memora
★ 744
vs
Co-Engram
★ 11
memora vs Co-Engram
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`.; Co-Engram: Co-Engram is an advanced, self-evolving memory system designed for AI agents and teams, which models memory after the human brain's plasticity. It allows memories to strengthen with use, weaken upon failure, consolidate, and verify themselves, all stored as plain Markdown in Git for transparent version control.
01
TL;DR
memoraMemory & Context · ★ 744
AI Agent Memory Management: Giving AI agents long-term, structured memory for complex, multi-session tasks.
Co-EngramMemory & Context · ★ 11
AI agent knowledge base: Agents can automatically capture, search, and update team knowledge through natural conversation.
02
Side-by-Side Comparison
Field
memoraCo-EngramCategory
Memory & Context
Memory & Context
Stars
★ 744
★ 11
License
MIT
MIT
Install Command
pip install git+https://github.com/agentic-box/memora.git
npm install -g @co-engram/claude-code && mkdir -p ~/team-memory && cd ~/team-memory && git init && co-engram config data-root $HOME/team-memory
Updated
2d ago
2mo ago
Open Source
Yes
Yes
Tags
AI Agents, Persistent Memory, Knowledge Graph
AI Memory System, Self-Evolving, Knowledge Management
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
Co-Engram01Self-maintaining memory: An engine automatically strengthens, consolidates, and refutes memories based on usage and feedback.
02Git-based storage with Stable IDs: Memories are stored as Markdown files in Git with stable ULIDs, allowing clean diffs and persistent references.
03Tiered disclosure for LLM context: Provides different tiers of memory detail (catalog, digest, content) to optimize LLM context window usage.
04Learning loop & plasticity: Memories strengthen with successful use (LTP) and decay with failures or disuse (LTD), similar to brain plasticity.
05Synapse graph for related context: Connects memories with typed synapses, allowing automatic traversal to retrieve related context during search.
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.
Co-Engram↳AI agent knowledge base: Agents can automatically capture, search, and update team knowledge through natural conversation.
↳Team decision memory: Store important decisions and their rationale, which get reinforced when proven effective.
↳Procedural knowledge (Skills): Capture workflows, checklists, and operational capabilities that agents can learn, invoke, and chain.
↳Collaborative memory for developers: Maintain a shared, self-evolving knowledge base for coding patterns, system configurations, and project details.
↳Onboarding new team members: Provide new team members with a living, self-curating memory system that quickly surfaces relevant information.
05
Best For
memoraTrendingHidden Gem
Co-Engram—
FAQ
FAQ
What is the difference between memora and Co-Engram?
Both memora and Co-Engram are in the Memory & Context category. memora has 744 stars, while Co-Engram has 11 stars.
Which is better, memora or Co-Engram?
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 Co-Engram if AI agent knowledge base: Agents can automatically capture, search, and update team knowledge through natural conversation..
Is memora free or open source?
Yes, memora is open source on GitHub (MIT).
Is Co-Engram free or open source?
Yes, Co-Engram is open source on GitHub (MIT).
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