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MakeaMouse/fish-bridge-mcp

by Various

An economical and fuel(token) efficient AI tool, graph session memory for looong chat session

M

MCP

MakeaMouse/fish-bridge-mcp

Added 1 June 2026

#ai-context-management #claude-code #context-compression #conversation-graph #copilot #cursor #knowledge-graph #llm-memory

Overview

A Python-based MCP (Model Context Protocol) tool that provides graph-structured session memory for extended AI chat conversations. It aims to reduce token consumption by storing and retrieving context efficiently, enabling long-running sessions without excessive fuel costs.

Best for

Best for
Developers building cost-sensitive AI chat applications requiring long-term session memory

Use cases

  • Maintaining conversation history across very long chat sessions with minimal token usage
  • Building AI assistants that need persistent memory without reloading full context
  • Reducing API costs in applications with extended user interactions

Notes

A Python-based MCP (Model Context Protocol) tool that provides graph-structured session memory for extended AI chat conversations. It aims to reduce token consumption by storing and retrieving context efficiently, enabling long-running sessions without excessive fuel costs.

1 stars on GitHub. Last updated 2026-05-20. Licensed MIT.

Use cases

  • Maintaining conversation history across very long chat sessions with minimal token usage
  • Building AI assistants that need persistent memory without reloading full context
  • Reducing API costs in applications with extended user interactions

Pros

  • Token-efficient memory management lowers operational costs
  • Graph-based structure allows flexible context retrieval
  • Open source and lightweight Python implementation

Cons

  • Very early stage with only 1 GitHub star, indicating limited community validation
  • Documentation and examples may be sparse
  • Python-only, limiting integration with non-Python stacks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Token-efficient memory management lowers operational costs
  • Graph-based structure allows flexible context retrieval
  • Open source and lightweight Python implementation

Cons

  • Very early stage with only 1 GitHub star, indicating limited community validation
  • Documentation and examples may be sparse
  • Python-only, limiting integration with non-Python stacks