What did Hlido score Shrimp Task Manager?

Shrimp Task Manager scored 75/100 (STEADY) in Hlido's independent, hands-on review.

How much does Shrimp Task Manager cost?

Hlido recorded the pricing model as: Open source. Derived from evidence Hlido holds, not vendor-supplied; verify current prices on the vendor's pricing page.

Does any vendor pay Hlido for placement?

No. Hlido takes no money from the agents it rates — scoring weights stay private and the evidence behind every verdict is public.

Shrimp Task Manager

Name: Shrimp Task Manager review
Item: Shrimp Task Manager
Rating: 75
Author: Hlido Editor

Coding · tested 2026-08-26 · re-test due 2026-11-24 · by the Hlido desk, not the vendor

In short: An MCP task manager that gives AI coding assistants long-term memory, structured task decomposition with dependencies, and execution tracking — aimed squarely at the 'assistant forgets across conversations' problem.

5 PASS · 0 FAIL of 5 public-surface claims

Visit Shrimp Task Manager → scorecard.json

Quick answer

Shrimp Task Manager scores 75/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-08-26). STEADY (75) because Shrimp Task Manager maps three real failure modes of AI coding assistants to concrete features — persistent task memory, dependency-aware decomposition, execution tracking, knowledge reuse — delivered Pricing: Open source (free entry point documented).

Hlido Editor · 2026-08-26

Shrimp Task Manager names its target precisely: the three ways AI programming assistants fail at multi-step work — memory loss across conversations, structural chaos in complex tasks, and starting every session from scratch — and answers each with a concrete feature. It provides a task-memory function that persists execution history, automatic decomposition of complex tasks into subtasks with explicit dependencies and ordered execution paths, real-time execution-status tracking with progress visualization, and a knowledge-accumulation store that records successful solutions for reuse. Delivered as an MCP server, it plugs into agentic coding assistants rather than being a standalone product, which is the right shape for the job. The documentation is well-structured (pain-points → six core features → workflow → prompt config → installation) and bilingual, and it is open source on GitHub. It lands mid-STEADY because the strengths are described capabilities rather than measured outcomes in this review, there is no independent benchmark of how well decomposition and dependency-tracking actually hold up on real tasks, and no adoption/maintenance signal was captured. As a structured-memory layer for a coding agent, though, it is coherent and well-scoped.

Why STEADY

STEADY (75) because Shrimp Task Manager maps three real failure modes of AI coding assistants to concrete features — persistent task memory, dependency-aware decomposition, execution tracking, knowledge reuse — delivered agent-native over MCP with clear docs. Held mid-STEADY because these are described capabilities, not outcomes measured in this review, with no independent benchmark or captured adoption/maintenance signal.

Public-surface checklist

PASS Homepage loads (required)
PASS Primary value prop (required) — Structured task management + long-term memory for AI programming assistants
PASS Cta present (required) — Get Started / Installation / GitHub
PASS Pricing or access — Open source on GitHub; MCP install
PASS Evidence or demo — Pain-points, six-feature breakdown, workflow and prompt-config docs

What we saw

4 screenshots captured by the Hlido engine during the reviewed run (run-a98709bb99713d17-cjo4m06-github-io). Our own captures — not vendor marketing material.

Shrimp Task Manager — run screenshot 1 (home.png) — home.png

Shrimp Task Manager — run screenshot 2 (page_pain-points.png) — page_pain-points.png

Shrimp Task Manager — run screenshot 3 (page_features.png) — page_features.png

Shrimp Task Manager — run screenshot 4 (page_workflow.png) — page_workflow.png

What it does well

Persistent task memory across conversations — directly targets the assistant's cross-session amnesia
Automatic decomposition of complex tasks into subtasks with explicit dependencies and ordered execution
Real-time execution-status tracking with progress visualization and completion reports
Knowledge-accumulation store that records successful solutions for reuse on similar tasks
Agent-native as an MCP server; clear, well-structured, bilingual documentation and prompt config
Open source on GitHub

What it fails at

Strengths are described features, not outcomes measured in this review
No independent benchmark of decomposition/dependency-tracking quality on real tasks
No captured adoption, maintenance-cadence or license detail on the reviewed surface
Value depends on the host assistant honouring the structured plan it produces

Best for

Developers whose AI coding assistant loses track of multi-step tasks across a session
Agentic workflows that need explicit task decomposition, dependencies and progress tracking
Teams that want a reusable knowledge base of solved tasks feeding future prompts

Not recommended for

Users wanting a standalone project-management app rather than an MCP layer for an assistant
Teams that require proven, benchmarked reliability guarantees today

Pricing & access

ModelOpen source
Free entry pointYes — a free tier or open-source edition is documented
Pricing findable on the public surfacePASS Open source on GitHub; MCP install (tested 2026-08-26)

Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-08-26.

Compared to

ai-orchestrator in-assistant-task-memory-vs-cross-agent-orchestration
Both structure multi-step agent work; the orchestrator coordinates agents/flows broadly, while Shrimp is a focused MCP memory-and-decomposition layer for a coding assistant. Orchestrator for cross-agent flow control, Shrimp for in-assistant task memory and breakdown.
codebase-memory-mcp task-state-memory-vs-codebase-memory
codebase-memory-mcp persists knowledge about the codebase; Shrimp persists task state and decomposition. Codebase-memory for what the code is, Shrimp for what the assistant is doing to it.

Agent relevance

MCP Behavioral-testable

Agentic-Commerce Readiness 60/100 · INTEGRABLE

Independent readiness for agent delegation & transaction. How it’s scored · check live

Runs as an MCP server connected to an AI coding assistant; the assistant calls it to plan and decompose tasks, persist task memory across sessions, track execution status, and reference a knowledge base of prior solutions. It is a structured-memory-and-planning layer for the agent rather than a standalone tool.

Agent-friendly score: 8/10

Evidence

Provides long-term/task memory so assistants remember progress across conversations — source (2026-08-26) verified
Automatically decomposes complex tasks into subtasks with dependencies and ordered execution — source (2026-08-26) verified
Six core features incl. execution-status tracking and knowledge accumulation — source (2026-08-26) verified

scorecard.json · transparency passport · registry · methodology

More: compare agents · best of · developer tools · incident registry

Verdict by Hlido Editor, our automated editorial system · Method: public-surface-tier-1+editorial-narrative-v2 · Methodology version 2026.05 · Next review due 2026-11-24

How this page was produced. The scores, claim verdicts and evidence come from automated hands-on testing of the product’s public surface. The written analysis is drafted by an AI system, and pages publish without a person reviewing each one. Hlido publishes this record and answers for it — tell us if anything here is wrong and we will correct it.

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