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Which messaging stack should I use for send/receive/store messages for my ai agent service?
r/aiagents
8/15/2025
Content Summary
The post asks for recommendations on which open-source messaging stack to use for an AI agent service with hundreds of users. The user is looking for something easy to start with and has good Python integration. Several options were suggested, including Redis, Kafka, and RabbitMQ, each with their own advantages. Redis was recommended as the best option for quick setup, while Kafka was suggested for high-throughput scenarios.
Opinion Analysis
Mainstream opinion suggests that Redis is the best choice for quick setup and Python support, making it ideal for the user's scale. However, Kafka and RabbitMQ were also mentioned as viable alternatives depending on specific needs. Some users emphasized the importance of choosing a tool that aligns with the primary use case, such as real-time chat, task queuing, or event streaming. There was no significant controversy, but the discussion highlights the trade-offs between simplicity, scalability, and performance.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Redis | https://redis.io | Messaging / Data Store | Supports Streams, Keys, and has strong Python support (redis-py) |
| Kafka | https://kafka.apache.org | Message Broker | High throughput, good for event streaming |
| RabbitMQ | https://www.rabbitmq.com | Message Broker | Reliable, supports Python via pika library |
| Apache Kafka | https://kafka.apache.org | Message Broker | High throughput, suitable for large-scale systems |
| Redis Streams | https://redis.io/docs/latest/streams/ | Messaging | Lightweight and fast for real-time applications |
USER NEEDS
Pain Points:
- Need a scalable and easy-to-use messaging stack for an AI agent service with hundreds of users
- Looking for a solution with good Python APIs and SDKs
- Concerned about setup complexity and performance
Problems to Solve:
- Efficient message sending, receiving, and storage for an AI agent
- Ensuring reliability and performance at scale
Potential Solutions:
- Redis for its simplicity and Python support
- Kafka for high-throughput scenarios
- RabbitMQ for reliable message queuing
GROWTH FACTORS
Effective Strategies:
- Focusing on scalability and ease of use to attract developers
- Leveraging open-source tools to reduce initial development costs
Marketing & Acquisition:
- Engaging with developer communities like r/aiagents for feedback and visibility
- Highlighting technical strengths and ecosystem compatibility
Monetization & Product:
- Emphasizing the importance of choosing the right tech stack to ensure product-market fit
- Building solutions that are flexible and can be adapted to different use cases
User Engagement:
- Encouraging community interaction through discussions and feedback loops