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याद Yaad

Give your coding agent persistent memory.

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

License: MIT Go Tests CI Discord

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.

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.

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- Auth subgraph outdated: src/middleware/auth.ts modified 2h ago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.

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.

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"

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

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

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.

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

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

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

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

Community

MIT © 2026 GrayCodeAI

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