Research agent

Skip the wiring — install a ready-made researcher from the Registry and adapt it.

Research agents are where the runtime, tools, and memory layers start to shine together. The common shape is:

• search and browsing
• multi-step synthesis
• source tracking
• optional long-running or scheduled execution

#Typical stack

npm install @agentskit/adapters @agentskit/runtime @agentskit/tools @agentskit/memory @agentskit/observability

#Recommended package mix

#What the architecture usually looks like

1. A task arrives as a prompt, CLI command, or scheduled job.
2. The runtime decomposes the task into multiple tool-assisted steps.
3. Search and browser tools collect evidence.
4. The agent writes structured findings to files or downstream systems.
5. Observability captures traces for replay and quality review.

#Good defaults

• Keep maxSteps explicit so research runs stay bounded.
• Save source links and snippets as structured output.
• Use memory to avoid re-researching the same topics in follow-up runs.
• Add evals if the research output feeds external customers or exec workflows.

#Best follow-up guides

• Build your first agent
• Runtime agent example
• RAG pipeline example
• Research team recipe
• Background agents
• Eval suite

#When AgentsKit is especially strong here

Research agents benefit from AgentsKit when you want a clean separation between provider, runtime, tools, and operations. It becomes easy to run the same core workflow in a CLI, a job runner, or a UI without rebuilding the agent from scratch.