Research question — agent memory + reliability in production #114
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Great question — I've been working on exactly this problem space. Here are the key gaps I've found when running CrewAI agents in production with persistent memory: 1. Memory integrity / poisoning — The biggest gap nobody talks about. If an agent persists memory across sessions (long-term memory, entity memory), a single malicious tool response or injected context can corrupt the memory store permanently. The agent then acts on poisoned data in every future session. This is OWASP ASI06. 2. Cross-session leakage — In multi-tenant setups, memory isolation between users/tasks is entirely your responsibility. CrewAI doesn't enforce boundaries. 3. Audit trails — You're right that there's no built-in audit. You need to wrap memory operations to log what was written, by whom, and when. For (1), I maintain Agent Memory Guard — it's an open-source middleware (OWASP Incubator) that wraps CrewAI's memory backend and screens every read/write for injection patterns, secret exfiltration, and integrity attacks. ~59µs overhead per operation. Drop-in integration: from agent_memory_guard import protect
from crewai import Crew
crew = Crew(agents=[...], tasks=[...])
protect(crew) # wraps memory with security scanningFor (2) and (3), you'll want to combine AMG's event hooks with a proper observability layer (Langfuse or similar) to get the audit trail. Happy to share more details on the production patterns I've seen work. Full disclosure: I'm the maintainer of AMG. |
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Hi — I've been looking at CrewAI Studio and it's a really solid project. I'm doing research on the infrastructure gaps developers hit when building seriously on top of CrewAI — things like persistent memory across restarts, task reliability, audit trails.
I imagine building Studio gave you a clear view of what CrewAI doesn't provide out of the box. Would you have 15 minutes to share what you had to work around? I'm trying to understand the real pain before building anything. No pitch — just research.
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