Dakera vs LangMem

Can I use Dakera with LangChain instead of LangMem?

Yes. Dakera's Python SDK works with any framework — you can replace LangMem's memory backend with Dakera API calls in your LangChain agent. This gives you persistent, hybrid retrieval with decay and knowledge graphs while keeping your LangChain agent orchestration. Dakera is framework-agnostic by design, so it integrates with LangChain, CrewAI, AutoGen, or any agent framework.

What happens to my conversation history when my LangChain process restarts?

With LangMem's default in-memory backends (ConversationBufferMemory, ConversationSummaryMemory), your history is lost on process restart. Persistent backends exist but require additional configuration. Dakera persists all memories to disk by default — process restarts, server reboots, and deployments don't affect stored memories. If your agents need to survive restarts, Dakera's persistence is built-in.

Does LangMem work outside the LangChain ecosystem?

No — LangMem is tightly coupled to LangChain's abstractions (chains, agents, callbacks). If you switch to another framework or build custom agents, LangMem doesn't transfer. Dakera is a standalone server with REST/gRPC APIs and SDKs in Python, TypeScript, Go, and Rust — use it from any language or framework without vendor lock-in to a specific agent toolkit.

Can multiple agents share memory with LangMem?

LangMem's memory is typically scoped to a single chain or agent instance. Sharing memory between agents requires custom wiring. Dakera has built-in multi-agent support with namespace isolation and scoped API keys — multiple agents can read from shared namespaces or maintain isolated memory spaces, with cryptographic separation between tenants.

How does retrieval quality compare between LangMem and Dakera?

Dakera scores 88.2% on the LoCoMo benchmark (1,540 questions testing long-conversation memory) using on-device ONNX inference with no LLM in the retrieval loop — meaning the number reflects real production performance with no ongoing API cost. LangMem's retrieval depends on which backend you configure — buffer memory has no retrieval (just recent messages), vector memory uses basic similarity search. Dakera's hybrid BM25+HNSW with cross-encoder reranking is purpose-built for memory retrieval quality, not adapted from a conversational buffer.

LangMem has no encryption — how does Dakera handle security?

LangMem stores memories in-process or in whatever external backend you configure — encryption is your responsibility and varies by backend. Dakera applies AES-256-GCM encryption at rest by default, scopes API keys per namespace, and enforces rate limiting. If your agents handle user data with any compliance requirement (GDPR, HIPAA, SOC 2), Dakera's built-in encryption means you are not relying on a misconfigured Redis instance or in-memory store.

How do I migrate from LangMem to Dakera in an existing LangChain app?

Replace your LangMem memory calls with Dakera SDK calls in your LangChain agent or tool definitions. The flow is: (1) install dakera-python, (2) on each conversation turn call client.store(text, session_id=...) instead of appending to a LangChain buffer, (3) on retrieval call client.recall(query, session_id=...) and inject results into your prompt. You keep LangChain's orchestration and replace only the memory layer. Session IDs map directly to LangChain's conversation IDs.

What hardware do I need to add Dakera to my LangChain deployment?

A separate process running the 44 MB Dakera binary. For a small LangChain app, a 1 vCPU / 1 GB RAM VPS handles memory operations alongside your agent. If you're containerizing your LangChain app already, add a Dakera sidecar container — it uses about 90 MB RAM for the default embedding model plus storage for your memories. Significantly less overhead than adding a Redis cluster and a separate vector database.