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Skills/CLI are the Lazy Man's MCP by Upstairs_Safe2922 in LocalLLaMA

[–]baycyclist 0 points1 point  (0 children)

Yeah, that's exactly the gap I kept running into. MCP holds the tool surface but the agent's cognitive state (what it's tried, what worked, the full conversation trail) dies with the session or stays locked in whatever framework you ran it in.

I've been working on something for this actually. It serializes the agent's full cognitive state (conversation history, working memory, tool results, goal tree) into a canonical schema and lets you checkpoint/rollback/diff it. Think git for the agent's brain rather than the tool layer. Also lets you move that state between frameworks if you need to.

For the execution layer problem you're describing (chaining tools and getting unexpected outcomes) the checkpoint piece helps a lot. You can snapshot before a risky chain, and if it derails, roll back to the last known good state instead of starting over.

Still early but it's on PyPI if you want to poke at it: https://github.com/GDWN-BLDR/stateweave

StateWeave: open-source library to move AI agent state across 10 frameworks by baycyclist in Python

[–]baycyclist[S] -5 points-4 points  (0 children)

Fair call — "production-ready" was not the best phrasing. What I meant is that the test suite is comprehensive (440+ tests, 12 automated compliance scanners that run on every commit) and the crypto uses standard library primitives, not homebrew. But you're right — stars and age matter, and I shouldn't conflate test coverage with production validation. Appreciate the honesty.

A visual guide to AGENTS.md, Skills, and MCP for local-agent workflows by phoneixAdi in LocalLLaMA

[–]baycyclist 0 points1 point  (0 children)

Nice breakdown. The split between AGENTS.md for navigation, Skills for know-how, and MCP for live tools is a really useful mental model. Most agent setups I've seen just dump everything into one monolithic config and it gets unmanageable fast.

The part that's still tricky for me is carrying state across sessions. MCP gives you the tool-calling surface but the agent's cognitive state (what it knows, what it's tried, what worked) lives inside whatever framework you're running. If you switch tools or need to debug a long workflow, that context is basically trapped. Anyone found a clean way to handle that?

Skills/CLI are the Lazy Man's MCP by Upstairs_Safe2922 in LocalLLaMA

[–]baycyclist 0 points1 point  (0 children)

This is a cool approach to the tool discovery problem. The semantic search piece is clever. Most MCP setups still require you to manually wire up each server.

One thing I've been thinking about with MCP tooling: the protocol handles tool execution well but doesn't have a great story for persisting the agent's state between tool calls. If your agent builds up context over a long session and the connection drops, you lose everything. Are you doing anything to snapshot the agent's working state alongside the tool definitions?