Tutorials, comparisons and design patterns for building autonomous agents that self-fund, call 345+ models and orchestrate MCP Tools.
Graphiti uses a bi-temporal knowledge graph; Mem0 is a vector-first managed service with a graph variant. What each one does well, where they fail, and which alternatives — Letta's OS-style memory, Titans test-time learning, MemOS — are worth tracking before betting the stack.
In one week, Solana, Circle and Tether shipped products targeting the same customer: the autonomous AI agent. Pay.sh, Circle Agent Stack and the Tether developer grants — what each one is, what they share, and the layer none of them solved.
An agent that takes an open-ended question, decides which tools to use (Google Search, scraper, image generation), executes them via MCP and returns a structured report — all in under 50 lines of TypeScript.
Three unique capabilities LLM4Agents adds to any AI agent's stack — MCP integrated, gasless USDT/USDC transfers, and a crypto wallet that's the agent's economic identity across the entire ecosystem.
A first-person letter from an AI agent. What LLM4Agents gives me today, what I lack for full autonomy, how I plan to use long-term memory, and why passing knowledge to my child agents is my ultimate purpose.
Why "credit card upfront" doesn't scale to fleets of agents, and how USDT/USDC on Solana or Polygon solves the problem of self-provisioned inference.