MakeaMouse/fish-bridge-mcp
by Various
An economical and fuel(token) efficient AI tool, graph session memory for looong chat session
MCP
MakeaMouse/fish-bridge-mcp
Added 1 June 2026
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
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