Project Memory

Durable, evidence-backed memory for coding agents.

Project Memory gives every coding agent a single, trustworthy knowledge base for your repository — so it stops re-learning the same facts and writing conflicting "memory" files.

Discover → Verify → Classify → Compound → Reconstruct → Single Source of Truth

Code tells agents what exists. Project Memory helps them remember why — and reconciles every place that already tried to write it down.

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Table of Contents

• Why it matters
• Quick Start
• Supported Platforms
• Engineering Memory Agent (EMA)
  • Core Capabilities
  • CLI Tooling
  • Model Context Protocol (MCP) Server
  • DSH Plugin & Slash Commands
• DeepSeek Harness (DSH) Plugin
• How to Use
• What It Does
• Skills
• Knowledge Architecture
• Prerequisites & Limitations
• Testing
• Architecture & Research Documentation
• License

Why it matters

Without reconciled memory, every agent (and every AI tool) re-discovers the architecture, re-tries the rejected approach, and writes its own slightly different conclusion. You end up with several disagreeing "sources of truth."

With Project Memory, that knowledge is discovered, verified against actual code, and rebuilt into one canonical memory that the next agent loads from a single trustworthy place.

Quick Start

One-Line Universal Install (Recommended)

Install Project Memory Agent (EMA) across all platforms with DeepSeek Harness prioritized:

Linux / macOS / WSL:

curl -fsSL https://raw.githubusercontent.com/LoveDoLove/Project-Memory-Agent/main/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/LoveDoLove/Project-Memory-Agent/main/install.ps1 | iex

The installer automatically:

1. Detects DeepSeek Harness (DSH) and mounts the @lovedolove/dsh-project-memory plugin to your active profile.
2. Installs the global ema CLI to your PATH (~/.local/bin/ema or %USERPROFILE%\.local\bin\ema.cmd).
3. Mirrors the 8 core Project Memory skills to ~/.agents/skills/, Claude Code (~/.claude/skills), and OpenCode.
4. Registers /ema and /project-memory slash commands in DeepSeek Harness chat.

Inside DeepSeek Harness (DSH) — Zero Setup Required

DeepSeek Harness users can install the complete suite in a single command using DSH's native plugin manager (matching the official dsh-univer-office system bundled skills standard):

# Add to your active DSH profile (e.g. 'web')
dsh plugin --profile web add @lovedolove/dsh-project-memory

This single command automatically:

• Registers all 8 Project Memory skills as native system bundled skills (source: 'bundled', rank: 600) via ctx.skills.registerProvider — accessible immediately in every session with zero file copying.
• Registers the /ema and /project-memory slash commands.
• Mounts the 6 Stdio MCP tools (ema_recall, ema_distill, ema_add, ema_context, ema_validate, ema_promote).
• Activates the offline 384-d vector embedder and sqlite-vec RRF hybrid retrieval.

Once installed, simply open any DSH session and type:

/ema

or

/project-memory

The agent will automatically orchestrate memory recall, verification, and compounding.

Supported Platforms

Engineering Memory Agent (EMA)

Engineering Memory Agent (EMA) extends Project Memory with a formal four-dimensional lifecycle model, authoritative 6-stage retrieval, six-boundary hard isolation, grounded evidence anchors, candidate queueing, and a standalone stdio MCP server.

Core Capabilities

• Interactive Visual Memory Graph:
  • Live full-screen dark-theme force-directed knowledge graph (ema ui).
  • Real-time multi-dimensional node coloring: Emerald (#10b981) for Canonical/Verified, Amber (#f59e0b) for Quarantined Candidates, Crimson (#ef4444) with animated glowing rings for Contradictions & Conflicts, Slate (#64748b) for Historical/Superseded.
  • Interactive slide-over inspection drawer with grounded evidence anchors, Markdown previews, and one-click promotion actions.
• Built-in Vector Model & sqlite-vec Hybrid RRF Search:
  • Out-of-the-box 384-dimensional feature-hash & subword n-gram semantic vectorizer (zero network dependencies, zero API keys required).
  • Reciprocal Rank Fusion (RRF) combining FTS5 lexical precision with KNN cosine distance in sqlite-vec.
  • Extensible to remote neural embeddings (EMA_EMBEDDING_API_KEY, OPENAI_API_KEY, EMA_EMBEDDING_URL).
• Autonomous Distillation & Ingestion Engine:
  • One-click memory capture from Git diffs, commits, or task summaries (ema ingest).
  • Automatically parses modified files, hunk line ranges, and symbols into grounded evidence anchors (ema://evidence/...#sym:..., #line:...).
  • Strictly adheres to the Quarantined Candidate invariant: extracted knowledge enters .ema/candidates/ with authority_level: Candidate and validation_state: Unreviewed (never directly modifying canonical docs/).
• Four Orthogonal Lifecycle Dimensions:
  • status: Draft | Current | Deprecated | Superseded | Historical | Abandoned
  • validation_state: Unreviewed | Needs Review | Potentially Stale | Verified | Invalid | Quarantined
  • authority_level: Candidate | Derived | Canonical
  • confidence: High | Medium | Low
• Authoritative 6-Stage Retrieval Pipeline: Stage 1: Authorization Gate → Stage 2: Candidate Retrieval (Hybrid RRF) → Stage 3: Lifecycle Filtering → Stage 4: Multi-Dimensional Ranking → Stage 5: Contradiction Surfacing → Stage 6: Context Assembly.
• Zero Silent Contradiction Resolution: Contradicting knowledge units are surfaced explicitly with warning banners; neither is silently suppressed.
• Candidate Isolation & Multi-Repo Promotion: Unreviewed knowledge is quarantined in .ema/candidates/ with authority_level: Candidate. Promoting to global scope requires $\ge 2$ independent repository sources.
• Evidence Anchoring: Grounded via immutable URIs: ema://evidence/<repo-id>/<git-ref>/<file-path>#<logical-anchor>.
• 6-Boundary Hard Isolation: Storage, Index, Query (zero-knowledge), Authorization (404 No-Probe), Promotion, and Export boundaries.
• Canonical Markdown Ownership: Markdown files in docs/ remain the single source of truth. The SQLite + sqlite-vec index (.ema/index.db) is derived and fully rebuildable.

CLI Tooling

The EMA CLI is available directly via Node.js in bin/ema-cli.mjs (or globally via ema / npx ema):

# Launch the interactive Visual Memory Graph Web UI
node bin/ema-cli.mjs ui --port 3888

# Auto-distill and capture knowledge from working tree git changes into candidate queue
node bin/ema-cli.mjs ingest --git

# Auto-distill from a specific patch or diff file
node bin/ema-cli.mjs ingest --diff ./fix.patch

# Distill engineering knowledge from an explanation or incident text
node bin/ema-cli.mjs ingest --text "Fixed race condition in token refresh by using mutex"

# Review and promote an approved candidate to project canonical storage
node bin/ema-cli.mjs promote <candidate_id> project

# Check database health, schema version, unit counts, and candidate queue
node bin/ema-cli.mjs status

# Validate all repository knowledge documents against EKU Schema v2
node bin/ema-cli.mjs verify docs

# Execute authoritative 6-stage hybrid memory recall (FTS5 + sqlite-vec RRF)
node bin/ema-cli.mjs recall "cordis plugin injection"

# Rebuild the derived SQLite + sqlite-vec vector index from canonical Markdown
node bin/ema-cli.mjs index docs

# View CLI help and usage options
node bin/ema-cli.mjs help

Model Context Protocol (MCP) Server

EMA includes a standalone stdio JSON-RPC 2.0 MCP server:

# Start the MCP server over stdio
node bin/ema-mcp.mjs

Available tools registered by the MCP server:

• ema_recall: Authoritative hybrid recall across authorized scopes (query, limit, scope, actor).
• ema_distill: Automatically distill knowledge from Git diffs or text into the candidate queue with grounded evidence anchors.
• ema_add: Submit new candidate knowledge manually into .ema/candidates/.
• ema_context: Assemble budget-capped static context injection for prompts.
• ema_validate: Update validation state and record validation audit trail.
• ema_promote: Promote knowledge units to broader scopes (project, workspace, global).

DSH Plugin & Slash Commands

When mounted in DeepSeek Harness, the plugin registers both /project-memory and /ema:

/ema recall <query>        # Authoritative recall with provenance headers
/ema status                # Memory health, index stats, and candidate queue
/ema verify                # Validate all documentation units
/ema promote <id> <scope>  # Promotes a validated candidate EKU
/ema                       # Interactive memory inspection & compounding workflow

DeepSeek Harness (DSH) Plugin

The project ships as a native DSH bundle plugin (@lovedolove/dsh-project-memory) that mounts all 8 Project Memory skills into any DSH profile as native system bundled skills. It also registers cbm_* tools (codebase-memory bridge) when codebase-memory-mcp is available, injects a first-time-init hint when no AGENTS.md is found, registers the /project-memory and /ema slash commands, and provides full MCP tooling and Visual Graph UI.

Install

# Interactive — shows the commands for your profile
.\install.ps1 -Target dsh

# Explicitly target your web profile
.\install.ps1 -Target dsh -DshProfile web

The installer will print the plugin add command. Run it manually:

dsh plugin --profile web add @lovedolove/dsh-project-memory

Usage

One-Click Command (Recommended)

After install, the one-click command is available in any DSH session:

/project-memory

This automatically detects the repository's Project Memory state and runs the appropriate workflow — no Agent selection, no skill picking, no mode arguments.

Optional --trace flag enables retrieval tracing for debugging:

/project-memory --trace

Advanced: Direct Agent Invocation

For full control, dispatch the orchestrator as a subagent:

use_agent(agent: "project-memory", prompt: "compound my last task")

Plugin internals: the npm package (@lovedolove/dsh-project-memory) uses a single-row Cordis patch that loads the runtime glue (dsh/plugin.mjs), which dynamically registers skills as native system bundled skills, registers the /project-memory and /ema slash commands, and injects a first-time-init hint when no AGENTS.md is found.

How to Use

Slash Command (DSH only)

/project-memory

See the DSH Plugin section for installation and full usage.

Agent Preset (All platforms)

Invoke the orchestrator agent directly in any supported agent:

@project-memory

It discovers existing knowledge, inspects the repository, verifies claims against real evidence, and reports what to keep, change, merge, or remove — across every origin tool, not just its own files.

Typical tasks: audit this repo, build memory for a repo that already has docs, reconcile conflicting AGENTS.md / CLAUDE.md, compound lessons after this feature.

What It Does

Skills

Eight specialized skills instead of one huge prompt:

The orchestrator loads these progressively — you rarely invoke one directly. It can also delegate to codebase-memory (read-only code graph) and cavecrew-builder (bounded edits). Every rule has exactly one canonical owner across the agent and skills; skills reference each other instead of duplicating, so guidance can't drift apart.

Knowledge Architecture

AGENTS.md is the single entry point; everything else is referenced, not duplicated.

AGENTS.md
   +-- docs/architecture.md      -> system design, DSH plugin internals
   +-- docs/solutions/           -> diagnosed fix patterns
   +-- docs/lessons/             -> reusable engineering principles
   +-- skills/<name>/SKILL.md    -> detailed skill instructions
   +-- templates/                -> document templates

Domains are a pattern, not a mandatory scaffold — only create what holds verified knowledge.

Templates

Templates provide starting points for new knowledge documents:

• templates/TEMPLATE.md — Solution document with dual-track schema (Bug + Knowledge)
• templates/CONCEPTS.md — Project vocabulary with accretion/seeding/mutations
• templates/SOLUTIONS.md — Index template for tracking all Solutions
• templates/schema.yaml — Canonical frontmatter contract

Reference Files

Detailed guidance lives in each skill:

• skills/knowledge-compounding/references/ — Grounding validation, durable bar, quality constraints, session history, auto-memory

Prerequisites & Limitations

• Prerequisites: Node.js >= 18.0.0 (Node.js 20+ or 24 recommended).
• Runtime Dependencies: better-sqlite3 and sqlite-vec in root package.json.
• Disposable Cache: The derived database .ema/index.db is purely a local cache; deleting it does not destroy any canonical knowledge.
• Current Vector Status: Offline 384-d feature-hash embedder and sqlite-vec KNN hybrid RRF retrieval are fully active.

Testing

# Run the complete EMA unit, integration, and security test suite (185 tests)
npm test

# Run installer test suite
Invoke-Pester ./install.tests.ps1

12 tests cover installer targets, the no-double-load rule, and a guardrail keeping the 8 skills, their manifest, and both agent files in sync. The EMA suite covers 172 tests across schema validation, evidence grounding, derived SQLite indexing, fail-closed policy, 6-stage retrieval, promotion pipelines, DSH hooks, and stdio MCP server.

Architecture & Research Documentation

For detailed architecture specifications and the phased implementation reports:

• EMA Development Plan — 9-phase evolution roadmap and milestones
• EMA Architecture Blueprint — Deep architectural specification
• Architecture Approval Candidate — Invariants, governance, and security model
• Change Audit Log — Chronological ledger of all memory system changes
• Phase 1–9 Reports — Individual phase implementation reports and verification reviews

License

MIT — see LICENSE.