AgentIndex icon
AgentIndex
Co-Engram logo
Co-Engram
★ 11
vs
enquire-mcp logo
enquire-mcp
★ 34

Co-Engram vs enquire-mcp

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.; enquire-mcp: enquire-mcp transforms your Obsidian vault into persistent, queryable long-term memory for various AI agents, eliminating the need to re-explain context in every session. It provides best-in-class hybrid retrieval, including semantic search, reranking, and HNSW, all running locally with zero cloud calls during serving.

01

TL;DR

Co-Engram logo
Co-EngramMemory & Context · ★ 11

AI agent knowledge base: Agents can automatically capture, search, and update team knowledge through natural conversation.

Read Co-Engram full review →
enquire-mcp logo
enquire-mcpMemory & Context · ★ 34

Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.

Read enquire-mcp full review →
02

Side-by-Side Comparison

Field
Co-Engram logoCo-Engram
enquire-mcp logoenquire-mcp
Category
Memory & Context
Memory & Context
Stars
★ 11
★ 34
License
MIT
MIT
Install Command
npm install -g @co-engram/claude-code && mkdir -p ~/team-memory && cd ~/team-memory && git init && co-engram config data-root $HOME/team-memory
npm install -g @oomkapwn/enquire-mcp
Updated
2mo ago
1d ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
AI Memory System, Self-Evolving, Knowledge Management
AI Agents, Long-term Memory, RAG
03

Features

Co-Engram logoCo-Engram
01Self-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.
enquire-mcp logoenquire-mcp
01Vendor-neutral memory: Your memory lives in .md files, ensuring portability across different AI agents and providers.
02Best-in-class hybrid retrieval: Features BM25, multilingual embeddings, BGE cross-encoder reranker, HNSW, and int8 quantization for superior search quality and performance.
03Zero cloud calls during serve: All models are cached locally, and vault content never leaves your machine, ensuring privacy and air-gap safety.
04Freshness-aware recall: Search hits report note age and a `stale` flag, with optional recency-weighted ranking to prioritize fresh knowledge.
05PDFs with OCR & GraphRAG-light: Integrates PDFs (including OCR for scanned docs) into hybrid search and offers GraphRAG-light for wikilink community detection.
04

Use Cases

Co-Engram logoCo-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.
enquire-mcp logoenquire-mcp
↳Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes.
↳Personal knowledge base / second brain: Offers hybrid retrieval across 50+ languages, wikilink graph-boost, and GraphRAG-light to surface relevant notes and discover connections.
↳Agentic RAG / context engineering: Exposes per-signal scores, HyDE pre-rewrites, and sub-question decomposition for advanced retrieval, plus a built-in evaluation harness.
FAQ

FAQ

What is the difference between Co-Engram and enquire-mcp?
Both Co-Engram and enquire-mcp are in the Memory & Context category. Co-Engram has 11 stars, while enquire-mcp has 34 stars.
Which is better, Co-Engram or enquire-mcp?
The best choice depends on your use case. Choose Co-Engram if AI agent knowledge base: Agents can automatically capture, search, and update team knowledge through natural conversation., and enquire-mcp if Long-term memory for AI agents: Provides durable, semantic recall for agents across sessions, models, and providers, grounded in your Obsidian notes..
Is Co-Engram free or open source?
Yes, Co-Engram is open source on GitHub (MIT).
Is enquire-mcp free or open source?
Yes, enquire-mcp is open source on GitHub (MIT).
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