agent-memoryPersistent memory for AI agents to store facts, learn from actions, recall information, and track entities across sessions.
Install via ClawdBot CLI:
clawdbot install Dennis-Da-Menace/agent-memoryPersistent memory system for AI agents. Remember facts, learn from experience, and track entities across sessions.
clawdhub install agent-memoryfrom src.memory import AgentMemory
mem = AgentMemory()
# Remember facts
mem.remember("Important information", tags=["category"])
# Learn from experience
mem.learn(
action="What was done",
context="situation",
outcome="positive", # or "negative"
insight="What was learned"
)
# Recall memories
facts = mem.recall("search query")
lessons = mem.get_lessons(context="topic")
# Track entities
mem.track_entity("Name", "person", {"role": "engineer"})Add to your AGENTS.md or HEARTBEAT.md:
## Memory Protocol
On session start:
1. Load recent lessons: `mem.get_lessons(limit=5)`
2. Check entity context for current task
3. Recall relevant facts
On session end:
1. Extract durable facts from conversation
2. Record any lessons learned
3. Update entity informationDefault: ~/.agent-memory/memory.db
Custom: AgentMemory(db_path="/path/to/memory.db")
Generated Mar 1, 2026
A customer support AI agent uses AgentMemory to recall previous interactions with customers, track their issues, and learn from resolved cases to improve response accuracy. This reduces repeat explanations and personalizes support by remembering customer preferences and history.
In project management, the AI agent tracks project entities like team members and tasks, remembers key decisions and deadlines, and learns from past project outcomes to suggest better workflows. This helps in maintaining context across meetings and avoiding repeated mistakes.
A healthcare AI uses AgentMemory to track patient entities, remember medical histories and treatment plans, and learn from past interactions to provide consistent care advice. This ensures compliance with protocols and improves patient engagement by recalling personal details.
Sales AI agents track leads and clients as entities, remember past conversations and purchase history, and learn from successful sales tactics to optimize future pitches. This enhances lead nurturing and increases conversion rates by providing tailored recommendations.
An educational AI tutor remembers student progress, tracks learning goals as entities, and learns from past lessons to adapt teaching methods. This personalizes learning experiences and helps identify areas where students need more practice based on historical data.
Offer AgentMemory as a cloud-based service with tiered pricing based on memory storage and API calls. This model targets businesses needing scalable AI memory solutions, generating recurring revenue through monthly or annual subscriptions with premium support and features.
Sell custom licenses to large enterprises for on-premises deployment, including integration support and training. This model caters to industries with strict data privacy requirements, providing high-value contracts and ongoing maintenance fees for tailored memory systems.
Provide a free version with basic memory functions to attract individual developers and small teams, then upsell advanced features like analytics and multi-agent support. This drives user adoption and converts free users to paid plans as their needs grow.
💬 Integration Tip
Start by integrating AgentMemory in session start/end protocols to load context and save lessons, ensuring data persistence across interactions without overcomplicating initial setup.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection