memora
★ 744
vs
causal-memory
★ 99
memora vs causal-memory
memora: Memora provides AI agents with a persistent memory layer, featuring structured storage, semantic retrieval, and graph relations for cross-session context. It allows agents to absorb work into a durable knowledge graph and retrieve relevant information, TODOs, and related edges using `memory_digest`.; 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.
01
TL;DR
memoraMemory & Context · ★ 744
AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
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.
02
Side-by-Side Comparison
Field
memoracausal-memoryCategory
Memory & Context
Memory & Context
Stars
★ 744
★ 99
License
MIT
Apache-2.0
Install Command
pip install git+https://github.com/agentic-box/memora.git
git clone https://github.com/JingxuanC/causal-memory.git && cd causal-memory && cargo build --release
Updated
2d ago
1w ago
Open Source
Yes
Yes
Tags
AI Agents, Persistent Memory, Knowledge Graph
Agent Memory, Causal Reasoning, Long-term Memory
03
Features
memora01Persistent Storage: SQLite with optional cloud sync (S3, R2, D1)
02Semantic Search: Vector embeddings for advanced queries (TF-IDF, sentence-transformers, OpenAI)
03Knowledge Graph: Interactive visualization with Mermaid rendering and cluster detection
04Chat with Memories: RAG-powered chat panel with LLM tool calling for search, create, update, delete
05LLM Deduplication: AI-powered semantic comparison to find and merge duplicate memories
causal-memory01Causal 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).
04
Use Cases
memora↳AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
↳Knowledge Base for Agents: Building a retrievable knowledge base that agents can query semantically and contextually.
↳Project Management & Task Tracking: Automating the creation and tracking of TODOs and issues, and surfacing insights for project progress.
↳Document Analysis & Retrieval: Storing and searching structured documents (e.g., reports, plans) at a granular fragment level.
↳Contextual Conversation for AI: Powering RAG-based chat panels that allow agents or users to converse deeply about stored memories.
causal-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.
05
Best For
memoraTrendingHidden Gem
causal-memory—
FAQ
FAQ
What is the difference between memora and causal-memory?
Both memora and causal-memory are in the Memory & Context category. memora has 744 stars, while causal-memory has 99 stars.
Which is better, memora or causal-memory?
The best choice depends on your use case. Choose memora if AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks., and 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..
Is memora free or open source?
Yes, memora is open source on GitHub (MIT).
Is causal-memory free or open source?
Yes, causal-memory is open source on GitHub (Apache-2.0).
Cloud Infrastructure
AdDeploy on DigitalOcean
Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.
→