[Enhancement]: Add Optional MCP-Orchestrator + Hybrid RAG (Graph + Vector) Memory Layer

[CrazyAce25]

What features would you like to see added?

LibreChat is already one of the most dynamic open-source frontends for agentic workflows. But to truly unlock next-gen memory, local deployment speed, and structured reasoning I thought the following would be useful.

1. Support for an optional MCP-style orchestrator (à la ActiveContext)
2. Integration of a Hybrid RAG architecture with graph + vector memory retrieval
3. A modular, plugin-friendly design that preserves LibreChat's existing flexibility

These additions would enable scoped task memory, memory-driven toolchains, and adaptive retrieval—all without sacrificing the modularity that I think all LibreChat users cherish.

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🧠 Why This Matters

Vector-only RAG is beginning to show its limits. It struggles with:

• ❌ Relationship-based retrieval (e.g. causality, authorship, multi-hop reasoning)
• 🐌 Performance slowdowns in local/private deployments where context levels can quickly bring things to the point of unusability
• 🔎 Memory precision, especially across long or structured contexts
• 🤖 Agent workflows that need scoped recall across tools and tasks

At the same time, the ecosystem is rapidly moving toward HybridRAG, structured graph memory, and memory-aware agents.

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🔧 What's being Proposed

1. ✅ Optional Native MCP-Style Orchestrator

Introduce a lightweight memory/task orchestrator (inspired by ActiveContext) that enables:

• 📎 Scoped and persistent memory across tools, sessions, and agents
• 🧠 Memory selection (e.g., vector, graph, hybrid) at runtime
• 🔧 Tool routing and task awareness
• 📦 Reusable agent packages that reason over memory cleanly

Design Principle: Modular and opt-in—can be enabled, extended, or replaced.

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2. ✅ Hybrid RAG Support (Vector + Graph Retrieval)

Enable agents to retrieve memory from:

• 🔎 Vector memory (e.g., pgvector, Qdrant, Weaviate)
• 🧱 Graph memory (e.g., Neo4j, Memgraph, MemCP)
• 🤖 Hybrid memory (via router tool or smart fallback)

This unlocks:

• Rich relationship-based recall (e.g. who said what, and when)
• Structured memory graphs for document understanding and provenance
• Better performance in local environments with graph-friendly lookups

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🧩 Design Philosophy: Modularity First

Everything in this proposal follows a plug-and-play approach—just like LibreChat's current vector pipeline:

Component	How It's Modular
🧠 Orchestrator	Optional layer (e.g., ActiveContext-style) users can enable, extend, or replace
🧱 Graph memory	Integrates via adapters (Memgraph, Neo4j, etc.)—not hardcoded
🔄 Retrieval mode	Selectable per agent/task (vectorOnly, graphOnly, hybrid)
🔌 External tools	Compatible with LangChain, LlamaIndex, MemCP, EMVR, etc.

This ensures users who want a lightweight experience stay unburdened—while power users gain flexibility to build memory-aware agents, even in fully offline environments.

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✅ Key Benefits

Capability	Current	With Orchestrator + Hybrid Memory
Agent memory scoping	❌ Manual or flat	✅ Modular + persistent
Relationship-aware memory	❌ Absent	✅ Graph-based + traversable
Local performance	🐢 RAG slows with scale	⚡ Graphs scale better
Agent-tool coordination	🧠 Flat context passing	✅ Contextual toolchains
Memory modularity	Vector-only	✅ Vector + Graph + Hybrid
OSS extensibility	Medium	🔓 High—adapter driven

This would make LibreChat the most memory-capable local agent interface available today—and future-proof it for HybridRAG workflows.

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🚀 Suggested Implementation Paths

• Build on LangChain or LlamaIndex hybrid retrievers
• Add config-layer selection for memory strategy
• Include adapters for Memgraph, Neo4j, or MemCP
• Offer orchestrator via plugin/module (don’t bake it into core)

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🙌 Final Thoughts

LibreChat already leads the open source UI space for LLMs. Adding modular support for an MCP-style orchestrator and Hybrid RAG retrieval could unlock:

• 🌐 Full agent memory orchestration
• 📎 Traceable and structured recall
• 🔐 Efficient local and private LLM experiences

More details

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Which components are impacted by your request?

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Code of Conduct

• I agree to follow this project's Code of Conduct

[onestardao]

been thinking about this for a while — totally agree vector-only RAG starts falling apart when reasoning depth increases or memory has to cross contexts.

we actually implemented something pretty close to what you're describing:

hybrid retrieval stack (semantic vector + graph traversal)

memory-task scoping via internal orchestrator layer (MCP-style, reusable across agents)

modular fallback + WDT/WRI-style path locks to prevent reasoning drift

it's MIT-licensed and works in production. even got a few upstream tool authors to vouch for it (Tesseract.js dev included).

if you're building around this direction, ping me — happy to share the stack and real usage patterns.
it’s already live and helping fix most of what you outlined.