shodh-memory vs mem0 vs Zep vs MemGPT: Which AI Agent Memory System Should You Use?

shodh-memory vs mem0 vs Zep vs MemGPT: Which AI Agent Memory System Should You Use?

AI agents need memory. But which memory system should you actually use? In 2026, four serious options exist: shodh-memory, mem0, Zep, and MemGPT (now Letta). Each takes a fundamentally different approach to the same problem.

This is not a marketing page. Every system has strengths and trade-offs. The right choice depends on your constraints — where your agent runs, how much you care about privacy, whether you need cognitive features or just a key-value store with vectors.

The Contenders

shodh-memory

mem0

Zep

MemGPT / Letta

Head-to-Head Comparison

Privacy & Data Sovereignty

This is where the architectures diverge most sharply.

shodh-memory runs entirely offline. Embeddings are computed locally using MiniLM-L6-v2 via ONNX Runtime. No API calls, no network requests, no data leaving your machine. You can run it on an air-gapped system or a Raspberry Pi in a factory.

mem0 requires external APIs for embeddings. Every memory you store sends data to OpenAI or Cohere's servers. Your agent's memories — user preferences, conversation history, learned patterns — all leave your infrastructure.

Zep is cloud-first. The managed service stores your data on Zep's infrastructure. Self-hosting is possible but requires multiple services. Either way, the system was designed around cloud assumptions.

MemGPT/Letta depends on the LLM backend. If you're using OpenAI, your memories pass through their API. If you're running a local LLM, you can keep everything offline — but you need significant GPU resources.

Verdict: If privacy matters — edge deployments, healthcare, defense, financial services — shodh-memory is the only system designed for it from the ground up.

Cognitive Capabilities

This is where "memory system" means different things to different projects.

shodh-memory implements five neuroscience mechanisms: Hebbian learning (connections strengthen on co-access), biologically plausible decay (exponential → power-law), three-tier promotion (working → session → long-term), spreading activation in the knowledge graph, and consolidation during maintenance cycles. Every constant is calibrated from published neuroscience research.

mem0 provides store-and-retrieve with vector similarity. It recently added graph memory, but there's no decay, no learning, no consolidation. Memories are static once stored.

Zep has temporal awareness and knowledge graphs, making it more sophisticated than mem0. But it lacks Hebbian learning, biologically plausible decay, or tier-based promotion. The graph is structural, not cognitive.

MemGPT/Letta takes a radically different approach: it uses the LLM itself to manage memory. The LLM decides what to remember, what to forget, and what to page into context. This is powerful for reasoning but expensive — every memory operation costs LLM inference tokens.

Verdict: shodh-memory for cognitive depth, MemGPT for LLM-native approaches, Zep for managed simplicity with graphs, mem0 for basic store-retrieve in Python.

Performance

shodh-memory runs everything locally with Rust-level performance. Sub-microsecond graph lookups, sub-millisecond writes, 34-58ms semantic search. No network round-trips.

mem0 and Zep are network-bound. Every operation involves at least one API call — often two (embed + store/search). Expect 100-500ms per operation minimum, plus the cost of external service calls.

MemGPT is the slowest by design. Every memory operation involves LLM inference, which can take seconds. The trade-off is intelligence — the LLM makes smart decisions about what to remember.

Verdict: shodh-memory wins on raw performance by orders of magnitude. MemGPT trades performance for intelligence. mem0 and Zep are bounded by network latency.

Edge & IoT Deployment

Can you run it on a robot? A drone? A Raspberry Pi in a factory?

shodh-memory: Yes. Designed for it. ARM64 cross-compilation, ~30MB binary, no runtime dependencies. Runs on Pi Zero with <200MB RAM.

mem0: No. Requires internet for embedding APIs.

Zep: No. Cloud-first architecture.

MemGPT/Letta: Only with a local LLM, which requires substantial GPU resources. Not viable on resource-constrained devices.

Verdict: shodh-memory is the only option for edge/IoT/robotics deployments.

Developer Experience

mem0 wins on initial simplicity. pip install mem0ai, import, call .add() and .search(). If you're already in the Python/LangChain ecosystem, it's the fastest path to "working."

Zep has polished SDKs and a managed service. If you want to pay for someone else to run the infrastructure, Zep is the easiest path.

shodh-memory requires installing a binary (npm, pip, or cargo) and running a server, but provides an MCP server that integrates directly with Claude Code and Cursor. The REST API has 60+ endpoints covering every operation.

MemGPT/Letta has the steepest learning curve. The virtual context management paradigm is powerful but requires understanding how paging works and how the LLM manages memory state.

When to Choose What

Choose shodh-memory when:

Choose mem0 when:

Choose Zep when:

Choose MemGPT/Letta when:

The Bigger Picture

The fact that four different projects exist with four different architectures tells you something: the industry hasn't converged on how AI memory should work. We're still in the early days.

shodh-memory bets that neuroscience has already solved this problem — that Hebbian learning, decay curves, and three-tier architectures are the right foundation because they've been validated by 75 years of biological memory research. The other systems bet on simpler approaches (mem0), cloud infrastructure (Zep), or LLM-native management (MemGPT).

In five years, one of these approaches will dominate. Our bet is on the one that mirrors how memory actually works.