#python

Just released threadsafe_datastore, a simple, convenient thread-safe data store for Python.

I kept rebuilding this feature to pass around context within AI agents working on the same data across multiple threads. I kept wanting a simple atomic datastore that was convenient to work with. I originally built it for arkaine | git |. So - here it is as a stand alone package for easier use. I also improved context management for real easy multi-step operations in case you need to do something real custom.

from threadsafe_datastore import Datastore

store = Datastore()
store["counter"] = 0
store.increment("counter", 5)  # Returns 5

# Nested dictionary support
store["nested"] = {"items": []}
store.append(["nested", "items"], "value1")

# Thread safe multi-step w/ context management:
with store as unlocked:
    unlocked["a"] = 1

    # This would be unsafe without the context:
    unlocked["b"] = unlocked.get("a", 0) + 1

Give it a try: pip install threadsafe-datastore

Just released structured-parse, a multi-language parser for block labeled LLM output. It’s based on the parser I had built for arkaine.

Here’s what I mean by “block labeleled” output:

Thought: I need to search for information about robots
Action: search
Action Input: {"query": "robots and why they're so cool", "max_results": 5}

This output is not only more human readable, but also easier for LLMs to produce. But there’s a catch - LLMs tend to still introduce nondeterministic volatility towards these outputs; humans are just good about reading through that. structured-parse is a robust parser that can deal with this, allowing LLMs to reliably follow instructions and allowing your code to parse it into clean, typed data structures.

structured-parse is written in Go with exports to TypeScript/JavaScript and Python via WebAssembly; so it’s all golang at its core.

Give it a try!