Built for every AI agent · ingests every data source
ClaudeGPT-4CursorCopilotAiderContinueCodyPhindAnthropic MCPOpenAIGitHubGitLabLinearNotionSlackVercelSentryPagerDutyDataDogStripeClaudeGPT-4CursorCopilotAiderContinueCodyPhindAnthropic MCPOpenAIGitHubGitLabLinearNotionSlackVercelSentryPagerDutyDataDogStripe
01 · The Problem

AI agents have no memory.

Today's AI coding tools are brilliant — but amnesiac. They can't remember what happened yesterday, last month, or last year.

AI forgets architecture

Every new session starts blank. Your AI assistant has no memory of why services were split, why databases were chosen, or what tradeoffs were made.

context.lost = trueerr · 0x2F

Context disappears

Between chat sessions, sprints, and team members — critical engineering context evaporates. The 'why' behind every decision is lost forever.

context.lost = trueerr · 0x3C

Knowledge is scattered

Architecture lives in Notion. Bugs live in Jira. Decisions live in Slack. No AI can reason across this fragmented landscape.

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History becomes invisible

After 6 months, no one remembers why that refactor happened, what broke in the incident, or which PR introduced the regression.

context.lost = trueerr · 0x56

87%

of engineering context lost within 3 months

4.2h

wasted re-discovering decisions per week

23%

of bugs caused by forgotten context

$0

persistent memory in existing AI tools

02 · How It Works

Three steps to infinite memory.

From raw repository data to AI-ready organizational memory in minutes — fully automated.

STEP 01

INGEST

Connect your engineering universe

LawdMemory ingests every signal from your development workflow — from git commits to incident postmortems.

GitHub repositories & PRs
Issues & discussions
Incident reports
Internal docs & wikis
terminal01
$ lawdmemory connect github \
    --org acme-corp \
    --repos "*" \
    --history 365d

▸ scanning 48 repositories ...
✓ ingested 142,418 memories

STEP 02

UNDERSTAND

Build the knowledge graph

AI extracts semantic meaning, temporal relationships, and causal chains from every engineering signal.

Knowledge graph generation
Semantic embedding
Temporal memory indexing
Decision extraction
terminal02
// extracted automatically
{
  type:    "decision",
  subject: "Redis",
  reason:  "Session bottleneck",
  refs:    ["INC-047", "a3f92b"],
  impact:  "↑ 4x throughput"
}

STEP 03

RECALL

Instant engineering intelligence

AI agents get instant access to historical context, architectural decisions, and relevant incidents — in real time.

Semantic memory search
Context injection for AI
Historical reasoning
Graph traversal queries
terminal03
const memory = await lawdmemory.recall({
  query:   "why was Redis added?",
  depth:   3,
  include: ["commits", "incidents"]
})

▸ retrieved in 3ms · 96% recall
03 · Memory Graph

Your knowledge as a living graph.

Every commit, incident, decision, and AI agent interaction becomes a node in your persistent organizational memory.

memory-graph ~ live view
synced
Repository
Service
Commit
Incident
AI Agent
Decision
04 · Query Demo

Ask anything about your engineering history.

LawdMemory retrieves temporal, causal, and semantic context in milliseconds.

Sample Queries

lawdmemory ~ query terminal
... claude
Why was Redis introduced?
05 · Features

Everything your AI team needs to remember.

A complete memory layer that makes your engineering organization smarter over time.

Temporal Memory Engine

v1.0

Track how architectural decisions, code patterns, and team knowledge evolve over time with millisecond precision.

Semantic Code Intelligence

v1.0

Understand the meaning behind your codebase — not just the syntax. Map intent, patterns, and implicit contracts.

Architecture Timeline

v1.0

Visualize how your system architecture evolved — from monolith to microservices, from single DB to distributed.

Incident Recall

v1.0

Every incident, postmortem, and fix becomes searchable memory. Never fight the same fire twice.

Agent Context Injection

v1.1

Automatically inject relevant historical context into your AI agents before every task — no manual prompting required.

Repository Knowledge Graph

v1.0

Interactive knowledge graph connecting repositories, services, teams, and decisions in a unified visual layer.

Persistent AI Context

v1.1

AI agents retain context between sessions, across repositories, and even across teams — indefinitely.

Engineering Decision Tracking

v1.2

Automatically extract and index every significant engineering decision from PRs, issues, and docs.

06 · Analytics

Real-time memory intelligence.

Watch your organizational knowledge grow, monitor retrieval performance, and track agent collaboration.

Knowledge Growth

total memories indexed / month

Repository Complexity

knowledge nodes & edges per repo

nodes
edges

Memory Retrieval Latency

p50 / p95 / p99 (ms)

p50
p95
p99

Agent Collaboration Activity

memory queries by agent / week

claude
copilot
custom
07 · Open API

Build on LawdMemory.

Fully open API. Connect any agent, any data source, any workflow.

mcp_integration.ts
// Claude agent with LawdMemory
import { LawdMemory } from "@lawdmemory/mcp";

const mcp = new LawdMemory({ org: "acme-corp" });

const agent = new ClaudeAgent({
  tools:  mcp.tools,    // 25 memory tools
  memory: mcp.recall,   // auto-inject context
});

await agent.run("Why is Redis in the stack?");
MIT LicenseSelf-hostableOpenAPI 3.1GraphQLMCP v1.0TypeScript SDKPython SDKREST API
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