Compresses AI chat sessions into a typed knowledge graph. Local/offline via Ollama.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-makeamouse-fish-bridge-mcp": {
"args": [
"fish-bridge-mcp"
],
"command": "uvx"
}
}
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Session-scoped knowledge graph engine for AI chat context compression.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'fish-bridge-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
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Session-scoped knowledge graph engine for AI chat context compression.
Converts raw AI chat (40k+ tokens) into a compact typed knowledge graph (~300–800 tokens) and writes it to .github/copilot-instructions.md or CLAUDE.md — automatically included in every AI turn across all modes (ask, edit, agent). No MCP server required for the core workflow.
Raw session (40k tokens) → [fish_bridge] → Compressed graph (350 tokens)
written to copilot-instructions.md
picked up by every AI turn automatically
Don't have
uv? Get it first:curl -LsSf https://astral.sh/uv/install.sh | sh(macOS/Linux) or see uv docs. It replaces pip + pipx + pyenv in one tool — no virtualenv management needed.
Recommended — uv tool install (installs both the fish-bridge CLI and fish-bridge-mcp MCP server on your PATH):
# Local Ollama backend — free, offline (requires Ollama running)
uv tool install fish-bridge-mcp
# Gemini backend (~$0.0002/turn, ~95% quality — recommended cloud option)
uv tool install "fish-bridge-mcp[gemini]"
export GEMINI_API_KEY=...
# Claude backend (~$0.002/turn, ~97% quality)
uv tool install "fish-bridge-mcp[claude]"
export ANTHROPIC_API_KEY=sk-ant-...
# OpenAI backend (~$0.0003/turn, ~93% quality)
uv tool install "fish-bridge-mcp[openai]"
export OPENAI_API_KEY=sk-...
# Everything
uv tool install "fish-bridge-mcp[all]"
After install, two commands are available on your PATH:
fish-bridge — the main CLI (ingest, compile, show, serve, ...)fish-bridge-mcp — the MCP server for VS Code agent modeMCP config only (no permanent install needed): use uvx directly in your .vscode/mcp.json — it downloads and runs the MCP server on demand:
{ "command": "uvx", "args": ["fish-bridge-mcp"] }
See the MCP server section below for the full config.
pip install fish-bridge-mcp
pip install "fish-bridge-mcp[gemini]" # with Gemini backend
pip install "fish-bridge-mcp[claude]" # with Claude backend
pip install "fish-bridge-mcp[all]" # everything
# 1. Initialize for your project
fish-bridge init --tool copilot --project ./
# 2. Ingest the latest Copilot session (auto-discovers JSONL on macOS/Linux/Windows)
fish-bridge ingest --source copilot
# 3. View the graph
fish-bridge show
# 4. Compile to your instructions file (done automatically after ingest)
fish-bridge compile
That's it. .github/copilot-instructions.md now contains a ~350-token compressed summary of your session, replacing raw history in every future turn.
| Backend | Install extra | Model | Quality | Cost/turn |
|---|---|---|---|---|
local (Ollama) | (none — requires Ollama) | qwen2.5:7b | ~85% | $0 |
gemini | [gemini] | gemini-2.5-flash | ~95% | ~$0.0002 |
openai | [openai] | gpt-4.1-mini | ~93% | ~$0.0003 |
claude | [claude] | claude-opus-4-7 | ~97% | ~$0.002 |
hybrid | [claude] or [openai] | local+cloud | best | mixed |
Configure with:
fish-bridge config --backend gemini
# or set GEMINI_API_KEY / ANTHROPIC_API_KEY / OPENA
... [View full README on GitHub](https://github.com/makeamouse/fish-bridge-mcp#readme)