fish_bridge
Session-scoped knowledge graph engine for AI chat context compression.
Alimenter votre agent
La commande propre à l'éditeur. Ce n'est pas un endpoint PlugMCP.
Host a copy with our keys and logs.
uvx fish-bridge-mcp==0.1.3Claude Code n'a pas de lien d'installation pour une commande locale. Copiez la commande ci-dessous et exécutez-la dans un terminal.
ANTHROPIC_API_KEY=<secret> OPENAI_API_KEY=<secret> GEMINI_API_KEY=<secret> claude mcp add io.github.makeamouse-fish-bridge-mcp -- uvx fish-bridge-mcp==0.1.3À exécuter dans un terminal.
{ "mcpServers": { "io.github.makeamouse-fish-bridge-mcp": { "command": "uvx", "args": [ "fish-bridge-mcp==0.1.3" ], "env": { "ANTHROPIC_API_KEY": "<secret>", "OPENAI_API_KEY": "<secret>", "GEMINI_API_KEY": "<secret>" } } }}claude_desktop_config.json
URL distante ou commande sur votre machine ? Ce qui distingue les deux installations
Exécuter sur PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LancerConverts 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.
Configure with:
fish-bridge config --backend gemini
# or set GEMINI_API_KEY / ANTHROPIC_API_KEY / OPENAI_API_KEY as env vars
# --- Session init ---
fish-bridge init # create session for current project
fish-bridge init --tool claude # → writes to CLAUDE.md instead
# --- Ingest chat turns ---
fish-bridge ingest --source copilot # auto-discover latest VS Code Copilot session
fish-bridge ingest --source copilot --session <id> # target specific session
fish-bridge ingest --source paste # paste any chat text — opens $EDITOR (universal fallback)
fish-bridge ingest --source file --file export.json # from a saved export file
fish-bridge watch --source copilot # tail JSONL, auto-update on new turns
# --- Merge external knowledge ---
fish-bridge merge --source document --file HANDOVER.md
fish-bridge merge --source codebase --path ./ # git log + README
fish-bridge merge --source obsidian --vault ~/notes
fish-bridge merge --source deps --path ./ # package.json / pyproject.toml etc.
fish-bridge merge --source testout --file results.json # jest / pytest / JUnit
fish-bridge merge --source iac --path ./ # Terraform / CDK / CloudFormation
fish-bridge merge --source openapi --file openapi.yaml
fish-bridge merge --source session --file prior.chatgraph.json
# --- Compile & view ---
fish-bridge compile # update instruction file (runs after ingest by default)
fish-bridge compile --mode digest # full handover markdown
fish-bridge compile --mode focus --query "Redis caching"
fish-bridge show # pretty-print active nodes
fish-bridge show --all # include resolved/deferred items
fish-bridge serve # open Cytoscape.js graph viewer at localhost:8080
fish-bridge digest # generate handover digest
# --- Node management ---
fish-bridge resolve "DNC caching strategy"
fish-bridge defer "v16 index validation"
fish-bridge add "Use Redis for session cache" --type decision
fish-bridge conflict show
fish-bridge conflict resolve <node-id> --keep old
# --- Export / import / diff ---
fish-bridge export # save .chatgraph.json
fish-bridge import prior-session.chatgraph.json
fish-bridge diff session-a.chatgraph.json session-b.chatgraph.json
# --- Config ---
fish-bridge config --show
fish-bridge config --backend gemini
The MCP server adds real-time record_turn capture when using VS Code agent mode. It is not required — the file-based workflow above works in all modes without it.
Add to .vscode/mcp.json (uses uvx — no prior install needed):
{
"servers": {
"fish-bridge": {
"command": "uvx",
"args": ["fish-bridge-mcp"],
"env": { "FISH_BRIDGE_BACKEND": "gemini", "GEMINI_API_KEY": "${env:GEMINI_API_KEY}" }
}
}
}
If you used uv tool install fish-bridge-mcp, you can also reference the installed binary directly:
{ "command": "fish-bridge-mcp" }
See examples/ for Claude Desktop, Cursor, and Windsurf configs.
MCP tools: record_turn, get_context, get_focus, mark_resolved, add_node, export_session, import_session, show_active, list_deferred
.github/copilot-instructions.md (or CLAUDE.md)MIT — see LICENSE
Compresses AI chat sessions into a typed knowledge graph. Local/offline via Ollama.
{ "mcpServers": { "io.github.makeamouse-fish-bridge-mcp": { "command": "uvx", "args": [ "fish-bridge-mcp==0.1.3" ], "env": { "ANTHROPIC_API_KEY": "<secret>", "OPENAI_API_KEY": "<secret>", "GEMINI_API_KEY": "<secret>" } } }}Outils
Pas encore de liste d'outils. Les serveurs distants en obtiennent une après le premier contrôle d'état.
FAQ
Copiez sur cette page la configuration stdio pour Cursor, Claude Desktop, Claude Code ou VS Code. Elle utilise l'installation de l'éditeur, pas un proxy PlugMCP.
L'éditeur indique ces variables d'environnement : ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY.
Fish Bridge — Session Knowledge Graph s'exécute en local via stdio.
Dernière vérification : 2026-11-09. Compresses AI chat sessions into a typed knowledge graph. Local/offline via Ollama.