Rules & Instructions

# 1. Create an instruction file in your project
echo "Always use type hints" > AGENTS.md

# 2. Run any CLI command — instructions load automatically
praisonai run "Help me with Python"

from praisonaiagents import Agent, RulesConfig

agent = Agent(
    name="Assistant",
    instructions="You are helpful.",
    rules=RulesConfig(
        char_budget=6000,
        files=["docs/CONVENTIONS.md"],
        workspace_path="/path/to/project",
    ),
)
agent.start("Summarise the project conventions.")

monorepo/                    # Git root
├── .git/
├── CLAUDE.md               # Discovered for all subdirs
├── .praisonai/rules/       # Discovered for all subdirs
├── packages/
│   ├── frontend/
│   │   ├── CLAUDE.md       # Higher priority for frontend
│   │   └── .praisonai/rules/
│   └── backend/
│       └── CLAUDE.md       # Higher priority for backend

project/
├── CLAUDE.md              # Auto-loaded (Claude Code compatible)
├── CLAUDE.local.md        # Local overrides (gitignored)
├── AGENTS.md              # Auto-loaded (Codex CLI compatible)
├── GEMINI.md              # Auto-loaded (Gemini CLI compatible)
├── PRAISON.md             # Auto-loaded (PraisonAI native)
├── PRAISON.local.md       # Local overrides (gitignored)
├── .cursorrules           # Auto-loaded (Cursor legacy)
├── .windsurfrules         # Auto-loaded (Windsurf legacy)
├── .claude/
│   └── rules/             # Claude Code modular rules
│       ├── code-style.md
│       └── testing.md
├── .windsurf/
│   └── rules/             # Windsurf modular rules
├── .cursor/
│   └── rules/             # Cursor modular rules
├── .praisonai/
│   ├── rules/             # Workspace rules
│   │   ├── python.md      # globs: ["**/*.py"]
│   │   ├── testing.md     # globs: ["**/*.test.*"]
│   │   └── security.md    # activation: manual
│   ├── workflows/         # Reusable workflows
│   └── hooks.json         # Pre/post operation hooks
└── ~/.praisonai/
    └── rules/             # Global rules (all projects)
        └── global.md

from praisonaiagents.memory import RulesManager

# Create manager for your workspace
rules = RulesManager(workspace_path="/path/to/project")

# Get all loaded rules
all_rules = rules.get_all_rules()
for rule in all_rules:
    print(f"{rule.name}: {rule.description} (priority: {rule.priority})")

# Get active rules (always + ai_decision)
active = rules.get_active_rules()

# Get rules for a specific file (includes glob matches)
python_rules = rules.get_rules_for_file("src/main.py")

# Get a specific rule by name (for @mention)
security_rule = rules.get_rule_by_name("security")

# Build context string for LLM
context = rules.build_rules_context(
    file_path="src/main.py",
    include_manual=["security"]  # Include manual rules via @mention
)

# Register extra instruction files beyond auto-discovery.
# Accepts a concrete path OR a glob; each match wins over auto-discovered
# rules via a +200 priority bump. Returns the number of files added.
added = rules.add_rule_file("docs/CONVENTIONS.md")            # concrete path
added = rules.add_rule_file(".praisonai/team-rules/*.md")     # glob

from praisonaiagents.memory import RulesManager

rules = RulesManager(workspace_path="/path/to/project")

# Create a new rule
rules.create_rule(
    name="api_guidelines",
    content="""# API Guidelines
- Use RESTful conventions
- Version all endpoints
- Return proper status codes
- Document with OpenAPI
""",
    description="REST API development guidelines",
    globs=["**/api/**/*.py", "**/routes/**/*.py"],
    activation="glob",
    priority=15,
    scope="workspace"  # or "global"
)

# Delete a rule
rules.delete_rule("old_rule")

# Reload rules from disk
rules.reload()

# Get statistics
stats = rules.get_stats()
print(stats)
# {
#   'total_rules': 8,
#   'always_rules': 3,
#   'glob_rules': 2,
#   'manual_rules': 1,
#   'ai_decision_rules': 2,
#   'root_rules': 3,
#   'gitroot_rules': 1,
#   'workspace_rules': 2,
#   'claude_rules': 1,
#   'windsurf_rules': 1,
#   'global_rules': 0,
#   'sources': {'root': 3, 'workspace': 2, 'claude': 1, ...}
# }

from praisonaiagents.memory import RulesManager

rules = RulesManager(workspace_path="/path/to/project")

# Get an ai_decision rule
rule = rules.get_rule_by_name("security")

# Evaluate if it should be applied to current context
def llm_func(prompt):
    return agent.chat(prompt)

should_apply = rules.evaluate_ai_decision(
    rule=rule,
    context="User is asking about database queries",
    llm_func=llm_func
)

if should_apply:
    print("Security rule should be applied")

from praisonaiagents import Agent, RulesConfig

# Default: auto-discover + apply (equivalent to rules=True or omitting the param)
agent = Agent(name="Coder", instructions="Help with code")

# Opt out for a reproducible sandbox run
sandbox = Agent(name="Sandbox", rules=False)

# Full control
strict = Agent(
    name="Strict",
    rules=RulesConfig(char_budget=4000, files=["docs/CONVENTIONS.md"]),
)

from praisonaiagents import Agent

agent = Agent(name="Assistant", instructions="You are helpful.")
agent.start("hello")   # triggers discovery on first run

if agent.rules_manager:
    stats = agent.rules_manager.get_stats()
    print(f"Loaded {stats['total_rules']} rules from {list(stats['sources'].keys())}")

    # Preview the exact string injected into the system prompt
    context = agent.get_rules_context(
        file_path="src/main.py",
        include_manual=["security"]
    )
    print(context)

[rules]
auto = false        # Disable everywhere
max_chars = 16000   # Or shrink the budget