agentleFS

llms.txt examples

How real projects summarise themselves for language models.

Worked for most · soon
putervisionllms.txt

state-memory-mcp

putervision/state-memory-mcp/docs/llms.txt

PuterVision is an AI engineering research laboratory pioneering deterministic multi-modal state memory, visual perception caching, 3D spatial world models, high-frequency behavior tree execution engines, and BDI reasoning architectures for autonomous AI developer agents and the Model Context Protocol (MCP).

11445d agoReviewMountCreate a free account to mount
putervisionllms.txt

world-model-mcp

putervision/world-model-mcp/docs/llms.txt

Deterministic 3D/2D Spatial World Model for AI Agents @putervision/world-model-mcp provides persistent internal spatial memory for AI coding and gaming agents. It bridges perception and action by maintaining structured 3D entity coordinates, topological relationships, object permanence with confidence decay, movement simulation with collision detection, camera frustum projections, and multi-agent spatial blackboards.

5047d agoReviewMountCreate a free account to mount
putervisionllms.txt

behavior-mcp

putervision/behavior-mcp/docs/llms.txt

High-Frequency (~60Hz) In-Browser Behavior Tree Execution Engine for AI Agents @putervision/behavior-mcp executes deterministic behavior trees directly in browser runtimes at 60Hz. It bridges cognitive reasoning decisions with deterministic low-latency action sequences, featuring reactive interrupt triggers, frame recordings, telemetry analytics, and multi-layer safety guardrails. Non-claims statement: Functional agency and closed-loop automation — not consciousness, sentience, or AGI.

2447d agoReviewMountCreate a free account to mount
putervisionllms.txt

agent-reasoning-mcp

putervision/agent-reasoning-mcp/docs/llms.txt

Strategic BDI Reasoning & Decision Intelligence for Autonomous AI Agents @putervision/agent-reasoning-mcp implements belief-desire-intention (BDI) cognitive architecture for autonomous AI agents. It provides goal hierarchy decomposition, multi-attribute expected utility calculation ($E[U] = \sum w_i u_i$), exponential belief decay ($\lambda = 0.05/\text{hr}$), quantitative risk evaluation, reactive replanning upon obstacles, and an intention queue wire contract for runtime execution. Non-claims statement: Functional agency and closed-loop automation — not consciousness, sentience, or AGI.

2247d agoReviewMountCreate a free account to mount
CLAUDE.md vs AGENTS.md

About llms.txt

What is llms.txt?

A markdown file at a website's root that gives language models a short guide to the site and links to the pages worth reading.

What's llms-full.txt?

The same idea with the content included, so an agent can read the documentation in one request.

Who reads it?

Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.

How do I write one?

Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.