What would the best way to get agent instructions or other configurations from a remote server?

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The instructions will be fetched based on the participant's sip uri. Is there a way to cache the instructions for reuse when the same participant joins again?

Thanks — I’m responding to your question about caching instructions fetched by a participant’s SIP URI so they can be reused when the same participant rejoins. Short answer • LiveKit does not provide a built-in instruction cache keyed by SIP URI. You can implement a simple, reliable cache yourself. The common patterns are: ◦ Per-session cache (in-memory

userdata

) for the session lifetime. ◦ Persistent external cache (Redis, database) keyed by SIP URI or

participant.identity

for reuse across sessions. ◦ Preload shared/static instructions in

prewarm

for global reuse. Recommended approaches (when to use which) • External cache (recommended for reuse across reconnects) ◦ Use Redis or a database keyed by the SIP URI (or normalized phone number /

participant.identity

). ◦ On participant join: check cache first; if present, use cached instructions; if missing, fetch from remote server and write back to cache. ◦ TTLs: give cached instructions a sensible TTL so updates on your remote service eventually propagate. ◦ Benefit: very low latency on reconnects and resilient across process restarts. • Session-local cache (quick, for a single session) ◦ Store instructions in the agent session’s

userdata

so other parts of the session can reuse them without repeated fetches. ◦ Use when instructions only need to survive the current session and you want minimal complexity. • Prewarm/preload (for largely static or shared instructions) ◦ Load global or commonly-used instructions in your server

prewarm

and serve from

proc.userdata

. ◦ Use when many participants share the same baseline instructions and you want to avoid per-join fetches. Implementation outline (example flow) 1. Participant joins; determine key ◦ Use the participant’s SIP URI, phone number, or

participant.identity

as the cache key. 1. Check cache ◦ Lookup key in Redis/DB. If found, deserialize and use instructions. 1. If not cached, fetch and cache ◦ Fetch from your remote config service (HTTP call or MCP). ◦ Store result in cache with an appropriate TTL. ◦ Initialize the agent with those instructions. 1. Use session-local fallback ◦ Also save to

userdata

so repeated accesses inside the same session don’t hit external storage. Conceptual code sketch (pseudo-Python)

async def on_participant_join(ctx, participant):
    key = participant.attributes.get("sip.phoneNumber") or participant.identity

    # 1) Try external cache
    instructions = await redis.get(key)
    if instructions:
        instructions = json.loads(instructions)
    else:
        # 2) Fetch from remote service (or MCP)
        instructions = await fetch_instructions_from_remote(key)
        await redis.set(key, json.dumps(instructions), ex=3600)  # TTL in seconds

    # 3) Save to session-local userdata for this session
    ctx.proc.userdata["instructions"] = instructions

    # 4) Initialize agent/session with instructions...

Notes and best practices • Normalize the SIP URI/phone number to a stable canonical key so the same participant yields the same cache key. • Choose TTL based on how frequently instructions change. If instructions are updated by external systems, use a short TTL or add a cache-invalidation webhook from your config server. • If instructions are highly sensitive, encrypt them at rest and ensure proper access controls to your cache. • If you already use MCP for dynamic tools/config, you can still cache MCP responses in Redis to avoid repeated remote calls and reduce latency. If you want, I can: • Provide a full code example tailored to your runtime (async Python, Node, etc.). • Suggest a TTL strategy based on how often your instructions change. Which runtime and caching backend are you using (or planning to use)? Sources: SIP Lifecycle Management Agent | LiveKit Documentation | Agents and handoffs | LiveKit Documentation | Context Variables | LiveKit Documentation | sip | livekit/livekit_composite | DeepWiki | How can I reduce latency in voice agents using STT, TTS and LLM? | LiveKit

How can I create a cache that is available across sessions?

Thanks — I’m responding to your question about creating a cache that persists across sessions so instructions fetched by SIP URI can be reused when the same participant rejoins. High-level options • External in-memory cache (recommended): Redis or Memcached. Fast, simple, supports TTL, atomic ops, and works across processes and servers. • Persistent datastore: PostgreSQL, DynamoDB, etc. Use when you need durability, transactional updates, or complex queries. • Hybrid: Cache in Redis for speed and persist canonical data in a DB for durability. • Server-side prewarm: Load commonly used data into

proc.userdata

at server startup, but this is not cross-process unless you coordinate it with an external store. Recommended pattern (Redis example) 1) Key design • Use a canonical key based on the SIP URI or

participant.identity

. Normalize the value (strip whitespace, lowercase, remove formatting). • Example key:

agent_instructions:sip:+15551234567

2) Cache read/write flow • On participant join: ◦ Try Redis GET(key). If found, deserialize and use. ◦ If missing, fetch from your remote config service (or MCP), then SET key with JSON-serialized instructions and an appropriate TTL. • Optionally use Redis' SETNX / Lua scripts to prevent stampedes (only one fetcher writes). • Use TTL so updates eventually propagate; if you need immediate invalidation, provide an API or webhook from your config service to delete the key. 3) Concurrency and consistency • Use a short TTL (minutes to hours) for frequently changing instructions. • For atomic refresh, use a “refresh lock” key (SET key:lock NX PX 30000) or Redis Lua script to ensure only one process refreshes the cache. • If instructions must change instantly, trigger cache invalidation from the service that updates instructions. 4) Security • Encrypt sensitive instruction payloads at rest or restrict access to Redis with strong auth, VPC/private network, and RBAC. • Limit TTLs and rotate keys if instructions contain secrets. 5) Session-local optimization • After loading from Redis, also store instructions in the session (

ctx.proc.userdata

or similar) so repeated accesses during the same session avoid Redis calls. Conceptual async-Python sketch (Redis + TTL + simple lock)

import json
import asyncio
import aioredis

REDIS_URL = "<redis://localhost:6379>"
TTL = 3600  # seconds

async def get_instructions_for_sip(sip_uri):
    key = f"agent_instructions:{normalize(sip_uri)}"
    async with aioredis.from_url(REDIS_URL) as redis:
        cached = await redis.get(key)
        if cached:
            return json.loads(cached)

        lock_key = key + ":lock"
        got_lock = await redis.set(lock_key, "1", nx=True, ex=30)  # prevent stampede
        if got_lock:
            try:
                instructions = await fetch_instructions_from_remote(sip_uri)
                await redis.set(key, json.dumps(instructions), ex=TTL)
                return instructions
            finally:
                await redis.delete(lock_key)
        else:
            # Another process is fetching — wait and retry
            await asyncio.sleep(0.2)
            return await get_instructions_for_sip(sip_uri)

def normalize(sip_uri):
    return sip_uri.strip().lower()

When to use a DB instead • Use PostgreSQL or DynamoDB when you need: ◦ Durable history of instruction versions ◦ Complex queries or joins ◦ Transactional updates or strong consistency • Pattern: write-through or write-back with Redis as front-line cache. Cache invalidation strategies • TTL-based expiry (simple and robust). • Explicit invalidation: webhooks or API calls from your config service that call DEL on the key. • Versioning: store a version number in the key (e.g.,

agent_instructions:v42:<sip>

) and bump it when instructions change. MCP-specific note • If you use MCP for dynamic tools/config, you can cache MCP responses (same Redis pattern) to avoid repeated HTTP/SSE fetches for the same SIP participant. Next step Which runtime are you using (async Python, Node.js, Go), and which cache backend do you prefer (Redis, PostgreSQL, DynamoDB)? I can provide a full, runnable code example tailored to your stack and include lock/invalidaton details.

what is the memory limit if I use a memory cache?

Thanks — I’m responding to your question about memory limits when using an in-memory cache for agent instructions. There is no single fixed memory limit for an in-memory cache — the limit depends entirely on your deployment environment and design choices. Here are the factors to consider and practical guidance to help you choose a safe approach. What determines the memory limit • Host/container memory: The process can only use the RAM available on the machine or container it runs in. If your app runs in a container, memory limits set by the orchestrator (Kubernetes, Docker) will cap what’s available. • Language/runtime: The language runtime may add overhead (heap, garbage collector). For example, Python, Node.js, and Java have different memory usage characteristics. • Other processes: If multiple services share a host, they compete for the same physical RAM. • Data size and structure: The size of each cached instruction object, number of entries, and any indexing or serialization overhead determine total memory usage. • Retention policy: TTLs, eviction policies (LRU), and maximum entries you enforce affect peak memory. Practical guidance • Estimate item size: Measure or approximate the serialized JSON size of a typical instruction. Multiply by expected concurrent cached entries to estimate memory need. • Reserve headroom: Don’t allow your cache to consume all host memory. Leave room for the OS, runtime, and spikes (suggestion: reserve 20–40%). • Set limits in process/container: If using containers, set memory limits so the orchestrator can evict or restart the process before the host OOMs. • Use entry limits: Implement a max number of entries or a max total size for the cache in your code to keep memory bounded. • Eviction policy: Use LRU or TTL to automatically evict older entries when under memory pressure. • Monitor: Add metrics for cache size, hit rate, evictions, and process memory usage so you can tune sizes and TTLs. • Consider sharding: If a single process cannot hold the working set, shard the cache across multiple instances or use a distributed cache like Redis. When in-memory caching is appropriate • Small working sets where total cache size is modest relative to host RAM. • Low-latency needs and you control the hosting environment and memory limits. When to use an external cache instead • Large or unbounded working sets. • Need persistence across restarts or multi-process access. • Easier to enforce global TTLs, eviction, and monitoring (Redis, Memcached, etc.). Example calculation (how to size) 1. Measure average instruction size: e.g., 2 KB JSON per participant. 2. Multiply by expected unique participants to cache: e.g., 10,000 * 2 KB = ~20 MB. 3. Add overhead and runtime: multiply by 1.5–2 → ~30–40 MB. 4. Ensure host/container has headroom: pick a host with at least 4x that amount if you run other services on it. If you tell me: • Which runtime you’re using (Python, Node.js, Go), • Typical average size of an instruction (or paste one), • Expected number of concurrent cached participants, I can help you calculate a recommended memory allocation and provide code to enforce a size limit or LRU eviction in that runtime.

If I use the livekit cloud, what is my limit?

Thanks — I’m responding to your question about memory limits for an in-memory cache when you run agents on LiveKit Cloud. Short answer • LiveKit Cloud does not impose a specific, separate memory limit on an in-memory cache inside your agent code. Memory available to your agent processes is determined by the resources of the instance your agent runs on. • A common guidance for voice/agent servers is to target about 4 CPU cores and 8 GB RAM per agent server. Your in-memory cache must fit within whatever memory is available to that process. What that means in practice • If you store instruction payloads in-process, the total cache size is limited by the process/container memory on the instance. LiveKit Cloud manages the infrastructure but does not enforce a distinct cache quota. • If your cache grows to consume most of the instance memory you risk process instability, crashes, or autoscaling behavior that increases cost. Recommended approach • Use an external cache for cross-session reuse and predictable limits (Redis or Memcached). This avoids depending on a single process’s memory and scales across instances. • If you must use in-memory caching, enforce bounds: ◦ Set a maximum number of entries or max total byte size. ◦ Use TTLs and LRU eviction. ◦ Add a refresh-lock to prevent stampedes on cache miss. • Monitor memory usage and cache metrics (hit rate, evictions). Adjust instance size or switch to external caching as needed. • Normalize your SIP URI key and use sensible TTLs or an invalidation webhook from your config service so updates propagate. If you want, I can: • Calculate a recommended memory allocation given an average instruction size and expected number of cached participants. • Provide a runnable example (async Python / Node.js) that enforces max cache size and TTL. Which runtime and average instruction size should I use for that example? Sources: Self-hosted deployments | LiveKit Documentation

how do I use an external cache?

Thanks — I’m responding to your question about how to use an external cache so instructions fetched by SIP URI can be reused across sessions. Below is a clear, practical plan and sample code you can adapt to your runtime and backend (I’ll use Redis + async Python as a concrete example). If you prefer Node.js, Go, or a different store (Postgres, DynamoDB), tell me and I’ll provide that specific example. Overview (recommended pattern) • Use an external cache (Redis) keyed by a canonical participant identifier (SIP URI, phone number, or

participant.identity

). • On participant join: check the cache first. If present, use cached instructions. If missing, fetch from your remote config (HTTP/MCP), store them in Redis with a TTL, and then use them. • Also save to session-local storage (

proc.userdata

/

ctx.proc.userdata

) so repeated accesses during the same session avoid extra round-trips. • Prevent cache stampedes with a simple lock (SETNX or short lock key) or let one process refresh while others wait and retry. • Invalidate or update cache via TTLs, versioned keys, or a config-server webhook that deletes/updates the Redis key. Concrete async Python example (Redis) 1) Requirements: • aioredis (or redis-py asyncio) • An async HTTP fetch function or MCP client to retrieve remote instructions 2) Key design: • Normalize SIP URI, e.g., remove whitespace, lowercase, E.164 for phone numbers. • Key example: agent_instructionssip+15551234567 3) Example code (conceptual)

import json
import asyncio
import aioredis

REDIS_URL = "<redis://localhost:6379>"
TTL_SECONDS = 3600  # adjust to your freshness needs
LOCK_TTL = 30  # seconds to hold refresh lock

redis = None

async def get_redis():
    global redis
    if redis is None:
        redis = await aioredis.from_url(REDIS_URL)
    return redis

def normalize_key(sip_uri: str) -> str:
    return "agent_instructions:" + sip_uri.strip().lower()

async def fetch_instructions_from_remote(sip_uri: str) -> dict:
    # Implement your HTTP or MCP fetch logic here
    # Example: await http_client.get(f"<https://config.example.com/instructions/{sip_uri}>")
    raise NotImplementedError

async def get_instructions(sip_uri: str) -> dict:
    key = normalize_key(sip_uri)
    r = await get_redis()

    cached = await r.get(key)
    if cached:
        return json.loads(cached)

    lock_key = key + ":lock"
    got_lock = await r.set(lock_key, "1", ex=LOCK_TTL, nx=True)
    if got_lock:
        try:
            instructions = await fetch_instructions_from_remote(sip_uri)
            # store in cache with TTL
            await r.set(key, json.dumps(instructions), ex=TTL_SECONDS)
            return instructions
        finally:
            await r.delete(lock_key)
    else:
        # Another process is fetching — wait and retry a few times
        for _ in range(10):
            await asyncio.sleep(0.2)
            cached = await r.get(key)
            if cached:
                return json.loads(cached)
        # fallback: fetch directly (last resort)
        instructions = await fetch_instructions_from_remote(sip_uri)
        await r.set(key, json.dumps(instructions), ex=TTL_SECONDS)
        return instructions

4) Integrate into agent entrypoint • Load instructions before connecting the agent so the agent participant is ready when it appears.

async def entrypoint(ctx: JobContext):
    # Extract SIP URI from participant attributes or ctx
    sip_uri = extract_sip_uri_from_ctx(ctx)  # implement extraction

    # First, try session-local userdata
    instructions = ctx.proc.userdata.get("instructions_for:" + sip_uri)
    if not instructions:
        instructions = await get_instructions(sip_uri)
        # store in session-local userdata for quick reuse within session
        ctx.proc.userdata["instructions_for:" + sip_uri] = instructions

    # Initialize your agent with these instructions
    session = AgentSession(...)
    await session.start(
        room=ctx.room,
        agent=MyAgent(instructions=instructions),
        # ...
    )

Best practices and additional recommendations • TTL vs explicit invalidation: ◦ Use reasonable TTLs if instructions change periodically. ◦ If updates must propagate immediately, have your config service call a cache-invalidation endpoint or send a webhook to delete the Redis key. • Security: ◦ Protect Redis access (VPC, password, TLS). If payloads are sensitive, encrypt them before caching. • Size and memory: ◦ Store only necessary fields in cache (avoid bloat). If entries are large, prefer external DB + small cache pointers. • Stampede protection: ◦ Use lock keys (SET NX) or Redis-based algorithms (Redlock/Lua scripts) for atomic refresh. • Monitoring: ◦ Track cache hit rate, miss rate, average fetch latency, and Redis memory usage. • Scaling across instances: ◦ External cache lets multiple agent servers share cached instructions without duplicating memory. When to choose alternatives • Use Postgres/DynamoDB if you need durability, version history, or transactional updates. Then use Redis as a front-line cache for performance. • Use Memcached if you need a simple, memory-only cache without persistence and with very high perf. Next step Tell me: • Which runtime do you want the example in (async Python, Node.js, Go)? • Which cache backend do you prefer (Redis, Postgres, DynamoDB)? I’ll provide a full runnable example (including fetch logic and cache invalidation) tailored to your stack. Sources: External data and RAG | LiveKit Documentation | Building an Automated IVR Menu Caller | LiveKit Documentation | Server options | LiveKit Documentation

when you create the cache outside of the define agent, how much memory is there available?

Thanks — I’m responding to your question about how much memory is available when you create the cache outside of the agent definition. Short answer • Memory available for an external cache depends on where that cache runs: ◦ If your cache runs in-process (created outside the Agent class but inside the same process), it shares the process/container memory limit. ◦ If you run a dedicated external cache service (Redis, Memcached, a managed cache), memory depends on the instance size you provision for that service. • On LiveKit Cloud, agent servers are commonly sized around 4 CPU cores and 8 GB RAM per agent server — any in-process cache must fit within that memory. External cache services are provisioned separately and have their own limits based on the plan/instance you choose. Details to help you decide • In-process cache (same process as agent) ◦ Uses the same heap and memory as your agent. On LiveKit Cloud expect your process to share the host/container’s allocated RAM (typical guidance: ~8 GB for a voice/agent server). ◦ Pros: lowest latency. Cons: not shared across instances, vulnerable to restarts, and can cause process OOM if unbounded. ◦ Recommendation: enforce entry limits/byte limits, use TTL/LRU eviction, and monitor memory metrics. • External cache service (Redis/Memcached/DB) ◦ Memory is whatever you provision for that service (e.g., a Redis instance with 2 GB, 8 GB, etc.). It’s independent of agent process memory. ◦ Pros: shared across agent instances, scalable, predictable sizing. Cons: network latency, operational cost. ◦ Recommendation: pick instance size based on estimated total cached data, use TTLs, and monitor memory usage on the cache server. Sizing guidance (quick math) 1. Measure average instruction size (bytes). Example: 2 KB per entry. 2. Multiply by expected unique cached participants. Example: 10,000 * 2 KB = ~20 MB. 3. Add overhead (serialization, indexing, metadata). Multiply by 1.5–2 → ~30–40 MB. 4. If using Redis, pick an instance with headroom (e.g., 128 MB–1 GB for this small set). For larger sets scale accordingly. Operational tips • If running in-process on LiveKit Cloud, assume ~8 GB available for the server process but reserve room for other components — don’t let your cache consume all RAM. • Prefer external caches for cross-session reuse and multi-instance deployments. • Use TTLs, LRU, or entry-count limits to bound memory growth. • Add monitoring and alerts for memory usage and eviction rates. • If you need immediate invalidation, implement a webhook or API from your config service to delete keys in the cache. If you want, I can: • Calculate a recommended cache size given your average instruction size and expected number of unique participants. • Provide a runnable example that enforces a max memory or entry limit (your runtime: async Python, Node.js, or Go) and shows how to integrate Redis as an external cache. Which runtime and expected scale should I use for the example?

I cannot run a local redis server

Thanks — I’m responding to your note that you cannot run a local Redis server and want alternatives for an external cache. Good news: you don’t need to run Redis locally to use an external cache. Here are practical options and recommendations, including how to connect from LiveKit Cloud and example code for a remote Redis endpoint. Options (ranked by simplicity & reliability) • Managed Redis (recommended) ◦ Providers: Redis Cloud (Redis Ltd), AWS Elasticache (Redis), Azure Cache for Redis, Upstash (serverless Redis), DigitalOcean Managed Databases. ◦ Pros: no ops, secure connectivity options, backups, scaling, and predictable memory sizing. Many offer free/low-cost tiers (e.g., Upstash has serverless pricing). • Hosted Memcached ◦ Simpler memory-only cache; fewer features than Redis but good for simple key/value. • Cloud-managed key-value stores ◦ AWS DynamoDB, Google Firestore, Azure CosmosDB: durable, serverless, globally available. Use with a small TTL or a secondary cache layer. • Database-backed caching ◦ PostgreSQL or other DB as authoritative store and small in-process or managed cache for hot items. Useful if you already have a DB and need durability/transactions. • Serverless cache (e.g., Upstash or providers with HTTP APIs) ◦ Offers simple connection from serverless or cloud-hosted apps without managing a connection pool. How to pick • Need ultra-low latency & high throughput → Managed Redis (Elasticache/Redis Cloud). • Want minimal ops and pay-for-what-you-use → Upstash or other serverless Redis. • Need strong durability / complex queries → Postgres/DynamoDB, plus a small front-line cache. • Multi-instance sharing required → External cache (not in-process). Connecting from LiveKit Cloud • Provision a managed Redis instance and whitelist your LiveKit Cloud outbound IPs or use a VPC peering/private networking option if supported by your Redis provider. • Use TLS/auth (password or ACL tokens) when connecting from cloud agents. • For serverless Redis like Upstash, you may connect over HTTPS/WebSockets or standard Redis protocol depending on plan — this is convenient when you can’t open persistent connections. Example: using a remote Redis endpoint (async Python sketch) • Assume you have a managed Redis URL like redis//password@redis-host:6379/0

import aioredis
import json
import asyncio

REDIS_URL = "redis://:YOUR_PASSWORD@redis.example.com:6379/0"
TTL_SECONDS = 3600

async def get_redis():
    return await aioredis.from_url(REDIS_URL, encoding="utf-8", decode_responses=True)

async def get_instructions(sip_uri):
    key = "agent_instructions:" + sip_uri.strip().lower()
    r = await get_redis()
    cached = await r.get(key)
    if cached:
        return json.loads(cached)

    # fetch remote instructions (HTTP or MCP)
    instructions = await fetch_instructions_from_remote(sip_uri)
    await r.set(key, json.dumps(instructions), ex=TTL_SECONDS)
    return instructions

• For Node.js use ioredis or node-redis with the remote URL and password similarly. If you cannot open persistent Redis connections (firewall or platform limits) • Use an HTTP-friendly cache/API: ◦ Upstash has an HTTP REST API for Redis operations (suitable for serverless environments). ◦ Build a thin HTTP cache microservice (hosted on your cloud provider) that your agents call to get/set cached instructions; that service connects to Redis/DB. • Use a durable DB (DynamoDB, Postgres) directly and add application-level TTL/version checks. Security & ops reminders • Use strong auth (password/ACL tokens) and TLS for remote Redis. • Restrict access: IP allowlists, VPC peering, or private networking where possible. • Monitor connection counts, memory usage, hit/miss metrics. • Choose a TTL appropriate for how frequently instructions change; implement explicit invalidation webhooks if you need immediate updates. Next step I can help with • Give a concrete runnable example for your runtime (async Python, Node.js, or Go) using a specific managed provider (Upstash, AWS ElastiCache, Redis Cloud). • Show how to implement connection pooling, retry/backoff, and a simple refresh-lock pattern for stampede protection. Which runtime and managed cache provider would you like me to use for the sample?

then it's not quicker than the original http request

Thanks — I’m responding to your point that using a remote cache (e.g., managed Redis) may not be any faster than the original HTTP request. Short answer • A properly provisioned external cache can be significantly faster than repeated HTTP requests to your config service, but only if you minimize network hops and connection overhead. If the cache is remote with high latency or you open a new connection per request, it may indeed be no faster than the HTTP call. • There are several ways to get real performance wins: place the cache close to your agent servers, keep persistent connections, reduce round trips, and combine caching with session-local preloads. How a remote cache can be faster (and what to watch) • Network proximity: Host the cache in the same cloud region or VPC as your LiveKit agents. Cross-region or public-internet hops add latency that can erase gains. • Persistent connections: Redis clients reuse a pooled TCP/TLS connection. Reusing that connection is much faster than establishing new HTTP connections repeatedly. • Smaller payloads and serialization: Cache payloads are often compact JSON blobs; reading from Redis is usually quicker than HTTP processing on the origin service. • Lower server overhead: Cache reads avoid whatever app-layer logic the origin service runs (DB queries, auth, transformation). • Concurrency and throughput: Redis is optimized for many small reads/second with lower tail latency than an HTTP endpoint under load. Why you might not see improvements • Cache is remote and far: If your Redis instance is in a different region or suffers network flakiness, latency can match or exceed the HTTP call. • No connection pooling: Creating a new connection for every request (or using an HTTP-based cache without keep-alive) is slow. • Cache miss rate is high: If most requests miss the cache, you’ll still hit the origin service frequently, so caching adds overhead instead of saving it. • Small scale: For a handful of requests, the difference may be negligible and extra infrastructure complexity isn’t worth it. Practical options to get speed benefits • Use a managed Redis or serverless Redis in the same region as your LiveKit agents (Upstash, Redis Cloud, ElastiCache in same VPC/region). • Keep a persistent Redis client / connection pool in your agent process so reads are cheap. • Use session-local caching (in

ctx.proc.userdata

) for repeated accesses inside a session to avoid any network call after the first read. • Prewarm frequently used entries (load hot instructions into the process on startup) to avoid first-request latency. • Reduce RTTs: batch reads where possible or use small keys/values to reduce payload size. • Use TTL + proactive refresh: serve cached value while a background refresh updates the cache so clients never block waiting on origin fetch. • If persistent connections are not possible, consider an HTTP cache layer (a small cache microservice near your agents) that keeps a pooled Redis connection — agents call that local HTTP endpoint with low latency. Concrete patterns you can use • Fast path on join: 1) Check session-local userdata (zero network). 2) If missing, GET from Redis (single fast network call over a pooled connection). 3) If Redis miss, trigger remote fetch and write result to Redis (use lock to avoid stampede). • Background refresh: ◦ On cache hit with TTL nearing expiry, return current value and spawn an async background refresh to update Redis. This keeps agent startup fast. • Local HTTP cache proxy: ◦ Run a tiny cache service colocated with agents that exposes a simple HTTP API and keeps a single pooled Redis connection. This avoids per-agent Redis connections in constrained environments. Questions to recommend a concrete setup • What latency target do you need for instruction retrieval on participant join (e.g., <50 ms, <200 ms)? • Which runtime are you using (async Python, Node.js, Go)? • Which managed cache providers can you use / prefer (Upstash, AWS ElastiCache, Redis Cloud)? • Can you place the cache in the same cloud region as your LiveKit agents? If you tell me those, I’ll give a specific architecture and a runnable example (client pooling, prewarm + session-local caching, and a background refresh pattern) tuned to your constraints.