Official vendor OpenAPI document · agent-readyAI/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.
Use for: Store a fact about a user in their Memory Space for use in later turns, Retrieve the five most relevant memories for the current user query, Create a new Memory Space scoped to a specific agent run, Share my Memory Space with another team member
Not supported: Does not host LLM inference, generate embeddings on demand, or replace a relational database - use for AI agent long-term memory storage and retrieval only.
The Memara API is an external memory system designed for AI agents and multi-step workflows. It exposes Memory Spaces - scoped containers for facts, documents, and conversation history - together with semantic search, access controls, sharing, and analytics across 171 endpoints. Agents can write memories, retrieve relevant context for a new turn, share spaces with collaborators, and audit usage, which makes Memara a drop-in long-term memory layer for LLM applications that need to outlive a single context window.
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.
1
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%2Fmemara.io%2Fmemara" | sh2
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%2Fmemara.io%2Fmemara" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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
Track Memory Space analytics including memory count, storage usage, and query patterns
Authenticate via socket-based or HTTP bearer tokens for both server and edge agent runtimes
Enforce content limits and quotas on a per-space basis to control cost as agents accumulate state
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
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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
M
Memara API
- Foundational (C)56/100
52
Foundational Compliance62
Developer Experience & Jentic Compatibility50
AI-Readiness & Agent Experience57
Agent Usability60
Security68
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
52
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: C-Signals: 4
34%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
75%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
62
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
1%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
49%
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.
50
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: D+Signals: 4
44%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
54%
Summary Coverage
Coverage of summaries across operations/tags/info.
57
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: CSignals: 1
57%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
60
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).
68
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: B+Signals: 1
68%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Memara by hand means sending its HTTP bearer token and hand-coding each space and memory call against api.memara.io across a large operation surface. Through Jentic you install once, import the Memara API from the API Directory, store the token once, and your agent calls it.
Permission scoping
Memara puts the space id in the URL path (/spaces/{space_id}/...), so a rule can pin your agent to one space: it can store and recall memories there and nothing else. You choose the operations it may call, so sharing a space or reading its analytics is included only if you add it.
Credential isolation
Your Memara bearer token is stored once, encrypted, by your own Jentic One instance and injected on each /memories or /spaces call at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'store agent memory' or 'recall context for a user', and Jentic returns POST /memories or the matching search operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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.
Can I limit what my agent is allowed to do with the Memara API?
Yes. Because you run Jentic One yourself, your own rules decide which Memara operations and credentials the agent may use, and Memara puts the space id in the URL path (/spaces/{space_id}/...), so a rule can pin the agent to a single Memory Space. You can allow it to only write and recall memories in that space while withholding operations like sharing a space via POST /spaces/{space_id}/share or reading /spaces/{space_id}/analytics. The bearer token stays with your instance and is injected at execution time, so the agent calls only the endpoints you permit.
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