MCPHub LabRegistrysympozium-ai/sympozium
sympozium-ai

sympozium ai/sympozium

Built by sympozium-ai 378 stars

What is sympozium ai/sympozium?

Run a fleet of AI agents on Kubernetes. Administer your cluster agentically

How to use sympozium ai/sympozium?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add sympozium ai/sympozium using the following command: npx @modelcontextprotocol/sympozium-ai-sympozium 4. Restart the client and verify the new tools are active.
🛡️ Scoped (Restricted)
npx @modelcontextprotocol/sympozium-ai-sympozium --scope restricted
🔓 Unrestricted Access
npx @modelcontextprotocol/sympozium-ai-sympozium

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

sympozium ai/sympozium FAQ

Q

Is sympozium ai/sympozium safe?

Yes, sympozium ai/sympozium follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is sympozium ai/sympozium up to date?

sympozium ai/sympozium is currently active in the registry with 378 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for sympozium ai/sympozium?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub
<p align="center"> <img src="logo.png" alt="sympozium.ai logo" width="600px;"> </p> <p align="center"> <em> Agents don't need better prompts. They need shared situational awareness.<br> Sympozium is a <b>coordination layer</b> for multi-agent AI systems on Kubernetes &mdash;<br> selective permeability, structured handoffs, and shared memory.<br> Every agent is a Pod. Every policy is a CRD. Every execution is a Job.</em><br><br> From the creator of <a href="https://github.com/k8sgpt-ai/k8sgpt">k8sgpt</a> and <a href="https://github.com/AlexsJones/llmfit">llmfit</a> </p> <p align="center"> <b> This project is under active development. API's will change, things will break. Be brave. <b /> </p> <p align="center"> <a href="https://github.com/sympozium-ai/sympozium/actions"><img src="https://github.com/sympozium-ai/sympozium/actions/workflows/build.yaml/badge.svg" alt="Build"></a> <a href="https://github.com/sympozium-ai/sympozium/releases/latest"><img src="https://img.shields.io/github/v/release/sympozium-ai/sympozium" alt="Release"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue" alt="License"></a> </p> <p align="center"> <img src="demo.gif" alt="Sympozium TUI demo" width="800px;"> </p>

Full documentation: deploy.sympozium.ai/docs

The problem this solves: The Sticky-Note Problem — why message-passing between agents breaks down, and what to build instead.


The Problem

Most multi-agent systems communicate through messages — strings of tokens that one agent serialises and another deserialises. A detection agent spots a threat while a containment agent takes the server offline for maintenance. Neither knows what the other is doing. The breach is missed.

This is the sticky-note problem: agents passing notes instead of sharing a situational board. Kubernetes solved this for containers with a shared control plane. Agents need the same thing — not better message-passing, but shared coordination infrastructure.

Sympozium provides that infrastructure: a synthetic membrane that wraps agent teams with selective permeability, shared memory, structured handoffs, and circuit breakers — all expressed as Kubernetes-native CRDs.


Quick Install (macOS / Linux)

Homebrew:

brew tap sympozium-ai/sympozium
brew install sympozium

Shell installer:

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)

Advanced: Helm Chart

Prerequisites: cert-manager (for webhook TLS):

kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.17.1/cert-manager.yaml

Sympozium can be installed as two charts: sympozium-crds (the CRDs, so they can be upgraded) and sympozium (the control plane). Install the CRDs first, then the control plane:

helm repo add sympozium https://deploy.sympozium.ai/charts
helm repo update

helm upgrade --install sympozium-crds sympozium/sympozium-crds \
  --namespace sympozium-system --create-namespace

helm upgrade --install sympozium sympozium/sympozium \
  --namespace sympozium-system \
  --skip-crds --set createNamespace=false

--skip-crds on the second command assumes you installed sympozium-crds first. If you skip the CRDs chart, drop --skip-crds so the bundled CRDs in the sympozium chart are applied instead.

See charts/sympozium/values.yaml for configuration options, or the Helm Chart docs for the full guide.


Why Sympozium?

Containers needed orchestration. Agents need coordination.

Sympozium is a Kubernetes-native coordination layer for multi-agent AI systems. It solves the same problem Kubernetes solved for containers — but for agents that need to share context, hand off tasks, and maintain shared situational awareness.

Agent Coordination

Synthetic MembraneSelective permeability for agent teams — control what agents share via trust groups, visibility tags, and field-level gating. Read the paper
Agent WorkflowsDelegation, sequential pipelines, supervision, and stimulus triggers between personas — visualised on an interactive canvas
Shared Workflow MemoryPack-level SQLite memory pool for cross-persona knowledge sharing with per-persona access control and time decay
EnsemblesHelm-like bundles for AI agent teams — activate a pack and the controller stamps out instances, schedules, and memory

Platform Infrastructure

Local Model InferenceDeclare GGUF models as CRDs — weights are downloaded, llama-server deployed, and OpenAI-compatible endpoints exposed. No API keys required
Skill SidecarsEvery skill runs in its own sidecar with ephemeral least-privilege RBAC, garbage-collected on completion
Multi-ChannelTelegram, Slack, Discord, WhatsApp — each channel is a dedicated Deployment backed by NATS JetStream
Persistent MemorySQLite + FTS5 on a PersistentVolume — memories survive across ephemeral pod runs
Scheduled TasksCron-based recurring agent runs for periodic workflows, data syncs, and automated checks
Agent SandboxKernel-level isolation via kubernetes-sigs/agent-sandbox — gVisor or Kata with warm pools for instant starts
MCP ServersExternal tool providers via Model Context Protocol with auto-discovery and allow/deny filtering
TUI & Web UITerminal and browser dashboards with live workflow canvas, or skip the UI entirely with Helm and kubectl
Any AI ProviderOpenAI, Anthropic, Azure, Ollama, or any compatible endpoint — no vendor lock-in

Documentation

TopicLink
Getting Starteddeploy.sympozium.ai/docs/getting-started
Architecturedeploy.sympozium.ai/docs/architecture
Custom Resourcesdeploy.sympozium.ai/docs/concepts/custom-resources
Ensemblesdeploy.sympozium.ai/docs/concepts/ensembles
Skills & Sidecarsdeploy.sympozium.ai/docs/concepts/skills
Persistent Memorydeploy.sympozium.ai/docs/concepts/persistent-memory
Channelsdeploy.sympozium.ai/docs/concepts/channels
Agent Sandboxingdeploy.sympozium.ai/docs/concepts/agent-sandbox
Securitydeploy.sympozium.ai/docs/concepts/security
CLI & TUI Referencedeploy.sympozium.ai/docs/reference/cli
Helm Chartdeploy.sympozium.ai/docs/reference/helm
Local Modelsdeploy.sympozium.ai/docs/guides/local-models
Ollama & Local Inferencedeploy.sympozium.ai/docs/guides/ollama
Writing Skillsdeploy.sympozium.ai/docs/guides/writing-skills
Writing Toolsdeploy.sympozium.ai/docs/guides/writing-tools
LM Studio & Local Inferencedeploy.sympozium.ai/docs/guides/lm-studio
llama-serverdeploy.sympozium.ai/docs/guides/llama-server
Unslothdeploy.sympozium.ai/docs/guides/unsloth
Writing Ensemblesdeploy.sympozium.ai/docs/guides/writing-ensembles
Your First AgentRundeploy.sympozium.ai/docs/guides/first-agentrun

Development

make test        # run tests
make test-system # run envtest system tests (no cluster needed)
make lint        # run linter
make manifests   # generate CRD manifests
make run         # run controller locally (needs kubeconfig)

License

Apache License 2.0

Global Ranking

8.5
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "sympozium-ai-sympozium": { "command": "npx", "args": ["sympozium-ai-sympozium"] } } }