Build

Custom Software DevelopmentSystems built to your process

Web Application DevelopmentReact, Next.js, Rails, Django

Mobile App DevelopmentiOS, Android, React Native

App DevelopmentWeb and mobile apps, end to end

SaaS Product DevelopmentMulti-tenant from day one

MVP DevelopmentFirst release in 6–10 weeks

Enterprise SoftwareSSO, audit trails, scale

Connect & operate

API Development & IntegrationREST, GraphQL, webhooks

Cloud & DevOps EngineeringAWS, GCP, Azure, Terraform

QA & Software TestingAutomated suites, load, E2E

Legacy ModernizationStrangler-fig migrations

Cybersecurity EngineeringHardening and remediation

Data EngineeringPipelines, warehouses, reporting

Design & teams

UI/UX DesignInterfaces people finish tasks in

Prototyping & DiscoveryScope before you spend

Staff AugmentationSenior engineers on your team

Dedicated Dev TeamsA full pod, one contract

White-Label DevelopmentEngineering under your brand

Technology ArchitectureDecide once, correctly

Project ManagementDelivery you can audit

Start hereScope a build in one callBring a spec, a wireframe, or a paragraph. You leave with an architecture, a timeline, and a number.Book a scoping call

All software development services →How we price work →Talk to an engineer →

AI software

AI Application DevelopmentFull-stack AI products

AI Agent DevelopmentMulti-step autonomous agents

AI SaaS DevelopmentMulti-tenant AI products

Workflow AutomationReplace manual ops

Custom LLM AppsBranded internal assistants

LLM & data systems

Private LLM DevelopmentSelf-hosted Llama, Mistral, Qwen

RAG DevelopmentCited answers over your docs

MCP & WebMCPMake your systems agent-callable

Vector Database Setuppgvector, Pinecone, Qdrant

Document IntelligenceOCR and extraction pipelines

Vibe coding

AI-Assisted BuildsFast, with senior review

Rapid PrototypingClickable in days

Concept to CodeIdea to working software

Code Review & CleanupMake AI output shippable

Fractional AI TeamOngoing build capacity

Ready to ship?Put AI where the work isAgents, RAG, and private LLMs wired into the systems your team already uses — not a chatbot bolted to a homepage.Discuss an AI project

AI development services →AI readiness checklist →AI vs. traditional development →

Commercial

Financial Services

eCommerce & Retail

Marketing Agencies

Real Estate

Automotive

Regulated

Healthcare & HIPAA

Legal

Government

Education

Banking

By company size

Startups & MVPs

Small Business

Enterprise

Agriculture

Domain firstWe learn your workflow before we model itRegulated, operational, or high-volume — the constraints belong in the schema, not in a training doc.Talk about your domain

Selected workSoftware we own and operateMost agencies show screenshots. We run the products — the same stack, uptime, and on-call we hand to clients.View the portfolio

The DEV.co product network

AI ERP with agents inside the systems you run

AI phone and voice agents for real calls

LLM.coPrivate, self-hosted LLM deployments

Legal AI infrastructure for firms

AI RFP discovery and response drafting

Automatic.coBusiness process automation

CyberAttack.aiThreat detection and defense

Secure AI virtual data rooms

Compare

Custom Software vs. SaaS

In-House vs. Outsourced

MVP vs. Full Product

AI vs. Traditional Development

Tools & reading

Software Cost Calculator

Project Scope Template

Engineering Blog

Open Source

Plan smarterEstimate before you commitCost ranges, scope templates, and the questions we ask in discovery — free, no form.Open the cost calculator

DEV.co

About

Careers

Locations

Contact

Real conversationsTalk with a technical leadNo SDR, no discovery gauntlet. The person on the call is the one who scopes the build.Book a call

MCP Servers · parcadei

Continuous-Claude-v3

Continuous Claude is a Python-based framework that extends Claude Code with persistent multi-agent orchestration, context management across sessions via YAML handoffs, and automated learning extraction. It uses hooks, skills, and specialized agents to maintain code context without token waste.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/parcadei/Continuous-Claude-v3

3.9k

GitHub stars

295

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	parcadei/Continuous-Claude-v3
Owner	parcadei
Primary language	Python
License	MIT — OSI-approved
Stars	3.9k
Forks	295
Open issues	45
Latest release	Unknown
Last updated	2026-01-26
Source	https://github.com/parcadei/Continuous-Claude-v3

What Continuous-Claude-v3 is

Built on Claude Code CLI, the system provides 109 skills, 32 agents, and 30 hooks for intelligent task routing. Core mechanisms include 5-layer TLDR code analysis, PostgreSQL-backed memory ledgers, daemon-driven learning extraction, and MCP-compatible isolated context windows. MIT-licensed Python implementation with Docker-based optional PostgreSQL backend.

Quickstart

Get the Continuous-Claude-v3 source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/parcadei/Continuous-Claude-v3.git2cd Continuous-Claude-v33# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Long-running development sessions with context loss

Maintains state across Claude Code sessions via YAML handoffs and memory recall, eliminating token waste from context compaction. Useful for multi-week codebases or iterative feature development.

Multi-agent code orchestration workflows

Coordinates 32 specialized agents (scout, debug-agent, oracle, kraken, etc.) with isolated context windows. Ideal for complex tasks like full-stack feature builds, cross-service refactoring, or research-heavy development.

Token-efficient code analysis at scale

5-layer TLDR system with semantic indexing reduces token burn for large codebases. Supports AST-grep integration and shift-left validation via hooks (pyright/ruff before tests).

Implementation considerations

Install via 12-step wizard in opc/ subdirectory; requires uv package manager, Docker, Claude Code CLI, and Python 3.11+. Expect 30–60 min setup including database initialization.
Database setup: local Docker PostgreSQL auto-provisioned, or remote connection via CONTINUOUS_CLAUDE_DB_URL. Remote requires pgvector extension pre-enabled (AWS RDS needs Parameter Group edit; Supabase/Azure use Extensions UI).
Skills, agents, and hooks inject into ~/.claude/ directory; wizard provides uninstall with state preservation (archives old config, restores backups). Test on non-production machine first.
Memory system (daemon + recall) and TLDR code analysis are optional features; core agent orchestration works without them. Math features (SymPy, Z3, Pint) also opt-in during setup.
Skill activation is inference-based (Claude decides which of 109 skills to use) rather than command-driven. Rely on natural language prompts; direct `/fix`, `/build` workflows supported but not mandatory.

When to avoid it — and what to weigh

Simple, one-off Claude Code tasks — Setup complexity (12-step wizard, Docker, PostgreSQL, 3.11+ Python) outweighs benefit for ad-hoc code fixes or single scripts.
Teams requiring strict SLA/compliance frameworks — No documented security audit, penetration test results, or compliance certification (SOC2, FedRAMP, HIPAA). Memory and handoff data flows require independent security review.
Organizations with restricted Docker or PostgreSQL policies — Requires Docker stack + PostgreSQL (local or remote). Remote database setup needs pgvector extension, which may conflict with managed DB parameter groups or be unavailable in some regions.
Production systems without version stability — No releases published since creation (Dec 2025); latest push Jan 2026. Code is active but versioning and breaking-change communication are unclear.

License & commercial use

MIT License (MIT) is a permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution.

MIT License permits commercial use without royalty or special agreement. No proprietary lock-in on skills, agents, or hooks (they are Python code in ~/.claude/). However, Continuous Claude is a *wrapper* around Claude Code (Anthropic product); commercial use of underlying Claude API remains subject to Anthropic's terms of service and API pricing. Review Anthropic's commercial use policy separately.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	Medium

Security considerations

No security audit, penetration test report, or threat model documented. Memory system stores code context in PostgreSQL ledgers; review your data residency and encryption-at-rest requirements for that database. YAML handoffs transfer context between sessions—inspect serialization logic for injection risks. Hooks run pyright/ruff after edits; those tools are trusted but ensure linter configs are version-pinned. No secrets management framework mentioned; API keys stored in ~/.claude/settings.json or .env files—standard practice but requires OS-level file permissions enforcement. Recommend independent security review before use in regulated environments (healthcare, finance, government).

Alternatives to consider

Native Claude Code CLI (no wrapper)

Simpler setup, no external database dependency, no learning curve for agents/skills. Trade-off: manual context management, no cross-session memory, higher token waste on compaction.

LangChain / LlamaIndex agent frameworks

Broader model support (OpenAI, Anthropic, open-source LLMs), mature production tooling, larger community. Trade-off: steeper learning curve, not Claude Code–specific, requires custom integration work.

Anthropic Workbench or custom MCP servers

Full control over agent orchestration and memory logic; vendor-neutral. Trade-off: no pre-built skills/agents, higher development effort, smaller ecosystem of examples.

Software development agency

Build on Continuous-Claude-v3 with DEV.co software developers

Continuous Claude automates agent orchestration and context persistence. Start with the 12-step wizard to explore multi-agent development, persistent memory, and token-efficient code analysis.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

Claude-Code-Everything-You-Need-to-Know everything-claude-code-zh agents

Related on DEV.co

Explore the category and the services that help you build with it.

MCP Servers AI Development Services AI Application Development API Development Custom Software Development Talk to DEV.co

Continuous-Claude-v3 FAQ

Do I need Perplexity or NIA to use Continuous Claude?

No. Both are optional integrations. Core agent orchestration, skills, hooks, and memory work without them. Use them only if you need research-heavy tasks (oracle agent + Perplexity recommended for high-value-per-token research).

What happens if the PostgreSQL database is down?

Unknown—not documented. Assume agent execution and memory recall fail gracefully or error out. Test database failover and recovery procedures in staging before relying on production.

Can I use Continuous Claude with non-Claude models (GPT-4, Llama, etc.)?

No. The system is tightly coupled to Claude Code CLI and Claude API. No swappable model abstraction is documented.

Is there a managed / hosted version?

Not documented. Continuous Claude is self-hosted (local Docker + PostgreSQL or remote DB via connection string).

Work with a software development agency

Standing up Continuous-Claude-v3 properly takes more than a deploy. As a software development agency, DEV.co handles the mcp servers architecture, the web development on top, and the operational work underneath.

Ready to scale your Claude Code workflows?

Continuous Claude automates agent orchestration and context persistence. Start with the 12-step wizard to explore multi-agent development, persistent memory, and token-efficient code analysis.

Talk to DEV.co Schedule a Consultation