> your AI forgets everything between sessions.
> gingugu fixes that.█
      


      

        a local MCP server that gives AI coding assistants a real long-term
        brain — persistent, structured, searchable memory that survives across
        sessions, repos, and projects.
      


      

        [ python 3.11+ ]
        [ protocol: MCP ]
        [ storage: SQLite ]
        [ license: MIT ]
        [ 0% cloud · 0 telemetry ]
      

    


    
    

      
$ whoami

      

        

          every session with an AI assistant starts from zero. the decisions you
          made yesterday, the bug you fixed last week, the architecture you
          settled on a month ago — gone.
        

        

          existing memory tools dump observations into a flat pile. no structure,
          no staleness tracking, no relationships, no sense of what's relevant
          right now.
        

        

          gingugu is the actual brain — not a junk
          drawer. one SQLite file on your machine. no cloud, no API keys,
          no telemetry.
        

      

    


    
    

      
$ cat features.txt

      

        
[namespaces] memories auto-scoped to repos/projects, cross-repo pattern sharing

        
[hybrid_search] FTS5 BM25 + local semantic embeddings fused with RRF — fast, local, zero API calls, no PyTorch

        
[trust_map] trust-led scoring, dormancy tracking (never forgets), last-confirmed tracking, spreading activation

        
[relations] link memories: supersedes · related_to · caused_by · contradicts

        
[confidence] verified → inferred → stale → deprecated lifecycle

        
[consolidate] merge duplicates, summarize clusters, dedupe on demand

        
[auto_context] surfaces relevant memories on session start — zero manual effort

        
[cred_vault] API keys/tokens in the OS keychain — never in plaintext

        
[explorer_ui] interactive knowledge graph + dashboard for your memory data

        
[health] memory stats, dormancy reports, namespace overviews

      

    


    
    

      
$ ls tools/ # 16 MCP tools

      

        memory_storememory_recallmemory_contextmemory_update
        memory_relatememory_consolidatememory_forgetmemory_namespaces
        memory_exportmemory_importmemory_statsmemory_search
        credential_storecredential_getcredential_listcredential_delete
      

    


    
    

      
$ gingugu --demo

      
// session start — your agent runs:
memory_context(namespace="my-app", task_hint="fix auth bug")

→ surfaced 4 memories:
  [bug]      JWT refresh loop on expired tokens — FIXED in a81f2c
  [decision] auth lives in middleware/, not per-route guards
  [pattern]  all API errors wrap in ApiError(code, msg)
  [fact]     staging uses cognito pool us-east-1_x7Yq

// your AI now remembers. like it never left.

    


    
    

      
$ ./install.sh

      

        

          pip install gingugu
          
        

        

          or uv tool install gingugu. then point your MCP config at it
          and paste the memory protocol into your agent's rules file — full setup in the README.
        

      

    


    
    

      
$ cat links.txt

      

        [1] github.com/gingugu/gingugu ........ source
        [2] README#setup ..................... install guide
        [3] modelcontextprotocol.io .......... what's MCP?