Bring Agent Memory
to CrewAI

Absolutely — this is a core design goal. Use create_note to store decisions, context, or meeting summaries as markdown notes. Organize them into folders with create_note_folder and move_note_to_folder. Use toggle_note_share to publish findings to the team. Every edit is versioned via note_revisions so you can track how knowledge evolves.

Yes! The global_search tool performs a unified search across tasks, wiki pages, discussions, user stories, sprints, and notes. Results are grouped by resource type, so you instantly see where every mention lives. It's the fastest way to find anything in your workspace.

Yes. Use send_message to post updates to any discussion channel, and reply_to_message for threaded conversations. The agent can also create channels with create_channel, search message history with search_channel_messages, and review thread replies with thread_replies — enabling fully automated status updates and knowledge sharing.

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.