ContextAgent
The ContextAgent is a specialized agent for Context Engineering - comprehensive codebase analysis and Product Requirements Prompt (PRP) generation.
Not to be confused with Context Window Management (context=ContextConfig()).
This agent analyzes codebases and generates implementation guidance, while ContextConfig manages token budgets and compaction.
Quick Start
from praisonaiagents import create_context_agent
# Create context engineering agent
agent = create_context_agent(
llm="gpt-4o-mini",
name="Context Engineering Specialist"
)
# Analyze a codebase
analysis = agent.analyze_codebase_patterns(
project_path="/path/to/project",
file_patterns=["*.py"]
)
# Generate PRP (Product Requirements Prompt)
prp = agent.generate_prp(
feature_request="Add user authentication with JWT",
context_analysis=analysis
)
Key Features
| Feature | Description |
|---|
| Codebase Analysis | Analyze project structure, patterns, conventions |
| AST Analysis | Extract classes, functions, imports from Python |
| Pattern Extraction | Identify code patterns and architecture |
| PRP Generation | Create comprehensive implementation guidance |
| Validation Framework | Define success criteria and quality gates |
| Implementation Blueprint | Step-by-step implementation plan |
| GitHub Integration | Analyze GitHub repositories directly |
API Reference
Core Methods
# Protocol-compatible methods (recommended)
analysis = agent.analyze_codebase("/path/to/project")
prp = agent.generate_prp("Feature description", analysis)
blueprint = agent.create_implementation_blueprint("Feature", analysis)
# Original methods (still available)
analysis = agent.analyze_codebase_with_gitingest("/path/to/project")
prp = agent.generate_comprehensive_prp("Feature description", analysis)
blueprint = agent.build_implementation_blueprint("Feature description", analysis)
Async Methods
# Async versions for non-blocking execution
analysis = await agent.aanalyze_codebase("/path/to/project")
prp = await agent.agenerate_prp("Feature description", analysis)
blueprint = await agent.acreate_implementation_blueprint("Feature", analysis)
Configurable Output
Control output using the output= parameter (inherited from Agent):
# Silent mode - suppress all progress output
agent = create_context_agent(llm="gpt-4o-mini", output="silent")
# Verbose mode - show rich output with progress
agent = create_context_agent(llm="gpt-4o-mini", output="verbose")
# Actions mode - show actions/steps only
agent = create_context_agent(llm="gpt-4o-mini", output="actions")
# Default is silent (no output)
agent = create_context_agent(llm="gpt-4o-mini")
Protocol Compliance
ContextAgent implements ContextEngineerProtocol:
from praisonaiagents.agent import ContextEngineerProtocol
# Check protocol compliance
assert isinstance(agent, ContextEngineerProtocol)
GitHub Analysis
# Analyze GitHub repository
result = agent.start("https://github.com/user/repo Goal: Add authentication")
Multi-Agent Workflow
from praisonaiagents import Agent, Task, Agents, create_context_agent
# Create context engineer
context_engineer = create_context_agent(llm="gpt-4o-mini")
# Analyze codebase
analysis = context_engineer.analyze_codebase_patterns("/path/to/project")
# Create implementation agent with context
developer = Agent(
name="Developer",
instructions=f"""
Implement features following these patterns:
{analysis}
"""
)
# Create workflow
agents = Agents(agents=[developer], tasks=[...])
Output Directory
Context Engineering outputs are saved to .praison/prp/:
.praison/prp/
├── agent_responses/ # Individual agent responses
├── markdown_outputs/ # Formatted markdown reports
├── debug_logs/ # Debug logs (if enabled)
└── final_results/ # Final PRP and blueprints
Context Engineering vs Context Window Management
| ContextAgent (Context Engineering) | context=ContextConfig (Context Window) |
|---|
| Codebase analysis | Token budgeting |
| PRP generation | Auto-compaction |
| Pattern extraction | Session tracking |
| Implementation blueprints | Multi-memory aggregation |
| Validation frameworks | Overflow detection |
Next Steps
