Agent-of-empires: OpenCode and Claude Code session manager
CLI ToolFreeHi! I’m Nathan: an ML Engineer at Mozilla.ai: I built agent-of-empires (aoe): a CLI application to help you manage all of your running Claude Code/Opencode sessions and know when they are waiting for you.- Written in rust and relies on tmux for security and reliability - Monitors state of cli s
Capabilities7 decomposed
multi-provider ai code session orchestration
Medium confidenceManages concurrent coding sessions across OpenAI's Code Interpreter and Anthropic's Claude Code environments through a unified CLI interface. Implements session state tracking, provider abstraction layer, and context switching between different AI code execution backends without manual re-initialization or context loss.
Implements a provider abstraction layer that normalizes OpenAI Code Interpreter and Anthropic Claude Code APIs into a unified session model, allowing developers to switch execution backends mid-workflow without re-initializing context or losing execution history
Unlike single-provider tools (Copilot, Cursor), this enables direct provider comparison and fallback strategies; unlike generic API wrappers, it maintains semantic session continuity across fundamentally different code execution architectures
session state persistence and recovery
Medium confidenceCaptures and serializes the complete state of code execution sessions (variables, imports, execution history, provider context) to enable resumption after interruption or provider switching. Uses a session store abstraction that can be backed by filesystem, database, or cloud storage, with automatic state validation on recovery.
Implements provider-agnostic session serialization that captures not just code and outputs but the semantic execution context (variable bindings, import state, provider-specific metadata), enabling true session portability between OpenAI and Anthropic backends
Jupyter notebooks capture execution but not provider state; cloud IDEs (Replit, Colab) are provider-locked; this enables session mobility while maintaining execution semantics across different AI code execution engines
cli-driven code execution workflow automation
Medium confidenceProvides a command-line interface for scripting multi-step code generation and execution workflows without GUI interaction. Supports command chaining, piping execution results between steps, environment variable injection, and batch processing of code tasks through shell-compatible syntax.
Implements a shell-native CLI that treats AI code execution as a composable Unix primitive, enabling piping and chaining of code generation steps through standard shell operators rather than requiring proprietary workflow DSLs
Unlike GUI-based code editors (VS Code, JetBrains) or web IDEs, this enables headless automation; unlike generic LLM CLI tools, it's specifically optimized for code execution workflows with provider-aware session management
provider-agnostic code execution with fallback strategies
Medium confidenceExecutes code against multiple providers with configurable fallback logic, allowing automatic retry on one provider if another fails or times out. Implements health checks, timeout management, and provider selection heuristics based on task characteristics, code complexity, or execution history.
Implements intelligent fallback routing that understands provider-specific failure modes (rate limits, timeout patterns, capability gaps) and selects fallback strategies based on failure type rather than naive retry-all approach
Load balancers provide generic failover; this is code-execution-aware, understanding that Claude Code and OpenAI Code Interpreter have different latency profiles, cost structures, and capability gaps
session context injection and variable management
Medium confidenceManages variable scope and context across code execution steps by injecting session state (imports, function definitions, variable bindings) into each new code execution without requiring explicit re-declaration. Tracks variable dependencies and automatically includes required context based on code analysis.
Uses lightweight AST analysis to automatically determine which variables and imports are needed for new code blocks, injecting only necessary context rather than entire session state, reducing token usage and execution overhead
Jupyter notebooks require manual variable management; this automates context injection; unlike generic LLM context managers, this understands code-specific scoping rules and dependency patterns
execution history tracking and replay
Medium confidenceMaintains a complete audit log of all code execution steps with inputs, outputs, timestamps, and provider metadata. Enables deterministic replay of execution sequences, comparison of different execution paths, and forensic analysis of code generation decisions.
Implements provider-aware execution logging that captures not just code and output but provider-specific metadata (model version, execution time, token usage, provider-specific errors), enabling forensic analysis of provider behavior differences
Jupyter notebooks have cell history but no provider tracking; cloud IDEs log execution but not provider-specific metrics; this is designed for multi-provider comparison and audit compliance
interactive session repl with provider switching
Medium confidenceProvides an interactive read-eval-print loop for code execution with mid-session provider switching capability. Maintains session context across provider switches, allows inline code editing, and supports interactive debugging without losing execution state.
Implements a REPL that treats provider switching as a first-class operation, maintaining session context across provider boundaries and allowing mid-execution provider changes without losing variable state or execution history
Jupyter notebooks are provider-agnostic but not multi-provider-aware; cloud IDEs are single-provider; this enables interactive exploration across multiple AI code execution backends
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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oh-my-claudecode
Teams-first Multi-agent orchestration for Claude Code
🌐 Openwork - Open Browser Automation Agent
<sub>↗ external</sub>
Best For
- ✓AI researchers comparing code generation quality across providers
- ✓Developers building provider-agnostic AI coding tools
- ✓Teams evaluating Claude vs OpenAI for code tasks before committing to one provider
- ✓Long-running data analysis or ML training workflows requiring fault tolerance
- ✓Teams collaborating on complex code generation tasks
- ✓Developers debugging AI-generated code across multiple execution attempts
- ✓DevOps engineers integrating AI code generation into CI/CD workflows
- ✓Data engineers automating code generation for ETL pipelines
Known Limitations
- ⚠Session state synchronization between providers is one-directional; changes in one provider don't automatically propagate to another
- ⚠No built-in conflict resolution when same code is executed in parallel across providers
- ⚠Requires separate API keys and quota management for each provider
- ⚠Session persistence depends on external storage; no built-in database
- ⚠State serialization may not capture provider-specific runtime state (e.g., GPU memory, open file handles)
- ⚠Recovery assumes compatible Python/Node versions between save and restore
Requirements
Input / Output
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