Kaloyan Danchev 872ed24f0c Add performance features: caching, cost tracking, retry, compaction, classification, scrubbing
Inspired by zeroclaw's lightweight patterns for slow hardware:
- Response cache (SQLite + SHA-256 keyed) to skip redundant LLM calls
- History compaction — LLM-summarize old messages when history exceeds 50
- Query classifier routes simple/research queries to cheaper models
- Credential scrubbing removes secrets from tool output before sending to LLM
- Cost tracker with daily/monthly budget enforcement (SQLite)
- Resilient provider with retry + exponential backoff + fallback provider
- Approval engine gains session "always allow" and audit log

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-19 09:20:52 +02:00

xtrm-agent

Multi-agent AI automation system with shared message bus, specialized roles, and deny-by-default security.

Architecture

Multiple specialized agents share a message bus and can delegate to each other:

  • Coder Agent — Claude, file+bash tools, coding-focused
  • Researcher Agent — DeepSeek/Kimi, web tools, research-focused
  • Reviewer Agent — Claude, read-only tools, code review

Quick Start

# Install
uv sync

# Interactive chat (default: coder agent)
uv run xtrm-agent chat

# Target a specific agent
uv run xtrm-agent chat --agent researcher

# Single-shot message
uv run xtrm-agent chat -m "write a hello world script"

# Run all agents + Discord bot
uv run xtrm-agent serve

# Show status
uv run xtrm-agent status

Configuration

Edit config.yaml to configure providers, agents, tools, and channels. Agent definitions live in agents/*.md with YAML frontmatter.

Deployment

Deploy via Dockge on Unraid using the included compose.yaml.

Description
Multi-agent AI automation system
Readme 216 KiB
Languages
Python 99.9%
Dockerfile 0.1%