lossless-claude

lossless-claude
Shared memory infrastructure for coding agents

DAG-based summarization, SQLite-backed message persistence, promoted long-term memory, MCP retrieval tools

Website • Runtime Model • Installation • MCP Tools • Development

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lossless-claude replaces sliding-window forgetfulness with a persistent memory runtime for both humans and agents.

• Every message is stored in a project SQLite database.
• Older context is compacted into a DAG of summaries instead of being dropped.
• Durable decisions and findings are promoted into cross-session memory.
• Claude Code already has end-to-end hook integration, while VS Code and Codex use connector-based workflows on the same backend today.

Humans and agents use the same backend. The integration surface differs by client, but the memory model is shared.

This repo started as a fork of lossless-claw by Martian Engineering, adapted for Claude Code. The LCM model and DAG architecture originate from the Voltropy paper.

Runtime Model

flowchart LR
  subgraph Clients["Clients"]
    CC["Claude Code<br/>hooks + MCP"]
  end

  CC --> D["lossless-claude daemon"]

  D --> DB[("project SQLite DAG")]
  D --> PM[("promoted memory FTS5")]
  D --> TOOLS["MCP tools<br/>search / grep / expand / describe / store / stats / doctor"]

Capabilities by integration path

Path	Restore	Prompt hints	Turn writeback	Automatic compaction	Notes
Claude Code	Yes	Yes	Yes, via transcript/hooks	Yes	Primary hook-based integration
GitHub Copilot (VS Code)	No	Yes, via skill/rules	No	No	Repo-local skill can teach Copilot to call lcm, but there is no automatic restore or turn capture yet
Codex	No	Yes, via skill/rules	No	No	Repo-local or global skill plus lcm import --codex; MCP config in .codex/config.toml is still manual, and first-class runtime support is tracked in issue #232

LCM Model

Phase	What happens
Persist	Raw messages are stored in SQLite per conversation
Summarize	Older messages are grouped into leaf summaries
Condense	Summaries roll up into higher-level DAG nodes
Promote	Durable insights are copied into cross-session memory
Restore	New sessions recover context from summaries and promoted memory
Recall	Agents query, expand, and inspect memory on demand

Nothing is dropped. Raw messages remain in the database. Summaries point back to their sources. Promoted memory remains searchable across sessions.

flowchart TD
  A["conversation / tool output"] --> B["persist raw messages"]
  B --> C["compact into leaf summaries"]
  C --> D["condense into deeper DAG nodes"]
  C --> E["promote durable insights"]
  D --> F["restore future context"]
  E --> F
  F --> G["search / grep / describe / expand / store"]

Installation

Prerequisites

• Node.js 22+
• Claude Code if you want hook-based automation
• GitHub Copilot in VS Code if you want VS Code integration
• Codex CLI if you want Codex connector installation, summarization, or transcript import

Claude Code

Install the lcm binary first:

npm install -g @lossless-claude/lcm  # provides the `lcm` command

claude plugin add github:lossless-claude/lcm
lcm install

lcm install writes config, registers hooks, installs slash commands, registers MCP, and verifies the daemon.

VS Code (GitHub Copilot)

Install the lcm binary first:

npm install -g @lossless-claude/lcm

Then install the repo-local Copilot connector:

lcm connectors install github-copilot
lcm connectors doctor github-copilot

This creates a workspace skill under .github/skills/lcm-memory/SKILL.md so Copilot can search and store memory through the lcm CLI.

Codex

Install the lcm binary first:

npm install -g @lossless-claude/lcm

Then install the Codex connector:

lcm connectors install codex
lcm connectors doctor codex

Import older Codex sessions when needed: