turn will be transcribed with the greeting already in the model's context.
```python {2} theme={null}
params = {
"agent_context": "Welcome to the Krusty Krab, home of the Krabby Patty, may I take your order?",
"sample_rate": 16000,
"speech_model": "universal-3-5-pro",
}
```
```python {5} theme={null}
client.connect(
StreamingParameters(
sample_rate=16000,
speech_model="universal-3-5-pro",
agent_context="Welcome to the Krusty Krab, home of the Krabby Patty, may I take your order?",
)
)
```
```javascript {4-5} theme={null}
const params = {
sample_rate: 16000,
speech_model: "universal-3-5-pro",
agent_context:
"Welcome to the Krusty Krab, home of the Krabby Patty, may I take your order?",
};
```
```javascript {4-5} theme={null}
const transcriber = client.streaming.transcriber({
sampleRate: 16_000,
speechModel: "universal-3-5-pro",
agentContext:
"Welcome to the Krusty Krab, home of the Krabby Patty, may I take your order?",
});
await transcriber.connect();
```
### Updating agent context mid-stream
A typical voice agent loop looks like this:
1. User speaks → Universal-3.5 Pro Streaming emits a final turn.
2. Your agent runs an LLM step and generates a reply.
3. Your TTS speaks the reply to the user.
4. User responds → next turn.
During step 3, send the agent's reply text to the streaming session so the model knows what question the user will be answering next turn.
```python theme={null}
ws.send(json.dumps({
"type": "UpdateConfiguration",
"agent_context": "Sure, what date would you like to book?",
}))
```
```python theme={null}
client.update_configuration(
agent_context="Sure, what date would you like to book?",
)
```
```javascript theme={null}
ws.send(JSON.stringify({
type: "UpdateConfiguration",
agent_context: "Sure, what date would you like to book?",
}));
```
```javascript theme={null}
transcriber.updateConfiguration({
agent_context: "Sure, what date would you like to book?",
});
```
## Limits
* **Universal-3.5 Pro only.** `agent_context` is supported on `speech_model: "universal-3-5-pro"`. If you set it at connection time on any other model, the session is rejected; if you send it mid-stream on another model, it's stripped with a warning.
* **Per-value cap: 1750 characters.** Trim long agent replies down to the substantive question before sending.
## When conversation context helps most
`agent_context` has the largest impact on:
* **Voice agents:** short user responses to agent questions (`"yes"`, `"no"`, `"that's all"`, dates, times, single names).
* **Spelled-out entities:** emails, account IDs, addresses, and similar inputs read aloud after the agent has just asked for them. Setting `agent_context` to the agent's prompt (e.g. `"What's your email address?"`) primes the model for what's coming.
* **Disambiguation:** words that sound similar but only one fits the conversation (`"fleas"` vs `"please"`, `"to"` vs `"two"` vs `"too"`).
* **Entity recall:** names, products, or terms that were established earlier in the conversation.
It has less impact on long, self-contained turns where the audio already provides enough context on its own.
## Interactions with other parameters
**`prompt`.** Conversation context layers on top of the model's built-in transcription instruction and any [contextual `prompt`](/streaming/prompting-and-keyterms) you provide. The two stack cleanly.
**`keyterms_prompt`.** Use `keyterms_prompt` alongside `agent_context` as needed; the two don't conflict.
**Multilingual sessions.** Carrying prior turns biases the model toward the languages already seen in the conversation. For sessions that mix three or more languages, this can occasionally push the model toward translating rather than transcribing. If you see drift, set a single transcription language in your `prompt` (see [Specifying the transcription language](/streaming/getting-started/transcribe-streaming-audio)).