I have one doubt, if I wanted to use the observer LLM how can I create a seperate context management for this AI without mixing up with the voice agent?

Thanks for reaching out, Yethu. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

actually observer LLM for nudge genration only, how can I create the context accordingly so that it have the visibility to see the preivuous triggered nudges and we will pass the voice agent chat cotnext toio

Thanks — I’ll help you design the observer context for nudge generation that can see previous nudges while keeping the voice agent context safe. Quick clarifying question: which SDK/language are you using (Python or TypeScript)? Also confirm whether you want the observer to: • Read the voice agent’s chat context (read-only) and the previously triggered nudges, or • Receive a curated subset (e.g., last N messages + last M nudges) that you construct and pass in? Once you confirm, I’ll provide a short concrete pattern and code snippet for building the observer’s

ChatContext

, storing/reading prior nudges, and safely passing the agent context. , with interactive elements

Read the voice agent’s chat context (read-only) and the previously triggered nudges

Great — here’s a concise pattern and concrete examples (Python + TypeScript) to implement an observer LLM for nudge generation that has read-only access to the voice agent’s chat context and visibility into prior nudges, while keeping contexts isolated. Design summary • Observer uses its own

ChatContext

(isolated) for LLM calls. • Provide the observer a read-only snapshot of the agent’s chat context plus a curated list of prior nudges. • Keep the agent’s live

ChatContext

immutable to the observer; only pass copies/snapshots. • Store prior nudges in a small persistent store or in-memory buffer the observer can read (include metadata: timestamp, source, severity). • After nudge generation, return the nudge to the orchestrator; orchestrator decides whether to apply it to the agent (by updating the agent context via a controlled API). Python example (pattern)

# snapshot agent context (read-only)
agent_snapshot = agent_chat_ctx.copy_readonly()  # or create a lightweight snapshot

# load prior nudges
prior_nudges = nudge_store.get_recent(n=10)  # list of dicts: {text, ts, id}

# build observer context
observer_ctx = ChatContext()
observer_ctx.add_message(role="system", content="You are an observer that generates nudges.")
observer_ctx.add_message(role="system", content=f"Agent context snapshot:\n{agent_snapshot.summary()}")
observer_ctx.add_message(role="system", content=f"Prior nudges:\n{format_nudges(prior_nudges)}")
observer_ctx.add_message(role="user", content="Generate a nudge if necessary based on recent transcript and prior nudges.")

# call observer LLM
nudge_response = observer_llm.call(observer_ctx)

# orchestrator handles result
if is_valid_nudge(nudge_response):
    nudge_store.append(nudge_response)
    # optionally create a copy of agent context and add a system message if applying to agent
    agent_copy = agent_chat_ctx.copy()
    agent_copy.add_message(role="system", content=f"Applied nudge: {nudge_response.text}")
    update_chat_ctx(agent_copy)  # done by orchestrator, not observer

TypeScript example (pattern)

// snapshot agent context (read-only)
const agentSnapshot = agentChatCtx.snapshot(); // returns a serializable summary

// load prior nudges
const priorNudges = nudgeStore.getRecent(10); // [{text, ts, id}...]

// build observer context
const observerCtx = new ChatContext();
observerCtx.addMessage({role: "system", content: "Observer for nudge generation."});
observerCtx.addMessage({role: "system", content: `Agent snapshot:\n${agentSnapshot.summary}`});
observerCtx.addMessage({role: "system", content: `Prior nudges:\n${formatNudges(priorNudges)}`});
observerCtx.addMessage({role: "user", content: "Generate a nudge if needed."});

// call observer LLM
const nudge = await observerLLM.call(observerCtx);

// orchestrator handles nudge
if (isValidNudge(nudge)) {
  nudgeStore.append(nudge);
  const agentCopy = agentChatCtx.copy();
  agentCopy.addMessage({role: "system", content: `Applied nudge: ${nudge.text}`});
  updateChatCtx(agentCopy);
}

Key implementation notes • Snapshot vs full context: pass a summarized snapshot (last N user/agent turns) rather than the entire live context to keep prompt size reasonable. • Prior nudges format: include timestamp, id, and outcome (applied/rejected) so the observer can avoid repeating nudges. • Read-only enforcement: the observer should never call methods that mutate `agent_chat_ctx`; only read snapshots or copies. • Limits: rotate prior-nudge history (e.g., last 50) to avoid prompt bloat. • Orchestrator role: the orchestrator (or controller) decides to persist/apply nudges — the observer only suggests. Would you like a more detailed code snippet tied to a specific SDK (LiveKit Python or TypeScript) and an example prompt template for the observer? , with interactive elements

i think livekit has one redis management server I guess can I also resuse it on each request basis? if i am on cloud

Short answer: LiveKit Cloud’s internal Redis is not available for customer reuse. You should provision and use your own Redis (or other external store) for per-request nudge history or shared observer state. Why: • LiveKit uses Redis internally for operational pub/sub and it isn’t exposed for app-level storage. • Using your own Redis ensures you can persist nudges, share state between observer and agent, and control retention/partitioning. Recommended pattern: • Snapshot the agent chat context (read-only) and pass that plus a curated list of prior nudges (from your Redis) into the observer’s isolated

ChatContext

. • Store nudges with metadata (id, ts, applied/rejected) in your Redis; read the last N when building the observer prompt. • Keep the agent context immutable to the observer; any changes go through the orchestrator which updates the agent via a copy and

update_chat_ctx

. • Rotate/prune prior-nudges (e.g., last 50) to avoid prompt bloat. If you want, tell me which cloud Redis provider you plan to use (AWS ElastiCache, Redis Labs, etc.) and I’ll give a tiny code snippet showing read/write patterns and prompt construction for the observer. Sources: How to Build a Background Observer for Voice AI Guardrails | LiveKit, with interactive elements

but where i would store prior nudged

Good question — here’s where and how to store prior nudges so the observer can read them without touching the agent’s live context. Short answer • Store prior nudges in your own external persistent store (recommended: Redis, a key-value DB, or a small document DB). LiveKit Cloud’s internal Redis is not available for app data. What to store (recommended schema) • id (string) • session_id or room_id • timestamp • text (nudge content) • source (observer/auto/manual) • status (applied/rejected/pending) • metadata (severity, reason, related_message_id) Storage patterns • Per-session list: Redis list or sorted set keyed by

nudges:{session_id}

for quick recent-nudge reads. • Global dedupe set: Redis set keyed by

nudges_seen:{session_id}

or store hashes to avoid duplicates. • Small document store: Use DynamoDB / Firestore / Postgres if you need richer queries or audit logs. Read/write workflow (high level) 1. Observer receives a read-only snapshot of agent context. 2. Observer reads

nudges:{session_id}

(last N items) from your store. 3. Build observer

ChatContext

with the snapshot + formatted prior nudges. 4. Generate nudge. If valid, write new nudge to store (append to

nudges:{session_id}

) with metadata. 5. Orchestrator decides whether to apply it to agent by copying agent context and calling

update_chat_ctx

. Concise examples Python (Redis pseudocode)

# write
redis.rpush(f"nudges:{session_id}", json.dumps(nudge_obj))
# read last N
raw = redis.lrange(f"nudges:{session_id}", -N, -1)
prior_nudges = [json.loads(r) for r in raw]

TypeScript (ioredis pseudocode)

await redis.rpush(`nudges:${sessionId}`, JSON.stringify(nudge));
const raw = await redis.lrange(`nudges:${sessionId}`, -N, -1);
const priorNudges = raw.map(r => JSON.parse(r));

Best practices • Keep only the last N nudges (rotate/prune) to avoid prompt bloat. • Store applied/rejected status to avoid re-triggering the same nudge. • Include timestamps and message references to correlate with agent turns. • Protect access to the store and encrypt sensitive data. • If strict ordering matters, use Redis sorted sets with timestamps. If you tell me which store/provider you plan to use (Redis provider, DynamoDB, Postgres, etc.) and whether you want sample code integrated with your observer prompt, I’ll provide a short concrete snippet tailored to that environment. , with interactive elements