Memara API

Name: Memara API
Brand: Memara API
Availability: InStock

✓ Official Vendor SpecAI/MLEmbeddings Searchbearer171 EndpointsREST

For Agents

Read and write long-term memory for AI agents — store facts, documents, and turns in scoped Memory Spaces and retrieve relevant context via semantic search. Bearer-authenticated.

Install Jentic One Beta

Connect the Memara API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Memara API, 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://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | 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 Memara API.

Create and scope Memory Spaces so each agent or user has isolated long-term memory

Store memories with content, metadata, and tags via the /memories endpoint, then retrieve them by semantic similarity

Share a Memory Space with another user or grant scoped access for collaborative agent workflows

GET STARTED

Start building with Memara API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use Memara API for, with concrete tasks.

★ Long-Term Memory for Conversational Agents

Customer-facing chat agents need to remember user preferences, history, and prior decisions across sessions. Memara's /memories endpoints let an agent write each significant turn into a Memory Space and retrieve the top-k semantically relevant memories at the start of every new conversation, replacing brittle vector-DB plumbing with a managed memory service.

Create a Memory Space for user_id=u_123, write the fact 'prefers vegetarian recipes' on each confirmation, then on the next session retrieve the top 5 memories matching the user's new query before composing a response.

Multi-Agent Knowledge Sharing

When several agents collaborate on a task — say, a research agent and a drafting agent — they need a shared scratchpad. Memara's space-sharing endpoints let one agent grant another scoped access to the same Memory Space so findings flow forward without serialising the whole context window into prompts.

Create a Memory Space owned by the research agent, share it with the drafting agent via /spaces/{space_id}/share, then have the drafting agent query the space for citations during writing.

Workflow State Persistence

Long-running agent workflows — onboarding flows, multi-step research, scheduled reviews — need state that survives crashes and context-window resets. Storing intermediate results in a Memory Space gives the workflow a durable resume point, and Memara's analytics endpoints let operators see how much state is accumulating per run.

After each step in the workflow, write a memory tagged 'step:{n}' with the step result; on workflow resume, list memories tagged 'step:*' to reconstruct progress.

AI Agent Memory via Jentic

An agent built with Jentic can use Memara as its memory backend without writing direct HTTP calls. The agent searches Jentic for memory operations, loads the schema for /memories or /spaces, and persists or recalls state through normal tool calls — letting the framework handle bearer-token rotation and request shaping.

Search Jentic for 'store agent memory', load POST /memories, execute with the user-provided fact and a Memory Space ID, then on the next turn search 'recall agent memory' and run a top-k query.

Key Endpoints

171 endpoints — the memara api is an external memory system designed for ai agents and multi-step workflows.

METHOD

PATH

DESCRIPTION

GET

/auth/me

Get the authenticated user/agent identity

GET

/spaces

List Memory Spaces accessible to the caller

POST

/spaces

Create a new Memory Space

GET

/spaces/{space_id}

Get a single Memory Space

POST

/spaces/{space_id}/share

Share a Memory Space with another user

GET

/spaces/{space_id}/analytics

Get usage analytics for a Memory Space

POST

/memories

Write a new memory

GET

/memories/content-limits

Retrieve content limits and quotas

GET

/auth/me

Get the authenticated user/agent identity

GET

/spaces

List Memory Spaces accessible to the caller

POST

/spaces

Create a new Memory Space

GET

/spaces/{space_id}

Get a single Memory Space

POST

/spaces/{space_id}/share

Share a Memory Space with another user

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Memara uses HTTP bearer tokens. Jentic stores the token encrypted in the MAXsystem vault and injects it on each /memories or /spaces call, so the token never appears in agent prompts, logs, or chat transcripts.

Intent-based discovery

Agents search Jentic for intents like 'store agent memory' or 'recall context for user', and Jentic returns POST /memories or the search endpoint with the input schema, ready to execute.

Time to first call

Direct integration: 1-2 days to wire up auth, space management, write, and retrieve flows across 171 endpoints. Through Jentic: under 1 hour — the agent loads only the operations it needs.

FAQs

Specific to using Memara API through Jentic.

What authentication does the Memara API use?

The API uses HTTP bearer authentication declared as the HTTPBearer security scheme. Through Jentic, the bearer token is stored in the encrypted vault and injected on each request, so the raw token never enters agent context.

Can I share a Memory Space between two agents with the Memara API?

Yes. POST /spaces/{space_id}/share grants another user or agent scoped access to a Memory Space, which is the supported pattern for multi-agent workflows that need a shared scratchpad.

What are the rate limits for the Memara API?

The OpenAPI spec does not declare numeric rate limits, but per-space content limits are exposed via /memories/content-limits — query that endpoint to retrieve the current cap on memory size and total stored memories before designing high-volume writes.

How do I store and retrieve agent memory through Jentic?

Run `pip install jentic`, search for `store agent memory` and `recall agent memory`, then execute POST /memories to write and the search endpoint to retrieve. Jentic handles bearer-token injection and schema validation automatically.

Does the Memara API support semantic search over stored memories?

Yes. Memories are indexed for semantic retrieval so an agent can pass a natural-language query and receive the most relevant items in the Memory Space, rather than relying on exact-match metadata filters.

Can I see how much memory an agent has accumulated in the Memara API?

Yes. /spaces/{space_id}/analytics returns memory count, storage usage, and query statistics for a space, which lets operators monitor cost and prune stale state.

Memara API

Connect the Memara API to your agent

Capabilities

Start building with Memara API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs