Tinytasktree – Behavior-tree-style task orchestration for LLM agents
Behavior-tree orchestration for agents when LangGraph and AutoGen already exist.
Context Management System to let your agent find and build relevant context
llms.txt tree structure lets agents navigate context instead of dumping everything.
Developers building AI agents for support or operations tasks
Cursor Rules · Claude Memory · LangChain
The way we solve it now is a "tree of llms.txt". An llms.txt normally references what info is available on a website or docs — we use the same idea internally to organize the info the agent needs. The agent starts from a folder and navigates down:
. ├── llms.txt # references each folder at this level ├── stripe/ # info.md: how our stripe account is structured ├── firestore/ # info.md: how the schema looks └── support/ ├── info.md # how to resolve support tasks ├── runbooks/ # one file per task, with its own llms.txt │ ├── cancel-subscription.md │ ├── export-gym-data.md │ └── fix-membership-mismatch.md └── logs/ # one file per day, every task the agent resolved Copy With this we can steer the agent much better and create a new runbook every time a new support task comes.
You can add in every integration what the agent should and should not touch. The gcontext prompts make sure any guardrail is meticulously followed.
Behavior-tree orchestration for agents when LangGraph and AutoGen already exist.
Searchable directory for llms.txt files when general search engines could index these.
Transparent AI bot posture tracking, but 'what bots touch you' is already solved by uBlock Origin.
Yet another opinionated SSG when Astro and Docusaurus already dominate.
O(1) fork latency makes tree search 1000x faster than vLLM for agentic workloads.
Fresh context per task prevents bloat, but agent orchestration tools already exist.