# Send Engram to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "engram", "name": "Engram", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/Dannydvm/engram", "canonicalUrl": "https://clawhub.ai/Dannydvm/engram", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/engram", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=engram", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "engram", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-02T22:04:16.267Z", "expiresAt": "2026-05-09T22:04:16.267Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=engram", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=engram", "contentDisposition": "attachment; filename=\"engram-0.2.1.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "engram" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/engram" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/engram", "downloadUrl": "https://openagent3.xyz/downloads/engram", "agentUrl": "https://openagent3.xyz/skills/engram/agent", "manifestUrl": "https://openagent3.xyz/skills/engram/agent.json", "briefUrl": "https://openagent3.xyz/skills/engram/agent.md" } } ``` ## Documentation ### Engram: Persistent Memory for AI Agents Engram gives you durable semantic memory that survives sessions, compaction, crashes. All local, no cloud, no token cost. ### Boot Sequence (MANDATORY) On every session start, run: engram search "" --limit 10 Example: engram search "client onboarding status churn risk" --limit 10 This recalls relevant memories from previous sessions before you start work. ### Storing Memories 5 memory types: fact | decision | preference | event | relationship # Facts — objective information engram add "API rate limit is 100 req/min" --type fact --tags api,limits # Decisions — choices made engram add "We chose PostgreSQL over MongoDB for better ACID" --type decision --tags database # Preferences — user/client likes/dislikes engram add "Dr. Steph prefers text over calls" --type preference --tags dr-steph,communication # Events — milestones, dates engram add "Launched v2.0 on January 15, 2026" --type event --tags launch,milestone # Relationships — people, roles, connections engram add "Mia is client manager, reports to Danny" --type relationship --tags team,roles When to store: Client status changes (churn risk, upsell opportunity, complaints) Important decisions made about projects/clients Facts learned during work (credentials, preferences, dates) Milestones completed (onboarding steps, launches) ### Searching Semantic search (finds meaning, not just keywords): # Basic search engram search "database choice" --limit 5 # Filter by type engram search "user preferences" --type preference --limit 10 # Filter by agent (see only your memories + global) engram search "project status" --agent theo --limit 10 ### Context-Aware Recall Recall ranks by: semantic similarity × recency × salience × access frequency engram recall "Setting up new client deployment" --limit 10 Better than search when you need the most relevant memories for a specific context. ### Memory Relationships 7 relation types: related_to | supports | contradicts | caused_by | supersedes | part_of | references # Manual relation engram relate --type supports # Auto-detect relations via semantic similarity engram auto-relate # List relations for a memory engram relations Relations boost recall scoring — well-connected memories rank higher. ### Auto-Extract from Text Ingest extracts memories from raw text (rules-based by default, optionally LLM): # From stdin echo "Mia confirmed client is happy. We decided to upsell SEO." | engram ingest # From command engram extract "Sarah joined as CTO last Tuesday. Prefers async communication." Uses memory types, tags, confidence scoring automatically. ### Management # Stats (memory count, types, storage size) engram stats # Export backup engram export -o backup.json # Import backup engram import backup.json # View specific memory engram get # Soft delete (preserves for audit) engram forget --reason "outdated" # Apply decay manually (usually runs daily automatically) engram decay ### Memory Decay Inspired by biological memory: Every memory has salience (0.0 → 1.0) Daily decay: salience *= 0.99 (configurable) Accessing a memory boosts salience Low-salience memories fade from search results Nothing deleted — archived memories can be recovered ### Agent Scoping 4 scope levels: global → agent → user → session By default: Agents see their own memories + global memories --agent filters to specific agent Scope isolation prevents memory bleed between agents ### REST API Server runs at http://localhost:3400 (start with engram serve). # Add memory curl -X POST http://localhost:3400/api/memories \\ -H "Content-Type: application/json" \\ -d '{"content": "...", "type": "fact", "tags": ["x","y"]}' # Search curl "http://localhost:3400/api/memories/search?q=query&limit=5" # Recall with context curl -X POST http://localhost:3400/api/recall \\ -H "Content-Type: application/json" \\ -d '{"context": "...", "limit": 10}' # Stats curl http://localhost:3400/api/stats Dashboard: http://localhost:3400/dashboard (visual search, browse, delete, export) ### MCP Integration Engram works as an MCP server. Add to your MCP client config: { "mcpServers": { "engram": { "command": "engram-mcp" } } } MCP tools: engram_add, engram_search, engram_recall, engram_forget ### Configuration ~/.engram/config.yaml: storage: path: ~/.engram embeddings: provider: ollama # or "openai" model: nomic-embed-text ollama_url: http://localhost:11434 server: port: 3400 host: localhost decay: enabled: true rate: 0.99 # 1% decay per day archive_threshold: 0.1 dedup: enabled: true threshold: 0.95 # cosine similarity for dedup ### Best Practices Boot with recall — Always engram search "" --limit 10 at session start Type everything — Use correct memory types for better recall ranking Tag generously — Tags enable filtering and cross-referencing Ingest conversations — Use engram ingest after important exchanges Let decay work — Don't store trivial facts; let important memories naturally stay salient Use relations — auto-relate after adding interconnected memories Scope by agent — Keep agent memories separate for clean context ### Troubleshooting Server not running? engram serve & # or install as daemon: see ~/.engram/daemon/install.sh Embeddings failing? ollama pull nomic-embed-text curl http://localhost:11434/api/tags # verify Ollama running Want to reset? rm -rf ~/.engram/memories.db ~/.engram/vectors.lance engram serve # rebuilds from scratch Created by: Danny Veiga (@dannyveigatx) Source: https://github.com/Dannydvm/engram-memory Docs: https://github.com/Dannydvm/engram-memory/blob/main/README.md ## Trust - Source: tencent - Verification: Indexed source record - Publisher: Dannydvm - Version: 0.2.1 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-02T22:04:16.267Z - Expires at: 2026-05-09T22:04:16.267Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/engram) - [Send to Agent page](https://openagent3.xyz/skills/engram/agent) - [JSON manifest](https://openagent3.xyz/skills/engram/agent.json) - [Markdown brief](https://openagent3.xyz/skills/engram/agent.md) - [Download page](https://openagent3.xyz/downloads/engram)