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Sympozium

Kubernetes-native AI Agent Orchestration Platform

Every agent is an ephemeral Pod. Every policy is a CRD. Every execution is a Job. Orchestrate multi-agent workflows on Kubernetes — from single tasks to coordinated teams. Multi-tenant. Horizontally scalable. Safe by design.

Sympozium TUI demo

Quick Install

brew tap sympozium-ai/sympozium
brew install sympozium

curl -fsSL https://deploy.sympozium.ai/install.sh | sh

Then deploy to your cluster and activate your first agents:

sympozium install          # deploys CRDs, controllers, and built-in Ensembles
sympozium                  # launch the TUI — go to Personas tab, press Enter to onboard
sympozium serve            # open the web dashboard (port-forwards to the in-cluster UI)

New here?

See the Getting Started guide — install, deploy, onboard your first agent, and learn the TUI, web UI, and CLI commands.

Why Sympozium?

Sympozium is a Kubernetes-native platform for orchestrating AI agent teams. Deploy agents for customer support, code review, data pipelines, incident response, or any domain-specific workflow — each agent gets its own pod, RBAC, and network policy with proper tenant isolation.

Bundle agents into Ensembles with delegation, sequential pipelines, and supervision relationships. Give them persistent memory, external tools via MCP servers, and cron schedules — all declared as CRDs and reconciled by controllers.

Every concept that traditional agent frameworks manage in application code, Sympozium expresses as a Kubernetes resource — declarative, reconcilable, observable, and scalable.

Key Features

  • Ephemeral agent pods — each agent run is an isolated Kubernetes Job with its own security context
  • Skill sidecars — every skill runs in its own container with auto-provisioned, least-privilege RBAC
  • Ensembles — pre-configured bundles of agents that activate with a few keypresses
  • Multiple interfaces — k9s-style TUI, full web dashboard, or CLI
  • Channel integrations — Telegram, Slack, Discord, WhatsApp
  • Persistent memory — agents retain context across runs via ConfigMap-backed memory
  • Policy-as-CRD — feature and tool gating enforced at admission time
  • OpenTelemetry — built-in observability with traces and metrics
  • Web endpoints — expose agents as OpenAI-compatible APIs and MCP servers
  • Scheduled tasks — cron-based recurring agent runs
  • Local inference discovery — node-probe DaemonSet discovers Ollama/vLLM/llama-cpp on host nodes with automatic model listing and node pinning

Learn More

Topic Description
Getting Started Install, deploy, and onboard your first agent
Architecture System design and how it all fits together
Custom Resources The six CRDs that model every agentic concept
Ensembles Pre-configured agent bundles
Skills & Sidecars Isolated tool containers with ephemeral RBAC
Lifecycle Hooks PreRun and postRun containers for setup and teardown
Security Defence-in-depth at every layer
Writing Skills Build your own SkillPacks
Writing Tools Add new tools to the agent runner
Ollama & Local Inference Node-based and in-cluster Ollama setup with auto-discovery
LM Studio Local GGUF model serving with desktop GUI
llama-server llama.cpp server with full GPU control and node auto-discovery
Unsloth Fine-tuned models served via llama.cpp or vLLM
AWS Bedrock Amazon Bedrock setup with Claude, Nova, and other foundation models