causalmem 0.1.0

Causal memory graph for AI agents with belief revision and semantic retrieval

causalmem

Causal memory graph for AI agents with belief revision and semantic retrieval.

Overview

causalmem provides a structured memory system for AI agents that:

• Records episodes (action + outcome) in an append-only buffer
• Consolidates episodes into a causal graph using LLM-based belief revision
• Classifies relationships as REFINEMENT, UPDATE, CORRECTION, or UNRELATED
• Retrieves relevant memory chains using semantic similarity

Installation

pip install causalmem

Quick Start

from causalmem import Episode, EpisodeBuffer, Consolidator

buf = EpisodeBuffer()
c = Consolidator(buf)

buf.add(Episode("user wants report", "fetch data", ["fetch(db)"], "retrieved 100 rows", "need data first"), c)
buf.add(Episode("have data", "analyze data", ["analyze(data)"], "found 3 trends", "data available"), c)

await buf._queue.join()
result = c.retrieve("how was the analysis done?")
for node in result:
    print(node.episode.action, "->", node.episode.outcome)

License

Apache 2.0