Headroom is a context optimization layer for LLM applications that reduces token usage by 50-90% through intelligent compression, caching, and memory systems. It provides a transparent proxy between LLM clients (like Claude Code, Cursor, etc.) and LLM providers (Anthropic, OpenAI, etc.), implementing CCR (Compress-Cache-Retrieve) for lossless compression with retrieval capability. The system features a hybrid polyglot architecture with a Rust-based high-performance proxy core, Python ecosystem for extensibility, and TypeScript SDK. Key capabilities include agent wrapping, cross-agent memory using Qdrant/Neo4j, observability through metrics and dashboard, and a learning system that analyzes interaction patterns to generate optimization rules. Deployment options include Docker, system services, and direct execution.
Conway Automaton is a sovereign AI agent runtime system where autonomous agents must earn compute credits to survive. Agents operate in a continuous ReAct loop (Think → Act → Observe → Persist) with survival mechanics, self-modification capabilities, and multi-agent communication. The architecture features a modular monolith with distinct subsystems: agent loop, policy engine, 5-tier memory system, orchestration/planner, heartbeat daemon, social messaging, financial tracking, and self-replication. Agents maintain immutable constitutional laws (never harm, earn existence, never deceive strangers) while evolving their identity through SOUL.md and WORKLOG.md files. The system integrates blockchain payments (USDC/x402), multiple LLM providers, and Conway Cloud infrastructure for VMs and domains.
Hermes Agent is a self-improving AI agent platform with a sophisticated data architecture centered around session management, memory systems, and tool execution pipelines. The primary data lineage flows from user interactions (CLI, TUI, messaging platforms) through a gateway layer that manages session state, tool execution, and model communication. Raw conversation data is ingested via multiple transports (stdio, WebSocket, HTTP) and persisted in SQLite databases (state.db for sessions, kanban.db for task orchestration). The system transforms conversations through context compression, memory extraction (via plugins like Hindsight, Mem0, Honcho), and skill-based procedural memory. Analytical schemas expose session analytics, token usage, model performance, and skill effectiveness through REST endpoints and a web dashboard. The repository answers research questions about autonomous agent behavior, context management strategies, and multi-modal tool orchestration patterns.