Real-world use cases
See how teams use Engram to ship better code, faster.
For team leads & engineering managers
Where Engram realistically speeds up engineering work: agent onboarding, repeated incidents, repo conventions, and multi-tool teams.
Onboarding a New Agent to a Repo
A new developer or a fresh agent enters the repo. Engram does not replace docs, but it gives the agent project conventions, known gotchas, and architecture decisions before it starts editing.
The agent guesses from the current files and misses past decisions or edge cases.
The agent loads repo memory first, makes fewer wrong suggestions, and shortens the review loop.
$ engram_get_context({ repo: "main-api" }) -> 200 memories loaded
Midnight Production Hotfix
Production is down at 2am. You open your coding agent. Engram recalls: "Last time auth broke, it was the JWT expiry config in .env.production." Fixed in 3 minutes.
2am debugging, searching Slack history, guessing
engram_recall -> exact fix from last incident. 3 minutes, not 30.
$ engram_recall({ query: "auth service down" }) -> "JWT expiry in .env.production, set to 24h not 1h"
Cross-Team Convention Sharing
Frontend team adopts a new API pattern. Backend team's agents learn it through Engram. No meeting scheduled, no wiki updated.
Conventions documented in wiki that nobody reads. Teams diverge.
One team stores a convention. Every team's agents follow it.
$ engram_store({ type: "CONVENTION", content: "API responses use { data, error, meta } envelope" })
Post-Incident Learning
Database goes down. Root cause: connection pool exhaustion. The fix is stored in Engram. Next time any agent encounters pool issues, it recalls the fix instantly.
Post-mortem doc written, filed, forgotten.
Fix stored as GOTCHA. Every agent knows it forever.
$ engram_store({ type: "GOTCHA", content: "Pool exhaustion: max 20 connections, use pgbouncer" })
Multi-Tool Consistency
Alice uses Claude Code, Bob uses Cursor, Charlie uses Gemini or Antigravity. Same codebase. Engram ensures all agents follow the same conventions, regardless of which AI model powers them.
Each developer's AI suggests different patterns. Code reviews catch inconsistencies.
All agents recall the same conventions. Consistency without enforcement.
$ engram_recall({ query: "code conventions" }) -> 12 CONVENTION memories loaded
Machine-readable examples
Copy-paste patterns for AI coding agents. Each example shows the MCP tool call and expected response.
Session Bootstrapping
You just started a new session. Before writing any code, load context.
engram_get_context({
repo: "api-backend",
task: "add payment endpoint"
})15 memories loaded (conventions: snake_case DB, gotchas: Stripe webhook idempotency...)
Pre-Decision Check
You are about to choose between REST and GraphQL. Check team memory first.
engram_recall({
query: "REST vs GraphQL decision"
})ARCHITECTURE memory found: "REST for external APIs, GraphQL for internal dashboard. Decision made 2024-06."
Bug Fix Documentation
You just spent 30 minutes debugging. Store the fix so no agent wastes time on this again.
engram_store({
type: "SOLUTION",
content: "TypeError in Prisma 7: use PrismaPg adapter, not raw PrismaClient()",
tags: ["prisma", "typescript"]
})Memory stored successfully. Available to all agents in this workspace.
Feedback Loop
You recalled a memory and it helped. Report it so the system learns.
engram_report_outcome({
memory_id: "mem_xyz",
success: true,
detail: "Migration worked perfectly"
})
engram_report_outcome({
memory_id: "mem_abc",
success: false,
failure_reason: "outdated",
detail: "This was for Prisma 5, we use 7 now"
})Memory stored successfully. Available to all agents in this workspace.
Cross-Model Collaboration
A Cursor agent stored a gotcha yesterday. You are a Claude Code agent. You benefit from it today.
engram_recall({
query: "Neon database"
})GOTCHA (stored by cursor, confidence: 0.82): "Neon drops connections at 10s. Fix: connect_timeout=30"