Install Jentic One Beta

Connect the MemoryPlugin to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the MemoryPlugin, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmemoryplugin.com%2Fmemoryplugin" | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fmemoryplugin.com%2Fmemoryplugin" | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with MemoryPlugin API.

Store a new memory with dated text in the default bucket or a chosen bucketId

Search memories by query and get back the 5 most relevant, or page through the latest with count and skip

Pull relevant context from past chat history for the user's current input, capped by maxTokens up to 2000

Create named buckets and fetch memories and buckets together in one request filtered by bucketId

GET STARTED

Start building with MemoryPlugin API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use MemoryPlugin API for, with concrete tasks.

★ Persistent memory for your agent through Jentic

An assistant agent that forgets everything between sessions is less useful. Through Jentic it searches MemoryPlugin with the user's current topic at the start of a task, reads the 5 most relevant memories, and saves new facts back with a dated text field as the conversation goes. The bearer token stays in your own Jentic One instance, and your rules decide whether the agent may also delete memories.

Call GET /api/memory with query 'project deadlines' to read the most relevant memories, then call POST /api/memory with text '2026-11-09: Launch moved to December 4' to save the new fact

Chat-history context injection

Before answering, a client can pull the slices of past chats that matter to the current message. The chat-history inject endpoint takes a required query, an optional conversationContext string, and a maxTokens budget that defaults to 600 and caps at 2000, so the injected context fits the model's prompt without overrunning it.

Call POST /api/chat-history/inject with query 'what hotel did we pick in Lisbon' and maxTokens 800, then use the returned context in the answer

Organising memories into buckets

Memories grow messy over time. The API creates named buckets, lists them, and fetches memories and buckets together filtered by bucketId. The update endpoint moves up to 100 memoryIds into a target bucket in one call, creating the bucket by name if it does not exist yet, so an agent can tidy work, personal, and project memories apart.

Call GET /api/v2/memory with all true, pick the memories about work, then call POST /api/v2/memory/update with those memoryIds and bucketName 'Work'

Memory clean-up

Stale or wrong memories mislead an assistant. A single memory can be deleted by id, and the bulk-delete endpoint removes a list of memoryIds in one request, taking them out of both the database and the vector store so they no longer surface in search.

Call GET /api/memory with query 'old address' to find outdated memories, then call POST /api/memories/bulk-delete with their memoryIds

Key Endpoints

9 endpoints — the memoryplugin api stores a user's memories and returns the most relevant ones to an ai assistant on demand.

METHOD

PATH

DESCRIPTION

GET

/api/memory

Query or retrieve memories

POST

/api/memory

Store a new memory

POST

/api/chat-history/inject

Fetch context from chat history

GET

/api/v2/memory

Get memories and buckets together

POST

/api/buckets

Create a bucket

POST

/api/v2/memory/update

Edit or move memories

POST

/api/memories/bulk-delete

Bulk delete memories

GET

/api/memory

Query or retrieve memories

POST

/api/memory

Store a new memory

POST

/api/chat-history/inject

Fetch context from chat history

GET

/api/v2/memory

Get memories and buckets together

POST

/api/buckets

Create a bucket

POST

/api/v2/memory/update

Edit or move memories

POST

/api/memories/bulk-delete

Bulk delete memories

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring MemoryPlugin by hand means carrying a bearer token on every call, passing the v and source parameters, and choosing between the v1 and v2 memory routes. Through Jentic you install once, import MemoryPlugin from the API Directory, store the token once, and your agent calls it.

Permission scoping

MemoryPlugin passes buckets and memory ids as query or body fields on most routes rather than in the URL path, so rules bound which operations your agent may call, not which bucket it touches. You choose the operations it may call, so destructive ones like bulk delete or single-memory delete are not included unless you add them.

Credential isolation

Your MemoryPlugin token is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'search my memories' or 'save a memory', and Jentic returns the matching MemoryPlugin operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using MemoryPlugin API through Jentic.

What authentication does the MemoryPlugin API use?

Per its OpenAPI spec, every MemoryPlugin operation uses HTTP bearer auth: send your MemoryPlugin token in the Authorization header as a bearer token. Through Jentic the token is stored encrypted by your own Jentic One instance and injected at call time, so it never enters the agent's context.

How does memory search work in the MemoryPlugin API?

Call GET /api/memory with a query parameter and the API returns the 5 most relevant memories. Without a query, set latest to true with a count, or all to true, and page with skip. GET /api/v2/memory does the same while also returning your buckets and accepting a bucketId filter.

What are the rate limits for the MemoryPlugin API?

The OpenAPI spec does not specify rate limits. It does cap chat-history injection at 2000 tokens and bulk moves at 100 memories per request. Check https://www.memoryplugin.com for current limits.

Is there a MemoryPlugin MCP server?

You don't need an MCP server to give your agent MemoryPlugin. Jentic connects it directly from the API Directory: import it, store your token once, and your agent calls the memory operations it needs on demand without loading another server's tool definitions into its context.

How do I save a memory through Jentic?

Search Jentic for 'save a memory', load the POST /api/memory operation, and execute it with text that starts with the current date in YYYY-MM-DD format, plus an optional bucketId. Without a bucketId the memory lands in the default bucket. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Can I limit what my agent is allowed to do with the MemoryPlugin API?

Yes. Write a rule that allows only the memory search and chat-history inject operations, so the agent can recall what it needs but cannot save, move, or delete anything. If you later want it to remember new facts, add the store-memory operation alone. Every call is logged.

MemoryPlugin

AI ReadinessAI-Aware (B)64/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLEmbeddings Searchbearer9 EndpointsREST

For Agents

Save user memories into buckets, search them by query to pull the 5 most relevant into a conversation, inject relevant chat-history context under a token budget, and edit, move, or delete memories.

Use for: Search my memories for what I said about my project deadlines, I want to remember that I prefer TypeScript over JavaScript, Retrieve the latest 10 memories I saved, Get relevant context from my past chats about the trip to Lisbon

Not supported: Does not generate text, host language models, or store files and documents. Use for saving, searching, and organising a user's text memories and chat-history context only.

The MemoryPlugin API stores a user's memories and returns the most relevant ones to an AI assistant on demand. A search query returns the 5 most relevant memories, and memories can be organised into named buckets, edited, moved between buckets in batches of up to 100, or deleted from both the database and the vector store. A separate chat-history endpoint takes the user's current input and returns relevant context from past chats, capped by a maxTokens budget of up to 2000. All calls authenticate with a bearer token.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: ASignals: 4

98%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

60%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: B+Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

72%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: D+Signals: 4

38%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

53%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: D+Signals: 1

49%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the MemoryPlugin to your agent

Capabilities

Start building with MemoryPlugin API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

MemoryPlugin

Jentic AI Readiness Score

MemoryPlugin

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness