Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka client configuration for WarpStream.
Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka client configuration for WarpStream.
metadata
{"version":"1.0.0"}
Do NOT generate any code, scaffold any project, or modify any file until you have
explicitly asked and received answers for questions #1 (existing app or greenfield),
#2 (target environment), and #3 (producer, consumer, or both). If the user's prompt
partially answers some questions, still confirm your understanding before generating.
This applies to EVERY prompt regardless of how specific it appears.
Begin by announcing: "Using the Confluent Kafka Python Client skill to guide this project."
Confluent Kafka Python Client Creation
Generate a production-ready Python project for producing to and/or consuming from Kafka using confluent-kafka-python. Supports three target environments: Confluent Cloud (managed), Local Docker (open-source Kafka), and WarpStream (Kafka-compatible, object-storage-backed), and two producer styles: AsyncIO (non-blocking) and Synchronous (blocking). The generated code follows Confluent's best practices.
Step 1: Gather Requirements
Before generating any code, work through the questions below. Skip any question the user has already answered explicitly in their prompt — do not re-ask just for form's sake. For example, "build a producer and consumer on Confluent Cloud with an async producer" already answers #2, #3, and #4; only #1, #5, #6, #7, and #8 remain.
Mandatory confirmation gate — do not skip, even if the user answered every question. Before writing any file, you MUST send one message that:
Recaps the answers you extracted as a short bulleted list (e.g., "Target: Confluent Cloud · Components: producer + consumer · Producer style: async · From scratch: yes").
Asks any remaining open questions inline.
Explicitly asks the user to confirm or correct before you proceed.
Then STOP and wait for the user's reply. Do not generate files in the same turn as the recap, and do not proceed on the assumption that a fully-specified prompt implies consent to generate immediately — the recap catches misinterpretations of the prompt and is required even when questions #1–#8 are all pre-answered. The only way to skip the gate is if the user has already confirmed the recap earlier in this conversation.
Do not assume defaults for #1, #2, or #3 — if any of these are not answered by the prompt, you must ask.
Are you adding Kafka to an existing application, or starting from scratch?
If the user has existing Python code (mentions an existing project, has a main.py, uses Flask/FastAPI/Django, etc.), do not scaffold a new project. Instead: (a) identify their existing producer or data-sending code, (b) ask whether they already have schemas registered in Schema Registry, (c) add Schema Registry integration to their existing code following the patterns in the reference files. Generate only the files they are missing (e.g., common.py, schemas/value.schema.json) and modify their existing code inline.
If the user already produces to Kafka without Schema Registry (schemaless), help them migrate: (1) generate a JSON Schema from their existing message structure, (2) register it, and (3) replace their raw producer.produce() calls with serializer-backed calls. Do not discard their existing code.
If starting from scratch, proceed with the full scaffold below.
Target environment? — Confluent Cloud, local Kafka (Docker), or WarpStream. Always prompt for this, even if the user didn't mention it. If they mention "open source", "local", "docker", "self-hosted", or just want to try Kafka without a cloud account, choose local Docker. If they mention "Confluent Cloud", "CC", or have existing cloud credentials, choose Confluent Cloud. If they mention "WarpStream", choose WarpStream. Default to Confluent Cloud if they confirm they don't have a preference, but always ask first.
If WarpStream: Read references/warpstream-optimization.md and apply the librdkafka overrides from that reference. Key changes: disable idempotence, dramatically increase batch sizes and in-flight requests, set large fetch sizes, add ws_az=<az> to client.id for zone-aware routing. Prefer null message keys for sticky partitioning unless entity-based ordering is required.
Producer, consumer, or both?
Async or synchronous producer? (Only if producer is requested.) Help the user choose:
AsyncIO Producer (AIOProducer): Use when code runs under an event loop — FastAPI/Starlette, aiohttp, Sanic, asyncio workers — and must not block.
Synchronous Producer (Producer): Use for scripts, batch jobs, and highest-throughput pipelines where the user controls threads/processes and can call poll()/flush() directly.
If the user mentions an async framework (FastAPI, aiohttp, Sanic) or uses asyncio, default to AsyncIO. If they mention scripts, batch, ETL, or don't have a preference, default to Synchronous.
Do you have an existing schema you'd like to use? If yes, ask the user to paste it or provide the file path, then use it as the schemas/value.schema.json instead of generating one. If no, proceed to ask about their data fields.
What kind of data are you producing? (Only if the user doesn't have an existing schema. Get field names and types so you can generate a matching JSON Schema and sample data.)
Topic name? (Default: demo-topic)
Consumer group ID? (Only if consumer; default: python-consumer-group)
Don't ask about Schema Registry — always include it. For Confluent Cloud and local Docker, always use JSON Schema. If the target is WarpStream, ask which Schema Registry implementation they are using — WarpStream's built-in schema registry only supports Avro and Protobuf (GET /schemas/types returns ["AVRO","PROTOBUF"]), so if they are using it, ask whether they prefer Avro or Protobuf (default to Avro). If they are using a different SR (e.g., Confluent Cloud Schema Registry), JSON Schema is fine.
Common Agent Mistakes
Thought
Reality
"The user mentioned FastAPI, so I know it's async — skip the questions"
Still confirm. They might want a sync background worker alongside FastAPI.
"I'll use Avro since it's more widely used"
This skill uses JSON Schema by default. Exception: WarpStream's built-in schema registry only supports Avro and Protobuf — if the user is using WarpStream SR, use Avro by default. If they're using a different SR (e.g., Confluent Cloud SR), JSON Schema is fine regardless of the Kafka environment.
"I'll skip Schema Registry to keep it simple"
Schema Registry is non-negotiable. Every project includes it.
"I'll use auto.register.schemas=True for convenience"
Always False. Explicit registration is a core principle.
"I'll create a producer in produce() — it's cleaner"
One producer instance, created in main(), passed as a parameter. Always.
"The user wants sync, so the consumer should be sync too"
Consumer is always async (AIOConsumer). This is a deliberate design decision.
"I'll add headers= to the AIOProducer for schema ID"
AIOProducer.produce() raises NotImplementedError on headers. Only sync producers use headers.
"I'll swap AsyncJSONSerializer for AsyncAvroSerializer and keep the call site the same"
JSONSerializer takes schema_str first; AvroSerializer takes schema_registry_client first. Calling positionally across formats raises TypeError: ... got multiple values for argument 'schema_registry_client'. Always pass both as kwargs.
"I'll set message.max.bytes=64000000 on the producer config and fetch.max.bytes=50242880 on the consumer — they're independent"
Not in librdkafka. message.max.bytes is a client-global config (unlike Java's per-role max.request.size), so when get_kafka_config() is shared between producer and consumer, the consumer inherits it. librdkafka enforces fetch.max.bytes >= message.max.bytes at consumer construction and raises KafkaError{_INVALID_ARG, ... "fetch.max.bytes must be >= message.max.bytes"}. Use the WarpStream librdkafka consumer values in references/warpstream-optimization.md (fetch.max.bytes=67108864) — they're sized to satisfy this constraint.
Step 1b: Confirm Understanding
After gathering all answers, present a confirmation summary before generating any code:
Before I generate the project, let me confirm:
- Project type: [Greenfield scaffold / Migration of existing code]
- Environment: [Confluent Cloud (SASL_SSL) / Local Docker (PLAINTEXT) / WarpStream]
- Schema format: [JSON Schema / Avro / Protobuf] (Avro or Protobuf if using WarpStream's built-in SR)
- Components: [Producer only / Consumer only / Both]
- Producer style: [AsyncIO (AIOProducer) / Synchronous (Producer)] (if applicable)
- Schema: [brief description of user's data fields]
- Topic: [topic name]
- Consumer group: [group ID] (if consumer)
Does this look right?
Wait for user confirmation before proceeding to Step 2. If the user corrects anything, update your understanding and re-confirm.
NEVER read, open, or display .env files. They contain API keys and secrets. Only generate .env.example with placeholder values. If the user asks you to debug a connection issue, ask them to verify their .env values themselves — do not read the file.
Core Principles
These principles matter because they prevent the most common production issues with Kafka Python clients:
Reuse the producer instance. Creating a new producer per message is expensive — each one opens new TCP connections, does SASL handshakes, and fetches metadata. Create one producer and reuse it for all messages. The produce function should accept the producer as a parameter, not instantiate one.
Always use Schema Registry with JSON Schema. Schema Registry enforces a contract between producers and consumers. Without it, schema changes silently break downstream consumers. This skill uses JSON Schema by default. Schema Registry supports Avro, Protobuf, and JSON Schema — JSON Schema is chosen because: (1) Python has first-class JSON support with no code generation step, (2) confluent-kafka-python provides JSONSerializer/JSONDeserializer out of the box, (3) it is the most approachable format for Python developers already working with JSON/dict data.
WarpStream Schema Registry exception: WarpStream's built-in schema registry only supports Avro and Protobuf — its GET /schemas/types endpoint returns ["AVRO","PROTOBUF"]. JSON Schema is not available when using WarpStream's SR. If the user is running WarpStream and using its built-in SR, use Avro by default (or Protobuf if the user prefers). Use AvroSerializer/AvroDeserializer from confluent_kafka.schema_registry.avro (or ProtobufSerializer/ProtobufDeserializer from confluent_kafka.schema_registry.protobuf). For async producers, use AsyncAvroSerializer from confluent_kafka.schema_registry._async.avro (or AsyncProtobufSerializer from confluent_kafka.schema_registry._async.protobuf). Generate an Avro schema file at schemas/value.avsc (or schemas/value.proto for Protobuf) instead of schemas/value.schema.json — use references/value.avsc as the starting point and adapt to the user's domain. When generating producer/consumer code on the Avro path, copy from references/producer_avro.py (async), references/producer_avro_sync.py (sync), and references/consumer_avro.py (async) — not the JSON reference files — because the constructor signatures differ (see the Avro constructor warning below). If the user is running WarpStream but pointing at a different SR (e.g., Confluent Cloud Schema Registry), JSON Schema works fine — use the default path.
Register schemas as a separate explicit step before creating the serializer. Use a dedicated register_schema() function that calls sr_client.register_schema() and lets errors (auth failures, network errors, permission denials) propagate immediately — never wrap registration in a bare try/except. Then configure the serializer with auto.register.schemas=False and use.latest.version=True. This ensures the serializer never silently auto-registers and aligns with production practice where CI/CD registers schemas, not application startup.
Use the appropriate serializer for the chosen producer style and schema format:
JSON Schema (default):AsyncJSONSerializer / AsyncJSONDeserializer from confluent_kafka.schema_registry._async.json_schema for async, or JSONSerializer / JSONDeserializer from confluent_kafka.schema_registry.json_schema for synchronous.
Avro (default when using WarpStream SR):AsyncAvroSerializer / AsyncAvroDeserializer from confluent_kafka.schema_registry._async.avro for async, or AvroSerializer / AvroDeserializer from confluent_kafka.schema_registry.avro for synchronous.
Protobuf (alternative when using WarpStream SR):AsyncProtobufSerializer / AsyncProtobufDeserializer from confluent_kafka.schema_registry._async.protobuf for async, or ProtobufSerializer / ProtobufDeserializer from confluent_kafka.schema_registry.protobuf for synchronous.
Choose the right producer style. The confluent-kafka-python library offers two producer APIs:
AsyncIO Producer (AIOProducer from confluent_kafka.aio): Non-blocking, integrates with asyncio event loops. Use with AsyncJSONSerializer from confluent_kafka.schema_registry._async.json_schema and AsyncSchemaRegistryClient. Best for applications already running an event loop (FastAPI, aiohttp, Sanic, asyncio workers).
Synchronous Producer (Producer from confluent_kafka): Blocking calls with delivery callbacks. Use with JSONSerializer from confluent_kafka.schema_registry.json_schema and SchemaRegistryClient. Best for scripts, batch jobs, and highest-throughput pipelines where the user controls threads/processes and can call poll()/flush() directly.
Always ask the user which style fits their use case. The consumer always uses AIOConsumer (async) — long-running poll loops benefit from non-blocking I/O, and mixing sync/async consumer styles adds complexity with little benefit.
Graceful shutdown. Async producers must flush() and close() (both awaited) before exiting. Synchronous producers must call flush() before exiting — otherwise buffered messages are lost. Consumers must unsubscribe() then close() to leave the consumer group cleanly (avoiding unnecessary rebalances). Use try/finally blocks and handle KeyboardInterrupt / signals.
Support Confluent Cloud, local Docker, and WarpStream. When targeting Confluent Cloud, configure SASL_SSL with PLAIN mechanism and load API keys from .env. When targeting local Docker, use PLAINTEXT with no authentication. When targeting WarpStream, use SASL_SSL or PLAINTEXT depending on the user's WarpStream deployment, and apply the librdkafka overrides from references/warpstream-optimization.md (large batches, disabled idempotence, large fetches, zone-aware client.id). The KAFKA_ENV environment variable (cloud, local, or warpstream) controls which path is used. Load all settings from environment variables via .env.
Verify connectivity before running. Use AdminClient.list_topics() to verify the broker is reachable and the topic exists before producing or consuming. Verify Schema Registry connectivity with an HTTP health check.
Always set a message key for domain events. Pass key=<entity_id>.encode("utf-8") to producer.produce() for any message that represents an entity or event stream (order events, user actions, device telemetry, transactions). Kafka partitions by key, so messages with the same key land on the same partition and preserve ordering — critical for event streams like OrderCreated → OrderUpdated → OrderCancelled where consumers must see events in order. The produce() helper in every reference file accepts a key_field parameter naming the field to use as the key (e.g., key_field="order_id", key_field="transaction_id"). Ask the user which field identifies the entity and pass it to produce(). Only leave key_field=None if the user explicitly states ordering does not matter (e.g., stateless metrics where any partition is fine).
WarpStream exception: On WarpStream, null keys enable sticky partitioning, which builds larger batches and significantly improves throughput and cost. When the user's use case does not require per-entity ordering (e.g., independent telemetry readings, stateless metrics, logs), recommend key_field=None and explain the throughput benefit. When per-entity ordering is required (e.g., OrderCreated → OrderUpdated → OrderCancelled for the same order), still set a message key — correctness takes priority over batching efficiency. Ask the user whether their events need per-key ordering to decide.
common.py
This module handles configuration loading and connectivity verification. Use references/common.py as the template.
producer.py Pattern (AsyncIO)
When the user chooses the AsyncIO producer, use references/producer.py as the template.
Key points:
produce() takes a producer instance as a parameter — it never creates one
The producer is created once in main() and can be passed to multiple produce() calls
The async serializer (AsyncJSONSerializer) must be awaited when calling it on a message
AIOProducer.produce() is async and returns an asyncio.Future. You must await the method to get the Future, then await the Future to get the delivered Message: future = await producer.produce(...); result = await future
AIOProducer.flush() and close() are coroutines — they must be awaited in the finally block
Signal handlers set a shutdown event for graceful termination
Schema registration and serializer creation are separate steps. register_schema() explicitly registers the schema and returns the schema ID — errors propagate immediately. create_json_serializer() (or create_avro_serializer() / create_protobuf_serializer() when using Avro/Protobuf) creates the serializer with conf={'auto.register.schemas': False, 'use.latest.version': True}.
Constructor argument order differs across formats — always pass schema_registry_client and schema_str as keyword arguments, never positionally. The JSON, Avro, and Protobuf serializer/deserializer classes in confluent-kafka-python do not share the same positional signature: JSONSerializer/JSONDeserializer take schema_str first, while AvroSerializer/AvroDeserializer and ProtobufSerializer/ProtobufDeserializer take schema_registry_client first. Mixing positional and keyword forms across formats produces TypeError: ... got multiple values for argument 'schema_registry_client'. Use kwargs everywhere so the pattern is identical:
await AsyncProtobufSerializer(msg_type=MsgType, schema_registry_client=sr_client, conf=conf) (Protobuf takes a generated message class instead of a schema string)
See references/producer.py (JSON) and references/producer_avro.py (Avro) for the canonical templates.
Headers are NOT supported with AIOProducer batch mode. Do not pass headers= to AIOProducer.produce() — it will raise NotImplementedError. Schema identification is handled automatically by the serializer's wire format prefix (applies to JSON Schema, Avro, and Protobuf serializers). See "Schema ID in Headers vs Wire Format" below for details
producer.py Pattern (Synchronous)
When the user chooses the synchronous producer, use references/producer_sync.py as the template.
Key points:
produce() takes a producer instance as a parameter — it never creates one
The producer is created once in main() and can be passed to multiple produce() calls
Uses JSONSerializer (synchronous) from confluent_kafka.schema_registry.json_schema and SchemaRegistryClient from confluent_kafka.schema_registry. When using Avro/Protobuf (e.g., with WarpStream SR), use AvroSerializer from confluent_kafka.schema_registry.avro or ProtobufSerializer from confluent_kafka.schema_registry.protobuf
Producer.produce() is non-blocking — it enqueues the message. Call producer.poll(0) after each produce to serve delivery callbacks and keep the internal queue from filling up
Call producer.flush() after a batch to block until all in-flight messages are delivered
Use a delivery_callback(err, msg) function to handle per-message delivery reports
Signal handlers set a flag for graceful termination
flush() in the finally block ensures no buffered messages are lost
Schema registration and serializer creation are separate steps, same as the async pattern. register_schema() explicitly registers the schema. create_json_serializer() (or create_avro_serializer() / create_protobuf_serializer() when using Avro/Protobuf) creates the serializer with conf={'auto.register.schemas': False, 'use.latest.version': True}. Both return the schema ID
The same kwargs-only rule applies to the sync variants: call JSONSerializer(schema_str=..., schema_registry_client=..., conf=...), AvroSerializer(schema_str=..., schema_registry_client=..., conf=...), or ProtobufSerializer(msg_type=..., schema_registry_client=..., conf=...). The positional argument order is not consistent across formats — passing positionally will raise TypeError: ... got multiple values for argument 'schema_registry_client'. See references/producer_sync.py (JSON) and references/producer_avro_sync.py (Avro) for templates
The schema ID is passed as a Kafka record header (confluent.value.schemaId) on every produced message — this is the header-based schema identification pattern. It keeps the JSON payload clean and readable by non-Confluent consumers. See "Schema ID in Headers vs Wire Format" below for details
consumer.py Pattern
Use references/consumer.py as the template.
Key points in the consumer:
Signal-based graceful shutdown — unsubscribe() then close() to leave the consumer group cleanly
Deserialization via Schema Registry using AsyncJSONDeserializer (or AsyncAvroDeserializer / AsyncProtobufDeserializer when using Avro/Protobuf) — no fallback to raw parsing, Schema Registry is required. Construct the deserializer with keyword arguments only — e.g., await AsyncAvroDeserializer(schema_str=schema_str, schema_registry_client=sr_client). The positional signature differs across formats (Avro/Protobuf take schema_registry_client first; JSON takes schema_str first), and mixing positional and keyword forms produces TypeError: ... got multiple values for argument 'schema_registry_client'. See references/consumer.py (JSON) and references/consumer_avro.py (Avro) for templates
Continuous polling loop until shutdown signal
Schema ID in Headers vs Wire Format
Synchronous producer: The reference code passes the Schema ID as a Kafka record header (confluent.value.schemaId) on every message. This keeps the JSON payload clean (no magic byte prefix), making it readable by non-Confluent consumers and debuggable with tools like kcat. This is the recommended approach for synchronous producers.
Async producer (AIOProducer): The AIOProducer does not support custom headers in batch mode (produce() raises NotImplementedError if headers= is passed). Schema identification relies on the JSON Schema serializer's wire format prefix (magic byte + schema ID prepended to the payload). This is a known limitation — do not attempt to add headers to async-produced messages.
When to use which: If downstream consumers are all Confluent-aware (using Schema Registry deserializers), both approaches work transparently. If downstream consumers are non-Confluent (plain JSON consumers), the sync producer with header-based schema ID is preferable because the message value remains clean JSON. Document this tradeoff in the generated README when producing for mixed consumer ecosystems.
schemas/
Generate a schema file matching the user's data domain in the appropriate format for the chosen schema registry.
JSON Schema (default): Generate a JSON Schema file at schemas/value.schema.json.
For example, if the user is producing financial transactions:
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "Transaction",
"description": "A financial transaction event produced to Kafka.",
"type": "object",
"properties": {
"transaction_id": {
"type": "string",
"description": "Unique identifier for this transaction."
},
"amount": {
"type": "number",
"description": "Transaction amount in the specified currency.",
"default": 0
},
"currency": {
"type": "string",
"description": "ISO 4217 currency code.",
"default": ""
},
"timestamp": {
"type": "string",
"format": "date-time",
"description": "Time the transaction occurred, in ISO 8601 format."
},
"status": {
"description": "Current state of the transaction.",
"enum": ["pending", "completed", "failed", "refunded"],
"default": "pending"
},
"metadata": {
"oneOf": [{"type": "null"}, {"type": "object"}],
"description": "Optional metadata associated with the transaction.",
"default": null
}
},
"required": ["transaction_id", "amount", "currency", "timestamp", "status"]
}
Avro (when using WarpStream SR): Generate an Avro schema file at schemas/value.avsc (or schemas/value.proto for Protobuf). For the same financial transactions example in Avro:
{
"type": "record",
"name": "Transaction",
"namespace": "com.example.kafka",
"doc": "A financial transaction event produced to Kafka.",
"fields": [
{"name": "transaction_id", "type": "string", "doc": "Unique identifier for this transaction."},
{"name": "amount", "type": "double", "default": 0, "doc": "Transaction amount in the specified currency."},
{"name": "currency", "type": "string", "default": "", "doc": "ISO 4217 currency code."},
{"name": "timestamp", "type": "string", "doc": "Time the transaction occurred, in ISO 8601 format."},
{"name": "status", "type": {"type": "enum", "name": "Status", "symbols": ["pending", "completed", "failed", "refunded"]}, "default": "pending", "doc": "Current state of the transaction."},
{"name": "metadata", "type": ["null", "string"], "default": null, "doc": "Optional metadata associated with the transaction."}
]
}
Schema Generation Rules
Follow the rules in references/schema-generation-rules.md strictly when generating or adapting schemas to the user's domain.
Multi-Event Topics (Advanced)
When the user describes multiple event types on a single topic, follow references/multi-event-guide.md. Only suggest multi-event union schemas when the user explicitly describes multiple event types on one topic.
docker-compose.yml (Local Docker Path Only)
When the user chooses local Docker, you MUST generate a docker-compose.yml using references/docker-compose.yml as the template. This starts a single-node Kafka broker (using confluentinc/confluent-local) and a Confluent Schema Registry. The user just runs docker compose up -d to get a working Kafka environment.
IMPORTANT: The confluentinc/confluent-local image uses KRaft mode and has built-in listener names: PLAINTEXT (internal, port 29092), PLAINTEXT_HOST (external, port 9092), and CONTROLLER (port 29093). Do NOT invent custom listener names — this will conflict with the image's internal configuration and cause boot loops. Only override KAFKA_ADVERTISED_LISTENERS and KAFKA_LISTENERS using these exact listener names. The internal PLAINTEXT listener must advertise the kafka hostname (not localhost) so Schema Registry can reach the broker from within the Docker network.
.env.example
Generate the appropriate .env.example based on the target environment:
Every third-party package imported anywhere in the generated code (producer.py, consumer.py, common.py) must have a corresponding entry in requirements.txt. If the code does from confluent_kafka import ..., then confluent-kafka must be in requirements.txt. If it does from dotenv import load_dotenv, then python-dotenv must be listed. This includes transitive dependencies that aren't automatically installed — for example, the async Schema Registry client imports httpx and authlib at runtime, so both must be explicitly listed even though they aren't declared as dependencies of confluent-kafka. The user should be able to pip install -r requirements.txt and run the code with zero ModuleNotFoundErrors.
Always include pytest. Include pytest-asyncio if the project uses the async producer or consumer. Only include Faker if the producer generates sample data with it.
README.md
Generate a README following references/readme-template.md. Adapt to match what was actually generated — omit producer sections if only a consumer was requested, omit Docker sections for Confluent Cloud projects.
tests/test_project.py
Always generate unit tests. Use references/test_project.py as the template. The tests must run without a live Kafka cluster or Schema Registry — mock all external dependencies so tests pass in CI and eval environments.
The tests should verify these properties of the generated code:
common.py: load_config() returns all required keys and uses correct defaults. get_kafka_config() produces a config with SASL_SSL and PLAIN when KAFKA_ENV=cloud, or PLAINTEXT with no SASL when KAFKA_ENV=local. verify_kafka_setup() and verify_schema_registry() return the right booleans when mocked to succeed or fail.
producer.py (if generated): produce() accepts a producer instance and a schema_id as parameters (never creates a producer). The producer class (AIOProducer for async, Producer for sync) is instantiated exactly once in the module. Messages are passed through the serializer before producing. The schema ID is included as a confluent.value.schemaId Kafka record header on every produced message. For synchronous producers, verify flush() is called after producing.
consumer.py (if generated): Uses a Schema Registry deserializer (JSONDeserializer/AsyncJSONDeserializer, AvroDeserializer/AsyncAvroDeserializer, or ProtobufDeserializer/AsyncProtobufDeserializer depending on the chosen format) — no raw json.loads fallback. Calls unsubscribe() before close() for graceful shutdown.
Schema file: The test template includes three schema classes — TestJsonSchema, TestAvroSchema, TestProtobufSchema — each guarded by a pytest.mark.skipif that checks for the corresponding file (value.schema.json, value.avsc, value.proto). The class for the chosen format runs; the others are skipped. JSON Schema checks: type: object, title, properties with at least one property, descriptions on all properties, and format: date-time on timestamp-like fields. Avro checks: type: record, name, fields with at least one field, doc on the record and every field. Protobuf checks: proto3 syntax and at least one message definition.
Project structure: requirements.txt exists and contains confluent-kafka, python-dotenv, and requests. .env.example exists.
Adapt the tests to the user's specific schema and data domain — if they have fields like device_id and temperature, the schema tests can check for those specific field names.
After generating all files, run pytest tests/ to verify the tests pass. If any test fails, fix the generated code (not the tests) until they pass.
Step 3: Guide the User
After generating the files, give the user instructions based on their target environment. Adapt these to match what was actually generated: omit producer steps if only a consumer was requested, omit consumer steps if only a producer was requested.
Confluent Cloud:
Copy .env.example to .env and fill in their Confluent Cloud credentials
Create a virtualenv and install dependencies: pip install -r requirements.txt
If a producer was generated: the schema will be registered explicitly when the producer runs for the first time via the register_schema() function (auto.register.schemas is set to False). If only a consumer was generated: register the schema manually by pasting the contents of schemas/value.schema.json into the Confluent Cloud Console under Schema Registry > Schemas for the topic's value subject.
Run the producer (if generated): python producer.py
Run the consumer (if generated): python consumer.py
Remind them that they can find their bootstrap server, API keys, and Schema Registry URL in the Confluent Cloud Console under their cluster and environment settings.
Local Docker:
Start Kafka and Schema Registry: docker compose up -d
Copy .env.example to .env (defaults are pre-filled for local Docker, no edits needed)
Create a virtualenv and install dependencies: pip install -r requirements.txt
Create the topic (if auto-creation is disabled): docker compose exec kafka kafka-topics --create --topic demo-topic --bootstrap-server localhost:29092
Run the producer (if generated): python producer.py
Run the consumer (if generated): python consumer.py
When done, stop the containers: docker compose down (add -v to also remove stored data)
WarpStream:
Copy .env.example to .env and fill in the WarpStream bootstrap server, Schema Registry URL, and (if applicable) credentials. Set CLIENT_ID to include ws_az=<availability-zone> for zone-aware routing (e.g., python-client,ws_az=us-east-1a)
Create a virtualenv and install dependencies: pip install -r requirements.txt
Create the topic if it doesn't exist: kafka-topics.sh --create --topic demo-topic --bootstrap-server <warpstream-agent>:9092
Run the producer (if generated): python producer.py
Run the consumer (if generated): python consumer.py
Remind them that WarpStream has higher produce latency (~250ms p50) than standard Kafka — this is expected. If throughput is lower than expected, verify that the WarpStream-specific config overrides from references/warpstream-optimization.md are applied (especially enable.idempotence=false and large batch/fetch sizes).
