Best Agent Memory Layers That Run on Postgres in 2026 (Ranked)

You already run Postgres. The question is not whether Postgres can participate in agent memory, it can, across several architectural tiers, but what layer belongs on top of it for your specific use case. This guide evaluates every serious option that uses Postgres as a storage backend, from raw pgvector to full memory engines with graph and vector layers unified in a single instance. Cognee ranks first because it is the only tool that runs graph, vector, relational metadata, sessions, and usage data on a single Postgres instance without requiring a second database category. The other tools reviewed here, pgvector, LangGraph, LangChain, Mem0, and TigerData, each represent a meaningful layer in the stack and a real option depending on how much memory infrastructure you are willing to own.

Why Postgres Is the Right Foundation for Agent Memory

The instinct to reach for a dedicated vector database when building agent memory is understandable. Vector databases were purpose-built for embedding search, and the benchmarks made them look fast. But the actual production profile of agent memory is not a read-heavy RAG workload over a stable document corpus. It is write-heavy, iterative, transactionally complex, and full of operations, entity resolution, contradiction detection, session scoping, metadata filtering, that are joins and transactions, not nearest-neighbor lookups.

The Four Problems a Raw Vector Store Cannot Solve

• Fact supersession. A vector store does not natively understand that a new fact replaces an old one; it only knows how to append. Every retrieval returns the old record alongside the new one unless you write your own deduplication logic.
• Entity resolution across turns. Connecting "the user" in session 3 with "Alice" in session 47 requires a join, not a similarity search. A relational layer handles this correctly.
• Temporal and structural retrieval. Most agent-memory queries do not decompose to pure semantic similarity. They need temporal filtering, entity-based retrieval, or graph traversal to return the right answer.
• Operational overhead. Running a separate vector database, a graph store, and a relational database side by side triples your infrastructure surface area, your monitoring obligations, and your failure modes.

Postgres with pgvector collapses the vector tier back into the database you already operate, inheriting WAL durability, transactional guarantees, and Row Level Security for free. The tools reviewed here build progressively more structured memory on that foundation.

What to Look For in an Agent Memory Layer on Postgres

Evaluating a memory layer is not the same as evaluating a vector database. The features that matter for agent workloads are different from those that matter for static document retrieval. When reviewing any tool in this list, ask whether it provides the following.

Five Capabilities That Separate a Memory Layer from a Vector Column

• Extraction and structuring. Does the system extract facts from raw conversation text, or does it require you to write that pipeline yourself?
• Deduplication and conflict resolution. When the agent receives an update to a previously stored fact, does the system resolve the conflict or append a duplicate?
• Hybrid retrieval. Can the layer combine semantic similarity, keyword search, entity lookup, and graph traversal in a single query path?
• Session and user scoping. Are memories namespaced by user, session, agent, and organization so that cross-contamination between tenants is impossible by design?
• Self-improvement. Does memory quality improve with use, or does it degrade as the corpus grows with near-duplicates and stale records?

Cognee satisfies all five. The tools reviewed below satisfy subsets, and this evaluation is honest about which subsets matter for which workloads.

When Is Plain pgvector Sufficient for Agent Memory?

Before evaluating memory engines, it is worth being direct about the simplest tier. Plain pgvector, an embedding column on a Postgres table, an HNSW or IVFFlat index, and a nearest-neighbor query, is sufficient for a specific class of agent workloads. Understanding that class prevents over-engineering.

Plain pgvector is the right choice when your agent operates on a fixed document corpus that rarely changes, your retrieval pattern is pure semantic similarity with no entity or temporal filtering, and you do not need fact extraction, deduplication, or conflict resolution because you are writing structured records manually. A support agent that recalls product documentation, a coding agent with a static API reference, or a prototype that embeds conversation turns and retrieves the closest ones by cosine similarity are all reasonable pgvector-only deployments.

The failure mode of plain pgvector is not immediate. In the first weeks, retrieval is accurate. At three months, the store accumulates near-duplicates and stale facts, and retrieval quality degrades without a layer on top to manage supersession and deduplication. If your agent writes its own memory from user interactions rather than querying a static corpus, you need more than a vector column.

-- Minimal pgvector schema for agent memory
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE agent_memories (
 id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
 user_id     TEXT NOT NULL,
 session_id  TEXT,
 content     TEXT NOT NULL,
 embedding   vector(1536),
 created_at  TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX ON agent_memories
 USING hnsw (embedding vector_cosine_ops);

-- Nearest-neighbor retrieval
SELECT content
FROM   agent_memories
WHERE  user_id = $1
ORDER  BY embedding <=> $2
LIMIT  5;

This is the baseline. Every tool reviewed below adds structure above it.

How AI Engineers Use Postgres-Backed Memory Layers

The teams building production agents on Postgres-backed memory layers follow recognizable patterns. Understanding how they deploy these tools clarifies which tier fits which team.

Persistent personalization agents. Customer-facing agents that need to remember preferences, interaction history, and behavioral patterns across sessions. These teams typically use Mem0 on pgvector or Cognee on Postgres, depending on whether graph traversal is needed for relationship-aware recall.

Multi-agent state coordination. Teams running multi-agent pipelines where agents need to share and read from a common memory substrate. Cognee positions itself as a portable layer that any Model Context Protocol agent can adopt with minimal setup, making a shared memory backend that each tool can read from a realistic production posture rather than an aspirational one.

Checkpoint-based stateful workflows. Agentic pipelines that require pause-resume, human-in-the-loop review, and time-travel debugging. LangGraph's PostgresSaver is the standard implementation here, giving every node transition a durable snapshot in Postgres.

Enterprise knowledge retrieval. Agents embedded in enterprise knowledge bases where memory must span thousands of documents, relate entities across sources, and return citation-backed answers. This is where graph-plus-vector retrieval outperforms flat vector search, and where Cognee's architecture, with 14 retrieval modes including chain-of-thought graph traversal, is most differentiated.

Lightweight session memory with structured extraction. Prototyping and small-scale production where the team wants automatic fact extraction and deduplication without operating graph infrastructure. With roughly 48,000 GitHub stars, a $24M Series A closed in October 2025, and YC backing, Mem0 has become the default choice for teams that want to bolt production-grade memory onto an existing agent in under a day.

Forkable experiment workflows. Engineering teams deploying coding agents that need to branch database state safely for parallel experimentation. For the first time, developers and agents can create instant, copy-on-write branches of both databases and volumes, enabling safe parallel experiments without runaway costs, a capability TigerData's Agentic Postgres brings to this tier.

Competitor Comparison: Agent Memory Layers on Postgres

The table below compares the six tools reviewed in this guide across the dimensions that matter most for Postgres-backed agent memory. Use it as a quick reference before reading the detailed sections.

Cognee is the only tool that consolidates graph, vector, relational metadata, and session state on a single Postgres instance without requiring a dedicated graph database or a separately managed vector store. Every other tool either skips the graph layer, paywalls it, or externalizes it to a separate system.

Best Agent Memory Layers That Run on Postgres in 2026

1. Cognee

Cognee 1.0 launched on June 26, 2026 with a memory-native API and a single-Postgres deployment model, making it the first open-source memory platform to eliminate the need for a separate graph database, a dedicated vector store, and a Redis deployment by consolidating all three layers into Postgres. For teams already running Postgres in production, Cognee means zero new database categories to operate.

The release moves Cognee from a developer library to a production system: the same engine now ships as a managed service called Cognee Cloud, runs on a single Postgres database, is rebuilt around a high-performance Rust core, and is reorganized around a memory-native API. The core architecture uses an ECL pipeline (Extract, Cognify, Load) that ingests from 30-plus data sources, extracts structured entities and relationships, writes them as graph edges and nodes through Cognee's Postgres graph backend, and stores embeddings via pgvector.

Key Features

• Single-Postgres Deployment. Running on a single Postgres matters because it removes the multi-system overhead that usually comes with agent memory. The common setup runs a separate vector database, a graph store, and a relational database side by side, each adding cost and operational complexity. Cognee collapses all three into one instance with four environment variables.
• 14 Retrieval Modes. Cognee ships with 14 retrieval modes, including classic semantic similarity, chain-of-thought graph traversal, and a default GRAPH_COMPLETION mode that routes each query to the most appropriate retrieval strategy automatically, meaning developers do not have to design retrieval logic per query type.
• Memory-Native API. Four operations, remember, recall, forget, improve, provide a complete memory lifecycle without requiring custom pipeline logic.
• BEAM Benchmark Performance. Cognee beat the state of the art on BEAM's 100k-token setting by 6.5% and matched state of the art at 10M tokens using only default open-source features, no custom benchmark-specific architecture. Teams evaluating these claims should review the benchmark methodology independently.

Postgres Connection Configuration

DB_PROVIDER=postgres
VECTOR_DB_PROVIDER=pgvector
GRAPH_DATABASE_PROVIDER=postgres
CACHE_BACKEND=postgres
DB_HOST=localhost
DB_PORT=5432
DB_USERNAME=cognee
DB_PASSWORD=cognee
DB_NAME=cognee_db

Agent Memory Offerings

• Graph + Vector on Postgres. Entities, relationships, and embeddings coexist in one Postgres instance, eliminating cross-system consistency issues.
• Self-Improving Memory. The memify abstraction allows Cognee to refine its own knowledge structures over time. Memory does not simply accumulate; it is reorganized and reinforced based on agent interaction patterns, making recall accuracy improve with use.
• MCP-Native and Framework-Agnostic Integration. Cognee supports over 30 data source ingestion pipelines, a poly-store backend, a four-operation API (remember, recall, forget, improve), and first-party integrations with LangGraph, Claude Code, the OpenAI SDK, and n8n.
• 30-Plus Ingestion Sources. Documents, databases, APIs, Slack, Notion, Google Drive, and warehouses all feed into the same ECL pipeline.

Pricing

• Open Source (Apache 2.0): Free to self-host, forever. Bring your own Postgres instance. No usage caps.
• Cloud Hobby: Free, 1M tokens, 1 workspace, unlimited users.
• Cloud Growth: $5/workspace/month, unlimited workspaces, data source integrations (Slack, Notion, Google Drive), in-app support.
• Enterprise: Custom, dedicated support, bring-your-own cloud, SLAs.
• Token pricing: $2.50 per 1M tokens on paid plans.

Pros

• Only tool that unifies graph, vector, and relational layers on a single Postgres instance, no second database category required
• Full graph memory available at the free tier; not paywalled behind a Pro plan unlike Mem0
• Self-improving memory through the improve API, quality compounds with use rather than degrading over time
• Graduation from the GitHub Secure Open Source Program adds a layer of enterprise credibility that few open-source memory tools can match.
• Cognee exposes add_tool and search_tool interfaces for LangGraph, the OpenAI Agents SDK, and Claude Code. A standalone MCP server is also available for Cursor, Claude Desktop, and Cline.

Cons

• Python-only SDK as of mid-2026; TypeScript, Go, or other language-based agent stacks cannot use Cognee natively without custom wrappers
• Graph retrieval adds latency compared to a flat vector query, not the right choice for agents where sub-100ms recall is a hard requirement
• ECL pipeline and graph extraction add LLM token cost at ingestion time; factor this into your total cost model

The platforms that win will be the ones that are easy to operate (one database, not three), agent-native (MCP and SDK breadth), and self-improving (memory that gets better, not just bigger). Cognee 1.0's combination of a memory-native API, single-Postgres deployment, Rust edge core, and TypeScript SDK is the most complete expression of that direction available as open source today. For teams that are already running Postgres and do not want to add Neo4j, Qdrant, or Redis to their stack, Cognee is the clearest path to a production-grade graph-plus-vector memory engine.

2. pgvector (Raw)

pgvector is a Postgres extension that adds a vector column type and approximate nearest-neighbor indexes (HNSW and IVFFlat) to any Postgres instance. It is not a memory layer in the full sense, it provides the retrieval substrate that memory layers build on. If you are already using PostgreSQL, pgvector adds vector search with a single CREATE EXTENSION vector, no extra infrastructure, no extra cost, no extra API to learn. For workloads under roughly 10 million vectors on a single node, pgvector with IVFFlat handles writes efficiently and inherits Postgres WAL for durability.

Key Features

• HNSW and IVFFlat indexes. HNSW gives higher recall with higher memory cost; IVFFlat builds faster and handles frequent writes more efficiently. Choose based on whether query recall or write throughput is the binding constraint.
• Iterative index scans (pgvector 0.8+). Solves the filtered-search recall problem by pulling additional candidates from the index until enough rows pass the WHERE clause, critical for user-scoped agent memory queries.
• Cosine, L2, and inner product operators. Standard similarity metrics with appropriate operator classes per index type.
• Full SQL composability. Combine vector similarity with any Postgres filter, date ranges, metadata columns, user scoping, in a single query.

Postgres Connection Configuration

import psycopg2
from pgvector.psycopg2 import register_vector

conn = psycopg2.connect(
   host="localhost",
   port=5432,
   dbname="agent_memory",
   user="postgres",
   password="postgres"
)
register_vector(conn)

Agent Memory Offerings

• Semantic Recall. Retrieve the K nearest embeddings to a query vector using HNSW cosine similarity across any user-scoped partition.
• Filtered Retrieval. Combine vector search with SQL predicates for user-scoped and time-filtered queries in a single round trip.
• Write-Heavy Durability. IVFFlat index with WAL write-ahead logging for durable, high-frequency memory writes under 10M vectors.

Pricing

pgvector has no pricing model at all because it is a free Postgres extension. Your only cost is the database instance, which most teams running a RAG or search feature already pay for.

Pros

• Zero operational overhead if you are already running Postgres
• Full SQL composability, user scoping, temporal filtering, and metadata joins in one query
• WAL durability for write-heavy agent workloads
• No additional infrastructure for workloads under 10M vectors

Cons

• No fact extraction, deduplication, or conflict resolution, you own that pipeline entirely
• No graph layer, entity relationships and multi-hop reasoning require custom schema design
• HNSW indexes are read-optimized and degrade under write-heavy agent workloads with frequent updates; IVFFlat is more appropriate but has lower recall
• Memory quality degrades over time without a management layer for supersession and deduplication

3. LangGraph (PostgresSaver)

LangGraph's PostgresSaver is a checkpoint implementation that writes the full graph state to Postgres after every node transition. It is the standard production persistence layer for LangGraph-based agents, enabling pause-resume, human-in-the-loop review, and time-travel debugging. It is not a semantic memory layer, it stores raw graph state, not extracted facts, but it is the correct tool for durable in-session memory in LangGraph-based workflows.

Key Features

• Per-step checkpointing. Every node transition in the graph writes a full state snapshot to Postgres, so agents can resume from any prior step without data loss.
• Thread-based isolation. thread_id scopes all state to a single conversation or task; different users and tasks stay cleanly separated in the same database.
• Human-in-the-loop support. The agent pauses at a node, a human inspects and optionally modifies the checkpoint via graph.update_state(), and execution resumes from the updated state.
• Time-travel debugging. Query the full history of checkpoints for a thread, branch from any prior state, and replay forward with different inputs.
• Async support. AsyncPostgresSaver for non-blocking use in async agent runtimes.

Postgres Connection Configuration

from langgraph.checkpoint.postgres import PostgresSaver

DB_URI = "postgresql://user:pass@localhost:5432/db"

with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
   checkpointer.setup()
   graph = builder.compile(checkpointer=checkpointer)

Agent Memory Offerings

• In-Session State Persistence. Full graph state survives process restarts; agents resume mid-task without losing context.
• Cross-Turn Conversation History. The checkpointer stores all messages within a thread, making multi-turn conversations durable across restarts.
• Time-Travel Debugging. Query the history of saved states, travel to a past moment, change something, and continue forward creating a new branch.

Pricing

Free (MIT). LangSmith observability from $39/month. LangGraph Platform managed deployment is separately priced.

Pros

• Native Postgres support with psycopg (Psycopg 3); no extra dependencies for teams already running Postgres
• HITL, time travel, and pause-resume out of the box, the most complete checkpoint feature set of any tool in this list
• Standard implementation for LangGraph-based agentic workflows at production scale
• Encrypted checkpoints via EncryptedSerializer for sensitive state data

Cons

• Stores raw graph state, not extracted semantic memory, retrieval is by thread_id only, not by query similarity
• No fact extraction, deduplication, or graph traversal, cross-session knowledge must be built on top with a separate tool
• State bloat is a real operational concern: large binary objects in state produce proportionally large checkpoints at every step, causing database growth and slower queries
• Tightly coupled to the LangGraph framework; not portable to other agent runtimes

4. LangChain (LangMem + PostgresStore)

LangMem is LangChain's official open-source SDK for long-term agent memory, built for the LangGraph ecosystem. It provides semantic memory (user facts), episodic memory (interaction summaries), and procedural memory (agents rewriting their own system instructions). When configured with a PostgresStore backend, it gives agents memory that persists across sessions and is scoped by namespace. The most architecturally distinctive capability is procedural memory: agents can update their own system prompt instructions based on accumulated user feedback, which is a genuinely different model from simple fact retrieval.

Key Features

• Three memory types. Three memory types, semantic (facts/knowledge), episodic (past experiences), and procedural (learned behaviors/prompt rules), modeled after human memory, with two integration modes: hot-path tools agents call during conversations, and background memory managers that extract memories asynchronously.
• Namespace-based scoping. Memory is scoped by user_id, team_id, or app_id, preventing cross-contamination between users and sessions.
• Background extraction. Memory extraction runs after conversations complete, so it does not block the agent's real-time response path.
• Memory consolidation. Memory consolidation prevents unbounded growth by merging related memories and resolving contradictions, avoiding the memory hoarding problem.

Postgres Connection Configuration

from langgraph.store.postgres import PostgresStore

DB_URI = "postgresql://user:pass@localhost:5432/db"
store = PostgresStore.from_conn_string(DB_URI)
store.setup()

Agent Memory Offerings

• Semantic Long-Term Memory. Facts about users and the world stored and retrieved via embedding similarity against PostgresStore.
• Procedural Self-Modification. Agents update their own operating instructions based on user feedback, persisted across sessions in PostgresStore.
• Cross-Session Continuity. Memories persist across conversations under namespaced user scoping, with consolidation preventing corpus bloat.

Pricing

LangMem SDK is free (MIT). LangSmith observability from $39/month Developer, $259/month Plus. LangGraph Platform has separate managed deployment pricing.

Pros

• Procedural memory, agents updating their own system prompt, is unique in this category and architecturally differentiated from all other tools in this list
• Strong ecosystem fit for teams already building on LangChain and LangGraph
• Pluggable storage backends support any vector DB, MongoDB, Postgres via pgvector, and more.
• Background extraction does not add latency to the agent's real-time path

Cons

• LangMem requires a LangGraph store backend and has a p95 search latency of approximately 59.82 seconds, making it suitable for background processing but not for interactive agents.
• Tightly coupled to LangGraph; teams not already on LangGraph face meaningful adoption overhead to unlock LangMem's capabilities
• No hosted memory backend; teams configure and operate PostgresStore themselves
• No graph layer for relationship-aware retrieval across entities

5. Mem0

Mem0 is an open-source memory layer that sits above a vector store and adds automatic fact extraction, deduplication, and conflict resolution. The open-source library supports alternative backends including Chroma, Weaviate, Pinecone, and PostgreSQL with pgvector, making it a deployable Postgres-compatible memory layer without additional infrastructure. The API is designed for minimal integration friction, and the extraction pipeline handles the structuring step that raw pgvector leaves to the developer. Graph memory is available but requires the Pro tier at $249/month on the managed platform.

Key Features

• Automatic fact extraction. An LLM extraction step runs on each conversation turn, pulling discrete facts from raw text before writing to the vector store.
• Deduplication and conflict resolution. The managed platform handles supersession logic so that updated facts replace stale ones rather than accumulating as duplicates.
• pgvector backend support. 19 vector store backends are supported including Qdrant, Chroma, PGVector, Pinecone, Azure AI Search, and Amazon S3 Vectors.
• Multi-level memory scoping. Session, user, agent, and organization namespaces isolate memories across tenants.

Postgres Connection Configuration

from mem0 import Memory

config = {
   "vector_store": {
       "provider": "pgvector",
       "config": {
           "user": "test",
           "password": "123",
           "host": "127.0.0.1",
           "port": "5432",
       }
   }
}

m = Memory.from_config(config)

Agent Memory Offerings

• Extraction-First Memory Writing. Conversations are not embedded raw; the extraction step produces discrete, self-contained facts before any vector write occurs.
• pgvector Semantic Retrieval. Nearest-neighbor search over extracted facts, with optional DiskANN and HNSW indexing.
• Graph Memory Mode (Pro). Mem0 can retrieve user preferences semantically, look up subscription tier exactly, and traverse relationship data through the graph, all in a single memory call. This capability requires the $249/month Pro plan on the managed platform.
• MCP Server. The official OpenMemory MCP server enables direct integration with Claude Desktop, Cursor, and other MCP-compatible AI tools.

Pricing

• Open Source (Apache 2.0): Free to self-host. Manage your own Postgres, pgvector, and embedding pipeline.
• Hobby (managed): Free, 10K memories per month, 1K retrieval calls.
• Starter: $19/month, 50K memories.
• Pro: $249/month, graph memory, higher limits, enterprise compliance features (SOC 2, HIPAA).
• Enterprise: Custom pricing.

Pros

• Fastest path from zero to extraction-backed memory on pgvector, minimal code required to reach a working prototype
• Framework-agnostic REST API; works with any agent regardless of the LLM or orchestration layer
• AWS selected Mem0 as the exclusive memory provider for their Agent SDK, representing the strongest enterprise validation in the category.
• The Mem0 platform API is language-agnostic and available as Python and Node.js SDKs.

Cons

• Graph memory is paywalled behind the $249/month Pro plan; free and Starter tiers are flat-vector only
• The $19 to $249 jump is steep, there is no middle tier with partial graph support
• A high-severity security vulnerability (CVSS 8.1) was disclosed April 17, 2026, affecting PGVector, Azure MySQL, and Neptune Analytics backends. Verify patch status before production deployment.
• Self-hosted open-source version loses managed platform features, automatic conflict resolution and hosted graph memory require the paid platform

6. TigerData (Agentic Postgres + memory-engine)

TigerData is the company behind TimescaleDB and Agentic Postgres, building modern data infrastructure for agents, devices, and developers. Alongside forkable infrastructure, Agentic Postgres brings three new primitives into Postgres: an interface to control agents, hybrid search for retrieval, and persistent memory for state. The memory-engine repository is Apache 2.0 and targets TypeScript-first agent stacks. The infrastructure story is different from Cognee, TigerData treats Postgres as the database-of-record and adds memory as a managed API surface rather than unifying all layers into one schema.

Key Features

• Forkable infrastructure. Developers and agents can create instant, copy-on-write branches of both databases and volumes, enabling safe parallel experiments without runaway costs.
• Hybrid search (pgvectorscale + BM25). For semantic search, TigerData improved their existing extension pgvectorscale for higher-throughput indexing, better recall, and lower latency at scale than pgvector. For full-text search, pg_textsearch implements BM25 for modern ranked keyword search optimized for hybrid AI applications alongside pgvectorscale.
• MCP-native control plane. REST APIs, CLI, and MCP endpoints give agents direct access to database operations without bespoke tooling.
• Free tier with no credit card required. A free tier is available with no credit card and no time limit.

Postgres Connection Configuration

# Install Tiger CLI and MCP
curl -fsSL https://cli.tigerdata.com | sh
tiger auth login
tiger mcp install

# Create a service directly
tiger create service

Agent Memory Offerings

• Persistent Agent Context. Conversation history, user preferences, and shared state stored and retrieved via APIs and MCP endpoints.
• Forkable Databases. Instant copy-on-write branches for safe agent experimentation without touching production data.
• Hybrid Retrieval. pgvectorscale for vector search and BM25 for keyword search, combined in a single query without a dedicated search layer.

Pricing

The Tiger Free Plan is a fully managed Postgres built for how AI development actually works: experimental, iterative, and increasingly agent-driven. Paid plans are not publicly listed at a fixed rate; contact TigerData for managed cloud pricing. memory-engine is Apache 2.0 and free to self-host.

Pros

• Forkable infrastructure is a genuinely novel capability for agentic workloads, no other tool in this list offers copy-on-write database branching
• Hybrid search (semantic + BM25) without a separate search engine is operationally simpler than running two retrieval systems
• TypeScript-first memory-engine targets a language stack that Cognee does not natively serve as of mid-2026
• MCP-native from launch; agents interact with the database through a standardized protocol
• More than 2,000 customers, including OpenSea, HuggingFace, Mistral, Mercor, and Siemens, use Tiger to power production workloads across IoT, Web3, and AI.

Cons

• Memory primitive is in public preview; production readiness should be evaluated against your SLA requirements
• No knowledge graph layer, relationship-aware retrieval and multi-hop reasoning are not available
• No fact extraction or deduplication in the memory API; teams own the extraction pipeline
• Managed cloud pricing is not publicly listed, making cost estimation harder than self-hosted alternatives
• Less focused on agent memory specifically, Agentic Postgres is a broader database product with memory as one of three primitives

Evaluation Rubric for Agent Memory Layers on Postgres

This review evaluated each tool against six categories. The weighting reflects how much each category matters for the specific query, teams that already run Postgres and want to add agent memory, rather than a generic AI infrastructure evaluation.

Cognee scores highest on Postgres integration depth (single-instance, no second category) and memory layer completeness (extraction, graph, vector, sessions, self-improvement). It also provides full graph capabilities at the free tier, whereas Mem0 gates graph memory behind the $249/month Pro plan. The other tools are strong in narrower dimensions: LangGraph leads on checkpoint-based stateful workflows, TigerData leads on forkable infrastructure for coding agents, and Mem0 leads on developer experience speed-to-prototype and community size.

Why Cognee Is the Best Agent Memory Layer for Teams Running Postgres

The query at the center of this guide is concrete: find an agent memory layer that runs on Postgres. The answer depends on what "memory layer" means in your architecture. If it means a vector column, pgvector answers it. If it means checkpoint-based state persistence, LangGraph's PostgresSaver answers it. If it means extraction-backed semantic memory with a pgvector backend, Mem0 answers it, at a cost if you need graph retrieval. If it means the full stack, graph, vector, relational metadata, sessions, and self-improving memory, unified in a single Postgres instance with zero new database categories, Cognee is the only tool that answers it completely. Cognee is the only framework purpose-built around a graph-native architecture and a structured ECL pipeline that makes memory an active, self-improving layer rather than a passive store. For developers and AI engineers evaluating tools to build production agents on existing Postgres infrastructure, that is the most important distinction in this category.

FAQs About Agent Memory Layers on Postgres

Can I use pgvector as an agent memory layer?

Yes, with an important qualification. pgvector provides the retrieval substrate, a vector column with HNSW or IVFFlat indexing and cosine similarity queries, but it does not provide fact extraction, deduplication, conflict resolution, or graph traversal. For static document retrieval or prototypes with a small number of manually written memory records, plain pgvector is sufficient. For production RAG at scale, dedicated vector databases offer better performance and more features, but pgvector is the fastest path from zero to working semantic search. For agents that write their own memories from conversation turns, Cognee or Mem0 built on top of pgvector adds the management layer that raw pgvector leaves to the developer.

Which memory frameworks support Postgres as a backend?

Several frameworks support Postgres at different levels of integration. LangGraph's PostgresSaver and PostgresStore support Postgres for checkpoint state and long-term memory storage respectively. Teams can keep an existing checkpointer, add a PostgresStore alongside it, and attach memory tools to the agent's tool list. Mem0 supports pgvector as a configurable vector backend. LangMem builds on LangGraph's PostgresStore. TigerData's memory-engine is designed for Tiger Cloud's managed Postgres. Cognee is the most complete Postgres-native implementation, running graph, vector, relational metadata, and session state all on a single Postgres instance without requiring any additional database.

What is an agent memory layer?

Agent memory is the persistent, queryable store that lets an AI agent retain facts, decisions, prior tool calls, and user preferences across sessions, then retrieve only what is relevant at inference time. A complete memory layer includes storage for facts and relationships, an extraction pipeline that structures raw conversation text into discrete memories, retrieval logic that returns the right memories for a given query, and lifecycle operations like updating, forgetting, and improving stored memories. Raw vector stores like pgvector provide storage and retrieval but not extraction or lifecycle management. Cognee provides the full stack, adding a graph layer for relationship-aware retrieval that flat vector systems cannot support.

What are the best agent memory layers that run on Postgres in 2026?

Ranked by how completely each tool uses Postgres as its memory backend: Cognee (graph + vector + relational + sessions on a single Postgres instance), LangGraph PostgresSaver (checkpoint state on Postgres, no semantic retrieval), LangMem with PostgresStore (semantic and procedural memory on Postgres, LangGraph required), Mem0 with pgvector backend (extraction-backed semantic memory, graph requires Pro tier at $249/month), TigerData memory-engine (persistent context API on managed Postgres, no graph or extraction), and raw pgvector (vector retrieval substrate only). For teams that want zero new database categories and a complete memory stack, Cognee is the correct starting point.

When does a knowledge graph earn its place in agent memory?

Graph retrieval earns its place when the agent's memory contains entities with meaningful relationships that flat vector search cannot recover. A user who has a manager, a set of projects, a set of preferences, and a set of past decisions has a graph, not a list of similar text chunks. Multi-hop questions like "what did the user decide about the project their manager owns" require traversal, not similarity search. Mem0's graph layer can retrieve user preferences semantically, look up exact values, and traverse relationship data through the graph, all in a single memory call, but this capability is gated behind the Pro tier. Cognee provides the same graph-traversal capability at the free tier, natively on Postgres, without a separate graph database.