Why I Built My Own Multi-Agent Framework
And Why You Might Want to Try It
I've been writing about the shift from single Claude Code sessions to multi-agent orchestration for months. The productivity gains are real, but the existing tools? They're either enterprise-focused platforms with steep complexity or tools that lack robustness for production use.
So I built my own. Not because I wanted to join the crowded multi-agent framework space, but because I needed something that actually worked for how I develop software.
The Problem I Kept Running Into
Here's what using existing multi-agent frameworks feels like: You spend three days reading documentation, another two days configuring agents, and then discover the context management is broken. Your "specialized" agents keep forgetting what they're supposed to do. The conversation threads get polluted with irrelevant context. And your usage costs spiral unexpectedly (or you run into that dreaded "you've used up your Claude-Max plan message").
I tried the major players. LangChain—which I actually started with in this project—requires you to think in graphs and abstractions that make sense to AI researchers but not to developers shipping code. I pulled it out due to its complexity. AutoGen has powerful agent capabilities but demands you architect complex conversation flows. CrewAI works well for enterprises with dedicated AI teams, but setup takes weeks.
Meanwhile, I'm juggling client projects (I have the logs to show it!), building tools for HyperDev readers, and prototyping ideas. I need multi-agent capabilities, but I need them to work within my existing Claude Code workflow—not replace it entirely.
How Claude PM Actually Works
Claude PM Framework takes a different approach. Instead of trying to be everything to everyone, it focuses on one thing: making multi-agent coordination as simple as calling a function.
The core insight is this: most development tasks naturally break down into specialized roles. You need someone to research the problem, someone to implement the solution, someone to test it, someone to document it. In a traditional team, these are different people. With Claude PM, these are different agents—each with focused context and clear responsibilities.
Here's what a typical workflow looks like:
npm install -g @bobmatnyc/claude-multiagent-pm
Then, within Claude Code:
[claude-pm] "Research the latest Next.js app router patterns"
The PM agent automatically routes this to the Research agent, which has specialized prompts for gathering technical information. No complex configuration, no agent choreography, no context pollution.
When I need multiple agents working together:
[claude-pm] "push and publish the latest changes"
This triggers a coordinated workflow: Documentation agent updates docs, QA agent runs tests, Data Engineer agent validates any data changes, Version Control agent handles the commit and versioning. The built-in trackdown ticketing framework can sync with GitHub Issues, Linear, JIRA, and other project management tools. All automatically, with each agent seeing only the context relevant to their role.
Using It to Build Itself
The development process for Claude PM convinced me the framework was viable. I started with manually crafted prompts for basic functionality. Once I had minimal agent coordination working, I began using the Research agent to investigate multi-agent best practices, the Engineer agent to implement improvements, and the QA agent to validate changes.
What "smarter" actually means in practice: The Documentation agent learned to recognize patterns in my code comments and generate more relevant docs. The Engineer agent refined its approach to Node.js package management by analyzing successful patterns from previous commits. The QA agent developed better testing strategies by tracking which validation approaches caught real issues.
The 13-day development timeline (visible in the GitHub repository) involved 133 commits and 36 releases. Most of this work happened through agent coordination rather than manual coding—agents would propose changes, implement them, test them, and update documentation while I focused on architecture and coordination.
Performance Numbers With Context
The technical improvements were measurable:
Agent Loading Performance: Initial agent instantiation took 67+ seconds due to complex prompt loading and context setup. After implementing SharedPromptCache integration, this dropped to under 100ms—fast enough that agent switching feels instantaneous during development.
Task Routing: I tested the natural language routing with 50 common development tasks. The system correctly identified the appropriate agent 47 out of 50 times (94% accuracy), with routing decisions happening in under 1ms.
Memory Efficiency: The entire framework operates within 19.1MB memory footprint, smaller than most single-page applications. This matters when you're running multiple agents simultaneously.
Cache Performance: During typical development sessions, 94.3% of agent requests hit cache rather than requiring full prompt processing, making repeated operations nearly instantaneous.
These aren't theoretical benchmarks—they're measurements from daily usage across multiple client projects over the past month.
Where It Fits in Your Development Work
Claude PM works best when you're already comfortable with Claude Code but frustrated by context management and task switching. If you find yourself constantly re-explaining project context or manually coordinating different aspects of development, the framework can help.
It doesn't replace Claude Code—it orchestrates multiple Claude Code instances with specialized contexts. You still get the conversational development experience, but without the cognitive overhead of managing multiple threads manually.
The framework excels at:
Project coordination: Keeping documentation, testing, and deployment in sync
Context preservation: Each agent maintains specialized knowledge without pollution
Workflow automation: Standard processes like pre-commit validation happen automatically
Resource optimization: Only relevant agents activate for each task
Long-running orchestrated sessions: Maintaining project context across extended development cycles
Traditional best practices integration: When you start with a detailed design doc (still essential), the Research agent can analyze it for implementation best practices, the Ticketing agent builds epics and associated issues, multiple Engineer agents work on different project components, and QA agents build comprehensive tests and verification workflows
It's not ideal for:
Exploratory coding: When you need maximum flexibility and minimal structure
Learning new technologies: The specialized agents assume some domain knowledge
Why I'm Open Sourcing This
I'm releasing Claude PM under an open source license because the multi-agent space needs more practical alternatives. The enterprise solutions will continue serving large organizations with complex requirements. But individual developers and small teams deserve access to multi-agent capabilities without enterprise complexity or costs.
The framework is available immediately via NPM. Installation takes under five minutes. No complex configuration files, no architectural diagrams to study, no team of AI specialists required.
Making it open source means the community can extend and improve it. Custom agents can be created for specialized domains. Integration patterns can evolve based on real usage. The framework can grow beyond what any single developer could build.
What This Means for Practical Development
Claude PM represents something broader: multi-agent capabilities are becoming accessible to individual developers and small teams. These tools shouldn't require dedicated teams or massive budgets. They should be as accessible as installing any other development tool.
The rapid development timeline—from concept to production in 13 days—shows what's possible when AI tools actively participate in their own development. This isn't theoretical future development; it's working today.
For developers seeking practical multi-agent capabilities without enterprise overhead, Claude PM offers a bridge to AI-powered development that works within existing workflows. Install it, try it on a real project, and see if the productivity gains match your experience.
Multi-agent systems are gaining traction rapidly. The question isn't whether you'll adopt these capabilities, but which tools will make them accessible enough to actually use.
Claude PM Framework is available at npmjs.com/package/@bobmatnyc/claude-multiagent-pm with documentation and examples.