AgentRecall
Summary
The agent remembered everything in testing and nothing by Tuesday — because stateless sessions meant every conversation started cold, and your users noticed before you did.
AgentRecall is a memory layer that gives AI agents persistent context across sessions — so a support agent recalls a customer's past issue, a sales agent remembers where a deal stalled, and a coding assistant doesn't ask you to re-explain your architecture for the third time. The vendor describes a retrieval-and-storage infrastructure that indexes memories and surfaces relevant ones at query time, rather than stuffing the full conversation history into every prompt. The cloud tier caps at 1,000 stored memories, which is adequate for prototyping but a ceiling teams hit in production. Self-hosting under the MIT license removes that ceiling and keeps data inside your own infrastructure — the tradeoff is that you own the ops. API access covers JavaScript and Python environments.
Bottom line: Pick AgentRecall when your agent's value proposition is literally that it remembers — but plan for the memory cap and self-host overhead before you ship to users who will notice the difference.
Pricing Plans
Subscription- Price
- $9/month for Pro (cloud); self-hosted is free
- Free Tier
- 1,000 memories per month (cloud); unlimited for self-hosted MIT version
Free (Cloud)
1,000 memories per month with AI processing included, no server management required, 2-minute setup
- 1,000 memories/month
- AI-powered processing
- Graph relationships
- Semantic search
- Cloud-hosted
Pro (Cloud)
Unlimited memories with same features as Free tier, cloud-hosted with zero setup required
- Unlimited memories
- AI processing included
- Neo4j graphs
- Semantic search
- Cross-agent queries
Self-Hosted
MIT-licensed, run on own infrastructure with full control, unlimited memories and agents, no costs beyond infrastructure
- MIT license
- Unlimited memories
- Full control
- No API keys required
- Works with Claude Desktop MCP
View full pricing on agentrecall.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Persistent memory across sessions, so a support or sales agent can reference a customer's prior context without the user having to repeat themselves — which is the difference between an agent that feels useful and one that feels like a fresh chatbot every time.
- Self-hosted MIT-licensed deployment, so teams with data residency requirements can keep every stored memory inside their own infrastructure without negotiating a custom data agreement.
- API-first design with JavaScript and Python SDKs, which means the memory layer drops into an existing agent stack without a rewrite — teams avoid building and maintaining a bespoke retrieval system from scratch.
- Retrieval-at-query-time architecture, so only relevant memories surface per session rather than inflating every prompt with full history — which keeps token costs and latency from compounding as memory volume grows.
- Claude Desktop integration documented by the vendor, so teams already in that environment get memory persistence without standing up separate infrastructure.
Cons
Sign in to edit- The cloud tier caps at 1,000 stored memories — a solo developer's prototype fits, but a customer support deployment with hundreds of users hits that ceiling within days. Teams either move to the paid-only cloud tier or take on self-hosting, neither of which is free in time or money.
- Self-hosting transfers all ops responsibility to your team: infrastructure provisioning, uptime, upgrades, and any debugging when retrieval quality degrades. Teams without dedicated DevOps capacity discover this is not a one-afternoon setup.
- The scraped page content does not confirm a native vector database or specify retrieval ranking logic, which means teams with precision recall requirements — where surfacing the wrong memory is worse than surfacing none — have no documented way to audit or tune retrieval quality before they hit that problem in production.
- Teams that need memory scoped by user, tenant, or access role in a multi-tenant SaaS product will find no documented isolation model in available sources. When that requirement surfaces mid-build, the path forward is custom middleware or a competitor that ships tenant-aware memory out of the box.
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About
- Platforms
- Cloud (hosted API), Self-hosted (Docker/bare metal on user infrastructure)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-01T21:41:53.995Z
Best For
Who it's for
- Development teams building multi-session AI agents
- SaaS platforms integrating memory into chatbots or assistants
- Organizations requiring data residency or full infrastructure control
- Developers using JavaScript, Python, or Claude Desktop environments
- Applications where context persistence directly impacts user satisfaction
What it does well
- Customer support agents that recall past issues and preferences across conversations
- Sales agents that maintain context on leads, deals, and customer history without repetition
- Coding assistants that remember architectural decisions and debugging context across sessions
- Conversational AI that feels human by recalling prior interactions and preferences
- Multi-turn task workflows where agents execute complex plans across multiple sessions
Integrations
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Frequently Asked Questions
- Is AgentRecall free?
- AgentRecall is a paid tool ($9/month for Pro (cloud); self-hosted is free). No permanent free tier is offered.
- Is AgentRecall open source?
- No — AgentRecall is a closed-source tool. Source code is not publicly available.
- Does AgentRecall have an API?
- Yes. AgentRecall exposes a developer API. See the official documentation at https://agentrecall.com for details.
- Can I self-host AgentRecall?
- Yes. AgentRecall supports self-hosting on your own infrastructure.
- What platforms does AgentRecall support?
- AgentRecall is available on: Cloud (hosted API), Self-hosted (Docker/bare metal on user infrastructure).
Hours Saved & ROI Stories Community
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Most AI agents are amnesiac by design — each session starts blank, and the user bears the cost of re-explaining context. AgentRecall sits between your agent and its conversations, storing and retrieving memories so that context persists across sessions. The core workflow is retrieval-augmented: when a new session opens, AgentRecall queries stored memories for relevant context, surfaces the right ones, and injects them into the agent’s working state — rather than feeding a raw transcript that would blow the context window or degrade response quality.
The differentiating feature is the self-hosted option under an MIT license. Teams with data residency requirements — healthcare, legal, enterprise SaaS with strict data agreements — can run the full memory layer on their own infrastructure without routing customer data through AgentRecall’s cloud. The vendor states the self-hosted path is fully free, which means the infrastructure cost is compute you already control, not a per-seat or per-memory charge.
AgentRecall fits cleanly into any multi-session agent stack built on Python or JavaScript, and the vendor documents Claude Desktop integration for teams working in that environment. Where it shows its limits: the cloud tier’s 1,000-memory cap means a mid-sized customer support deployment hits the ceiling faster than a solo developer prototype. Teams that need memory at scale either upgrade to the paid-only cloud tier or take on the operational burden of self-hosting — there is no middle path.