EMOS
Cognition Recipes Overview¶
Build intelligent agents from the ground up using EmbodiedAgents, the EMOS intelligence framework. These recipes introduce the core Components – the modular building blocks that drive your physical agents.
Every capability – hearing, speaking, seeing, thinking – is a component you wire together in pure Python. No ROS XML, no boilerplate.
Your first agent – wire STT, VLM, and TTS into a multimodal dialogue system.
Run LLMs, VLMs, STT, and TTS entirely on-device — no server required.
Shape agent behavior with dynamic Jinja2 templates at the topic or component level.
Give your robot a graph-backed spatio-temporal memory built on eMEM – indexed simultaneously by meaning, location, and time.
Turn “go to the kitchen” into a PoseStamped goal – an LLM with Memory tools, plus a regex preprocessor.
Generalise the tool calling pattern: write your own Python function as the LLM’s tool, with full control over the schema and the published output.
Route messages to different graph branches based on meaning, not topic names.
Combine perception, memory, and reasoning into a fully embodied system.