Practical overview
When it fits
Use Continuum when an agent project needs more than model calls: persistent state, tool orchestration, multi-agent delegation, durable workflows or runtime observability.
Who it is for
- Python developers building AI agent applications
- Enterprise engineering teams operating multi-agent workflows
- Platform teams packaging self-hosted or Docker-based agent services
- Developers integrating MCP tools, model providers and observability into agent runtimes
Documented uses
- Build agents with structured outputs and lifecycle controls
- Route requests across multiple LLM providers
- Store long-term semantic recall and low-latency working memory
- Connect agents to MCP tool ecosystems
- Run restart-safe workflows with human approval stages
Requirements & limits
The repository is public, active and licensed under Apache-2.0. The documented setup requires Python 3.13+ and Docker; local infrastructure is started through the Continuum CLI.
- The minimal profile starts Redis and Qdrant, while standard adds Langfuse and full adds Temporal and Milvus.
- MCP is the documented tool-integration standard, but the supplied material does not include a concrete MCP integration example.
- Multi-LLM routing is supported, while provider configuration and routing policies are not detailed in the supplied setup excerpt.
- Persistent memory and durable workflows depend on the corresponding local infrastructure services.
- The project is Python-based and exposes a CLI for local infrastructure management.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Continuum’s local agent infrastructure
Install Continuum and start the minimal local services used for stateful agent development.
Before you start
- Python 3.13+
- Docker
- A provider API key for the model service you plan to use
Steps
Create and activate a Python 3.13 virtual environment.
Install the shyftlabs-continuum package.
Run continuum up to start the minimal Redis and Qdrant profile and write the environment configuration.
Add the provider API key to the generated .env file.
Expected outcome
A local Continuum environment with Redis and Qdrant running for stateful agent development.
Guide limitations
- The documented setup does not provide a complete agent implementation or execution example.
- The supplied instructions do not specify provider configuration beyond adding the API key to .env.
Compare options for Agent orchestration
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Continuum | Build agents with structured outputs and lifecycle controls | CLI, Docker, Linux, macOS | The repository is public, active and licensed under Apache-2.0. The documented setup requires Python 3.13+ and Docker; local infrastructure is started through the Continuum CLI. |
| Agenite | Define typed tools with JSON Schema validation and error handling. | CLI | |
| Veldra | Create an agent with a policy, tools, skills, knowledge base and reasoning method. | Self-hosted | The documented deployment is self-hosted and uses a sign-in gate after a first-run administrator account is created. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 3 October 2026
- Original website title
- shyftlabs/continuum · Official homepage title · retained in its source language