continuum-quickstart

name	continuum-quickstart
description	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.

Continuum Quickstart Skill

Use this skill when the user is starting from scratch. The goal is to get them to a running agent in a few minutes.

---

Path A — Library consumer

# 1. Create venv (Python 3.13 required) & install the package
python3.13 -m venv .venv && source .venv/bin/activate
pip install shyftlabs-continuum

# 2. Start infra (Redis :6380, Qdrant :6333) — `continuum up` ships with the
#    package, resolves the bundled stack, and writes a managed ./.env
continuum up

# 3. Add your provider key
echo "OPENAI_API_KEY=sk-..." >> .env

# 4. Run the smallest example (see snippet below)
python my_first_agent.py

Path B — Framework contributor (clone + editable install)

git clone https://github.com/bhavik-shyftlabs/continuum.git
cd continuum
cp .env.template .env                       # add OPENAI_API_KEY
continuum up                                # Redis + Qdrant (minimal); `continuum up full` adds Langfuse · Temporal · Milvus
python3.13 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,temporal,eval]"      # editable install with all extras

# Smoke-test against a runnable example app
python -m playground.sdk_feature_test

---

Smallest possible agent

import asyncio
from orchestrator.agent import BaseAgent, AgentRunner
from orchestrator.agent.config import AgentMemoryConfig
from orchestrator.core.container import Container, ContainerConfig

async def main():
    # No infra needed for this example
    container = Container(ContainerConfig(
        enable_memory=False, enable_session=False, enable_langfuse=False,
    ))
    agent = BaseAgent(
        name="hello",
        instructions="Reply in one short sentence.",
        model="gpt-4o-mini",
        memory_config=AgentMemoryConfig(search_memories=False, store_memories=False),
    )
    response = await AgentRunner(container=container).run(agent, "Say hi.")
    print(response.content)

asyncio.run(main())

---

Quick checks if something fails

Symptom	Fix
ModuleNotFoundError: orchestrator	source .venv/bin/activate
Failed to initialize mem0: Missing credentials	Set OPENAI_API_KEY in .env (mem0 needs it for embeddings, even if you use Anthropic/Gemini for chat)
redis ConnectionError on port 6380	continuum status — make sure the SDK Redis service is healthy
ImportError on Python startup	Wrong Python — must be 3.13
pip install -e . fails on Python <3.13	Switch to Python 3.13 (pyenv / uv)

---

Next steps to suggest

After "hello world" works, point them at one of:

• playground/memory-modes-demo/ — all four memory scopes
• playground/commerce-chat/ — plan-and-execute multi-agent + MCP
• docs/agent.md — full BaseAgent API reference
• .claude/skills/continuum-agent/ — the agent-building skill

---

What NOT to do

• Don't suggest pip install litellm or any LiteLLM code paths — it was removed.
• Don't change the default Redis (6380) or Qdrant (6333) ports — they're wired into Settings.
• Don't add new infra services to docker-compose.yml casually — Redis, Qdrant, and the Langfuse stack are the canonical set.
• Don't write to src/orchestrator/ if you only intended to consume the library — install via pip and write your code outside the source tree.