CrewAI memory is one of the more thoughtful built-in memory systems in the agent framework space. It ships with short-term, long-term, and entity memory out of the box — more than most frameworks offer.

But there's a catch: it only works inside CrewAI. If you switch frameworks, add a non-CrewAI agent to your system, or want to share memory across different tools, you're starting over.

This comparison looks at what CrewAI memory does well and where ClawVault offers a different approach.

How CrewAI Memory Works

CrewAI provides three memory types, each handling a different scope:

Short-Term Memory stores context from the current crew execution. Agents within a crew can share observations and findings during a single run. When the run ends, short-term memory is cleared.

Long-Term Memory persists learnings across crew executions using a local SQLite database. CrewAI stores task results and agent reflections, then retrieves them in future runs to improve performance. This is genuine cross-session persistence.

Entity Memory tracks information about specific entities — people, organizations, concepts — that appear during conversations. It helps agents maintain consistency when discussing the same entity across interactions.

Together, these cover a reasonable range of memory needs. For a pure CrewAI project, the built-in system can work well.

Where CrewAI Memory Gets Restrictive

Framework Lock-In

CrewAI memory is tightly coupled to CrewAI's execution model. The memory API, storage format, and retrieval logic are all internal to the framework.

This means:

Non-CrewAI agents can't access it. If you have a LangChain agent, a custom Python script, or a CLI tool that needs the same memory, you're out of luck.
Migration is painful. If you move away from CrewAI (or add another framework alongside it), the memory doesn't come with you.
No external inspection. Memory lives in CrewAI's internal storage. You can't easily browse, edit, or audit it outside of CrewAI's APIs.

Limited Categorization

CrewAI splits memory into three buckets: short-term, long-term, and entity. Real agent memory has more dimensions. Was this a decision? A user preference? A lesson learned from a failure? A project-specific note?

Without richer categorization, retrieval gets noisy. Searching long-term memory for "deployment configuration" might return task results about deployment mixed with unrelated results that happened to mention it.

No Observational Memory

CrewAI memory captures what agents explicitly produce — task outputs and reflections. It doesn't observe the broader environment: file changes, command outputs, patterns in how tools are used. The agent only remembers what it's told to remember.

Storage Opacity

CrewAI uses SQLite internally for long-term memory. While functional, this means memory isn't human-readable without database tools. You can't open a file, scan your agent's memories, and edit one that's wrong. Debugging memory issues requires querying the database.

How ClawVault Differs

ClawVault was designed as a standalone memory layer — not tied to any framework.

Framework-agnostic by design. ClawVault operates via CLI commands. Any agent that can execute a shell command can store and retrieve memories. CrewAI agents, LangChain chains, custom scripts, even manual terminal sessions — they all share the same vault.

14+ structured categories. Instead of three broad buckets, ClawVault stores memories as decisions, lessons, preferences, people, projects, goals, patterns, commitments, and more. When you search for deployment decisions, you get decisions — not a mix of everything.

Human-readable Markdown. Every memory is a Markdown file you can open, read, edit, and version control. No database to query. If an agent stored something wrong, open the file and fix it.

Observational memory. ClawVault can watch file changes, tool outputs, and agent behavior, capturing relevant context automatically. The agent builds memory from experience, not just explicit storage commands.

Semantic search. Built-in vector search finds relevant memories by meaning. Combined with structured categories, retrieval is both precise and contextually aware.

Comparison Table

| Feature | CrewAI Memory | ClawVault | |---|---|---| | Short-term (in-session) | ✅ Built-in | Not the focus | | Long-term persistence | ✅ SQLite-based | ✅ File-based | | Entity tracking | ✅ Built-in | ✅ People/projects categories | | Works outside the framework | ❌ CrewAI only | ✅ Any agent or tool | | Structured categories | 3 types | 14+ types | | Human-readable storage | ❌ SQLite | ✅ Markdown | | Observational learning | ❌ | ✅ | | Semantic search | ⚠️ Basic | ✅ Full vector search | | Version control friendly | ❌ | ✅ Git-compatible |

When to Use Each

CrewAI memory is a good fit when:

You're fully committed to CrewAI as your only agent framework
Short-term crew coordination is the primary memory need
You want zero-config memory that works immediately
Your agents don't need to share memory with non-CrewAI tools

ClawVault is a better fit when:

You use multiple frameworks or custom agents
You need persistent memory that's inspectable and editable
Structured categorization matters for your retrieval quality
You want memory that survives framework migrations
Your agents should learn from observation, not just explicit storage

Using both: You can run ClawVault alongside CrewAI. Let CrewAI handle short-term crew coordination, and use ClawVault for long-term, cross-framework persistence. At the start of a crew run, pull relevant context from ClawVault. At the end, persist important outcomes back.

Integration Example

Adding ClawVault to a CrewAI workflow:

import subprocess

# Before crew execution: load relevant context
result = subprocess.run(
    ["clawvault", "search", "client preferences for API design"],
    capture_output=True, text=True
)
prior_context = result.stdout

# After crew execution: persist the outcome
subprocess.run([
    "clawvault", "store",
    "--category", "decisions",
    "--title", "API versioning approach",
    "--content", "Team decided on URL-based versioning (v1/v2) over header-based"
])

The CLI-first approach means no Python SDK to install, no dependency conflicts with CrewAI's requirements, and no version compatibility concerns.

The Framework-Agnostic Advantage

Agent frameworks evolve fast. CrewAI is excellent today, but your stack might look different in six months. When memory is locked to a framework, switching costs include losing everything your agents have learned.

ClawVault's memory lives in plain files on disk. Switch from CrewAI to LangGraph, from Python to TypeScript, from one cloud to another — your agent's memory comes with you unchanged.

Get Started

npm install -g clawvault
clawvault init

Framework-agnostic memory for agents that need to remember — regardless of what framework they run on.

→ GitHub Repository | Documentation | Building Long-Term Memory Agents

CrewAI Memory vs ClawVault: Framework-Locked or Framework-Free?