Shannon

Shannon is a multi-agent orchestration framework built in Go with a Rust agent core and Python LLM layer. It manages complex AI workflows: task decomposition, multi-agent coordination, token budgets with automatic model fallback, and time-travel debugging that lets you replay any execution step. Supports 10+ LLM providers including Anthropic, OpenAI, and Ollama.

The architecture is serious infrastructure: Temporal for durable workflows, OPA for policy enforcement, Prometheus metrics, OpenTelemetry tracing, and human-in-the-loop approval gates. Multiple execution strategies (DAG, ReAct, Research, Swarm, Browser Use) cover different agent patterns. Docker Compose spins up Go gateway, Rust core, Python LLM service, Temporal, PostgreSQL, and Redis.

Teams building production agent systems who need observability, cost control, and multi-tenant isolation will find the feature set compelling. The token budget enforcement with automatic fallback to cheaper models is a useful idea for controlling LLM spend.

The catch: very early stage with minimal commit history. The feature list reads like aspirational docs more than battle-tested reality. Three languages (Go, Rust, Python) means three ecosystems to debug. Temporal alone is a significant operational dependency. Watch this project, but don't bet production on it today.