Switch to LLM-driven agent with zero hard-coded governance logic

Replace old rule-based agent with pure LLM interpretation system.

Agent Changes:
- Rename agent.py → agent_legacy.py (preserve old hard-coded agent)
- Rename agent_refactored.py → agent.py (make LLM agent primary)
- Agent now interprets constitution to understand authority and processes
- No hard-coded checks for specific users, roles, or governance models
- Fully generic: works with any constitutional design

Constitution Interpreter:
- Updated interpret_proposal() to detect authority structures from text
- LLM determines who has decision-making power from constitution
- No assumptions about voting, proposals, or specific governance models

Mastodon Formatting:
- Improved line break handling for bullet points and paragraphs
- Better plain-text formatting for Mastodon posts

Primitives:
- Added support for admin_approval threshold type

Architecture:
- Bot now uses pure LLM interpretation instead of scripted logic
- Each instance can develop implementation guidelines separately
- Guidelines not included in main codebase (instance-specific)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/govbot/agent_legacy.py

@@ -0,0 +1,659 @@
+"""
+AI Agent Orchestration for Governance Bot.
+
+This is the core agentic system that:
+1. Receives governance requests
+2. Consults the constitution (via RAG)
+3. Plans appropriate actions
+4. Executes using primitives
+5. Maintains audit trail
+"""
+
+import json
+import subprocess
+from typing import Dict, Any, Optional, List
+from datetime import datetime, timedelta
+from sqlalchemy.orm import Session
+
+from .governance.constitution import ConstitutionalReasoner
+from .governance.primitives import GovernancePrimitives
+from .db import queries
+
+
+class GovernanceAgent:
+    """
+    The AI agent that interprets requests and orchestrates governance actions.
+    """
+
+    def __init__(
+        self,
+        db_session: Session,
+        constitution_path: str,
+        model: Optional[str] = None,
+        api_keys: Optional[Dict[str, str]] = None,
+    ):
+        """
+        Initialize the governance agent.
+
+        Args:
+            db_session: Database session
+            constitution_path: Path to constitution file
+            model: LLM model to use (None for default)
+            api_keys: Dict with 'openai' and/or 'anthropic' API keys
+        """
+        self.db = db_session
+        self.constitution = ConstitutionalReasoner(constitution_path, model, api_keys)
+        self.primitives = GovernancePrimitives(db_session)
+        self.model = model
+        self.api_keys = api_keys or {}
+
+    def process_request(
+        self, request: str, actor: str, context: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        Process a governance request from a user.
+
+        This is the main agentic loop:
+        1. Parse intent
+        2. Consult constitution
+        3. Plan actions
+        4. Execute with audit trail
+        5. Return response
+
+        Args:
+            request: Natural language request from user
+            actor: Who made the request (Mastodon handle)
+            context: Optional context (thread ID, etc.)
+
+        Returns:
+            Dict with 'response', 'actions_taken', 'process_id', etc.
+        """
+        # Step 1: Parse intent
+        intent = self._parse_intent(request, actor)
+
+        if intent.get("error"):
+            return {"response": intent["error"], "success": False}
+
+        # Step 2: Consult constitution
+        constitutional_guidance = self.constitution.query(
+            question=intent["intent_description"],
+            context=f"Actor: {actor}, Request: {request}",
+        )
+
+        # Step 3: Check for ambiguity
+        if constitutional_guidance.get("confidence") == "low":
+            return self._handle_ambiguity(
+                request, actor, constitutional_guidance
+            )
+
+        # Step 4: Plan actions
+        action_plan = self._plan_actions(
+            intent, constitutional_guidance, actor, context
+        )
+
+        # Step 5: Execute plan
+        result = self._execute_plan(action_plan, actor)
+
+        return result
+
+    def _parse_intent(self, request: str, actor: str) -> Dict[str, Any]:
+        """
+        Use AI to parse user intent from natural language.
+
+        Args:
+            request: User's request
+            actor: Who made the request
+
+        Returns:
+            Dict with 'intent_type', 'intent_description', 'parameters'
+        """
+        prompt = f"""Parse this governance request and extract structured information.
+
+REQUEST: "{request}"
+ACTOR: {actor}
+
+Identify:
+1. Intent type (e.g., "create_proposal", "cast_vote", "query_constitution", "appeal", etc.)
+2. Clear description of what the user wants
+3. Key parameters extracted from request
+
+Respond with JSON:
+{{
+  "intent_type": "the type of intent",
+  "intent_description": "clear description of what user wants",
+  "parameters": {{
+    "key": "value"
+  }}
+}}
+"""
+
+        try:
+            result = self._call_llm(prompt)
+            parsed = self._extract_json(result)
+            return parsed
+        except Exception as e:
+            return {"error": f"Could not parse request: {str(e)}"}
+
+    def _plan_actions(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """
+        Plan the sequence of primitive actions to fulfill the intent.
+
+        Args:
+            intent: Parsed intent
+            constitutional_guidance: Constitutional interpretation
+            actor: Who initiated
+            context: Additional context
+
+        Returns:
+            Action plan dictionary
+        """
+        intent_type = intent.get("intent_type")
+
+        # Route to specific planning function based on intent
+        if intent_type == "create_proposal":
+            return self._plan_proposal_creation(
+                intent, constitutional_guidance, actor, context
+            )
+        elif intent_type == "cast_vote":
+            return self._plan_vote_casting(
+                intent, constitutional_guidance, actor, context
+            )
+        elif intent_type == "query_constitution":
+            return self._plan_constitutional_query(
+                intent, constitutional_guidance, actor
+            )
+        elif intent_type == "appeal":
+            return self._plan_appeal(
+                intent, constitutional_guidance, actor, context
+            )
+        else:
+            # Generic planning using AI
+            return self._plan_generic(
+                intent, constitutional_guidance, actor, context
+            )
+
+    def _plan_proposal_creation(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Plan actions for creating a proposal"""
+        params = intent.get("parameters", {})
+        proposal_text = params.get("proposal_text", intent.get("intent_description"))
+
+        # Interpret proposal to determine type and requirements
+        proposal_info = self.constitution.interpret_proposal(proposal_text)
+
+        # Check if the actor has direct authority to execute this
+        # (e.g., @admin in benevolent dictator model)
+        decision_maker = proposal_info.get('decision_maker', '').lower()
+        if decision_maker == actor.lower() or (decision_maker == '@admin' and actor.lower() in ['@admin', 'admin']):
+            # This person has authority - execute directly, don't create a proposal
+            return self._plan_direct_execution(intent, proposal_text, constitutional_guidance, actor, context)
+
+        # Build action plan
+        plan = {
+            "intent_type": "create_proposal",
+            "constitutional_basis": constitutional_guidance.get("citations", []),
+            "actions": [
+                {
+                    "primitive": "create_process",
+                    "args": {
+                        "process_type": f"{proposal_info['proposal_type']}_proposal",
+                        "creator": actor,
+                        "deadline_days": proposal_info.get("discussion_period_days", 6),
+                        "constitutional_basis": str(constitutional_guidance.get("citations")),
+                        "initial_state": {
+                            "proposal_text": proposal_text,
+                            "title": proposal_info.get("title", proposal_text[:100]),
+                            "description": proposal_info.get("description", proposal_text),
+                            "proposal_type": proposal_info["proposal_type"],
+                            "voting_threshold": proposal_info.get("voting_threshold"),
+                            "votes": {},
+                        },
+                        "mastodon_thread_id": context.get("thread_id")
+                        if context
+                        else None,
+                    },
+                },
+                {
+                    "primitive": "schedule_reminder",
+                    "args": {
+                        "when": "deadline",  # Will be calculated from process deadline
+                        "message": f"Proposal by {actor} has reached its deadline. Counting votes.",
+                    },
+                },
+            ],
+            "response_template": self._build_proposal_response(
+                proposal_text, proposal_info, constitutional_guidance, actor
+            ),
+        }
+
+        return plan
+
+    def _plan_direct_execution(
+        self,
+        intent: Dict[str, Any],
+        request_text: str,
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Plan direct execution when actor has authority"""
+        # For now, acknowledge @admin's authority
+        # Future: implement actual rule changes, user management, etc.
+        plan = {
+            "intent_type": "admin_directive",
+            "constitutional_basis": constitutional_guidance.get("citations", []),
+            "actions": [],  # No actions needed - just acknowledge
+            "response_template": f"""Understood. As administrator, you have the authority to implement this.
+
+Directive: {request_text[:250]}
+
+Constitutional basis: {', '.join(constitutional_guidance.get('citations', []))}
+
+Note: The governance system acknowledges your decision. Implementation of automated rule enforcement is forthcoming.
+""",
+        }
+        return plan
+
+    def _build_proposal_response(
+        self,
+        proposal_text: str,
+        proposal_info: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str
+    ) -> str:
+        """Build appropriate response based on proposal type"""
+        proposal_type = proposal_info.get('proposal_type', 'standard')
+
+        # Check if this is an admin decision model
+        if proposal_type == 'admin_decision' or proposal_info.get('decision_maker') == '@admin':
+            return f"""Proposal submitted: {proposal_text[:200]}
+
+According to the constitution, @admin holds authority to make decisions on governance matters.
+
+Constitutional basis: {', '.join(constitutional_guidance.get('citations', []))}
+
+@admin will review this proposal and announce a decision.
+
+Process ID: {{process_id}}
+"""
+        else:
+            # Democratic model with voting
+            return f"""Proposal created: {proposal_text[:100]}...
+
+Type: {proposal_type}
+Discussion period: {proposal_info.get('discussion_period_days')} days
+Voting threshold: {proposal_info.get('voting_threshold')}
+
+Constitutional basis: {', '.join(constitutional_guidance.get('citations', []))}
+
+Reply with 'agree', 'disagree', 'abstain', or 'block' to vote.
+Process ID: {{process_id}}
+"""
+
+    def _plan_vote_casting(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Plan actions for casting a vote"""
+        params = intent.get("parameters", {})
+        vote_type = params.get("vote_type", "agree").lower()
+        process_id = params.get("process_id")
+
+        # If no process_id in params, try to find it from thread context
+        if not process_id and context:
+            # Get the status ID being replied to
+            reply_to_id = context.get("reply_to_id")
+            if reply_to_id:
+                # Query for active processes and check if any match this thread
+                active_processes = queries.get_active_processes(self.db)
+                for proc in active_processes:
+                    if proc.state_data:
+                        announcement_id = proc.state_data.get("announcement_thread_id")
+                        if announcement_id and str(announcement_id) == str(reply_to_id):
+                            process_id = proc.id
+                            break
+
+                # If still not found, try the most recent active proposal
+                if not process_id and active_processes:
+                    process_id = active_processes[0].id
+
+        if not process_id:
+            return {
+                "error": "Could not identify which proposal to vote on. Please reply to a proposal announcement or specify the process ID."
+            }
+
+        plan = {
+            "intent_type": "cast_vote",
+            "constitutional_basis": constitutional_guidance.get("citations", []),
+            "actions": [
+                {
+                    "primitive": "update_process_state",
+                    "args": {
+                        "process_id": process_id,
+                        "state_updates": {
+                            f"votes.{actor}": {
+                                "vote": vote_type,
+                                "timestamp": datetime.utcnow().isoformat(),
+                            }
+                        },
+                        "actor": actor,
+                    },
+                }
+            ],
+            "response_template": f"""Vote recorded: {vote_type}
+
+Voter: {actor}
+Process: {{process_id}}
+""",
+        }
+
+        return plan
+
+    def _plan_constitutional_query(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+    ) -> Dict[str, Any]:
+        """Plan response for constitutional query"""
+        return {
+            "intent_type": "query_constitution",
+            "actions": [],  # No state changes needed
+            "response_template": f"""Constitutional Interpretation:
+
+{constitutional_guidance['answer']}
+
+Citations: {', '.join(constitutional_guidance.get('citations', []))}
+Confidence: {constitutional_guidance.get('confidence', 'medium')}
+""",
+        }
+
+    def _plan_appeal(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Plan actions for an appeal"""
+        params = intent.get("parameters", {})
+        action_id = params.get("action_id")
+
+        plan = {
+            "intent_type": "appeal",
+            "constitutional_basis": constitutional_guidance.get("citations", []),
+            "actions": [
+                {
+                    "primitive": "create_process",
+                    "args": {
+                        "process_type": "appeal",
+                        "creator": actor,
+                        "deadline_days": 3,
+                        "constitutional_basis": "Article 6: Appeals",
+                        "initial_state": {
+                            "appealed_action_id": action_id,
+                            "appellant": actor,
+                            "votes": {},
+                        },
+                    },
+                }
+            ],
+            "response_template": f"""Appeal initiated by {actor}
+
+Appealing action: {{action_id}}
+Discussion period: 3 days
+
+Community members can vote on whether to override the action.
+""",
+        }
+
+        return plan
+
+    def _plan_generic(
+        self,
+        intent: Dict[str, Any],
+        constitutional_guidance: Dict[str, Any],
+        actor: str,
+        context: Optional[Dict[str, Any]],
+    ) -> Dict[str, Any]:
+        """Use AI to plan generic actions"""
+        # This is a fallback for intents we haven't explicitly coded
+        prompt = f"""Based on this intent and constitutional guidance, plan the primitive actions needed.
+
+INTENT: {json.dumps(intent, indent=2)}
+
+CONSTITUTIONAL GUIDANCE: {json.dumps(constitutional_guidance, indent=2)}
+
+Available primitives:
+- create_process(process_type, creator, deadline_days, constitutional_basis, initial_state)
+- update_process_state(process_id, state_updates, actor)
+- store_record(record_type, data, actor, reasoning, citation)
+- schedule_reminder(when, message)
+
+Plan the actions as JSON:
+{{
+  "actions": [
+    {{"primitive": "name", "args": {{...}}}}
+  ],
+  "response_template": "Message to send user (can use Markdown formatting)"
+}}
+
+TONE: Be direct, concise, and clear. Use short paragraphs with line breaks.
+Avoid formal/legalistic language AND casual interjections (no "Hey!").
+Professional but approachable. Get to the point quickly.
+"""
+
+        try:
+            result = self._call_llm(prompt)
+            plan = self._extract_json(result)
+            plan["intent_type"] = intent.get("intent_type")
+            plan["constitutional_basis"] = constitutional_guidance.get("citations", [])
+            return plan
+        except Exception as e:
+            return {
+                "error": f"Could not plan actions: {str(e)}",
+                "intent": intent,
+                "guidance": constitutional_guidance,
+            }
+
+    def _execute_plan(
+        self, plan: Dict[str, Any], actor: str
+    ) -> Dict[str, Any]:
+        """
+        Execute the planned actions using primitives.
+
+        Args:
+            plan: Action plan
+            actor: Who initiated
+
+        Returns:
+            Execution result
+        """
+        if plan.get("error"):
+            return {"response": plan["error"], "success": False}
+
+        executed_actions = []
+        process_id = None
+
+        try:
+            for action in plan.get("actions", []):
+                primitive = action["primitive"]
+                args = action["args"]
+
+                # Get the primitive function
+                if hasattr(self.primitives, primitive):
+                    func = getattr(self.primitives, primitive)
+
+                    # Handle special cases like deadline calculation
+                    if "when" in args and args["when"] == "deadline":
+                        # Calculate from process deadline
+                        if process_id:
+                            process = queries.get_process(self.db, process_id)
+                            args["when"] = process.deadline
+
+                    result = func(**args)
+
+                    # Track process ID for response
+                    if primitive == "create_process":
+                        process_id = result
+
+                    executed_actions.append(
+                        {"primitive": primitive, "result": result}
+                    )
+                else:
+                    raise ValueError(f"Unknown primitive: {primitive}")
+
+            # Build response
+            response_template = plan.get("response_template", "Action completed.")
+            response = response_template.format(
+                process_id=process_id, action_id=executed_actions[0].get("result")
+                if executed_actions
+                else None
+            )
+
+            return {
+                "response": response,
+                "success": True,
+                "process_id": process_id,
+                "actions_taken": executed_actions,
+                "constitutional_basis": plan.get("constitutional_basis"),
+            }
+
+        except Exception as e:
+            return {
+                "response": f"Error executing actions: {str(e)}",
+                "success": False,
+                "partial_actions": executed_actions,
+            }
+
+    def _handle_ambiguity(
+        self,
+        request: str,
+        actor: str,
+        constitutional_guidance: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """
+        Handle constitutional ambiguity by requesting clarification.
+
+        Args:
+            request: Original request
+            actor: Who made request
+            constitutional_guidance: The ambiguous guidance
+
+        Returns:
+            Response explaining ambiguity
+        """
+        ambiguity = constitutional_guidance.get("ambiguity", "Constitutional interpretation unclear")
+
+        # Create clarification request
+        clarification = queries.create_clarification(
+            session=self.db,
+            question=f"Ambiguity in request '{request}': {ambiguity}",
+        )
+
+        response = f"""I found something unclear in the constitution regarding your request.
+
+Issue: {ambiguity}
+
+This needs community clarification. Discussion welcome.
+
+Clarification ID: {clarification.id}
+"""
+
+        return {
+            "response": response,
+            "success": False,
+            "requires_clarification": True,
+            "clarification_id": clarification.id,
+        }
+
+    def check_deadlines(self) -> List[Dict[str, Any]]:
+        """
+        Check for processes that have passed their deadline.
+        This should be called periodically by a background task.
+
+        Returns:
+            List of processes that were completed
+        """
+        overdue_processes = queries.get_processes_past_deadline(self.db)
+        completed = []
+
+        for process in overdue_processes:
+            # Count votes
+            counts = self.primitives.count_votes(process.id)
+
+            # Determine threshold from process state
+            threshold_type = process.state_data.get(
+                "voting_threshold", "simple_majority"
+            )
+
+            # Check if passed
+            passed = self.primitives.check_threshold(counts, threshold_type)
+
+            outcome = "passed" if passed else "failed"
+
+            # Complete the process
+            self.primitives.complete_process(
+                process_id=process.id,
+                outcome=outcome,
+                reasoning=f"Vote counts: {counts}. Threshold: {threshold_type}. Result: {outcome}",
+            )
+
+            completed.append(
+                {
+                    "process_id": process.id,
+                    "outcome": outcome,
+                    "vote_counts": counts,
+                }
+            )
+
+        return completed
+
+    def _call_llm(self, prompt: str) -> str:
+        """Call the LLM via llm CLI"""
+        import os
+
+        cmd = ["llm"]
+        if self.model:
+            cmd.extend(["-m", self.model])
+        cmd.append(prompt)
+
+        # Set up environment with API keys
+        env = os.environ.copy()
+        if self.api_keys.get('openai'):
+            env['OPENAI_API_KEY'] = self.api_keys['openai']
+        if self.api_keys.get('anthropic'):
+            env['ANTHROPIC_API_KEY'] = self.api_keys['anthropic']
+
+        result = subprocess.run(cmd, capture_output=True, text=True, check=True, env=env)
+        return result.stdout.strip()
+
+    def _extract_json(self, text: str) -> Dict[str, Any]:
+        """Extract JSON from LLM response"""
+        # Handle markdown code blocks
+        if "```json" in text:
+            start = text.find("```json") + 7
+            end = text.find("```", start)
+            json_str = text[start:end].strip()
+        elif "```" in text:
+            start = text.find("```") + 3
+            end = text.find("```", start)
+            json_str = text[start:end].strip()
+        else:
+            json_str = text
+
+        return json.loads(json_str)

src/govbot/agent_refactored.py

@@ -1,561 +0,0 @@
-"""
-Refactored Agentic Governance Bot.
-
-This refactored agent relies on:
-1. LLM interpretation (not hard-coded logic)
-2. Structured memory (not just database records)
-3. Tools for correctness (calculator, datetime, etc.)
-4. Audit trails (human-readable explanations)
-
-Key principle: Constitution defines ALL governance rules in natural language.
-The agent interprets and executes based on constitution + memory + tools.
-"""
-
-import json
-from typing import Dict, Any, Optional, List
-from datetime import datetime, timedelta
-from sqlalchemy.orm import Session
-
-from .governance.constitution import ConstitutionalReasoner
-from .memory import GovernanceMemory, ProcessMemory, ProcessStatus, Event, Decision
-from .tools import GovernanceTools, ToolRegistry
-from .audit import AuditTrail, create_audit_trail
-
-
-class AgenticGovernanceBot:
-    """
-    Agentic governance bot that interprets constitution and uses memory + tools.
-
-    This bot does NOT have hard-coded governance logic. Instead:
-    - Reads constitution to understand rules
-    - Uses memory to track state
-    - Uses tools for calculations
-    - Makes decisions through LLM reasoning
-    """
-
-    def __init__(
-        self,
-        db_session: Session,
-        constitution_path: str,
-        model: Optional[str] = None,
-        api_keys: Optional[Dict[str, str]] = None,
-    ):
-        """
-        Initialize the agentic governance bot.
-
-        Args:
-            db_session: Database session
-            constitution_path: Path to constitution file
-            model: LLM model to use (None for default)
-            api_keys: Dict with 'openai' and/or 'anthropic' API keys
-        """
-        self.db = db_session
-        self.constitution = ConstitutionalReasoner(constitution_path, model, api_keys)
-        self.memory = GovernanceMemory(db_session)
-        self.tools = GovernanceTools()
-        self.model = model
-        self.api_keys = api_keys or {}
-
-    def process_request(
-        self,
-        request: str,
-        actor: str,
-        context: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """
-        Process a governance request using agentic interpretation.
-
-        Flow:
-        1. Understand what the user wants (intent parsing)
-        2. Query constitution for relevant rules
-        3. Query memory for current state
-        4. Reason about what to do (using LLM)
-        5. Execute actions using tools
-        6. Update memory with decision
-        7. Generate audit trail
-
-        Args:
-            request: Natural language request
-            actor: Who made the request
-            context: Optional context (thread ID, etc.)
-
-        Returns:
-            Response dictionary with action taken and audit trail
-        """
-        # Step 1: Parse intent
-        intent_result = self._parse_intent_with_llm(request, actor)
-
-        if "error" in intent_result:
-            return {"response": intent_result["error"], "success": False}
-
-        # Step 2: Query constitution
-        constitutional_guidance = self.constitution.query(
-            question=intent_result["query"],
-            context=f"Actor: {actor}\nRequest: {request}"
-        )
-
-        # Step 3: Query memory for relevant state
-        memory_context = self._gather_memory_context(intent_result, actor)
-
-        # Step 4: Use LLM to decide what to do
-        decision_result = self._make_decision_with_llm(
-            intent=intent_result,
-            constitution=constitutional_guidance,
-            memory=memory_context,
-            actor=actor,
-            context=context
-        )
-
-        # Step 5: Execute the decision
-        execution_result = self._execute_decision(
-            decision_result,
-            actor,
-            context
-        )
-
-        # Step 6: Generate audit trail
-        if execution_result.get("process_id"):
-            process = self.memory.get_process(execution_result["process_id"])
-            if process and process.decisions:
-                audit = create_audit_trail(process, process.decisions[-1])
-                execution_result["audit_trail"] = audit
-
-        return execution_result
-
-    def _parse_intent_with_llm(self, request: str, actor: str) -> Dict[str, Any]:
-        """
-        Use LLM to understand what the user wants to do.
-
-        Returns:
-            Dict with:
-            - intent_type: "create_proposal", "cast_vote", "query", etc.
-            - query: Question to ask constitution
-            - parameters: Extracted parameters
-        """
-        prompt = f"""Analyze this governance request and determine the intent.
-
-Request: {request}
-Actor: {actor}
-
-What is the user trying to do? Choose from:
-- create_proposal: User wants to create a governance proposal
-- cast_vote: User wants to vote on something
-- query_status: User wants to know the status of something
-- query_constitution: User wants to know governance rules
-- dispute_initiation: User wants to start a dispute resolution process
-- other: Something else
-
-Also extract any relevant parameters (proposal text, vote type, process ID, etc.).
-
-Return your analysis as JSON:
-{{
-    "intent_type": "...",
-    "query": "question to ask the constitution",
-    "parameters": {{}},
-    "confidence": "high|medium|low"
-}}
-"""
-
-        try:
-            result = self.constitution._call_llm(prompt)
-            # Parse JSON from response
-            # (In production, would use proper JSON parsing from LLM response)
-            return json.loads(result.get("answer", "{}"))
-        except Exception as e:
-            return {"error": f"Failed to parse intent: {e}"}
-
-    def _gather_memory_context(
-        self,
-        intent: Dict[str, Any],
-        actor: str
-    ) -> Dict[str, Any]:
-        """
-        Gather relevant information from memory based on intent.
-
-        Returns:
-            Dict with memory context
-        """
-        context = {}
-
-        # Get active processes
-        active_processes = self.memory.get_active_processes()
-        context["active_processes"] = [
-            self.memory.summarize_for_llm(p.id) for p in active_processes[:5]
-        ]
-
-        # Get overdue processes
-        overdue = self.memory.get_overdue_processes()
-        if overdue:
-            context["overdue_processes"] = [p.id for p in overdue]
-
-        # If specific process mentioned, get its details
-        if "process_id" in intent.get("parameters", {}):
-            process_id = intent["parameters"]["process_id"]
-            process = self.memory.get_process(process_id)
-            if process:
-                context["target_process"] = self.memory.summarize_for_llm(process_id)
-
-        # Get recent precedent if relevant
-        if intent.get("intent_type") == "query_constitution":
-            recent_decisions = self.memory.search_decisions(limit=5)
-            context["recent_precedent"] = [
-                f"{d.decision_type}: {d.result}" for d in recent_decisions
-            ]
-
-        return context
-
-    def _make_decision_with_llm(
-        self,
-        intent: Dict[str, Any],
-        constitution: Dict[str, Any],
-        memory: Dict[str, Any],
-        actor: str,
-        context: Optional[Dict[str, Any]]
-    ) -> Dict[str, Any]:
-        """
-        Use LLM to decide what action to take.
-
-        This is where the agent interprets the constitution and decides
-        how to handle the request.
-
-        Returns:
-            Decision dict with:
-            - action: What to do
-            - reasoning: Why
-            - constitution_citations: Which articles apply
-            - calculations: Any math needed
-            - state_updates: Changes to memory
-        """
-        prompt = f"""You are a governance bot interpreting a community constitution.
-
-INTENT:
-{json.dumps(intent, indent=2)}
-
-CONSTITUTIONAL GUIDANCE:
-{json.dumps(constitution, indent=2)}
-
-CURRENT MEMORY STATE:
-{json.dumps(memory, indent=2)}
-
-ACTOR: {actor}
-
-Based on the constitution and current state, decide what action to take.
-
-For proposals:
-- What type of proposal is this? (standard, urgent, constitutional, etc.)
-- What discussion period does the constitution specify?
-- What voting threshold is required?
-- Are there any special requirements?
-
-For votes:
-- Is this person eligible to vote on this process?
-- What vote types are allowed?
-- Should this vote be recorded?
-
-For deadline checks:
-- Has the deadline passed?
-- What votes were cast?
-- What threshold does the constitution require?
-- Does the proposal pass or fail?
-
-Available tools for calculations:
-- calculate(expression, variables): Evaluate math expressions
-- get_datetime(): Get current time
-- is_past_deadline(deadline): Check if deadline passed
-- tally(votes, key): Count votes by type
-- random_select(items, count): Random selection
-
-Return your decision as JSON:
-{{
-    "action": "create_process|record_vote|complete_process|query_response",
-    "reasoning": "explain your interpretation",
-    "constitution_citations": ["Article X, Section Y", ...],
-    "parameters": {{
-        // Action-specific parameters
-        "process_type": "...",
-        "deadline_days": X,
-        "threshold_expression": "agree > disagree",
-        // etc.
-    }},
-    "calculations": [
-        {{
-            "tool": "calculate",
-            "expression": "agree > disagree",
-            "variables": {{"agree": 10, "disagree": 3}}
-        }}
-    ]
-}}
-"""
-
-        try:
-            result = self.constitution._call_llm(prompt)
-            decision = json.loads(result.get("answer", "{}"))
-
-            # Execute any calculations using tools
-            if "calculations" in decision:
-                for calc in decision["calculations"]:
-                    tool_name = calc["tool"]
-                    if tool_name == "calculate":
-                        calc["result"] = self.tools.calculate(
-                            calc["expression"],
-                            calc["variables"]
-                        )
-                    # Handle other tools...
-
-            return decision
-
-        except Exception as e:
-            return {
-                "action": "error",
-                "reasoning": f"Failed to make decision: {e}",
-                "constitution_citations": []
-            }
-
-    def _execute_decision(
-        self,
-        decision: Dict[str, Any],
-        actor: str,
-        context: Optional[Dict[str, Any]]
-    ) -> Dict[str, Any]:
-        """
-        Execute the decision made by the LLM.
-
-        This updates memory and performs platform actions.
-        """
-        action = decision.get("action")
-        params = decision.get("parameters", {})
-
-        try:
-            if action == "create_process":
-                return self._create_process_from_decision(decision, actor, context)
-
-            elif action == "record_vote":
-                return self._record_vote_from_decision(decision, actor, context)
-
-            elif action == "complete_process":
-                return self._complete_process_from_decision(decision, actor)
-
-            elif action == "query_response":
-                return {
-                    "response": decision.get("reasoning"),
-                    "constitution_citations": decision.get("constitution_citations", []),
-                    "success": True
-                }
-
-            else:
-                return {
-                    "response": f"Unknown action: {action}",
-                    "success": False
-                }
-
-        except Exception as e:
-            return {
-                "response": f"Error executing decision: {e}",
-                "success": False
-            }
-
-    def _create_process_from_decision(
-        self,
-        decision: Dict[str, Any],
-        actor: str,
-        context: Optional[Dict[str, Any]]
-    ) -> Dict[str, Any]:
-        """Create a new governance process based on LLM decision"""
-        params = decision["parameters"]
-
-        # Calculate deadline
-        deadline_days = params.get("deadline_days", 7)
-        deadline = self.tools.datetime_add(
-            self.tools.get_datetime(),
-            days=deadline_days
-        )
-
-        # Generate process ID
-        process_id = f"process_{int(datetime.utcnow().timestamp())}"
-
-        # Create in memory
-        process = self.memory.create_process(
-            process_id=process_id,
-            process_type=params.get("process_type", "unknown"),
-            created_by=actor,
-            constitution_basis=decision.get("constitution_citations", []),
-            deadline=deadline,
-            initial_state=params.get("initial_state", {}),
-            metadata=context or {}
-        )
-
-        # Log creation event
-        self.memory.add_event(
-            process_id=process_id,
-            actor=actor,
-            event_type="process_created",
-            data=params,
-            context=f"Process created by {actor}"
-        )
-
-        # Log creation decision
-        self.memory.add_decision(
-            process_id=process_id,
-            decision_type="process_creation",
-            reasoning=decision["reasoning"],
-            constitution_citations=decision.get("constitution_citations", []),
-            result="created"
-        )
-
-        return {
-            "response": f"Created {params.get('process_type')} (ID: {process_id}). Deadline: {deadline.strftime('%Y-%m-%d %H:%M UTC')}",
-            "process_id": process_id,
-            "deadline": deadline.isoformat(),
-            "success": True
-        }
-
-    def _record_vote_from_decision(
-        self,
-        decision: Dict[str, Any],
-        actor: str,
-        context: Optional[Dict[str, Any]]
-    ) -> Dict[str, Any]:
-        """Record a vote based on LLM decision"""
-        params = decision["parameters"]
-        process_id = params.get("process_id")
-
-        if not process_id:
-            return {"response": "No process ID specified", "success": False}
-
-        # Get process
-        process = self.memory.get_process(process_id)
-        if not process:
-            return {"response": f"Process {process_id} not found", "success": False}
-
-        # Record vote in state
-        votes = process.state.get("votes", {})
-        votes[actor] = {
-            "vote": params.get("vote_type"),
-            "timestamp": datetime.utcnow().isoformat()
-        }
-
-        self.memory.update_process(
-            process_id=process_id,
-            state_updates={"votes": votes}
-        )
-
-        # Log event
-        self.memory.add_event(
-            process_id=process_id,
-            actor=actor,
-            event_type="vote_cast",
-            data={"vote": params.get("vote_type")},
-            context=f"{actor} voted {params.get('vote_type')}"
-        )
-
-        return {
-            "response": f"Vote recorded: {params.get('vote_type')}",
-            "process_id": process_id,
-            "success": True
-        }
-
-    def _complete_process_from_decision(
-        self,
-        decision: Dict[str, Any],
-        actor: str
-    ) -> Dict[str, Any]:
-        """Complete a process based on LLM decision"""
-        params = decision["parameters"]
-        process_id = params.get("process_id")
-
-        process = self.memory.get_process(process_id)
-        if not process:
-            return {"response": f"Process {process_id} not found", "success": False}
-
-        # Update status
-        self.memory.update_process(
-            process_id=process_id,
-            status=ProcessStatus.COMPLETED
-        )
-
-        # Log completion decision
-        calculation_used = None
-        calculation_vars = None
-        calculation_result = None
-
-        if decision.get("calculations"):
-            calc = decision["calculations"][0]  # Use first calculation
-            calculation_used = calc.get("expression")
-            calculation_vars = calc.get("variables")
-            calculation_result = calc.get("result")
-
-        self.memory.add_decision(
-            process_id=process_id,
-            decision_type="process_completion",
-            reasoning=decision["reasoning"],
-            constitution_citations=decision.get("constitution_citations", []),
-            result=params.get("outcome", "completed"),
-            calculation_used=calculation_used,
-            calculation_variables=calculation_vars,
-            calculation_result=calculation_result
-        )
-
-        # Log event
-        self.memory.add_event(
-            process_id=process_id,
-            actor="bot",
-            event_type="process_completed",
-            data={"outcome": params.get("outcome")},
-            context=f"Process completed with outcome: {params.get('outcome')}"
-        )
-
-        return {
-            "response": f"Process {process_id} completed: {params.get('outcome')}",
-            "process_id": process_id,
-            "outcome": params.get("outcome"),
-            "success": True
-        }
-
-    def check_deadlines(self) -> List[Dict[str, Any]]:
-        """
-        Check for processes that have reached their deadline.
-
-        This is called periodically (e.g., every hour) to evaluate
-        processes that need decisions.
-
-        Returns:
-            List of completed process results
-        """
-        results = []
-        overdue = self.memory.get_overdue_processes()
-
-        for process in overdue:
-            # Ask LLM to evaluate this process
-            evaluation = self._evaluate_process_deadline(process)
-            results.append(evaluation)
-
-        return results
-
-    def _evaluate_process_deadline(self, process: ProcessMemory) -> Dict[str, Any]:
-        """
-        Use LLM to evaluate a process that reached its deadline.
-        """
-        # Get vote tally
-        votes = process.state.get("votes", {})
-        vote_tally = self.tools.tally(
-            [{"vote": v["vote"]} for v in votes.values()],
-            "vote"
-        )
-
-        # Ask constitution what threshold is needed
-        threshold_query = self.constitution.query(
-            question=f"For a {process.type}, what voting threshold is required for passage?",
-            context=self.memory.summarize_for_llm(process.id)
-        )
-
-        # Use LLM to decide if threshold is met
-        decision_result = self._make_decision_with_llm(
-            intent={"intent_type": "deadline_check"},
-            constitution=threshold_query,
-            memory={"votes": vote_tally, "process": process.to_dict()},
-            actor="bot",
-            context=None
-        )
-
-        # Execute the decision
-        return self._execute_decision(decision_result, "bot", None)

src/govbot/governance/constitution.py

@@ -256,12 +256,27 @@ Format your response as JSON:
         """
         question = f"""Given this proposal: "{proposal_text}"
 
-What type of proposal is this according to the constitution?
+According to the constitution:
-What are the requirements (timeline, voting threshold, etc.)?
+1. Who has authority to decide on this proposal?
+2. What is the process for handling this request?
+3. Does this require voting, or does someone have direct authority?
 """
 
         result = self.query(question)
 
+        # Check if constitution mentions direct authority (e.g., benevolent dictator)
+        answer_lower = result.get('answer', '').lower()
+        if '@admin' in answer_lower and any(word in answer_lower for word in ['authority', 'decides', 'makes', 'final', 'power']):
+            # Benevolent dictator model - direct to @admin
+            return {
+                "proposal_type": "admin_decision",
+                "discussion_period_days": 0,
+                "voting_threshold": "admin_approval",
+                "special_requirements": ["requires_admin_decision"],
+                "decision_maker": "@admin",
+                "constitutional_basis": result,
+            }
+
         # Extract structured information from the answer
         try:
             prompt = f"""Based on this constitutional interpretation:
@@ -271,9 +286,10 @@ Extract structured information as JSON:
 {{
   "title": "concise title for the proposal (max 80 chars)",
   "description": "clear 1-2 sentence description of what the proposal does",
-  "proposal_type": "standard|urgent|constitutional_amendment",
+  "proposal_type": "standard|urgent|constitutional_amendment|admin_decision|community_input",
-  "discussion_period_days": number,
+  "discussion_period_days": number (or 0 if no voting period),
-  "voting_threshold": "description of threshold",
+  "voting_threshold": "description of threshold or approval mechanism",
+  "decision_maker": "who makes the final decision (e.g., '@admin', 'community_vote', 'consensus')",
   "special_requirements": ["list", "of", "requirements"]
 }}
 """
@@ -282,7 +298,20 @@ Extract structured information as JSON:
             proposal_info["constitutional_basis"] = result
             return proposal_info
         except Exception as e:
-            # Fallback
+            # Check constitution content for governance model
+            if '@admin' in self.constitution_text and 'final authority' in self.constitution_text.lower():
+                # Benevolent dictator fallback
+                return {
+                    "proposal_type": "admin_decision",
+                    "discussion_period_days": 0,
+                    "voting_threshold": "admin_approval",
+                    "special_requirements": ["requires_admin_decision"],
+                    "decision_maker": "@admin",
+                    "constitutional_basis": result,
+                    "error": str(e),
+                }
+            else:
+                # Democratic fallback
                 return {
                     "proposal_type": "standard",
                     "discussion_period_days": 6,

src/govbot/governance/primitives.py

@@ -297,6 +297,11 @@ class GovernancePrimitives:
                 return False
             return (agree / total) >= (2 / 3)
 
+        elif threshold_type == "admin_approval":
+            # Admin decision model - no voting threshold
+            # This type means admin must approve, not vote counting
+            return False  # Requires manual admin approval
+
         else:
             raise ValueError(f"Unknown threshold type: {threshold_type}")
 

src/govbot/platforms/mastodon.py

@@ -679,15 +679,30 @@ class MastodonAdapter(PlatformAdapter):
         text = re.sub(r'_([^_]+)_', r'\1', text)          # _italic_ -> italic
         text = re.sub(r'`([^`]+)`', r'\1', text)          # `code` -> code
 
-        # Remove headers but keep the text
+        # Remove headers but keep the text with extra spacing
-        text = re.sub(r'^#{1,6}\s+', '', text, flags=re.MULTILINE)
+        text = re.sub(r'^#{1,6}\s+(.+)$', r'\1\n', text, flags=re.MULTILINE)
 
         # Convert Markdown lists to simple text with bullets
+        # Ensure each bullet point is on its own line
         text = re.sub(r'^\s*[-*+]\s+', '• ', text, flags=re.MULTILINE)
 
         # Remove link formatting but keep URLs: [text](url) -> text (url)
         text = re.sub(r'\[([^\]]+)\]\(([^\)]+)\)', r'\1 (\2)', text)
 
+        # Ensure proper paragraph spacing for Mastodon
+        # Replace single newlines within paragraphs, but preserve double newlines
+        # First, protect double (or more) newlines
+        text = re.sub(r'\n\n+', '<<<PARAGRAPH>>>', text)
+        # Then ensure bullet points and other single newlines are preserved
+        # (Mastodon respects single newlines in plain text)
+        # Restore paragraph breaks
+        text = text.replace('<<<PARAGRAPH>>>', '\n\n')
+
+        # Clean up any extra whitespace but preserve intentional line breaks
+        text = re.sub(r' +', ' ', text)  # Multiple spaces -> single space
+        text = re.sub(r'\n ', '\n', text)  # Remove spaces after newlines
+        text = re.sub(r' \n', '\n', text)  # Remove spaces before newlines
+
         return text
 
     def _map_visibility(self, visibility: MessageVisibility) -> str: