याद Yaad

Give your coding agent persistent memory.

One config line. Works with any MCP agent. Zero setup.

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The Problem

Every coding agent forgets everything when the session ends.

Session 1: You explain your stack, conventions, architecture. Agent writes great code.
Session 2: Agent has forgotten everything. You start over.
Session 50: You've wasted hours re-teaching the same context.

The Fix

Add one line to your agent's MCP config:

{ "mcpServers": { "yaad": { "command": "yaad", "args": ["mcp"] } } }

Now your agent remembers everything — across sessions, across models, across projects.

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30-Second Setup

# Install
go install github.com/GrayCodeAI/yaad/cmd/yaad@latest

# Add to your project
cd your-project && yaad init

# Connect your agent (generates .mcp.json + hooks)
yaad setup

That's it. Your agent now has persistent, graph-native memory.

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What Happens Next

Your agent starts a session → Yaad injects context from previous sessions:

## Project Memory (Yaad)

### Conventions (always follow)
- Use `jose` library, not `jsonwebtoken` (Edge compatibility)
- Named exports only, no default exports
- Run `pnpm test --coverage` before committing

### Active Tasks
- ✓ JWT token issuance endpoint
- → Rate limiting on /auth/token (in progress)

### ⚠ Stale Warnings
- Auth subgraph outdated: src/middleware/auth.ts modified 2h ago

### Previous Session
- Implemented rate limiting skeleton, hit NATS backpressure issue

Your agent works → stores decisions, bugs, conventions automatically.
Session ends → Yaad compresses and links everything in a memory graph.
Next session → picks up exactly where you left off. Zero re-explaining.

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How It Works

Yaad is a memory layer — it doesn't call LLMs. Your agent handles the LLM. Yaad handles memory.

Your Agent                          Yaad
   │                                  │
   ├─ starts session ──────────────▶  │ returns hot-tier context (~2K tokens)
   │                                  │
   ├─ needs context ───────────────▶  │ graph-aware search (BM25 + vector + graph + temporal)
   │  "auth middleware"               │ returns: decisions + conventions + bugs + specs
   │                                  │
   ├─ learns something ────────────▶  │ stores node, extracts entities, links edges
   │  "Use RS256 for JWT"            │ auto-detects: file refs, libraries, functions
   │                                  │
   ├─ ends session ────────────────▶  │ compresses → summary node → links to graph
   │                                  │
   └─ next session ────────────────▶  │ picks up from summary. zero re-explaining.

Under the Hood

Relaxed DAG — memories are nodes, relationships are edges:

[decision: "Use RS256"] ──led_to──▶ [convention: "Always RS256"]
        │                                     │
        │ led_to                              │ touches
        ▼                                     ▼
[spec: "Auth subsystem"] ◀──part_of── [file: src/middleware/auth.ts]
        │
        │ relates_to
        ▼
[bug: "Token refresh race"] ──supersedes──▶ [bug: "Token expiry (FIXED)"]

Intent-aware retrieval — "why" queries traverse causal edges, "when" queries traverse temporal edges:

"why did we choose NATS?"  → Intent: Why  → boost caused_by, led_to edges
"when did we fix auth?"    → Intent: When → boost temporal backbone
"what is the auth spec?"   → Intent: What → boost spec, part_of edges

4-path search — BM25 + vector + graph (intent-aware) + temporal recency, fused with RRF.

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Memory Types

Type	What it stores	Example
convention	Coding rules & patterns	"Use jose not jsonwebtoken"
decision	Architecture choices + why	"Chose NATS for backpressure"
bug	Symptom → Cause → Fix	"Token race → use mutex"
spec	How a subsystem works	"Auth: RS256 JWT with jose"
task	Done / in-progress / blocked	"✓ auth, → rate limiting"
skill	Reusable step sequences	"Deploy: test → build → fly"
preference	User coding style	"Functional style, tabs"
file	File/module anchor	"src/middleware/auth.ts"
entity	Auto-extracted entity	"jose", "PostgreSQL"

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Key Features

Graph-Native Memory (Relaxed DAG)

Not a flat list of memories. A directed graph with 8 edge types:

• Causal (acyclic): led_to, supersedes, caused_by, learned_in, part_of
• Relational (cycles OK): relates_to, depends_on, touches

Enables: subgraph extraction, impact analysis, causal chain traversal.

Intent-Aware 4-Path Search

Based on MAGMA (arxiv:2601.03236):

1. BM25 (FTS5) — keyword matching
2. Vector (optional) — semantic similarity
3. Graph (intent-aware BFS) — edge weights boosted by query intent
4. Temporal — recency-aware for "when" queries

Fused with Reciprocal Rank Fusion (RRF).

Dual-Stream Ingestion

Based on MAGMA + GAM research:

• Fast path (sync): store node + temporal edge, return in <1ms
• Slow path (async goroutine): infer causal edges, link entities

Agent is never blocked waiting for memory processing.

Git-Aware Staleness

When source files change, Yaad walks the graph backwards to flag stale subgraphs:

"Auth subgraph may be stale: src/auth.ts modified 2h ago. Affected: [decision: RS256], [convention: jose], [bug: token refresh]"

Impact Analysis

"What memories break if I change schema.sql?" → reverse graph traversal → "3 decisions + 2 specs + 1 convention affected"

Auto-Decay & Compaction

• Half-life decay: unused memories fade automatically
• Compaction: low-confidence memories merge into summaries
• Pinned memories never decay (core architecture decisions, deploy process)
• Auto-decay runs on every session start — zero maintenance

Privacy & Security

• API keys, tokens, secrets auto-stripped on ingest (regex + entropy detection)
• Localhost-only binding (127.0.0.1)
• HTTPS with auto self-signed cert generation
• All data stays local (SQLite, your machine)
• No LLM API calls — Yaad never sends your code anywhere

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MCP Tools (23 tools)

Your agent gets these tools automatically via yaad mcp:

Tool	What it does
yaad_remember	Store a memory (convention, decision, bug, spec, task, skill, preference)
yaad_recall	Graph-aware search with intent classification
yaad_hybrid_recall	4-path search: BM25 + vector + graph + temporal
yaad_context	Get hot-tier context for session injection
yaad_link	Create typed edge between memories
yaad_forget	Archive a memory (sets confidence to 0)
yaad_feedback	Approve / edit / discard a memory
yaad_pin	Pin/unpin a memory (pinned = always in context)
yaad_stale	Find memories invalidated by git changes
yaad_proactive	Predict what context the agent needs next
yaad_compact	Merge low-confidence memories into summaries
yaad_mental_model	Auto-generated project summary
yaad_skill_store	Save a reusable step sequence
yaad_skill_get	Retrieve and replay a skill
yaad_session_recap	Summary of the previous session
yaad_subgraph	Extract neighborhood around a memory
yaad_impact	What memories are affected by a file change?
yaad_status	Graph stats (nodes, edges, sessions)
yaad_decay	Manually trigger confidence decay
yaad_gc	Garbage collect archived memories
yaad_embed	Generate vector embedding for a node
yaad_export	Export graph as JSON/Markdown/Obsidian
yaad_import	Import graph from JSON

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│              YOUR CODING AGENT                                   │
│  Hawk · Claude Code · Cursor · Gemini CLI · Any MCP Agent       │
└──────┬───────────────┬──────────────────────────────────────────┘
       │ MCP (stdio)   │ REST/HTTPS (127.0.0.1:3456)
       ▼               ▼
┌─────────────────────────────────────────────────────────────────┐
│                       YAAD                                      │
│  Memory Engine · Graph Engine · 4-Path Search · Dual-Stream     │
├─────────────────────────────────────────────────────────────────┤
│  SQLite (WAL mode) · FTS5 · Embeddings (optional)              │
└─────────────────────────────────────────────────────────────────┘

Single binary. Zero dependencies. Pure Go. No CGO. No Docker. No cloud.

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CLI Commands

yaad init              # Initialize .yaad/ in current project
yaad setup             # Configure MCP + hooks for your agent
yaad serve             # Start REST API server
yaad mcp               # Start MCP server on stdio (used by agents)

yaad remember "..."    # Store a memory
yaad recall "..."      # Search memories
yaad link A B type     # Create edge between nodes
yaad status            # Show graph stats
yaad doctor            # Diagnose setup issues

yaad decay             # Apply confidence decay
yaad gc                # Garbage collect low-confidence nodes
yaad bench             # Run retrieval benchmark

yaad export-json       # Export as JSON
yaad export-md         # Export as Markdown
yaad export-obsidian   # Export as Obsidian vault
yaad import-json       # Import from JSON

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Configuration

Generated at .yaad/config.toml:

[server]
port = 3456
host = "127.0.0.1"

[memory]
hot_token_budget = 800
warm_token_budget = 800
max_memories = 10000

[search]
bm25_weight = 0.5
vector_weight = 0.5
default_limit = 10

[decay]
enabled = true
half_life_days = 30
min_confidence = 0.1
boost_on_access = 0.2

[git]
watch = true
auto_stale = true

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Development

git clone https://github.com/GrayCodeAI/yaad.git
cd yaad
make build           # Build binary
make test            # Run all tests
make install         # Install to $GOPATH/bin

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Documentation

Doc	What
ARCHITECTURE.md	Technical architecture
COMPARISON.md	vs Mem0, Letta, Engram, agentmemory
CONTRIBUTING.md	How to contribute
CHANGELOG.md	Release notes
openapi.yaml	OpenAPI spec

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Community

• Discord: GrayCodeAI
• Issues: GitHub Issues
• Contributing: CONTRIBUTING.md

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MIT © 2026 GrayCodeAI

yaad (याद) — Hindi/Urdu for memory, remembrance