Get a Continuum agent up and running — Python 3.13 venv, infra via `continuum up`, smallest possible BaseAgent + AgentRunner example. Invoke when the user asks "how do I start", "set up Continuum", "run my first agent", or is at the very beginning of a project.
Set up Claude Code for a Continuum project — CLAUDE.md wiring, skill imports, project settings, and hooks. Invoke when the user asks "set up Claude Code for my project", "CLAUDE.md for Continuum", "add skills to my project", or "configure Claude Code hooks".
Set up OpenAI Codex CLI for a Continuum project — AGENTS.md content, project conventions, and what Codex needs to write correct Continuum code. Invoke when the user asks "set up Codex for my project", "AGENTS.md for Continuum", or "configure Codex CLI".
Set up Cursor for a Continuum project — .cursor/rules/ MDC file, glob patterns, and what to include so Cursor generates correct Continuum code. Invoke when the user asks "set up Cursor for my project", "Cursor rules for Continuum", ".cursor/rules", or "MDC file for Continuum".
Build BaseAgent instances and run them with AgentRunner — covers fields, lifecycle hooks, structured outputs, ReAct mode, instruction modifiers, and the full execution flow. Invoke when the user asks "create an agent", "configure max_turns", "add lifecycle hooks", "structured output with Pydantic", or anything around the core agent abstraction.
Pick the right LLM provider, configure structured outputs, control context-window compression, and use the LLMClient directly. Provider routing is by model-string prefix; LiteLLM has been removed. Also covers Smart Gateway integration for multi-provider routing. Invoke when the user asks about "switch to Claude", "Gemini structured output", "context length error", "rate limiting", "JSON schema", "Smart Gateway", "gateway_mode", or "how does the LLM client work".
Configure and use Continuum's two-tier memory system — mem0+Qdrant/Milvus for long-term facts, Redis for short-term sessions, with multi-tenant scopes (USER / AGENT / SHARED / RUN / CONVERSATION). Invoke when the user asks about "remember", "user preferences", "long-term memory", "vector search over memories", "multi-tenant isolation", "PII filtering on memory writes", "Milvus", "Qdrant", or memory-related errors.
Evaluate agent quality with the `EvaluatorAgent`, build golden datasets from Langfuse traces, and run DeepEval/RAGAS metrics over conversations. Invoke when the user asks "test agent quality", "evaluate output", "RAG metrics", "DeepEval", "RAGAS", or "regression-test my agent".