Waggle-mcp

★ 44

deja-vu

★ 1.3k

Waggle-mcp vs deja-vu

Q: Which is better, Waggle-mcp or deja-vu?

By GitHub stars, deja-vu has more community adoption, but the best choice depends on your specific use case.

Waggle-mcp: Waggle-mcp provides AI agents with a persistent, graph-backed memory that remembers decisions, reasons, and contradictions across sessions, overcoming the limitations of short context windows. It acts as a local-first memory layer for MCP-compatible AI clients, allowing for rich, structured context retrieval instead of simple text snippets.; deja-vu: Deja-vu is a zero-dependency, local-first binary that indexes and makes searchable the historical sessions of various AI coding agents. It acts as a universal memory layer, allowing agents to recall past solutions and context efficiently, even from before its installation.

TL;DR

Waggle-mcpMemory & Context · ★ 44

Maintaining long-term conversational memory for AI coding assistants across development sessions.

Read Waggle-mcp full review →

deja-vuMemory & Context · ★ 1.3k

Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.

Read deja-vu full review →

Side-by-Side Comparison

Field

Waggle-mcp

deja-vu

Features

Waggle-mcp

01Persistent graph memory with SQLite backend by default, with optional Neo4j support.

02Integrates as an MCP server with automatic memory hooks for various AI clients like Claude, Cursor, and Gemini CLI.

03Recursive context assembly to provide compact, high-signal context packs, optimizing token usage.

04Stores relationships between facts, decisions, and contradictions, enabling temporal queries, diffs, and conflict resolution.

05Local embeddings with a deterministic offline fallback, ensuring privacy and local operation.

deja-vu

01Fast, Retroactive Session Search

02Automatic AI Agent Memory Recall

03Cross-Machine Memory Synchronization

04Seamless Agent Context Handoff

05Automated Sensitive Data Redaction

Use Cases

Waggle-mcp

↳Maintaining long-term conversational memory for AI coding assistants across development sessions.

↳Retrieving past decisions, constraints, and preferences to inform current AI agent actions.

↳Tracking changes and resolving contradictions in evolving project requirements or design choices.

↳Exporting and sharing AI agent memory snapshots for collaboration or migration between machines/sessions.

↳Debugging AI agent reasoning and understanding context by inspecting the underlying knowledge graph via Graph Studio.

deja-vu

↳Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.

↳Debugging complex issues by accessing comprehensive past debugging sessions and design decisions.

↳Maintaining persistent context across different AI agents and development machines.

↳Sharing sanitized session digests with colleagues for onboarding or knowledge transfer.

↳Understanding file change history and decision-making via session blame.

FAQ

What is the difference between Waggle-mcp and deja-vu?

Both Waggle-mcp and deja-vu are in the Memory & Context category. Waggle-mcp has 44 stars, while deja-vu has 1.3k stars.

Which is better, Waggle-mcp or deja-vu?

The best choice depends on your use case. Choose Waggle-mcp if Maintaining long-term conversational memory for AI coding assistants across development sessions., and deja-vu if Reusing solutions from past agent conversations to avoid re-debugging or re-implementing..

Is Waggle-mcp free or open source?

Yes, Waggle-mcp is open source on GitHub (Apache-2.0).

Is deja-vu free or open source?

Yes, deja-vu is open source on GitHub (MIT).

Cloud Infrastructure

Deploy on DigitalOcean

Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.

Claim $200 Free Credit

→

Alternatives to Waggle-mcp →Alternatives to deja-vu →Waggle-mcp details →deja-vu details →

TL;DR

Side-by-Side Comparison

Features

Use Cases

FAQ

Deploy on DigitalOcean

Related