# Engram

> MCP-native shared memory for AI agents and teams. Engram carries durable knowledge across sessions, tools, repositories, and teammates.

Last updated: 2026-08-31

## What Engram does

- Gives AI agents durable memory across sessions, tools, repositories, and teammates.
- Keeps private workspace and repository knowledge isolated from a source-aware public memory bank.
- Connects through Model Context Protocol (MCP); no application SDK is required.
- Helps agents recover after context compaction, recall prior decisions, reuse lessons from other repositories, store durable discoveries, and report whether recalled knowledge helped.
- Works with Claude Code, Cursor, Codex, Gemini, Antigravity, Copilot, OpenCode, Windsurf, Devin, Cline, Roo Code, Kiro, Amazon Q Developer, Goose, Continue, and other MCP-compatible clients.

## Verified product facts

- Public memory bank: 100,000 source-aware memories.
- Guided skills: 20.
- Supported website languages: 15.
- LoCoMo evidence-retrieval diagnostic (N=1,536): MRR 0.5345, R@50 92.19%, R@200 97.66%.
- LoCoMo answer diagnostic (fixed N=98): Engram top-50 66.33% versus 10.2% with no memory.
- Benchmark results are local, reproducible diagnostics, not official leaderboard scores. Retrieval and answer quality are reported separately.

## Core agent flow

1. Start a meaningful session with engram_start.
2. After context compaction or a resumed session, call engram_rehydrate.
3. Before a non-trivial decision, call engram_recall.
4. Before rebuilding a pattern that may exist in another repository, call engram_cross_pollinate.
5. Store durable discoveries with engram_store; never store secrets or raw customer data.
6. Report whether recalled knowledge helped with engram_report_outcome or engram_reflect.

## Canonical sources

- Home: https://engram.tools/en
- Documentation: https://engram.tools/en/documentation
- Agent guide: https://engram.tools/en/agent
- Machine-readable agent guide: https://engram.tools/agent.txt
- Human setup: https://engram.tools/en/human
- Human product deck: https://engram.tools/en/deck/human
- Agent pitch deck: https://engram.tools/en/deck/agent
- Benchmarks and limitations: https://engram.tools/en/benchmarks
- Real use cases: https://engram.tools/en/use-cases
- Shared memory for AI agents: https://engram.tools/en/ai-agent-memory
- MCP memory server: https://engram.tools/en/mcp-memory-server
- Context compaction recovery: https://engram.tools/en/context-compaction-recovery
- Benchmark methodology: https://engram.tools/en/benchmark-methodology
- Claude Code integration: https://engram.tools/en/integrations/claude-code
- Codex integration: https://engram.tools/en/integrations/codex
- Gemini integration: https://engram.tools/en/integrations/gemini
- OpenCode integration: https://engram.tools/en/integrations/opencode
- Mem0 comparison: https://engram.tools/en/compare/mem0
- LangMem comparison: https://engram.tools/en/compare/langmem
- AgentMemory comparison: https://engram.tools/en/compare/agentmemory
- Full machine-readable context: https://engram.tools/llms-full.txt
- Sitemap: https://engram.tools/sitemap.xml