Squish - AI Memory System for Coding Agents

Connect your sources. Click ingest. Your AI remembers everything.

Squish is an AI memory system for coding agents. Local-first MCP runtime with connectors, knowledge graphs, and multi-tier deployment. Free locally, paid Cloud for sync and teams.

npm i -g squish-memory && squish install --all

Core Concepts

The Problem: Agents Forget Everything

Every AI coding agent starts from zero when a new session begins. The architecture decision from last week, the config you spent an hour debugging, the preference you mentioned yesterday — gone.

Built-in memory files like CLAUDE.md and .cursorrules help, but they have hard limits. They cap out around 200 lines, require manual curation, and do not work across agents. You end up copy-pasting the same context into every tool.

Squish gives you persistent memory for coding agents that scales without limits. No manual maintenance. No token waste. No agent lock-in.

Three Layers of Memory

Token Cost Comparison

Squish retrieves only the relevant memories for the current task. The average context injection is 50-200 tokens — a fraction of what you would paste manually.

Quick Start

Step 1: Install

npm install -g squish-memory && squish install --all

This installs the Squish CLI, MCP server, and plugin hooks for all detected agents.

Step 2: Work

Start your coding agent as usual. Squish runs in the background, auto-capturing decisions, constraints, preferences, and context.

squish remember "We chose PostgreSQL for Squish Cloud team mode" --type decision
squish recall "project decisions"

Step 3: Search Past Sessions

After a few sessions, search your agent history:

squish sessions search "postgres migration"
squish sessions related --repo-path .

Step 4: Restart

Close your session and open a new one. Your agent picks up where you left off — all context is restored automatically.

squish context    # See what your agent remembers
squish stats      # Check memory health

Works locally free. Paid Squish Cloud is available at squishplugin.dev for sync, dashboard, and team features.

Works with Every Agent

Squish works with any AI coding agent that supports MCP (Model Context Protocol) or HTTP connections. One memory server, shared across all of them.

Works with any agent that speaks MCP or HTTP. One server, memories shared across all of them.

MCP Server Configuration

Add Squish to any MCP-compatible client:

{
  "mcpServers": {
    "squish": {
      "command": "squish-mcp",
      "args": ["--http", "--port", "8767"],
      "env": {
        "SQUISH_DB_PATH": "./squish-data"
      }
    }
  }
}

For cloud-connected agents:

{
  "mcpServers": {
    "squish-cloud": {
      "type": "url",
      "url": "https://api.squishplugin.dev/mcp",
      "headers": {
        "Authorization": "Bearer <your-token>"
      }
    }
  }
}

Why Squish

Most memory tools need a second LLM for embeddings and retrieval. That means extra API costs, latency, and infrastructure you have to manage.

Squish uses local embeddings by default. Zero LLM dependency. 1-5ms latency. $0 runtime cost in local mode.

Bring your own LLM if you want — Squish supports external embeddings and reasoning, but nothing requires it.

Comparison

Features

Memory Intelligence

• Auto-captures decisions, constraints, and preferences as you work
• Restores relevant context when an agent restarts
• Handles contradictions and temporal facts with expiration
• Graph-boosted retrieval connects related memories across sessions
• Contradiction detection flags conflicting information
• Temporal reasoning tracks when facts were true vs. now
• Confidence scoring adjusts memory relevance over time
• Decay system automatically ages low-value memories

Session Search

• Search previous Claude Code, Codex, and OpenCode sessions
• Find related sessions by project path or file overlap
• Inspect past decisions, errors, and commands as evidence
• Separate from long-term memory — raw session history, not distilled facts

Interfaces

• CLI: squish remember, recall, inspect, context, stats, search, sessions
• MCP Server: 18 tools for any MCP client — recall, health, graph, recency, maintenance
• Web UI: Local dashboard at localhost:37777 for visualizing memories
• Cloud Dashboard: Paid analytics and management at squishplugin.dev

Storage

• SQLite (local, default) or Squish Cloud team workspaces
• Hybrid retrieval: keyword + semantic similarity with RRF fusion
• AES-256-GCM encryption for sensitive memories
• Places routing: organize memories by project, feature, or context
• Full-text search with BM25 ranking
• Vector search with TF-IDF embeddings (768-dimensional)

Memory Pipeline

Squish uses a 4-stage pipeline to process memories:

1. Capture — Filters noisy tool output, keeps new memories private-first, and suggests what should be promoted into project/team/company scopes
2. Filter — Deduplicates, resolves contradictions, scores importance
3. Store — Persists to SQLite/PostgreSQL with graph relationships and embeddings
4. Retrieve — Hybrid search combines keyword, semantic, recency, and importance scoring

Architecture

Three-Layer Memory Model

Storage Layer

Connectors

Squish connects to your existing tools and ingests context automatically:

| Connector | What It Ingests | |-----------|----------------| | Google Drive | Documents, sheets, slides, and files | | GitHub | Issues, PRs, discussions, code context, and repo metadata | | Slack | Messages, threads, channel context, and decisions | | Notion | Pages, databases, docs, and wikis |

Connectors are available on Cloud tiers. Install with:

squish connect google-drive
squish connect github
squish connect slack
squish connect notion

Ingestion Pipeline

Squish uses a 4-stage pipeline to process content from your connectors:

1. Chunk -- Splits documents into meaningful segments
2. Embed -- Generates TF-IDF or external embeddings (768-dimensional vectors)
3. Store -- Persists to SQLite/PostgreSQL with graph relationships
4. Extract -- Builds knowledge graph with entity resolution and relationship mapping

Optional LLM-powered features (bring your own key):

• Entity extraction and relationship mapping
• Automatic memory organization and tagging
• Summarization and consolidation

Squish Cloud

Persistent memory across ChatGPT, Claude Desktop, Claude Code, and local agents. One account, synchronized everywhere.

Cloud features: OAuth 2.1 + PKCE login, cross-platform sync, team workspaces, admin dashboard, priority support.

Pricing

Sign up at squishplugin.dev -- 30 seconds, no credit card needed.

Installation Guides

• Claude Code — MCP server + plugin hooks for auto-capture
• OpenCode — MCP server + hooks for OpenCode agent
• OpenClaw — MCP server setup for OpenClaw

Quick install for all detected agents:

npm install -g squish-memory && squish install --all

Squish auto-detects which agents you have installed and configures hooks for each one.

Benchmarks

Squish is tested against real-world memory retrieval tasks and synthetic benchmarks.

Full benchmark details: docs/BENCHMARK.md

Documentation

FAQ

What is Squish?

Squish is a local-first memory runtime for AI coding agents. It gives your agents stable orientation, durable memory, and searchable session history across runs. Think of it as a brain that persists between sessions — your agents remember decisions, constraints, preferences, and context without you having to re-explain everything. In v1.6.0, Squish also searches past agent sessions as evidence, so agents can inspect prior work instead of starting from zero.

Does Squish require an API key?

No. Squish works locally by default with zero API keys. It uses local embeddings (TF-IDF) and SQLite storage. You can optionally configure an external LLM for enhanced reasoning, but it's not required. An API key is only needed if you want to use the paid Squish Cloud for cross-device sync.

How does Squish compare to mem0 or agentmemory?

Squish is the only option that works locally with zero external dependencies. mem0 requires Qdrant (a vector database) and cloud API calls. agentmemory requires iii-engine. Squish uses SQLite and local embeddings by default. See the full comparison in the Why Squish section above.

Can I use Squish with multiple AI agents?

Yes. Squish works with any MCP-compatible agent. One memory server is shared across Claude Code, Cursor, Codex, Copilot, Gemini CLI, and any other agent that supports MCP. Memories are available to all connected agents.

Is my data private with Squish?

Yes. In local mode, all data stays on your machine in an encrypted SQLite database. Nothing is sent to any cloud service. AES-256-GCM encryption protects sensitive memories. In cloud mode, data is encrypted in transit and at rest.

What databases does Squish support?

Squish supports SQLite (default, local) and Squish Cloud team workspaces backed by PostgreSQL. SQLite requires zero configuration. Team workspaces are only available on Squish Cloud and are used for shared memory across multiple users.

What is the difference between recall and sessions?

squish recall searches your long-term memory — distilled facts, decisions, and preferences that Squish has captured and organized. squish sessions search searches raw past agent runs — the actual messages, commands, and file changes from previous Claude Code, Codex, or OpenCode sessions. Recall gives you what the system decided to remember. Sessions give you the evidence.

Contributing

See docs/CONTRIBUTING.md for guidelines on how to contribute to Squish.

License

MIT -- see LICENSE for details.