AgentIndex icon
AgentIndex
causal-memory logo
causal-memory
★ 99
vs
anneal-memory logo
anneal-memory
★ 12

causal-memory vs anneal-memory

causal-memory: Causal-memory is an agent memory system with a causal core, uniquely modeling both excitatory and inhibitory connections for robust long-term retention. It enables agents to understand not just what happened, but why it worked or failed, and anticipate outcomes, thereby preventing repeated mistakes.; anneal-memory: anneal-memory provides living, auditable, and quality-controlled memory for AI agents, featuring structural defenses against sycophancy and junk accumulation. It implements a complementary learning systems architecture with episodic, continuity, crystallized, and prospective memory layers, all built with zero external dependencies.

01

TL;DR

causal-memory logo
causal-memoryMemory & Context · ★ 99

Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices.

Read causal-memory full review →
anneal-memory logo
anneal-memoryMemory & Context · ★ 12

Integrating robust, auditable memory into custom AI agent frameworks.

Read anneal-memory full review →
02

Side-by-Side Comparison

Field
causal-memory logocausal-memory
anneal-memory logoanneal-memory
Category
Memory & Context
Memory & Context
Stars
★ 99
★ 12
License
Apache-2.0
MIT
Install Command
git clone https://github.com/JingxuanC/causal-memory.git && cd causal-memory && cargo build --release
pip install anneal-memory
Updated
1w ago
5d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Agent Memory, Causal Reasoning, Long-term Memory
AI Agent Memory, Memory Management, Cognitive Architecture
03

Features

causal-memory logocausal-memory
01Causal Core with Excitatory and Inhibitory Connections: Models 'caused', 'enabled', and 'prevented' relationships using typed causal semantics and negative activation spread (GABA analogue).
02Immutable Consolidation & Q-value Dynamics: Employs hippocampus-style SWR (Sleep-Wake Replay) consolidation with LTP/LTD/GC, Q-value dynamics, and novelty-entropy triggers for robust memory evolution.
03Intervention & Counterfactual Query: Enables forward simulation to predict outcomes before action and contrastive analysis to compare outcomes of different alternatives.
04Compaction Survival: Stores causal information in an external SQLite database, making it immune to context window compaction and preserving critical 'why' knowledge.
05Unified Memory Graph: Integrates facts, temporal state, and decision-outcome causal edges into a single graph, accessible via RRF-fused retrieval (BM25 + semantic).
anneal-memory logoanneal-memory
01Citation-Validated Memory Graduation
02Tamper-Evident Audit Trail
03Sycophancy and Junk Accumulation Prevention
04Complementary Learning Systems Architecture
05Zero-Dependency Python Library
04

Use Cases

causal-memory logocausal-memory
↳Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices.
↳Proactive Warning and Intervention: Before committing risky actions (e.g., `git push --no-verify`), the system can fire DANGER warnings based on recorded past negative outcomes.
↳Root Cause Analysis: Trace multi-hop backward causal chains to identify the ultimate root cause of failures or unexpected outcomes.
↳Informed Decision Making: Agents can perform counterfactual comparisons between alternative actions, leveraging known outcomes to choose the best path.
↳Cross-Task Lesson Transfer: Discover and apply meta-edges (e.g., `similar_to`, `repeated`) to transfer lessons and patterns across different tasks or sessions.
anneal-memory logoanneal-memory
↳Integrating robust, auditable memory into custom AI agent frameworks.
↳Developing AI systems that require high integrity and verifiable decision-making.
↳Building multi-session AI agents that resist memory drift, sycophancy, and junk accumulation.
↳Implementing long-term knowledge management for LLM-powered applications, separating active context from crystallized wisdom.
↳Compliance and auditing for AI systems, providing traceability for AI actions and decisions.
FAQ

FAQ

What is the difference between causal-memory and anneal-memory?
Both causal-memory and anneal-memory are in the Memory & Context category. causal-memory has 99 stars, while anneal-memory has 12 stars.
Which is better, causal-memory or anneal-memory?
The best choice depends on your use case. Choose causal-memory if Preventing Repeated Mistakes: Agents can recall past `decision → outcome` causal edges, avoiding re-fixing the same bug or repeating past failed architectural choices., and anneal-memory if Integrating robust, auditable memory into custom AI agent frameworks..
Is causal-memory free or open source?
Yes, causal-memory is open source on GitHub (Apache-2.0).
Is anneal-memory free or open source?
Yes, anneal-memory is open source on GitHub (MIT).
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