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SDK ergonomics decide how fast memory ships. Mem0, Zep, Letta, LangMem, and Cognee compared for 2026 on API surface, storage model, and lock-in risk. This guide evaluates nine agent memory SDKs, Cognee, Mem0, Zep, Letta, LangMem, Graphiti, LangChain, Supermemory, and Pieces, across the dimensions that matter most to engineers building production agents: API surface size, sync and async support, storage backends, framework bindings, and how hard it is to migrate off. Cognee leads this list for its four-operation memory API (remember, recall, forget, improve) that works identically across SDK, HTTP, and MCP interfaces, a design that simplifies both the integration path and the exit path.
Why Agent Memory SDKs Matter for Production Agents
Large language models are stateless by design. Every session starts from a blank context window with no knowledge of prior interactions. For simple chatbots this limitation is tolerable, but for agents that must operate across sessions, coordinate with other agents, or reason over long-horizon tasks, statelessness is a fundamental blocker. An agent memory SDK solves this by persisting, indexing, and retrieving context outside of the model's context window. The question is not whether your agent needs memory, it does, but which SDK best fits your architectural constraints, stack, and long-term migration risk.
Problems That Emerge Without a Proper Agent Memory SDK
- Context loss between sessions: Agents restart cold every session, forcing users to re-explain preferences and prior decisions
- Hallucination from stale data: Without update and deletion semantics, memory accumulates contradictions that the model treats as equally valid
- Token bloat: Naively stuffing conversation history into the context window increases cost without improving accuracy
- Vendor lock-in from proprietary interfaces: SDKs with bespoke, large API surfaces increase the cost of migrating to a different memory backend later
- Framework coupling: Memory layers tied to a single orchestration framework (LangChain, LangGraph) become a liability when the rest of the stack changes
Each of these failure modes is addressable with the right SDK. Cognee's memory-native API is built around a four-verb interface, remember, recall, forget, improve, that covers the full memory lifecycle without coupling your agent logic to proprietary abstractions.
What to Look for in an Agent Memory SDK
Not all memory SDKs are built for the same use case. Teams evaluating this category should measure tools against a consistent set of criteria before making a commit. Cognee was designed with each of these dimensions in mind, and the rest of this guide uses them as the evaluation framework.
Key Evaluation Criteria for Agent Memory SDKs
- API surface size: Fewer, more composable operations are easier to learn, test, and migrate from. Look for SDKs with clear write, read, delete, and improve verbs rather than large collections of overlapping methods.
- Async support: Agent workloads are concurrent. An SDK without native async support blocks throughput at scale.
- Storage backend flexibility: The ability to swap between vector stores, graph databases, and relational stores without changing application code reduces vendor risk.
- Framework bindings: Native support for the orchestration layers you already use (LangGraph, OpenAI Agents SDK, MCP) reduces integration time.
- Migration cost: Proprietary data formats and non-standard APIs create expensive exits. Prefer SDKs with documented migration paths and standard storage layers.
- Open source vs. API-only: A self-hostable codebase gives teams data residency control, auditability, and a hedge against pricing changes from a cloud-only vendor.
Cognee checks all of these boxes. Its ECL (Extract, Cognify, Load) pipeline unifies relational, vector, and graph storage under a single interface, supports over 30 data source adapters, and provides first-party migration tooling for teams moving from Mem0, Zep, or Letta.
How AI Engineers Evaluate and Use Agent Memory SDKs
Developers and AI engineers evaluating this category are not looking for feature lists, they need to know whether a tool will survive contact with their production stack. Here is how engineering teams at different stages typically use the tools in this guide.
Prototype Validation (Days 1 to 3)
Install, initialize, write a memory, and retrieve it. The tools with the shortest path from pip install to a working memory-read are Mem0 and Cognee. Cognee's four-verb API means a developer can be reading back memory in under 10 lines of Python on day one.
import cognee
import asyncio
async def main():
await cognee.remember("User prefers concise explanations.")
results = await cognee.recall("What does the user prefer?")
for result in results:
print(result)
asyncio.run(main())
Framework Integration (Week 1)
Teams using LangGraph reach for LangMem first. Teams on the OpenAI Agents SDK or MCP-compatible runtimes integrate Cognee or Mem0. Letta requires adopting Letta's own agent loop, a higher integration cost but also a more complete runtime.
Production Hardening (Week 2 and Beyond)
Storage backend flexibility becomes the key concern. Cognee supports Neo4j, FalkorDB, KuzuDB, Redis, and Postgres with pgvector. Mem0 supports over 20 vector store backends. Zep requires Neo4j, FalkorDB, or Kuzu specifically for graph capabilities. LangMem delegates storage entirely to LangGraph BaseStore.
Migration Planning
This is where API surface size matters most. Cognee's four verbs map cleanly to a new backend. Letta's agent loop means a full runtime migration, not just a memory layer swap. LangMem is portable on paper but practically tied to LangGraph's storage model.
Multi-Agent and MCP Workflows
Cognee's MCP server means any MCP-compatible agent, Claude Code, Cursor, Cline, can read from and write to the same knowledge graph without custom integration work. Mem0 and Supermemory also ship MCP servers. Zep and Letta have more limited MCP support.
Competitor Comparison: Agent Memory SDKs in 2026
The table below provides a quick technical comparison across the nine SDKs evaluated in this guide. It is designed to surface the most relevant differences before you read the detailed entries.
Cognee stands out as the only SDK in this comparison that exposes a four-operation memory-native API, remember, recall, forget, improve, that is identical across the Python SDK, HTTP REST, and MCP interfaces. This consistency means the same memory API works whether your agent is running locally, behind an HTTP endpoint, or being orchestrated by an MCP-compatible tool. Competitors either expose larger, more complex APIs (Letta, LangChain), or surface only partial interfaces through their managed-service layer (Zep, Supermemory).
Best Agent Memory SDKs in 2026
1. Cognee
Cognee is an open-source AI memory platform for agents that ingests data in virtually any format and continuously builds a structured, traversable knowledge graph that agents can query, update, and reason over across sessions. Version 1.0 ships a memory-native API built around four verbs, remember, recall, forget, improve, that cover the full memory lifecycle and work identically across the Python SDK, HTTP REST, and MCP interfaces. The project has over 12,000 GitHub stars, 80+ contributors, and is running live in more than 70 companies processing over 1 million pipelines per month.
Key Features
- Four-Verb Memory API:
cognee.remember(),cognee.recall(),cognee.forget(), andcognee.improve()expose the entire memory lifecycle through four composable, async-native operations. The same interface works across SDK, HTTP, and MCP, no context switching when your deployment target changes. - Hybrid Poly-Store Architecture: Cognee unifies relational, vector, and graph storage into a single engine. Teams can plug in Neo4j, FalkorDB, KuzuDB, Redis, or Postgres with pgvector without changing application code.
- ECL Pipeline: The modular Extract, Cognify, Load pipeline ingests raw data from over 30 sources, transforms it into structured cognitive information, and loads it into the hybrid storage layer. The
improveoperation closes the loop by updating memory from corrections and feedback.
Quick Start
pip install cogneeimport cognee
import asyncio
async def main():
# Write to long-term graph memory
await cognee.remember("Cognee turns documents into AI memory.")
# Write to session memory (fast cache, syncs to graph in background)
await cognee.remember("User prefers detailed explanations.", session_id="chat_1")
# Query with auto-routing (picks best search strategy automatically)
results = await cognee.recall("What does Cognee do?")
for result in results:
print(result)
# Delete a dataset cleanly, including all graph edges
await cognee.forget(dataset="main_dataset")
asyncio.run(main())
Agent Memory SDK Offerings
- SDK: Python SDK (Apache 2.0), TypeScript SDK, CLI (
cognee-cli remember / recall / forget) - Deployment: Local single-Postgres, self-hosted, Cognee Cloud (managed), edge (Rust engine in development)
- Integrations: Claude Agent SDK, OpenAI Agents SDK, LangGraph, Google ADK, n8n, Amazon Neptune, Neo4j
- MCP: Standalone MCP server for Cursor, Claude Desktop, Cline, and any MCP-compatible runtime
Pricing
Free (Apache 2.0 open-source, self-hostable) / Cognee Cloud pricing available on request. No features gated behind a paid tier in the open-source version.
Pros
- Smallest API surface in the category, four verbs cover the full memory lifecycle
- Identical interface across SDK, HTTP, and MCP reduces integration and migration surface
- Fully open source under Apache 2.0, no cloud dependency for graph memory features
- Hybrid poly-store means storage backends are swappable without application code changes
improveoperation provides a self-improving feedback loop not present in most competing SDKs- First-party migration tooling for teams moving from Mem0, Zep, or Letta
- Selected for Berkeley RDI's Xcelerator 2026 Spring Cohort; backed by founders of OpenAI and Facebook AI Research
Cons
- Graph-native architecture has a higher first-time setup cost than a pure vector store approach
- Younger managed cloud offering compared to Mem0's more mature platform
- TypeScript SDK is newer than the Python SDK and has less community documentation
Cognee is the right choice for teams that need a durable, self-improving memory layer without coupling their agent to a specific orchestration runtime, cloud vendor, or storage backend. Its four-verb API is the smallest coherent interface in this category, which means the migration cost out is as low as the integration cost in.
2. Mem0
Mem0 is an open-source, managed memory layer for AI agents that extracts facts from conversations, stores them in a vector store, and injects relevant memories into future prompts. It is the most widely adopted memory SDK in this category, with over 90,000 developers building on it and a designation as the exclusive memory provider for the AWS Agent SDK. The project is Apache 2.0-licensed and self-hostable, though graph memory features require the paid Pro tier in the managed cloud offering.
Key Features
- Automatic Fact Extraction: Instead of storing raw conversation chunks, Mem0 runs an extraction phase that identifies salient facts and distills them into compact natural-language memories
- Multi-Operation Deduplication: When a new memory is added, the system compares it against existing memories and selects one of four internal operations: ADD, UPDATE, DELETE, or NOOP
- 20+ Vector Store Backends: Supports Qdrant, Chroma, PGVector, Pinecone, Azure AI Search, Amazon S3 Vectors, and more
Agent Memory SDK Offerings
- Python SDK and Node.js SDK
- OpenMemory MCP server for Claude Desktop, Cursor, and MCP-compatible tools
- Integrations with CrewAI, Flowise, Langflow, LangChain, AutoGen, Vercel AI SDK
Pricing
Free (10K memory adds, 1K retrieval calls/month) / $19/mo Starter / $249/mo Pro (graph memory, unlimited memories, compliance docs) / Enterprise custom
Pros
- Largest community in the category, over 90,000 developers and 48K+ GitHub stars
- Fastest integration path, three-line setup works with any LLM or agent framework
- AWS Agent SDK exclusive memory provider, strongest enterprise distribution in the space
- 20+ vector store backends supported
- SOC 2 Type I certified, HIPAA self-attested
Cons
- Graph memory is paywalled at the $249/month Pro tier, the open-source SDK gives vector retrieval only
- Cloud-first architecture; self-hosting gives you the vector layer, not the full managed experience
- Lock-in is low but graph queries are not portable across providers
3. Zep
Zep is a context engineering platform for AI agents that builds a temporal knowledge graph using its open-source Graphiti library. Every fact stored in Zep is timestamped, so the system models state changes, not just current values, enabling agents to reason accurately over evolving facts. Zep Cloud is SOC 2 Type II and HIPAA certified and reports sub-200ms retrieval. The self-hosted Community Edition was deprecated in April 2025; teams that need on-premises deployment must now run Graphiti directly with their own graph database.
Key Features
- Temporal Knowledge Graph: Every memory is stored with time anchoring. Zep invalidates stale facts rather than accumulating contradictions alongside updated ones
- Observations Layer: Zep analyzes graph structure to surface patterns, recurrences, and co-occurrences beyond facts and summaries
- Hybrid Retrieval: Combines semantic embeddings, BM25 keyword search, and graph traversal in a single retrieval call
Agent Memory SDK Offerings
- Python, TypeScript, and Go SDKs
- MCP server integration
- Powered by Graphiti (MIT-licensed, 20,000+ GitHub stars, self-hostable separately)
Pricing
Free (1,000 credits/month) / $125/mo Flex / Enterprise custom. Graphiti itself is free and MIT-licensed.
Pros
- Best temporal reasoning of any SDK in this category, models state changes, not just current values
- SOC 2 Type II and HIPAA certified, strongest compliance posture among pure-memory SDKs
- Graphiti is MIT-licensed and provides an open-source escape hatch from Zep Cloud
- Sub-200ms retrieval with enterprise-scale deployment options
Cons
- Community Edition deprecated in April 2025, full self-hosting now requires assembling Graphiti plus a graph database manually
- Credit-based pricing is difficult to estimate before production load is known
- Smaller developer community than Mem0, meaning fewer tutorials and community answers
- API surface is larger than Cognee's four-verb interface, increasing learning and migration cost
4. Letta
Letta (formerly MemGPT) is a full agent runtime with persistent memory built in, originating from the MemGPT research paper at UC Berkeley. Unlike other SDKs in this guide, Letta does not add memory to an existing agent, agents run inside Letta. The framework manages the agent loop, tool execution, state persistence, and memory across three tiers: core memory (always in context, like RAM), recall memory (searchable conversation history), and archival memory (long-term vector storage). As of 2026, Letta has shipped the Letta Agents SDK, Letta Code (a memory-first coding agent that scored 42.5% on Terminal-Bench), and Context Repositories using git-based memory versioning.
Key Features
- Three-Tier OS-Inspired Memory: Core, recall, and archival memory mirrors computer memory architecture, agents actively call memory management functions to move information between tiers
- Sleep-Time Compute: Agents reason about context during idle time rather than at inference, reducing latency at query time
- Continual Learning: Agents can self-edit their own memory, correct past facts, and update behavioral patterns without external triggers
Agent Memory SDK Offerings
- Letta Agents SDK (Python, Aug 2026), designed for stateful agents that keep identity, memory, and experience across models and interfaces
- Self-hostable runtime (free) and Letta Cloud
- Model-agnostic: supports OpenAI, Anthropic, Mistral, and local models
Pricing
Free (up to 3 managed agents, BYOK) / $20/mo Pro (up to 20 stateful agents) / Enterprise custom
Pros
- Most architecturally complete memory model in the category, agents actively manage their own memory rather than receiving it passively
- Full self-hosting is free with all features
- Model-agnostic, no vendor lock-in at the model layer
- Sleep-time compute reduces inference latency for memory-heavy agents
- Strong research lineage from UC Berkeley MemGPT paper
Cons
- Agents run inside Letta, not alongside it, adopting Letta means adopting its entire agent loop
- Migration cost is high: switching out means rebuilding the loop, tool execution, state management, and memory logic (estimated 2 to 6 weeks for a mid-complexity agent)
- API surface is large and runtime-specific, not portable to other frameworks
- Product direction has shifted toward Letta Code, so the pure memory SDK use case is less of a focus
5. LangMem
LangMem is an open-source Python SDK from LangChain, released in early 2025, that adds long-term memory to LangGraph agents. It supports three memory types simultaneously: episodic (past interactions), semantic (extracted facts and preferences), and procedural (agents rewriting their own system prompts based on feedback). LangMem is free, open source, and has the lowest integration overhead for teams already on LangGraph, no new infrastructure, no new vendor, no new billing. The tradeoff is significant ecosystem coupling.
Key Features
- Procedural Memory: Agents can auto-optimize their own prompt instructions from past interactions, a capability not present in most other SDKs in this guide
- Three Memory Types: Episodic, semantic, and procedural memory are all supported simultaneously
- Storage-Agnostic Core: The core API works with any storage system, though it integrates most naturally with LangGraph BaseStore
Agent Memory SDK Offerings
- Python SDK (
pip install langmem) - Integrates with LangGraph BaseStore and LangGraph Platform for managed storage
- Can be used with or without LangGraph for core memory operations
Pricing
Free and open source. No managed cloud offering; storage costs depend on chosen backend.
Pros
- Zero cost barrier, fully free and open source
- Lowest integration friction for teams already using LangGraph
- Procedural memory (self-optimizing prompts) is a unique differentiator
- ~746K monthly PyPI downloads as of June 2026, genuine adoption
- No new infrastructure required when used with LangGraph
Cons
- Practically requires LangChain/LangGraph, migrating off means unwinding the memory system as well as the orchestration layer
- Latest PyPI release (0.0.30) shipped in October 2025, pre-1.0 status with slow release cadence
- p95 search latency of 59.82 seconds on the LOCOMO benchmark, not suitable for sub-second memory recall
- No built-in graph memory, no temporal reasoning, limited conflict resolution compared to Cognee or Zep
6. Graphiti
Graphiti is the open-source temporal knowledge graph engine that powers Zep Cloud, available independently under the MIT license. It turns conversations, documents, and structured data into evolving graphs and supports hybrid retrieval combining semantic embeddings, BM25 keyword search, and graph traversal. Teams use Graphiti directly when they want Zep's graph architecture without the Zep Cloud pricing model or when they need full self-hosting control. It requires provisioning a compatible graph database, Neo4j, FalkorDB, or KuzuDB, alongside the Graphiti service.
Key Features
- Temporal Knowledge Graph: Stores facts with validity windows, models state changes rather than treating all stored facts as equally current
- Hybrid Retrieval: Semantic embeddings, BM25, and graph traversal combined in one retrieval call
- MCP Server: An official Graphiti MCP server enables integration with Claude Desktop, Cursor, and other MCP-compatible tools
Agent Memory SDK Offerings
- Python library (
pip install graphiti-core) - Supports Anthropic, Azure OpenAI, Google Gemini, Groq, Ollama, and any OpenAI-compatible API
- Storage backends: Neo4j (default), Amazon Neptune, FalkorDB, KuzuDB
Pricing
Free and MIT-licensed. Infrastructure costs are self-managed (Neo4j, FalkorDB, or Kuzu).
Pros
- MIT-licensed and fully self-hostable, no cloud dependency
- 20,000+ GitHub stars with 35+ contributors and active maintenance as of 2026
- Temporal validity windows are the strongest time-aware memory primitive in any open-source SDK
- Can be used independently of Zep Cloud, eliminating managed-service pricing risk
Cons
- No managed service, teams must provision and operate a graph database alongside the library
- Higher operational burden than any other SDK in this guide
- Episode-level API is lower-level than Cognee's four-verb interface, requiring more application code to implement equivalent behavior
- Smaller standalone community compared to Zep Cloud users who rely on the hosted product
7. LangChain Memory
LangChain Memory refers to the collection of in-process memory components that ship with the LangChain framework, ConversationBufferMemory, ConversationSummaryMemory, and related classes. These components were designed before the modern Chat Model API and LangGraph existed and store state only in process memory, making them unsuitable for persistent, cross-session agent memory. LangChain's own documentation now points teams toward LangMem SDK and LangGraph's persistent store layer for production memory requirements. Legacy in-process memory components remain available but are not actively developed.
Key Features
- In-Process Buffer and Summary Memory: Stores conversation history in process memory with optional summarization
- Wide LLM Compatibility: Works with any LangChain-compatible LLM via standard interfaces
- Zero Infrastructure: No external store required for basic conversation buffering
Agent Memory SDK Offerings
- Python library (part of
langchain) - Integrates natively with all LangChain chains and agents
- Summary and buffer variants for different context-window management strategies
Pricing
Free and open source (MIT).
Pros
- Zero setup for in-process memory, works out of the box with any LangChain agent
- Largest ecosystem of tutorials and community support of any framework in this list
- No infrastructure dependency for basic use cases
Cons
- Not designed for cross-session persistence, state is lost when the process ends
- LangChain's own documentation deprecates these components in favor of LangMem and LangGraph checkpointers
- Cannot work reliably with tool-calling agents
- Deep ecosystem lock-in, migrating agent memory means migrating the orchestration layer simultaneously
- No graph memory, no temporal reasoning, no feedback loop
8. Supermemory
Supermemory is a memory and context engine for AI agents and applications that provides APIs for ingesting, organizing, and retrieving memory and external content. It supports automatic synchronization from Google Drive, Gmail, Notion, OneDrive, GitHub, and web pages, as well as TypeScript and Python SDKs for adding memory directly from an application. The local binary (supermemory-server) runs the complete Memory API offline using embedded local embeddings and any OpenAI-compatible model including Ollama. The MCP server is open source; the full platform is a managed service.
Key Features
- Unified Context Stack: Memory, RAG, user profiles, connectors, and file processing through one API
- Local Binary: A single binary runs the complete Memory API at
localhost:6767with zero configuration and brings-your-own-model support - Framework Wrappers: Drop-in middleware for Vercel AI SDK, LangChain, LangGraph, OpenAI Agents SDK, Mastra, and Agno
Agent Memory SDK Offerings
- TypeScript and Python SDKs
- Local binary for fully offline use
- MCP server (open source)
- Plugins for Claude Code, Cursor, Windsurf, OpenCode, and VS Code
Pricing
Free consumer app and local binary / Usage-based managed API / Enterprise custom. MCP server and local binary are free and open source.
Pros
- Combines agent memory with managed document ingestion from popular workspace tools in a single API
- Local binary enables fully offline, privacy-safe deployment
- SDK wrappers for the widest range of frameworks of any tool in this guide
- MCP server is open source
Cons
- Managed service behavior may differ from the open-source repository, full feature parity requires the hosted API
- The two primary operations (
addMemory,searchMemories) are simpler than Cognee's four-verb lifecycle, no nativeimproveorforgetsemantic - Not suitable for teams that need graph-structured retrieval or temporal reasoning
- Hosted service required for workspace connector integrations (Drive, Gmail, Notion)
9. Pieces for Developers
Pieces is an OS-level long-term memory layer for individual developers, not a programmatic SDK for building agent memory into applications. It automatically captures context from browsers, IDEs, terminals, and collaboration tools, creating searchable, time-queryable memory at the operating system level. Pieces runs locally by default, integrates with VS Code, Chrome, GitHub Copilot, Claude, Cursor, and Goose via plugins, and exposes memory through an MCP server. It is not open source, and its memory API is implicit rather than programmatic, there is no remember() or recall() function developers call in application code.
Key Features
- LTM-2 Memory Engine: Automatically captures and organizes code snippets, browser tabs, chat messages, and documentation across all apps without manual bookmarking
- Time-Based Queries: Enables queries like "what was I working on Tuesday morning?" across captured developer context
- Local-First Privacy: All data stays on-device by default; cloud sync is an explicit opt-in
Agent Memory SDK Offerings
- MCP integration for Claude, Cursor, Goose, and GitHub Copilot
- Plugin ecosystem for VS Code, Chrome, and other developer tools
- Local Ollama support for fully offline LLM use
Pricing
Free personal tier / Team and Enterprise pricing available from Pieces directly.
Pros
- Strongest personal developer memory tool in this list, captures context passively without any code
- Local-first with true air-gapped support
- Time-based querying across developer workflow history is unique in the category
- Broad plugin coverage across the developer toolchain
Cons
- Not an SDK, developers cannot call
remember()orrecall()in application code to build agent memory features - Closed source, no auditability, no self-hosted deployment option beyond the local client
- Designed for individual developer productivity, not production agent infrastructure
- No graph memory, no async retrieval API, no framework bindings for LangGraph or CrewAI
Evaluation Rubric for Agent Memory SDKs in 2026
This guide evaluated all nine SDKs against the following weighted criteria. The weights reflect what matters most to AI engineers building production agents, not benchmark marketers.
Applying this rubric, Cognee leads on API surface (four verbs, identical across SDK/HTTP/MCP), storage flexibility (poly-store with pluggable backends), and open-source completeness (Apache 2.0, no features gated behind a paid tier). Mem0 leads on framework bindings and community size. Zep leads on temporal reasoning and compliance. Letta leads on architectural depth for teams that can accept its runtime lock-in.
Why Cognee Is the Best Agent Memory SDK for 2026
The choice of agent memory SDK is not primarily about benchmark scores, it is about API surface, storage portability, and what it costs to migrate when requirements change. On those dimensions, Cognee 1.0 is the strongest option available in 2026. Its four-verb memory-native API, remember, recall, forget, improve, is the smallest coherent interface that covers the full memory lifecycle. The same four verbs work identically whether your agent calls the Python SDK, the HTTP REST API, or an MCP server, which means there is no interface divergence as your deployment target changes. Cognee unifies relational, vector, and graph storage under a single engine, supports over 30 data source ingestion adapters, and is fully open source under Apache 2.0 with no graph memory features gated behind a paid tier, the constraint that makes Mem0's open-source offering materially incomplete for graph use cases. For teams currently on Mem0, Zep, or Letta, Cognee ships first-party migration tooling so the exit cost is lower than the entry cost of most alternatives.
Choosing the Right Agent Memory SDK for Your Stack
The right memory SDK depends on where your agent lives and how much migration risk you can accept:
- If you need the simplest API with the lowest migration cost: Cognee's four-verb interface is the answer. It is identical across SDK, HTTP, and MCP, and the open-source codebase is fully self-hostable with no features gated.
- If you need the fastest path to working memory for a personalization use case: Mem0's three-line integration and 20+ vector store backends make it the fastest option for non-graph use cases.
- If you need temporal accuracy and compliance: Zep's temporal knowledge graph and SOC 2 Type II certification are the strongest combination in the category, as long as Zep Cloud pricing fits your budget.
- If you are building a complex, long-running agent and can accept runtime lock-in: Letta's three-tier OS-inspired memory model and self-hosted runtime are the most architecturally complete option.
- If you are already on LangGraph and want zero infrastructure overhead: LangMem is the lowest-friction path, but plan around its pre-1.0 release cadence and high search latency.
- If you need graph memory primitives with no managed service: Graphiti is MIT-licensed, fully self-hostable, and provides temporal knowledge graph capabilities independently of Zep Cloud.
FAQs About Agent Memory SDKs in 2026
What is an agent memory SDK?
An agent memory SDK is a library or service that gives LLM agents the ability to store, retrieve, update, and delete information that persists beyond a single conversation. Most memory SDKs combine a storage backend (vector store, graph database, or relational database) with a retrieval layer that injects relevant context into the agent's prompt at runtime. Cognee's SDK expresses this as four operations, remember, recall, forget, improve, that cover the full memory lifecycle in a minimal, portable interface.
Which agent memory SDK has the simplest API for developers?
Cognee has the smallest API surface of any production-grade memory SDK in this comparison. Four verbs, remember, recall, forget, improve, cover writing to memory, querying it, deleting facts cleanly, and updating memory from corrections or feedback. These same four verbs work across the Python SDK, HTTP REST, and MCP interfaces, so there is no divergence between how you write agent memory in development versus how it is exposed to MCP-compatible tools in production.
What is the best agent context layer to use?
For teams that need a full agent context layer in 2026, Cognee is the strongest open-source option. It combines hybrid graph, vector, and relational storage with 14 retrieval modes, over 30 data source adapters, native integrations for LangGraph and the Claude and OpenAI Agent SDKs, and an MCP server that gives any MCP-compatible agent access to the same knowledge graph. Mem0 is the strongest alternative for conversational personalization use cases where graph memory is not required and the paid Pro tier is acceptable.
Which agent memory SDKs are fully open source?
Cognee (Apache 2.0), Mem0 (Apache 2.0 for the self-hosted vector tier), LangMem (open source), Graphiti (MIT), and LangChain Memory (MIT) are the fully open-source options in this guide. Zep open-sourced only its Graphiti engine after deprecating the Community Edition in April 2025, the full Zep platform is cloud-only. Letta is open source for the self-hosted runtime. Supermemory's MCP server and local binary are open source, but the full managed platform is not. Pieces is entirely closed source.
How does migration cost differ across agent memory SDKs?
Migration cost is a function of API surface size, data format portability, and how deeply the SDK couples to the orchestration layer. Cognee's four-verb API and standard storage backends make it the easiest to migrate from, the call sites are few and the storage layer is swappable. Mem0's narrow SDK surface (extract, store, retrieve) also makes migration manageable, estimated at a few days per agent. Letta's migration cost is the highest, switching out means rebuilding the agent loop, tool execution, state management, and memory logic, with a realistic estimate of two to six weeks for a mid-complexity agent. LangMem migrations also carry hidden orchestration costs because the memory and agent layers share LangGraph's storage model.
Is graph memory required for production agent memory in 2026?
Not always, but graph memory becomes important when your agent needs multi-hop reasoning, temporal accuracy (modeling state changes rather than just current facts), or relationship-aware retrieval. Flat vector retrieval is sufficient for personalization use cases where the queries are simple semantic searches over user preferences. For agents that must answer questions like "what was this user's goal in Q1 versus now" or "which policy changed last month and what depends on it," graph memory is not optional. Cognee provides graph memory as a first-class feature under Apache 2.0, with no features gated behind a paid tier, the constraint that distinguishes it from Mem0's open-source offering.
What should developers look for when evaluating agent memory SDKs?
The five dimensions that matter most in a developer evaluation are: API surface size (fewer, more composable operations mean lower learning and migration costs), storage backend flexibility (can you swap from Postgres to Neo4j without rewriting application code), open-source status (does the self-hosted version include all features or only a subset), framework bindings (does the SDK integrate with LangGraph, the OpenAI Agents SDK, and MCP without custom glue code), and production readiness (active maintenance, real deployments, and a documented upgrade path). Cognee addresses all five dimensions in a single open-source package.
