I will build multi ai agent system with crewai, autogen pinecone, langchain and n8n

Yes. I architect role-based CrewAI crews alongside AutoGen conversation agents, enabling task delegation, parallel execution and result synthesis all orchestrated through a shared LangGraph state machine with full memory persistence.

I implement short-term memory via Redis, long-term retrieval through Pinecone or Weaviate vector stores, and structured state via PostgreSQL giving agents full contextual awareness across sessions without token bloat or hallucination drift.

LangGraph suits deterministic, graph-based workflows needing fine control over state transitions. AutoGen excels at dynamic multi-agent conversations. For complex systems I combine both LangGraph as the backbone, AutoGen handling agent-to-agent negotiation layers.

Yes. Semantic Kernel handles enterprise plugin orchestration and planner logic while LangChain manages chain composition and tool routing. Both can share the same vector store backend Pinecone, Weaviate or Chroma depending on your latency requirements.

Each agent service runs in an isolated Docker container. Kubernetes manages horizontal scaling, health checks and load balancing. FastAPI exposes agent endpoints, Celery or BullMQ handles async task queues, and Redis manages inter-agent messaging and caching.

Absolutely. I expose agent capabilities via FastAPI webhooks that n8n, Make.com and Zapier trigger natively. This allows your agents to act on CRM events, emails, form submissions or scheduled triggers without custom integration code on your end.

Yes. I build code-execution agents using AutoGen's code interpreter, sandboxed inside Docker containers. Agents write, run, catch errors and self-correct iteratively with human-in-the-loop checkpoints configurable at any step in the LangGraph flow.

I use Redis pub/sub for real-time agent messaging, BullMQ for priority task queuing between agents, and PostgreSQL for audit logging every agent action ensuring zero message loss and full traceability even under high-concurrency workloads.

I implement structured tool schemas with LangChain's ToolCallingAgent, combined with a Semantic Kernel planner for intent classification. Agents score tool relevance per subtask, preventing wrong tool selection and reducing hallucinated API calls significantly

Yes. I configure Kubernetes HPA tied to BullMQ or Celery queue metrics via KEDA automatically scaling agent pods up under load and down during idle periods, keeping infrastructure costs proportional to actual workload demand.