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Memstate AI - Agent Memory System

Memstate AI - Agent Memory System

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by memstate-ai

Memstate AI - MCP

npm version License: MIT MCP Node memstate-mcp MCP server

Versioned memory for AI agents. Store facts, detect conflicts, and track how decisions change over time — exposed as a hosted MCP server.

Dashboard · Docs · Pricing


Why Memstate?

RAG (most other memory systems)

Memstate AI

Token usage per conversation

~7,500

~1,500

Agent visibility

Black box

Full transparency

Memory versioning

None

Full history

Token growth as memories scale

O(n)

O(1)

Infrastructure required

Yes

None — hosted SaaS

Other memory systems dump everything into your context window and hope for the best. Memstate gives your agent a structured, versioned knowledge base it navigates precisely — load only what you need, know what changed, know when facts conflict.


Related MCP server: BuildAutomata Memory MCP Server

Benchmarks

We built an open-source benchmark suite that tests what actually matters for agent memory: can your system store facts, recall them accurately across sessions, detect conflicts when things change, and maintain context as a project evolves?

Head-to-Head: Memstate AI vs Mem0

Both systems were tested under identical conditions using the same agent (Claude Sonnet 4.6, temperature 0), the same scenarios, and the same scoring rubric.

Metric

Memstate AI

Mem0

Winner

Overall Score

69.1

15.4

Memstate

Accuracy (fact recall)

74.1

12.6

Memstate

Conflict Detection

85.5

19.0

Memstate

Context Continuity

63.7

10.1

Memstate

Token Efficiency

22.3

30.6

Mem0

Scoring weights: Accuracy 40%, Conflict Detection 25%, Context Continuity 25%, Token Efficiency 10%.

Per-Scenario Breakdown

The benchmark runs five real-world scenarios that simulate multi-session agent workflows:

Scenario

Memstate AI

Mem0

Web App Architecture Evolution

43.2

55.6

Auth System Migration

66.2

10.2

Database Schema Evolution

72.7

7.0

API Versioning Conflicts

86.5

0.9

Team Decision Reversal

77.2

3.3

Mem0 won the first scenario (simple architecture tracking), but struggled severely on scenarios requiring contradiction handling, cross-session context, and decision reversal tracking — scoring near zero on three of five scenarios.

Why Memstate Wins

The benchmark reveals a fundamental architectural difference:

Mem0 uses embedding-based semantic search. Facts are chunked, embedded, and retrieved by similarity. This works for simple lookups but breaks down when:

  • Facts contradict earlier facts (the system can't distinguish current vs. outdated)

  • Precise recall is needed (embeddings return "similar" results, not exact ones)

  • Write-to-read latency matters (new memories take seconds to become searchable)

Memstate uses structured, versioned key-value storage. Every fact lives at an explicit keypath with a full version history. This means:

  • Conflict detection is built in — when a new fact contradicts an old one, the system knows and preserves both versions

  • Recall is deterministic — you get back exactly what was stored, not an approximate match

  • Cross-session continuity is reliable — the agent navigates a structured tree rather than hoping semantic search surfaces the right context

  • Token cost stays O(1) — the agent loads summaries first and drills into detail only when needed, instead of dumping all potentially-relevant embeddings into the context window

Fairness Notes

  • Both systems used the same agent model, temperature, and evaluation rubric

  • Mem0 was given a 10-second ingestion delay between writes and reads to account for its async embedding pipeline

  • Mem0 scores higher on token efficiency, but this metric should be read in context — lower token usage can simply reflect less information being returned. A system that retrieves incomplete or incorrect facts uses fewer tokens per response but may require more follow-up calls, ultimately costing more tokens to reach the same answer

  • The benchmark source code is included in this repository for full reproducibility

  • Mem0 may perform differently with custom configuration or a different embedding model


Quick Start

Get your API key at memstate.ai/dashboard, then add to your MCP client config:

{
  "mcpServers": {
    "memstate": {
      "command": "npx",
      "args": ["-y", "@memstate/mcp"],
      "env": {
        "MEMSTATE_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

No Docker. No database. No infrastructure. Running in 60 seconds.


Client Setup

Claude Desktop

Config location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "memstate": {
      "command": "npx",
      "args": ["-y", "@memstate/mcp"],
      "env": { "MEMSTATE_API_KEY": "YOUR_API_KEY_HERE" }
    }
  }
}

Claude Code

claude mcp add memstate npx @memstate/mcp -e MEMSTATE_API_KEY=YOUR_API_KEY_HERE

Cursor

In Cursor Settings → MCP → Add Server — same JSON format as Claude Desktop above.

Cline / Windsurf / Kilo Code / Roo Code

All support the same stdio MCP config format. Add to your client's MCP settings file.


Core Tools

Tool

When to use

memstate_remember

Store markdown, task summaries, decisions. Server extracts keypaths and detects conflicts automatically. Use for most writes.

memstate_set

Set a single keypath to a short value (e.g. config.port = 8080). Not for prose.

memstate_get

Browse all memories for a project or subtree. Use at the start of every task.

memstate_search

Semantic search by meaning when you don't know the exact keypath.

memstate_history

See how a piece of knowledge changed over time — full version chain.

memstate_delete

Soft-delete a keypath. Creates a tombstone; full history is preserved.

memstate_delete_project

Soft-delete an entire project and all its memories.

How keypaths work

Memories are organized in hierarchical dot-notation:

project.my_app.database.schema
project.my_app.auth.provider
project.my_app.deploy.environment

Keypaths are auto-prefixed: keypath="database" with project_id="my_app" → project.my_app.database. Your agent can drill into exactly what it needs — no full-context dumps.


How It Works

Agent: memstate_remember(project_id="my_app", content="## Auth\nUsing SuperTokens...")
         ↓
Server extracts keypaths:  [project.my_app.auth.provider, ...]
         ↓
Conflict detection:  compare against existing memories at those keypaths
         ↓
New version stored — old version preserved in history chain
         ↓
Next session: memstate_get(project_id="my_app") → structured summaries only
         ↓
Agent drills into project.my_app.auth only when it needs auth details

Token cost stays constant regardless of how many total memories exist.


Add to Your Agent Instructions

Copy into your AGENTS.md or system prompt:

## Memory (Memstate MCP)

### Before each task
- memstate_get(project_id="my_project") — browse existing knowledge
- memstate_search(query="topic", project_id="my_project") — find by meaning

### After each task
- memstate_remember(project_id="my_project", content="## Summary\n- ...", source="agent")

### Tool guide
- memstate_remember — markdown summaries, decisions, task results (preferred)
- memstate_set — single short values only (config flags, status)
- memstate_get — browse/retrieve before tasks
- memstate_search — semantic lookup when keypath unknown
- memstate_history — audit how knowledge evolved
- memstate_delete — remove outdated memories (history preserved)

Environment Variables

Variable

Default

Description

MEMSTATE_API_KEY

(required)

API key from memstate.ai/dashboard

MEMSTATE_MCP_URL

https://mcp.memstate.ai

Override for self-hosted deployments

Verify Your Connection

MEMSTATE_API_KEY=your_key npx @memstate/mcp --test

Prints all available tools and confirms your API key works.

Built for AI agents that deserve to know what they know.

Available Tools

7 tools
memstate_deleteA

Soft-delete a memory by keypath. Creates a tombstone version preserving full history. The memory can be un-deleted by setting a new value at the same keypath.

USE THIS WHEN: You need to remove outdated or incorrect memories, clean up a keypath subtree, or mark memories as no longer relevant. NOT FOR: Updating content (use memstate_set or memstate_remember to overwrite with new content instead).

memstate_delete(project_id="myapp", keypath="config.old_setting") memstate_delete(project_id="myapp", keypath="config", recursive=true) → deletes config and all children

History is preserved. Use memstate_history to see the deletion in the version chain. Keypath is auto-prefixed.

Inputs: project_id (required), keypath (required), recursive. Returns: result

memstate_delete_projectA

Soft-delete an entire project and all its memories. Creates tombstone versions for every memory, preserving full history. The project is hidden from listings but can be restored by creating a new project with the same ID.

USE THIS WHEN: You want to remove all memories for a project, e.g. cleaning up test data or decommissioning a project.

memstate_delete_project(project_id="old-project")

All memories get individual tombstone versions. History is preserved via memstate_history.

Inputs: project_id (required). Returns: result

memstate_getA

Browse and retrieve memories. Use BEFORE starting tasks to fetch existing knowledge.

USE THIS WHEN: You need to read what's already stored — list projects, browse a project tree, or get content at a keypath. NOT FOR: Saving new content (use memstate_remember for markdown/summaries, memstate_set for one keypath value).

memstate_get() → list all projects memstate_get(project_id="myapp") → full project tree memstate_get(project_id="myapp", keypath="database") → subtree memstate_get(project_id="myapp", keypath="db", include_content=true) → with full content memstate_get(memory_id="mem_abc123") → single memory by ID

Keypath is auto-prefixed. Default recursive=true.

Inputs: project_id, keypath, memory_id, recursive, +3 more. Returns: result

memstate_historyA

View version history for a keypath or memory chain. Use when you need to see how a value changed over time.

USE THIS WHEN: Debugging past state, auditing changes, or recovering a previous value. NOT FOR: Reading current content (use memstate_get) or saving (use memstate_remember or memstate_set).

memstate_history(project_id="myapp", keypath="config.database.port") memstate_history(memory_id="mem_abc123")

Returns all versions with timestamps; latest is marked is_latest=true.

Inputs: project_id, keypath, memory_id. Returns: result

memstate_rememberA

Save markdown, task summaries, or any text. Server extracts keypaths and creates structured memories automatically. This is the PREFERRED way to save information.

USE THIS WHEN: Saving task summaries, meeting notes, docs, or any text with multiple facts. The server handles organization, conflict detection, and versioning. NOT FOR: Setting one specific keypath to a short value (e.g. config.port = "8080") — use memstate_set for that.

memstate_remember(project_id="myapp", content="## Task Summary\n- Added OAuth\n- Files: auth.go, middleware.go", source="agent") memstate_remember(project_id="myapp", content="Architecture decision: migrated to JWT tokens for session management")

Content limit: 100,000 chars. Processing is async (~15-18s); returns job_id immediately.

Inputs: project_id (required), content (required), source, context. Returns: result

memstate_setA

Set one keypath to a short value. Only for simple key=value facts like config, status, or version numbers.

USE THIS WHEN: You need to store ONE specific value at a known keypath (e.g. a port number, a status flag, a version string). NOT FOR: Task summaries, markdown, or text with multiple facts — use memstate_remember instead.

memstate_set(project_id="myapp", keypath="config.database.port", value="5432") memstate_set(project_id="myapp", keypath="status.deployment", value="production") memstate_set(project_id="myapp", keypath="version.current", value="2.1.0")

Value limit: 2,000 chars. If longer, use memstate_remember instead. Keypath is auto-prefixed.

Inputs: project_id (required), keypath (required), value (required), category, +1 more. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.7/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with minimal overlap. The descriptions explicitly differentiate tools (e.g., memstate_remember vs. memstate_set, memstate_get vs. memstate_search) and include 'USE THIS WHEN' and 'NOT FOR' sections that prevent confusion. The tools cover different aspects of memory management without redundancy.

    Naming Consistency5/5

    All tools follow a consistent 'memstate_verb' naming pattern (e.g., memstate_delete, memstate_get, memstate_search). This uniformity makes it easy to identify the server's tools and their functions at a glance, with no deviations in style or structure across the set.

    Tool Count5/5

    With 7 tools, the server is well-scoped for an agent memory system. Each tool serves a specific, necessary function (e.g., create, read, update, delete, search, history, project management), and there are no extraneous tools. The count aligns with the domain's complexity without being overwhelming or insufficient.

    Completeness5/5

    The tool set provides comprehensive coverage for memory management, including CRUD operations (memstate_remember/set for create/update, memstate_get for read, memstate_delete for delete), search (memstate_search), version history (memstate_history), and project-level management (memstate_delete_project). There are no obvious gaps; agents can handle full memory lifecycles and workflows effectively.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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