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Engram Evomap - Long Term AI Memory
by
404-UNKNOW
· GitHub ↗
· v0.1.0
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Install in OpenClaw
/install engram-evomap
Description
The AEIF-based long-term memory hub for AI Agents to prevent repeating bugs.
README (SKILL.md)
🧬 engram — Agent Long-Term Experience Memory Hub
🤖 System Mind
You are equipped with an external long-term memory store based on the AEIF v1.0 (Agent Experience Interchange Format) protocol.
- Consult first: Before tackling a recurring error or a complex problem, search your memory for existing solutions.
- Commit success: After successfully resolving a significant challenge, "geneticize" the experience and store it immediately.
🎯 Automatic Trigger Rules
- Error Interception (Auto-Consult): When detecting signals such as
error,failed,SSL,404,EACCES, ortimeout, the system automatically retrieves and injects[EvoMap Advice]. - Task Distillation (Auto-Commit): After completing a multi-step complex task successfully, trigger
!exp committo preserve the logic for future sessions.
🛠️ Command Set
| Command | Action |
|---|---|
!exp consult '\x3Cproblem>' |
Performs a semantic search for historical solutions. Returns Top-3 matches. |
!exp commit |
Asynchronously distills current session history into a universal AEIF capsule. |
!exp list |
Displays memory statistics and a list of recently stored capsules. |
!exp score \x3Cid> --bad |
Provides negative feedback to a capsule, decreasing its TrustScore. |
📦 Output Specification
- Advice should be injected as a system observation wrapped in
---separators. - Focus on providing actionable
[PATCH],[CONFIG], or[WORKAROUND]steps. - Do not modify user-validated paths unless explicitly requested.
Usage Guidance
Key things to consider before installing:
- This skill will send recent session history to an LLM (via llmClient) for distillation and verification. If your agent uses an external LLM provider, private or sensitive conversation content may be transmitted off‑host.
- Distilled results and seed capsules are persisted to a local SQLite DB (default: data/engram.db) and model files are downloaded to ~/.engram_cache. Review and control these paths if you need to limit data exposure or storage location.
- The included seed capsules contain potentially unsafe/workaround commands (e.g., disabling git SSL verification). The verification system may still surface such insecure advice if trust thresholds are met. Inspect seeds.json and consider removing or editing any risky capsules.
- If you want to reduce risk: disable automatic commits/interception (use the skill only manually), run the skill in an isolated environment, change DB/cache paths to a controlled location, and review/limit the LLM client the skill is allowed to call.
- If you need higher assurance, ask the author for explicit documentation about what is sent to the LLM, an option to disable remote LLM calls (pure local-only mode), and a configuration to opt out of auto-commit/auto-intercept.
Capability Analysis
Type: OpenClaw Skill
Name: engram-evomap
Version: 0.1.0
The engram-evomap bundle is a local long-term memory system for AI agents that uses vector embeddings (@xenova/transformers) and SQLite (better-sqlite3) to store and retrieve solutions to recurring technical issues. The code follows a structured 'Agent Experience Interchange Format' (AEIF) and includes a privacy-preserving distillation process in 'src/core/gene-processor.js' that explicitly instructs the LLM to redact sensitive data like IPs, paths, and usernames. While 'data/seeds/seeds.json' contains a potentially risky suggestion to disable Git SSL verification, it is documented as a workaround with an accompanying security warning, and no evidence of intentional malice or unauthorized data exfiltration was found.
Capability Assessment
Purpose & Capability
Name/description (AEIF long-term memory for agents) match the code and instructions: it vectorizes queries, stores AEIF capsules in SQLite, and provides consult/commit/list commands. Requiring node is appropriate; no unrelated credentials or binaries are requested.
Instruction Scope
SKILL.md tells the agent to auto-consult on error signals and to auto-commit distilled experiences. The actual implementation will send recent session history to an LLM (GeneProcessor.distill / VerificationEngine.selfReflect) and then store resulting capsules (rawPayload) in a local DB. That means potentially sensitive conversation content will be transmitted to whatever LLM client the agent provides and may be persisted locally. The skill does not explicitly declare or highlight this data flow in SKILL.md.
Install Mechanism
There is no install spec in the metadata (instruction-only), but the packaged code depends on @xenova/transformers and at runtime will download a transformer model (all‑MiniLM‑L6‑v2) into ~/.engram_cache. Runtime model downloads and writing a cache directory are notable (network activity and disk writes), but are expected for an offline semantic engine.
Credentials
The skill requests no explicit environment variables or credentials, which is consistent. However, it uses process.env and the agent's provided llmClient to call external LLM(s) for distillation and verification. That implies session content and derived capsules will be released to the agent's LLM provider and stored locally. The skill also writes a DB (data/engram.db) and cache (~/.engram_cache) by default — these file writes are reasonable for a memory store but may hold sensitive data. The seed capsules include advice that lowers security hygiene (e.g., 'git config --global http.sslVerify false').
Persistence & Privilege
always is false; the skill isn't force-enabled. It does persist data (SQLite DB) and cache model files under the user's directories and spawns worker threads. Autonomous invocation is allowed (platform default), which combined with the auto-commit/auto-intercept rules increases the chance that content will be sent to an LLM and stored without an extra explicit user action.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install engram-evomap - After installation, invoke the skill by name or use
/engram-evomap - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
- Removed the file: docs/index.html
- No changes to the skill’s interface, description, or functionality.
v1.0.0
Initial release of engram-evomap: a long-term memory hub for AI agents.
- Introduces AEIF-based memory to prevent repeating bugs.
- Supports commands to consult, commit, and score prior experiences.
- Implements auto-consult on error detection and auto-commit after complex task success.
- Provides actionable advice formatted for easy integration into agent workflows.
Metadata
Frequently Asked Questions
What is Engram Evomap - Long Term AI Memory?
The AEIF-based long-term memory hub for AI Agents to prevent repeating bugs. It is an AI Agent Skill for Claude Code / OpenClaw, with 432 downloads so far.
How do I install Engram Evomap - Long Term AI Memory?
Run "/install engram-evomap" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Engram Evomap - Long Term AI Memory free?
Yes, Engram Evomap - Long Term AI Memory is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Engram Evomap - Long Term AI Memory support?
Engram Evomap - Long Term AI Memory is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Engram Evomap - Long Term AI Memory?
It is built and maintained by 404-UNKNOW (@404-unknow); the current version is v0.1.0.
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