Claude Code forgets. claude-mem remembers fragments. lossless-code remembers everything.

Try it in 60 seconds

/plugin marketplace add GodsBoy/lossless-code
/plugin install lossless-code

That's it. Start a new session and search your history:

lcc_grep "database migration"

The Problem

Claude Code forgets everything between sessions. Memory tools like ClawMem, context-memory, and claude-mem use flat retrieval: keyword search over snippets, no structure, no hierarchy, no way to trace a summary back to its source conversation.

When a project spans weeks and hundreds of sessions, flat search fails. You get fragments without lineage.

What Makes lossless-code Different

lossless-code uses DAG-based lossless preservation, the same approach pioneered by lossless-claw for OpenClaw:

• Nothing is ever deleted. Every message stays in vault.db forever.
• Summaries form a directed acyclic graph. Messages cascade to depth-0 summaries, which roll up to depth-1, depth-2, and beyond.
• Full drill-down. lcc_expand traces any summary back to the exact messages that created it.
• Automatic. Claude Code hooks capture every turn and trigger summarisation transparently. Zero manual effort.
• Cross-session recall. Start a new session and your full project history is immediately searchable.
• Lossless Dream. Extracts recurring patterns (corrections, preferences, conventions) from vault history and injects them into future sessions — like Auto-Dream but without forgetting.

                              ┌──────────────────┐
                              │   Claude Code     │
                              │   Session         │
                              └────────┬─────────┘
                                       │
              ┌────────────────────────┼────────────────────────┐
              │                  │                │              │
        ┌─────▼─────┐    ┌──────▼──────┐  ┌──────▼──────┐ ┌────▼─────┐
        │  Hooks     │    │   Skills    │  │   CLI       │ │  MCP     │
        │  (write)   │    │  (shell)    │  │   Tools     │ │  Server  │
        │            │    │             │  │             │ │  (stdio) │
        │ SessionStart│   │ lcc_grep    │  │ lcc_status  │ │          │
        │ Stop       │    │ lcc_expand  │  │             │ │ 6 tools  │
        │ PreCompact │    │ lcc_context │  │             │ │ read-only│
        │ PostCompact│    │ lcc_sessions│  │             │ │          │
        │ UserPrompt │    │ lcc_handoff │  │             │ │          │
        └─────┬──────┘    └──────┬──────┘  └──────┬──────┘ └────┬─────┘
              │                  │                │              │
              └──────────────────┼────────────────┼──────────────┘
                                 │                │
                        ┌────────▼────────────────▼──┐
                        │         vault.db            │
                        │         (SQLite)            │
                        │                             │
                        │  messages    summaries       │
                        │  summary_sources  sessions   │
                        │  FTS5 indexes                │
                        └──────────────────────────────┘

Comparison

Why lossless-code Costs Less

Memory tools that inject context on every prompt are silently expensive. Here's why lossless-code's design saves tokens:

1. On-demand recall, not automatic injection

ClawMem injects relevant memory into 90% of prompts automatically (their stated design). claude-mem injects a context index on every SessionStart. Both approaches front-load tokens whether or not the agent needs that context.

lossless-code injects nothing by default. Context surfaces only when the agent explicitly calls an MCP tool or the PreCompact hook fires. Most coding turns (writing code, running tests, reading files) don't need historical context at all. You pay for recall only when recall matters.

2. Fewer MCP tool definitions = fewer tokens per turn

Every MCP tool registered in ~/.claude.json has its schema injected into every single API call as available tools. Claude Code's own docs warn: "Prefer CLI tools when available... they don't add persistent tool definitions."

• ClawMem: 28 MCP tools (query, intent_search, find_causal_links, timeline, similar, etc.)
• claude-mem: 10+ search endpoints via worker service
• lossless-code: 6 MCP tools (grep, expand, context, sessions, handoff, status)

Over a 200-turn session, that difference in tool schema overhead compounds significantly.

3. No background embedding costs

ClawMem runs a watcher service (re-indexes on file changes) and an embed timer (daily embedding sweep across all collections). These require GGUF models (~2GB minimum) and consume CPU/GPU continuously. claude-mem runs a persistent worker service on port 37777.

lossless-code has zero background processes. Hooks fire only during Claude Code events. The vault is pure SQLite with FTS5 (built into SQLite, no external models). Nothing runs between sessions.

4. DAG summarisation reduces compaction waste

When Claude Code hits its context limit, it compacts: summarising earlier context to make room. With flat memory systems, compaction loses fidelity and the agent may re-explore territory it forgot, costing more tokens ("debugging in circles").

lossless-code's DAG captures the full conversation before compaction happens (PreCompact hook). After compaction, the PostCompact hook re-injects only the top-level summaries. The agent can drill down via lcc_expand if it needs detail, but the DAG ensures nothing is truly lost. This means:

• Fewer repeated explorations after compaction
• One long session is cheaper than multiple short sessions covering the same ground
• Context survives compaction without paying to re-read everything

5. No runtime dependencies

Fewer dependencies means less to maintain, fewer failure modes, and lower resource consumption.

Install

Option A: Claude Code Plugin (recommended)

/plugin marketplace add GodsBoy/lossless-code
/plugin install lossless-code

Hooks, MCP server, and skill are activated automatically. No manual setup needed.

Option B: Standalone Install

git clone https://github.com/GodsBoy/lossless-code.git
cd lossless-code
bash install.sh

The installer:

1. Creates ~/.lossless-code/ with vault.db and scripts
2. Configures Claude Code hooks in ~/.claude/settings.json
3. Installs the skill to ~/.claude/skills/lossless-code/
4. Adds CLI tools to PATH

Idempotent: safe to run again to upgrade.

Requirements

• Python 3.10+
• SQLite 3.35+ (for FTS5)
• Claude Code CLI

Optional: anthropic Python package for AI-powered summarisation (falls back to extractive summaries without it).

MCP Server

lossless-code includes an MCP (Model Context Protocol) server so Claude Code can access the vault as native tools without shelling out to CLI commands.

Setup

The installer (install.sh) automatically:

1. Copies the MCP server to ~/.lossless-code/mcp/server.py
2. Installs the mcp Python SDK
3. Registers the server in ~/.claude.json

After installation, every new Claude Code session auto-discovers 6 MCP tools:

Manual Registration

If you need to register the MCP server manually:

// ~/.claude.json
{
  "mcpServers": {
    "lossless-code": {
      "command": "python3",
      "args": ["~/.lossless-code/mcp/server.py"]
    }
  }
}

Architecture

  Claude Code  ──stdio──▶  MCP Server  ──read-only──▶  vault.db
                            (server.py)
                            6 tools

The MCP server is read-only. All writes to the vault happen through hooks (SessionStart, Stop, UserPromptSubmit, PreCompact, PostCompact). The MCP server imports db.py directly for SQLite access.

Commands

lcc_grep <query>

Full-text search across all messages and summaries.

lcc_grep "database migration"
lcc_grep "auth refactor"

lcc_expand <summary_id>

Expand a summary node back to its source messages.

lcc_expand sum_abc123def456
lcc_expand sum_abc123def456 --full

lcc_context [query]

Surface relevant DAG nodes for a query. Without a query, returns highest-depth summaries.

lcc_context "auth system"
lcc_context --limit 10

lcc_sessions

List recorded sessions with timestamps and handoff status.

lcc_sessions
lcc_sessions --limit 5

lcc_handoff

Show or generate a session handoff.

lcc_handoff
lcc_handoff --generate --session "$CLAUDE_SESSION_ID"

lcc_status

Show vault statistics: message count, summary count, DAG depth, dream stats, and FTS index health.

lcc_status

lcc_dream

Run the dream cycle — extract patterns from vault history and consolidate the DAG.

lcc_dream --run                    # Dream for current working directory
lcc_dream --run --project /path    # Dream for a specific project
lcc_dream --run --global           # Cross-project dream

See Lossless Dream for details.

Terminal UI (lcc-tui)

lcc-tui is a terminal-based browser for your vault. Built with Textual.

lcc-tui

Views

Navigation

• 1 to 4: switch tabs
• /: open search modal from any view
• Enter: drill into selected session or summary
• Esc: go back
• q: quit

Full reference: docs/tui.md

How It Works

Hooks (Automatic)

DAG Summarisation

1. Collect unsummarised messages, chunk into groups of ~20
2. Summarise each chunk (via Claude API or extractive fallback)
3. Write summary nodes to summaries table (depth=0)
4. Link to sources in summary_sources
5. Mark source messages as summarised
6. If depth-N exceeds threshold: cascade to depth-N+1
7. Repeat until under threshold at every depth

Lossless Dream

Dream is the intelligence layer on top of the DAG. It analyzes vault history to extract recurring patterns and consolidate redundant summaries — all without deleting anything.

Three-phase cycle:

1. Pattern extraction — Queries messages and summaries since the last dream, chunks them, and sends each chunk to the LLM. Extracts patterns in 5 categories: corrections, preferences, anti-patterns, conventions, decisions. Falls back to keyword heuristics when no LLM API is available.
2. DAG consolidation — Finds summaries with overlapping sources (>50% shared), merges them into tighter nodes via LLM, marks originals as consolidated=1. Nothing is deleted.
3. Report generation — Writes a timestamped report and updates the dream log for idempotent reruns.

Auto-trigger: Dream runs automatically from the stop.sh hook when configurable conditions are met (default: 5+ sessions or 24+ hours since last dream). Runs as a background process with file-based locking to prevent concurrent races.

Context injection: On SessionStart, per-project and global dream patterns are injected alongside existing handoff and summaries, within a configurable token budget (default 2000 tokens).

Lineage: Every pattern in patterns.md includes source reference IDs. Use lcc_expand to trace any pattern back to the original conversation.

# Run dream manually
lcc dream --run

# Dream for a specific project
lcc dream --run --project /path/to/project

# Cross-project dream
lcc dream --run --global

Semantic Search (v1.1.0+)

Hybrid FTS5 + vector search, activated optionally. Default is FTS5-only — install nothing and the plugin behaves exactly as before.

Enable semantic search:

# Install the optional embedding dependency
pip install fastembed

# Turn it on in ~/.lossless-code/config.json
{"embeddingEnabled": true}

# Index your existing vault (one-time backfill)
lcc reindex --embeddings

How it works: New messages are embedded in the background after each session (same non-blocking pattern as dream auto-trigger). Queries combine FTS5 keyword results with vector cosine similarity using Reciprocal Rank Fusion (k=60). When hybrid search is active, lcc grep shows a [hybrid] tag on results.

Provider tiers:

• fastembed (default local): ONNX-based, no PyTorch, ~200 MB install. Default model: BAAI/bge-small-en-v1.5
• openai / anthropic: API-based, higher quality. Set embeddingProvider in config and export the API key
• Fallback: pure-Python numpy cosine similarity (no extra install, works for smaller vaults)
• Always: FTS5-only if no provider installed

Config keys (all optional, flat in ~/.lossless-code/config.json):

Switching models: Change embeddingModel and run lcc reindex --embeddings --force to re-embed with the new model.

Check status:

lcc status   # shows "Vector search: active (fastembed, BAAI/bge-small-en-v1.5)"
             #       "Embeddings: 4,231 / 4,400 messages indexed (169 pending)"

Storage

~/.lossless-code/
  vault.db       # SQLite: all messages, summaries, DAG, sessions, dream_log
  config.json    # Settings (summary model, thresholds, dream config)
  scripts/       # Python modules and CLI tools
  hooks/         # Shell scripts called by Claude Code hooks
  dream/         # Dream output
    reports/     # Timestamped dream reports
    projects/    # Per-project pattern files (keyed by working dir hash)
    global/      # Cross-project pattern files
    dream.log    # Dream cycle log (for debugging background execution)

Configuration

~/.lossless-code/config.json:

{
  "summaryModel": "claude-haiku-4-5-20251001",
  "summaryProvider": "anthropic",
  "anthropicBaseUrl": null,
  "chunkSize": 20,
  "depthThreshold": 10,
  "incrementalMaxDepth": -1,
  "workingDirFilter": null,
  "autoDream": true,
  "dreamAfterSessions": 5,
  "dreamAfterHours": 24,
  "dreamModel": "claude-haiku-4-5-20251001",
  "dreamTokenBudget": 2000,
  "contextTokenBudget": 8000
}

Compaction Configuration

lossless-code supports multiple LLM providers for compactions. Configure your provider in ~/.lossless-code/config.json:

{
  "summaryModel": "gpt-4.1-mini",
  "summaryProvider": "openai",
  "chunkSize": 20,
  "depthThreshold": 10
}

Supported Providers

Anthropic (default)

Authentication is resolved automatically from multiple sources (in priority order):

1. ANTHROPIC_API_KEY environment variable (standard API key)
2. Claude Code OAuth token from ~/.claude/.credentials.json (setup-token)
3. CLAUDE_CODE_OAUTH_TOKEN environment variable

Note: OAuth/setup-tokens require ANTHROPIC_BASE_URL pointing to a compatible proxy (e.g. OpenClaw), since api.anthropic.com does not accept OAuth tokens directly. If you only have a setup-token and no proxy, use the OpenAI provider instead.

{ "summaryProvider": "anthropic", "summaryModel": "claude-haiku-4-5-20251001" }

Model examples: claude-haiku-4-5-20251001, claude-sonnet-4-20250514

OpenAI

Set OPENAI_API_KEY in your environment.

{ "summaryProvider": "openai", "summaryModel": "gpt-4.1-mini" }

Model examples: gpt-4.1-mini, gpt-4.1-nano, gpt-4o-mini

You can use any model your provider supports. These are just common choices.

Custom Anthropic-compatible endpoints

Any provider that exposes an Anthropic Messages API endpoint works (e.g. MiniMax, Cloudflare AI Gateway, custom proxies). Set anthropicBaseUrl in config (or ANTHROPIC_BASE_URL env var) and provide the provider's API key via ANTHROPIC_API_KEY.

{
  "summaryProvider": "anthropic",
  "summaryModel": "MiniMax-M2.7",
  "anthropicBaseUrl": "https://api.minimax.io/anthropic"
}

export ANTHROPIC_API_KEY="your-minimax-api-key"

Reasoning models that return ThinkingBlock responses are handled automatically.

Model examples: MiniMax-M2.7, MiniMax-M2.7-highspeed

Cost Comparison

Estimated Monthly Costs

For typical compaction workloads using gpt-4.1-mini:

Compactions are triggered automatically before context compaction (PreCompact hook) and at session end (Stop hook). The extractive fallback runs automatically when no API key is configured: no hard dependency on any LLM provider.

CLI Usage

The lcc CLI provides direct access to vault operations.

# Run compaction manually
lcc summarise --run

# Run compaction for a specific session
lcc summarise --run --session <session-id>

# Check vault status
lcc status

# Search all messages and summaries
lcc grep "auth refactor"

# Show handoff from last session
lcc handoff

# Generate and save a handoff for current session
lcc handoff --generate --session "$CLAUDE_SESSION_ID"

# List recent sessions
lcc sessions

# Expand a summary node
lcc expand sum_abc123def456

# Run dream cycle
lcc dream --run

# Dream for a specific project directory
lcc dream --run --project /path/to/project

Schema

sessions        -- session_id, working_dir, started_at, last_active, handoff_text
messages        -- id, session_id, turn_id, role, content, tool_name, working_dir, timestamp, summarised
summaries       -- id, session_id, content, depth, token_count, created_at, consolidated
summary_sources -- summary_id, source_type, source_id
dream_log       -- id, project_hash, scope, dreamed_at, patterns_found, consolidations, sessions_analyzed, report_path
messages_fts    -- FTS5 index on messages.content
summaries_fts   -- FTS5 index on summaries.content

Uninstall

rm -rf ~/.lossless-code
# Remove hooks from ~/.claude/settings.json manually
# Remove skill: rm -rf ~/.claude/skills/lossless-code

Prior Art and Acknowledgements

lossless-code is a Claude Code adaptation of the Lossless Context Management (LCM) architecture created by Jeff Lehman and the Martian Engineering team. Their lossless-claw plugin for OpenClaw proved that DAG-based context preservation eliminates the information loss problem in long-running AI sessions. lossless-code brings that same architecture to Claude Code.

Additional references:

• ClawMem by yoloshii (hooks architecture patterns)
• Voltropy LCM paper (theoretical foundation)

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feat/your-feature)
3. Write tests for new functionality
4. Ensure tests pass
5. Open a pull request

Roadmap

lossless-code currently supports Claude Code natively. The hook and plugin ecosystem across coding agents is converging fast, and we're tracking compatibility:

MCP works everywhere today. Any agent that supports MCP servers can already use lcc_grep, lcc_expand, lcc_context, lcc_sessions, lcc_handoff, and lcc_status for manual recall. The roadmap above tracks automatic capture via hooks.

Contributions welcome for any of the planned integrations.

Star History

Contributors

Licence

MIT