AI Tool

Decispher Memory Review

Decispher Memory is an AI tool that stores durable preferences and team norms for engineers, connecting with coding agents to provide contextual knowledge automatically.

shipped Sep 2, 2026videofreemium

Monthly visits104/mo

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Why it matters

1Offers a freemium pricing model with an 'Early Access: Free' tier.

2Integrates with platforms such as Slack, GitHub, Jira, Claude Code, Cursor, and Codex.

3Provides a developer API for custom integrations and extensions.

4Designed for software development teams, developers, and coding agents.

About Decispher Memory

Business Model

Subscription SaaS

Free Credits

Free to read

Platforms

Web

Target Audience

Software development teams

Pricing Plans

Early Access

Free

• Connect with Claude Code, Cursor, Codex, Grok
• Free to read
• No credit card
• Delete anything, anytime

Cost Examples

• Memory arriving before your prompt costs nothing
• Looking one up by name costs nothing
• A write, import batch, or a question needing ranking costs a credit

API Docs GitHub

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is Decispher Memory?

Decispher Memory is an AI context and memory layer tool developed by DecispherHQ that enables developers and engineering teams to store persistent working preferences and team norms. It connects seamlessly with various coding agents to provide contextual knowledge automatically, enhancing AI coding sessions and streamlining engineering workflows.

features

Key Features of Decispher Memory

Decispher Memory provides a robust set of features designed to manage and retrieve engineering context efficiently for AI agents and human developers.

Stores preferences scoped to individual persons, specific projects, and company-wide norms.
Ensures immediate deletion of content upon user request, removing all associated data.
Maintains distinct separation between personal and team memory scopes for privacy and organization.
Automatically provides relevant memories before each AI agent prompt, enhancing contextual accuracy.
Logs every memory retrieval, creating an audit trail for transparency and compliance.
Combines fragmented engineering context from various platforms into retrievable units.
Records AI coding sessions and converts execution into structured handoffs on Pull Requests (Branch Story).
Enables autonomous worker agents to retrieve relevant context from sources like Jira and Slack.

use cases

Who Should Use Decispher Memory?

Decispher Memory is primarily designed for individuals and teams involved in software development, particularly those leveraging AI coding agents.

Developers: For storing personal working preferences and accessing contextual knowledge during AI-assisted coding sessions.
Engineering Teams: To establish and maintain team norms, project conventions, and shared engineering context across projects.
Coding Agents: As a persistent memory layer to retrieve relevant information from various sources like Jira and Slack, enabling more autonomous and context-aware operations.

how to use

How to Use Decispher Memory

Decispher Memory integrates into existing engineering workflows to provide persistent context. Users can begin by connecting their preferred coding agents and platforms.

1Sign up for the Early Access program via the Decispher Memory website.
2Connect Decispher Memory with integrated platforms such as Slack, GitHub, or Jira.
3Integrate with AI coding agents like Claude Code, Cursor, or Codex.
4Define and store personal working preferences and team norms within the system.
5Allow agents to automatically retrieve relevant context before prompts or for specific tasks.
6Utilize the Branch Story feature to record AI coding sessions and generate structured handoffs on PRs.

pricing

Decispher Memory Pricing & Plans

Decispher Memory operates on a freemium model, offering an 'Early Access: Free' tier. The cost structure for usage beyond free allowances is credit-based, with specific actions consuming credits.

Early Access: Free (includes core functionalities).
Credit-based Usage: Memory arriving before your prompt costs nothing; looking up a memory by name costs nothing. A write operation, import batch, or a question requiring ranking consumes a credit.

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Pros

+Provides persistent, scoped memory for individual engineers, teams, and projects.
+Integrates with major engineering tools like Slack, GitHub, and Jira.
+Offers a developer API for custom extensions and integrations.
+Ensures data privacy with a policy of never training on user data.
+Facilitates structured handoffs on PRs by recording AI coding sessions (Branch Story).
+Freemium model allows for initial access and evaluation without upfront cost.

Cons

−Specific pricing details for credit packs or paid tiers are not explicitly detailed beyond the freemium model.
−The tool's capabilities regarding AI models and multimodality are currently unknown.
−Requires integration with existing coding agents and platforms, which may involve initial setup.
−Limited to 'Web' platform, without native desktop or mobile applications.
−The 'Early Access' status suggests ongoing development and potential feature changes.

Policies

Pricing Page

View Pricing→

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