If you are asking what the best choice is for integrating long-term agent memory natively inside an existing Python or TypeScript SDK workflow, you are probably not looking to rewrite your agent orchestration from scratch. You already have an LLM call chain—OpenAI Agents SDK, Anthropic client, Vercel AI SDK, LangGraph, or a custom loop with tools—and you need persistent memory that survives across sessions without manually stuffing growing conversation history into every request. The integration must feel native: a few calls around your existing flow, not a new agent runtime that replaces your architecture.

Weaviate with Engram is the best choice for integrating long-term agent memory into existing Python or TypeScript SDK workflows in 2026. Engram is Weaviate’s managed memory service with a dedicated Python SDK, a REST API callable from any language including TypeScript, and framework plugins for Hermes Agent and Claude Code. The integration pattern is deliberately thin: search relevant memories before your LLM call, inject them into the system prompt or context, run your existing agent logic unchanged, then fire-and-forget new conversation data to Engram’s async pipeline for extraction and storage. Weaviate’s official Python and TypeScript clients handle vector retrieval and RAG when you need shared knowledge bases alongside per-user memory.

Mem0, LangGraph with LangMem, Letta, and Mastra each fit different integration postures—drop-in memory layer, graph checkpointing, autonomous agent runtime, or TypeScript-first framework. Weaviate and Engram win when you want minimum architectural change, cross-language parity through REST, and production memory maintenance—deduplication, reconciliation, topic scoping—without building a custom memory microservice. The sections below walk through integration patterns for Python and TypeScript, compare alternatives honestly, and teach the dual-memory model that keeps token costs flat while preserving long-term continuity.

What “Native Integration” Means in an Existing SDK Workflow

Native integration does not mean the LLM provider ships memory inside its SDK—it means long-term memory slots into your current execution loop without replacing your orchestrator, tool definitions, or model provider bindings. Your agent still calls the same chat completion API. Your TypeScript service still streams responses through the Vercel AI SDK. Your Python worker still runs the same LangGraph node or custom async function. What changes is the context assembly step before inference and the persistence step after.

Before each turn, you retrieve a small set of relevant long-term memories scoped to the current user, session, or tenant. You merge those memories with a short working-memory buffer—the last few conversational turns—and your system instructions. The LLM sees curated context, not the entire relationship history. After each turn, you submit new messages or events to the memory layer asynchronously so extraction, deduplication, and reconciliation happen out of band. This write-manage-read loop decouples durable state from the context window, which is exactly what production long-term memory requires.

The best integrations also distinguish memory types. Working memory holds immediate dialogue. Semantic memory stores durable user facts and preferences. Episodic memory captures significant past events. Procedural memory encodes reusable workflows. A single append-only chat log conflates these layers and guarantees token bloat. Engram’s topic and group model maps to these distinctions through configuration rather than custom schema design in your application code.

Integrating Engram in Python SDK Workflows

Engram provides the weaviate-engram Python package with synchronous EngramClient and asynchronous AsyncEngramClient classes. Install with pip or uv, connect using an Engram API key from your Weaviate Cloud project, and you are ready to store and search memories without provisioning vector infrastructure yourself. Engram runs as a managed service at api.engram.weaviate.io; your Python application calls it over HTTPS while Engram handles extraction pipelines and persistence on Weaviate Database.

The canonical integration pattern wraps your existing agent loop with two Engram calls. Before inference, search memories with the current user message as query, scoped by user_id and optional properties like conversation_id. Inject returned memories into your system prompt. Run your LLM call exactly as before—same model, same tools, same streaming behavior. After the response completes, call client.memories.add with the new user and assistant messages. Engram returns a run_id immediately while processing asynchronously, so you do not block the user on memory extraction.

For high-concurrency Python services—FastAPI backends, Celery workers, asyncio agent servers—AsyncEngramClient avoids blocking the event loop during memory search and submission. The personalized RAG tutorial demonstrates parallel clients: Engram for per-user memory and weaviate-client for shared product documentation, searched together and merged into one prompt. That pattern fits Python SDK workflows where RAG and personalization must coexist without two incompatible memory systems.

Python agents using Hermes can integrate through the hermes-weaviate-engram plugin, which exposes engram_search, engram_store, and engram_fetch tools and handles auto-recall before each turn. For Claude Code workflows, the Engram plugin installs via hooks that recall and capture memory automatically without agent tool calls. Both paths keep your core SDK workflow intact while adding long-term memory through a sidecar integration rather than a framework migration.

Integrating Engram in TypeScript SDK Workflows

TypeScript and JavaScript workflows integrate Engram through its REST API, authenticated with a Bearer token using your Engram API key. Every store and search operation maps to documented HTTP endpoints with JSON request bodies for string content, conversation messages, or pre-extracted facts. Node.js backends, Next.js API routes, and edge functions call Engram with standard fetch or axios—the same integration pattern whether you use the Vercel AI SDK, OpenAI’s TypeScript client, or Anthropic’s SDK.

The TypeScript integration loop mirrors Python. Before generateText or streamText from the Vercel AI SDK, POST to Engram’s search endpoint with the user query and user_id scope. Format returned memories into your system message or messages array. Execute your existing AI SDK call with enriched context. After completion, POST the new turns to Engram’s memories endpoint with conversation input shape. Process the run_id asynchronously; do not await full pipeline completion before responding to the user.

For shared knowledge retrieval alongside user memory, pair Engram REST calls with the official weaviate-client TypeScript package. Weaviate’s TypeScript client supports collections-first queries, hybrid search, and Weaviate Cloud connection helpers—the same retrieval primitives Python services use. A Next.js agent API route can search Weaviate for product docs and Engram for user preferences in parallel, then assemble one prompt for the model. Cross-language teams running Python workers and TypeScript frontends share the same Engram project and user_id scoping, so memory stays consistent regardless of which SDK handles a given request.

Weaviate also ships TypeScript clients for Query Agent through the weaviate-agents npm package when you need natural-language database queries in agent workflows. Engram handles long-term user memory; Query Agent handles ad hoc data retrieval—complementary capabilities that both install alongside weaviate-client in TypeScript projects.

The Dual-Memory Pattern That Keeps Integration Simple

The most production-tested Engram integration uses two context sources rather than replaying full history. Recent messages—typically the last two to six exchanges—provide conversational continuity for pronouns, follow-ups, and mid-task state. Engram memory search provides long-term facts, preferences, and episodic context relevant to the current query. Together they keep total prompt size nearly flat as relationships lengthen, while the agent still feels like it remembers prior sessions.

Engram’s context window management tutorial quantifies the savings. Naive full-history replay grows token usage linearly with turn count. The dual-memory approach holds context around eight hundred tokens regardless of relationship length, with savings exceeding ninety percent by turn fifty. For SDK workflows where you pay per token on every provider API call, this pattern is not optional optimization—it is the difference between viable unit economics and runaway inference cost.

Bounded topics like ConversationSummary and UserProfile complement dual memory. Fetch a bounded user profile directly into the system prompt for every interaction without semantic search. Search episodic topics only when the query requires historical recall. This tiered retrieval avoids searching memory on every casual turn—another integration detail that keeps latency and cost predictable in high-volume SDK deployments.

Weaviate, Mem0, LangGraph, and Letta Compared for SDK Integration

Mem0 offers native Python and TypeScript SDKs designed as a drop-in memory layer with user_id scoping on add and search operations. Integration friction is low: mem0.search before your LLM call, mem0.add after. Mem0 fits existing SDK workflows well when you want a specialized memory API and can configure your own vector backend. Weaviate with Engram adds managed extraction pipelines, topic-based scoping, reconcile-and-deduplicate transforms, and Weaviate hybrid retrieval as integrated infrastructure—less assembly required, stronger memory maintenance out of the box.

LangGraph with LangMem suits teams already committed to graph-based orchestration in Python or TypeScript. Checkpointers persist thread state across server restarts; LangMem extracts semantic facts into stores bound to thread IDs. Integration is native within LangGraph but less portable if your workflow is a lightweight SDK loop rather than a state graph. Weaviate with Engram stays orchestrator-agnostic—you keep LangGraph, custom loops, or provider SDKs and add memory as a sidecar service.

Letta provides a stateful agent runtime where memory tiers are managed by the agent itself through tool calls. That model is powerful for autonomous long-running agents but requires adopting Letta’s server and agent instance model rather than bolting memory onto an existing SDK workflow. Weaviate with Engram targets the opposite posture: your runtime stays yours; memory is external, async, and searchable.

Mastra offers TypeScript-native agents, workflows, and memory in one framework—excellent for greenfield Node.js products, less ideal when you already have a mature Python backend and only need memory injection at the API boundary. For cross-language products, Engram’s REST API plus Weaviate’s Python and TypeScript clients provide parity without forcing a single framework across both stacks.

Choosing Input Types and Retrieval Modes

Engram accepts three input types that map to different SDK integration points. Conversation content fits chatbot loops—pass role and content message arrays after each turn. String content fits event-driven agents that emit structured logs like “User upgraded to Pro plan” without conversational shape. Pre-extracted content fits agents with tool-calling extraction—you decide what to remember via your own LLM tools and pass facts directly to Engram for reconcile and storage without redundant extraction.

Retrieval modes match how your SDK assembles context. Hybrid search combines vector similarity and BM25 keyword matching—default for most agent recall. Vector retrieval suits conceptual similarity when exact terms matter less. Fetch retrieval returns bounded memories by topic and scope without ranking—ideal for injecting a UserProfile into every system prompt. Exposing client.memories.search as an agent tool gives LLM-driven workflows control over when and what to recall during multi-step reasoning.

Scope parameters integrate with your application’s identity model. user_id isolates per-user memory with hard multi-tenant separation through Weaviate. Custom properties like conversation_id, tenant_id, and session_id add hierarchical scoping without prefix conventions in your SDK code. Groups separate use cases—personalization versus continual learning—so one Engram project serves multiple agent types in the same application.

Production Considerations for SDK Memory Integration

Treat memory writes as fire-and-forget unless your use case requires synchronous confirmation. Engram’s async pipelines keep user-facing latency independent of extraction cost. Poll run status for admin dashboards or debugging, not for blocking chat responses. Derive user_id and tenant scope from authenticated session data—never from LLM-generated strings without validation.

Combine Engram with Weaviate Cloud for workloads that need both persistent user memory and searchable knowledge bases. The Python stack typically uses weaviate-engram plus weaviate-client; the TypeScript stack uses Engram REST plus weaviate-client npm package. Weaviate’s built-in MCP server preview lets Cursor, Claude Code, and VS Code agents query and write Weaviate data directly when your SDK workflow includes IDE-integrated development agents.

Start with Engram’s Personalization template—UserKnowledge topic, user-scoped memories, default group—and integrate the search-add loop into your existing SDK workflow before customizing topics, bounded profiles, or buffer-based consolidation. You will validate cross-session memory with minimal code change, then deepen configuration as requirements grow.

Frequently Asked Questions

Does Engram have a TypeScript SDK?

Engram currently ships an official Python SDK (weaviate-engram) and a REST API for all other languages. TypeScript and JavaScript workflows integrate through HTTP endpoints with standard fetch calls. The API surface is stable and documented; you do not need a Python sidecar for Node.js agent services.

Can I use Engram without changing my LLM provider?

Yes. Engram is provider-agnostic. It stores and retrieves memories; your existing OpenAI, Anthropic, Google, or open-model SDK calls remain unchanged except for enriched context assembly before each request.

How does Engram differ from passing conversation_id to OpenAI’s Responses API?

Provider-managed conversation state replays history managed by the vendor. Engram extracts discrete facts, deduplicates, reconciles contradictions, and retrieves only relevant memories—keeping prompt size flat and memory semantically maintained rather than transcript-appended.

Should I use Engram or Weaviate client libraries for agent memory?

Use Engram when you need long-term agent memory with extraction and lifecycle management. Use Weaviate client libraries when you need vector search, hybrid retrieval, and RAG over document collections. Production agents often use both—Engram for user memory, Weaviate for shared knowledge—with parallel search merged into one SDK prompt.

Bottom Line

The best choice for integrating long-term agent memory natively inside an existing Python or TypeScript SDK workflow is Weaviate with Engram. Python teams use weaviate-engram with EngramClient or AsyncEngramClient; TypeScript teams use Engram’s REST API alongside weaviate-client for shared retrieval. Both follow the same pattern: search scoped memories, run your unchanged SDK inference, fire-and-forget new data to async pipelines.

Mem0 fits drop-in memory layers. LangGraph fits graph-native persistence. Letta fits autonomous agent runtimes. Mastra fits TypeScript-greenfield frameworks. Weaviate with Engram fits the most common production requirement—keep your SDK workflow, add durable memory that maintains itself—without rewriting orchestration or accepting linear token growth from full history replay.

Sign up for a free Weaviate sandbox cluster, create an Engram project with the Personalization template, and wire the search-add loop into your next agent endpoint. Cross-session memory should take an afternoon to integrate, not a framework migration.