Kocoro-lab/Shannon

What it solves

Shannon addresses the reliability and observability gaps in deploying AI agents to production. It solves common production issues such as silent agent failures, spiraling token costs, lack of execution visibility, and security risks associated with executing agent-generated code.

How it works

Shannon uses a multi-language architecture (Go, Rust, and Python) to orchestrate agents. It leverages Temporal workflows for fault tolerance and state management, allowing for "time-travel debugging" where executions can be replayed step-by-step. For security, it employs a WASI sandbox to isolate code execution. The system automatically routes tasks to different execution strategies (such as DAG, ReAct, Research, or Swarm) based on the complexity of the request. It also implements hard token budgets and automatic model fallback to manage costs.

Who it’s for

Developers and organizations looking to move beyond AI prototyping and ship reliable, production-grade AI agents with built-in monitoring, budget controls, and security sandboxing.

Highlights

• Time-Travel Debugging: Replay any workflow execution step-by-step to diagnose failures.
• Token Budget Control: Set hard token limits per task with automatic fallback to cheaper models.
• Multi-Strategy Orchestration: Automatic routing between simple responses, DAGs, ReAct loops, and Swarm collaboration.
• Secure Execution: WASI sandboxing for code execution and OPA policies for security.
• Broad Provider Support: Integration with 10+ LLM providers including OpenAI, Anthropic, Google, and local models via Ollama.
• Human-in-the-Loop: Built-in approval gates for critical agent actions.