# Product Requirements Document (PRD)
## CodeTether Agent
**Version:** 1.0
**Date:** February 2025
**Status:** In Development
**Author:** CodeTether Team
---
## 1. Executive Summary
### 1.1 Product Vision Statement
> **CodeTether Agent is a high-performance, A2A-native AI coding agent that autonomously implements software features through PRD-driven development, enabling developers to transform product specifications into production-ready code with minimal human intervention.**
CodeTether Agent represents a paradigm shift in AI-assisted software development. Built from the ground up in Rust for maximum performance and reliability, it serves as both an intelligent coding companion and an autonomous implementation engine. The agent combines the power of multiple AI providers with a sophisticated tool system, Language Server Protocol (LSP) integration, and recursive language model processing to handle complex development tasks at unprecedented speed and scale.
Unlike traditional AI coding assistants that require constant human guidance, CodeTether Agent can operate autonomously through its "Ralph" loop—reading product requirements, implementing features, running quality checks, and iterating until all acceptance criteria are met. This capability has been proven through dogfooding: the agent implemented 20 of its own user stories (LSP client, RLM pool, truncation utilities, and more) with a 100% pass rate on quality checks.
### 1.2 Key Objectives for the Three Main Features
#### Feature 1: Autonomous PRD-Driven Development (Ralph)
**Objective:** Enable fully autonomous feature implementation from structured product requirements documents, eliminating the manual overhead of translating specifications into code.
**Key Goals:**
- Parse structured PRD documents containing user stories with acceptance criteria, priorities, and dependencies
- Automatically select and implement the highest-priority stories with satisfied dependencies
- Execute comprehensive quality checks (type checking, linting, testing, building) after each implementation
- Maintain memory across iterations through git history, progress tracking, and PRD state management
- Achieve 100% pass rate on all quality checks for implemented stories
- Reduce feature implementation time by 163x compared to manual development
#### Feature 2: Language Server Protocol (LSP) Client Integration
**Objective:** Provide first-class code intelligence capabilities by implementing a complete LSP client that communicates with language servers for real-time code analysis.
**Key Goals:**
- Implement full LSP transport layer using stdio with proper JSON-RPC message handling
- Support core LSP lifecycle: initialize handshake, document synchronization, and graceful shutdown
- Enable go-to-definition, find-references, hover information, and code completion requests
- Manage multiple concurrent LSP connections for polyglot development environments
- Maintain server health monitoring with automatic restart capabilities
- Support industry-standard language servers (rust-analyzer, typescript-language-server, etc.)
#### Feature 3: Recursive Language Model (RLM) Processing
**Objective:** Handle contexts that exceed model window limits through intelligent chunking and recursive analysis, enabling the agent to work with arbitrarily large codebases and documents.
**Key Goals:**
- Implement intelligent content chunking based on content type (code, logs, documents, conversations)
- Create an internal REPL-like environment where the LLM can explore content programmatically
- Enable sub-LM queries for semantic questions across chunks
- Synthesize coherent answers from multiple chunk analyses
- Provide connection pooling for RLM agents to minimize latency and resource usage
- Support parallel processing of independent chunks for improved throughput
### 1.3 Success Metrics
| **Autonomous Development** | Story implementation pass rate | ≥95% | Quality check results from `cargo check`, `cargo clippy`, `cargo test`, `cargo build` |
| | Average time per user story | <5 minutes | Timestamp analysis from PRD iteration logs |
| | Token efficiency vs. manual coding | 3x reduction | Token usage comparison with baseline |
| **LSP Integration** | Language server compatibility | 5+ servers | Verified with rust-analyzer, typescript-language-server, pylsp, gopls, clangd |
| | Request-response latency | <100ms p95 | Instrumented timing on LSP operations |
| | Server uptime (with auto-restart) | 99.9% | Health check monitoring over 7-day period |
| **RLM Processing** | Maximum context size handled | 10MB+ | Stress testing with large files |
| | Chunk processing throughput | >100 chunks/sec | Benchmark with standard test corpus |
| | Answer accuracy on large contexts | ≥90% | Human evaluation on 50 test queries |
| **Performance** | Binary startup time | <15ms | `time codetether --help` |
| | Memory usage (idle) | <20MB | `ps` memory reporting |
| | Memory usage (swarm, 10 agents) | <60MB | Peak RSS during swarm execution |
| | Sub-agent spawn time | <2ms | Instrumented spawn latency |
| **Adoption** | Quality gate pass rate | 100% | All stories must pass 4 quality checks |
| | Documentation coverage | ≥80% | `cargo doc` coverage analysis |
| | Test coverage | ≥70% | `cargo tarpaulin` report |
### 1.4 Target User Personas
#### Persona 1: Senior Software Engineer - "Alex"
**Background:** Alex is a senior engineer at a mid-sized tech company, working on a complex Rust codebase. They are highly productive but constantly face tight deadlines and technical debt.
**Goals:**
- Automate repetitive implementation tasks to focus on architecture and design
- Maintain high code quality standards without sacrificing velocity
- Quickly understand large, unfamiliar codebases
- Reduce time spent on boilerplate and test writing
**Pain Points:**
- Context switching between high-level design and low-level implementation
- Reviewing and refactoring code written by junior developers
- Navigating legacy code without proper documentation
- Limited time for deep work due to meeting overhead
**How CodeTether Helps:**
- Alex writes a PRD for a new feature, and Ralph implements it autonomously while Alex attends meetings
- LSP integration provides instant code intelligence for unfamiliar code
- RLM processing allows Alex to query entire codebases for patterns and dependencies
- Swarm mode parallelizes complex refactoring tasks across multiple sub-agents
#### Persona 2: Tech Lead / Engineering Manager - "Maya"
**Background:** Maya leads a team of 8 engineers and is responsible for technical strategy, code quality, and delivery timelines. She needs visibility into development progress and consistent output from her team.
**Goals:**
- Standardize development practices across the team
- Accelerate feature delivery without compromising quality
- Reduce onboarding time for new team members
- Ensure consistent code review and testing practices
**Pain Points:**
- Inconsistent code quality across team members
- Difficulty estimating timelines for complex features
- Onboarding new developers to large, complex systems
- Balancing feature work with technical debt reduction
**How CodeTether Helps:**
- PRD-driven development enforces structured requirements and acceptance criteria
- Quality gates ensure all code passes type checking, linting, and tests
- LSP integration provides consistent code intelligence regardless of IDE choice
- Autonomous implementation frees senior engineers to mentor and architect
#### Persona 3: Indie Developer / Startup Founder - "Jordan"
**Background:** Jordan is a solo developer building a SaaS product. They wear multiple hats (frontend, backend, DevOps) and need to move fast with limited resources.
**Goals:**
- Ship features quickly as a single developer
- Maintain professional code quality without a dedicated QA team
- Minimize time spent on infrastructure and tooling
- Scale development capacity without hiring immediately
**Pain Points:**
- Limited time to learn new languages and frameworks deeply
- Difficulty maintaining context across large codebases
- No team to review code or catch bugs early
- Expensive to hire senior developers for early-stage startup
**How CodeTether Helps:**
- Autonomous implementation acts as a "virtual teammate" for feature development
- Multi-language LSP support helps Jordan work across the full stack
- RLM processing compensates for limited familiarity with all parts of the codebase
- 3x cost reduction compared to manual development extends runway
#### Persona 4: AI Research Engineer - "Dr. Chen"
**Background:** Dr. Chen works at an AI research lab, building tools and infrastructure for machine learning experiments. They need to process large datasets and codebases for analysis.
**Goals:**
- Analyze large codebases for patterns and anti-patterns
- Extract insights from log files and experiment outputs
- Build internal tools quickly without diverting from research
- Process documentation and papers efficiently
**Pain Points:**
- Model context windows are too small for large codebases
- Manual analysis of logs and outputs is time-consuming
- Building tools distracts from core research objectives
- Need to work across multiple programming languages
**How CodeTether Helps:**
- RLM processing handles arbitrarily large contexts through recursive analysis
- LSP integration provides code intelligence across Python, Rust, C++, and more
- Swarm mode parallelizes analysis tasks across multiple files
- Autonomous tool building through Ralph enables rapid prototyping
### 1.5 Scope Boundaries
#### In Scope
**Core Agent Functionality:**
- A2A protocol implementation for agent-to-agent communication
- Tool system with 24+ built-in tools for file operations, code search, execution, and web access
- Session management with persistent history and git-aware storage
- Interactive TUI for manual agent interaction
- HTTP server mode for API access
**Autonomous Development (Ralph):**
- PRD parsing and user story extraction
- Priority-based story selection with dependency resolution
- Automated implementation using available tools
- Quality check execution (typecheck, lint, test, build)
- Progress tracking and memory persistence across iterations
- Git integration for change tracking
**LSP Client:**
- stdio transport layer with JSON-RPC message handling
- LSP lifecycle management (initialize, document sync, shutdown)
- Core LSP requests: definition, references, hover, completion
- Multi-server management for concurrent language support
- Server health monitoring and auto-restart
**RLM Processing:**
- Content type detection and intelligent chunking
- Internal REPL environment for content exploration
- Sub-LM query routing and result synthesis
- Connection pooling for agent reuse
- Parallel chunk processing
**Security & Configuration:**
- HashiCorp Vault integration for secure API key storage
- Configuration management via TOML files
- Support for multiple AI providers (OpenAI, Anthropic, Google, Moonshot, etc.)
#### Out of Scope
**Not Implemented (Future Considerations):**
- **LSP Server Implementation:** CodeTether Agent acts as an LSP client only; it does not expose LSP server capabilities to other editors
- **GUI/IDE Integration:** No VS Code, JetBrains, or other IDE extensions; TUI and CLI only
- **Collaborative Editing:** No real-time multi-user editing or conflict resolution
- **Version Control Operations:** Git integration is limited to change tracking; no branch management, merging, or conflict resolution
- **Deployment Automation:** No CI/CD pipeline integration, container building, or deployment orchestration
- **Code Review UI:** No dedicated interface for reviewing AI-generated code changes
- **Natural Language PRD Creation:** PRDs must be written in structured JSON; no natural language to PRD conversion
- **Mobile Applications:** No iOS or Android support; desktop/server only
- **Offline Operation:** Requires internet connection for AI provider APIs
- **Custom Model Training:** No fine-tuning or custom model training capabilities
**Explicit Exclusions:**
- Language server binaries (rust-analyzer, etc.) must be installed separately
- HashiCorp Vault server must be provided externally
- A2A server implementation (CodeTether Agent is a worker, not a server)
- MCP (Model Context Protocol) server implementation (client only)
---
## 2. User Stories
*See `prd.json` and `prd_missing_features.json` for detailed user stories with acceptance criteria, priorities, and dependencies.*
---
## 3. Technical Requirements
*To be completed in subsequent sections.*
---
## 4. Quality Assurance
### 4.1 Quality Gates
All code changes must pass the following quality checks:
```bash
# Type checking
cargo check
# Linting
cargo clippy -- -D warnings
# Testing
cargo test
# Release build
cargo build --release
```
### 4.2 Definition of Done
- [ ] All acceptance criteria for the user story are met
- [ ] Code passes all quality gates (typecheck, lint, test, build)
- [ ] Documentation is updated (README, inline docs, architecture diagrams)
- [ ] Changes are committed to git with descriptive messages
- [ ] PRD is updated to mark the story as passing
- [ ] No regression in existing functionality
---
## 5. Appendix
### 5.1 Glossary
- **A2A (Agent-to-Agent):** Protocol for communication between AI agents
- **LSP (Language Server Protocol):** Standard protocol for code intelligence
- **RLM (Recursive Language Model):** Technique for processing contexts larger than model windows
- **Ralph:** Autonomous PRD-driven agent loop
- **PRD (Product Requirements Document):** Structured specification for features
- **TUI (Terminal User Interface):** Interactive command-line interface
### 5.2 References
- [LSP Specification 3.17](https://microsoft.github.io/language-server-protocol/specifications/lsp/3.17/specification/)
- [A2A Protocol](https://github.com/google/A2A)
- [JSON-RPC 2.0](https://www.jsonrpc.org/specification)
- [Recursive Language Model Paper](https://arxiv.org/abs/2305.14976)
---
**Document History:**
| 1.0 | 2025-02-03 | CodeTether Team | Initial PRD with executive summary and goals |