# Personas (Unified Agent Profiles) [Top](../Readme.md) | [MMA](guide_mma.md) | [Tools & IPC](guide_tools.md) | [Architecture](guide_architecture.md) --- ## Overview A **Persona** is a unified agent profile that consolidates the model choice, system prompt, tool preset, bias profile, context preset, and aggregation strategy into a single named, reusable entity. Personas eliminate the need to configure these settings individually per session or per Tier — the developer names a persona, and the entire configuration is applied atomically. Personas are persisted in TOML files with **scope-based inheritance** (Global vs Project). The PersonaManager merges both scopes at load time, with project-level entries overriding global entries of the same name. This guide covers: 1. **Data Model** — The `Persona` schema and its fields 2. **Storage & Scope** — Global and project TOML files, merge rules 3. **PersonaManager** — CRUD operations 4. **MMA Integration** — How personas are applied to workers 5. **Editor Modal** — The GUI for creating and editing personas 6. **Testing** — Test areas for persona behavior --- ## Data Model ### `Persona` (`src/models.py:704`) ```python class Persona: name: str preferred_models: List[Dict[str, Any]] = field(default_factory=list) system_prompt: str = '' tool_preset: Optional[str] = None bias_profile: Optional[str] = None context_preset: Optional[str] = None aggregation_strategy: Optional[str] = None ``` | Field | Type | Purpose | |---|---|---| | `name` | `str` | Unique identifier. Used as the key in TOML. | | `preferred_models` | `List[Dict]` | Ordered list of `{provider, model, temperature, top_p, max_output_tokens}` dicts. The first entry is the initial model; subsequent entries are escalation targets. | | `system_prompt` | `str` | Replaces the default system prompt when this persona is active. | | `tool_preset` | `Optional[str]` | Name of a `ToolPreset` to apply. See [guide_tools.md](guide_tools.md). | | `bias_profile` | `Optional[str]` | Name of a `BiasProfile` to apply. See [guide_tools.md](guide_tools.md). | | `context_preset` | `Optional[str]` | Name of a `ContextPreset` to apply for file selection. | | `aggregation_strategy` | `Optional[str]` | One of `auto`, `full`, `summarize`, `skeleton`. Drives how files are aggregated for this persona. | **Convenience Properties**: ```python @property def provider(self) -> Optional[str]: if not self.preferred_models: return None return self.preferred_models[0].get("provider") @property def model(self) -> Optional[str]: if not self.preferred_models: return None return self.preferred_models[0].get("model") @property def temperature(self) -> Optional[float]: if not self.preferred_models: return None return self.preferred_models[0].get("temperature") # Similar: top_p, max_output_tokens ``` These read from `preferred_models[0]` (the primary model). The full escalation list is used by `multi_agent_conductor.run_worker_lifecycle` for retry-based model escalation. ### `preferred_models` Schema Each entry in the list is a dict with optional keys: ```python { "provider": "gemini", # Required "model": "gemini-3.1-pro-preview", # Required "temperature": 0.0, # Optional, defaults to 0.0 "top_p": 1.0, # Optional, defaults to 1.0 "max_output_tokens": 8192, # Optional, defaults to provider default } ``` The list is ordered: index 0 is the first attempt, index 1 is the first escalation, etc. The ConductorEngine's model escalation logic iterates this list on retry. --- ## Storage & Scope ### File Locations | Scope | Path | Configured By | |---|---|---| | Global | `/personas.toml` | `src/paths.py:get_global_personas_path()` | | Project | `/personas.toml` | `src/paths.py:get_project_personas_path(project_root)` | Both files use the same TOML schema: ```toml [personas.] preferred_models = [ { provider = "gemini", model = "gemini-3.1-pro-preview", temperature = 0.0 }, { provider = "gemini", model = "gemini-3-flash-preview", temperature = 0.0 }, ] system_prompt = "You are a senior backend engineer with deep knowledge of Python asyncio." tool_preset = "read_only" bias_profile = "discovery_heavy" context_preset = "codebase_full" aggregation_strategy = "summarize" ``` ### Scope Inheritance `PersonaManager.load_all()` merges global and project personas: 1. Load global personas first. 2. If `project_root` is set, load project personas and **overwrite** entries with matching names. 3. Return the merged dict. **Example**: If `personas.toml` (global) has a `code_reviewer` persona and `personas.toml` (project) also has a `code_reviewer` persona, the project version wins. This allows projects to override global defaults without losing the global fallback. ### Scope Lookup `get_persona_scope(name)` returns `"global"` or `"project"` based on which file contains the persona. Used by the editor modal to show the user where a persona is defined. ### Save / Delete - `save_persona(persona, scope="project")` writes the persona to the specified scope's TOML file. - `delete_persona(name, scope="project")` removes the entry. - The default scope for both is `"project"`. Use `"global"` for application-wide personas. --- ## PersonaManager `PersonaManager` is the CRUD interface. ```python from src.personas import PersonaManager from src.models import Persona manager = PersonaManager(project_root=Path("/path/to/project")) all_personas = manager.load_all() # Merged global + project manager.save_persona(my_persona, scope="project") manager.delete_persona("old_persona", scope="project") scope = manager.get_persona_scope("code_reviewer") ``` ### `load_all()` Returns a `Dict[str, Persona]` mapping name to Persona. Project entries override global entries of the same name. ### `save_persona(persona, scope)` Writes a Persona to the specified scope's TOML file. If the file doesn't exist, it's created (parent directories included). If the file exists, the entry is added or updated; other entries are preserved. ### `delete_persona(name, scope)` Removes the named entry from the specified scope's TOML file. If the entry doesn't exist, the operation is a no-op. ### `get_persona_scope(name)` Returns `"global"` or `"project"` based on which file contains the persona. Returns `"project"` if neither (effectively saying "would be saved to project if you save"). ### Internal Helpers - `_load_file(path) -> Dict[str, Any]`: Reads a TOML file, returns `{}` on error or missing file. - `_save_file(path, data)`: Writes a TOML file using `tomli_w`. Creates parent directories as needed. --- ## MMA Integration When a `Ticket` has `persona_id` set, or when a Tier has a default persona, the ConductorEngine applies the persona to the worker. See [guide_mma.md#persona-application](guide_mma.md#persona-application) for the full integration details. **Application order** (in `run_worker_lifecycle`): 1. **Model selection**: - `ticket.model_override` (if set) — used unconditionally - Persona `preferred_models[0]` (if persona applied) — initial model - Default tier model — fallback 2. **System prompt**: `ai_client.set_custom_system_prompt(persona.system_prompt)` replaces the default. 3. **Bias profile**: `ai_client.set_bias_profile(persona.bias_profile)` applies semantic nudging. 4. **Tool preset**: `ai_client.set_tool_preset(persona.tool_preset)` configures enabled tools. 5. **Aggregation strategy**: Used by the `aggregate.py` pipeline to choose `full`/`summarize`/`skeleton`. **Failure handling**: If the persona fails to load (file not found, parse error, missing fields), the worker logs a warning and falls back to the default model list. The persona is **not** a hard failure point. ### Tier-Scoped vs Ticket-Scoped Personas - **Tier-scoped**: Set in `tier_usage[]["persona"]`. Applied to every worker for that tier when no ticket-level persona is set. - **Ticket-scoped**: Set in `ticket.persona_id`. Overrides tier-level for that specific ticket. --- ## Editor Modal The GUI provides a Persona Editor modal (`src/gui_2.py:_render_persona_editor_modal` or similar) for creating and editing personas without manually editing TOML. **Fields exposed in the modal**: - Name (text input, required) - Preferred Models (editable list, with provider/model/temperature/top_p/max_output_tokens per entry) - System Prompt (multi-line text input) - Tool Preset (dropdown of available presets) - Bias Profile (dropdown) - Context Preset (dropdown) - Aggregation Strategy (radio buttons: auto / full / summarize / skeleton) - Scope (radio: Global / Project) **Actions**: - **Save** — Writes the persona to the selected scope. - **Delete** — Removes the persona (with confirmation). - **Duplicate** — Creates a copy with a new name. - **Cancel** — Discards changes. The modal validates the name (must be unique, must be a valid TOML key) and the model entries (provider and model are required) before allowing save. --- ## Configuration Personas are project-scoped (or global) configuration. There is no central `config.toml` setting for personas themselves; they're standalone TOML files. **Related settings** (in `manual_slop.toml` or `config.toml`): ```toml [mma] default_personas = { "Tier 1": "orchestrator", "Tier 2": "tech_lead", "Tier 3": "code_worker", "Tier 4": "qa_reviewer", } ``` This sets the default persona for each tier. Tickets without `persona_id` use the tier's default. --- ## Testing ### Unit Tests - `tests/test_persona_manager.py` — `PersonaManager` CRUD, scope merging, file I/O - `tests/test_persona_models.py` — `Persona` serialization/deserialization - `tests/test_persona_id.py` — `Persona` ID validation and uniqueness ### Integration Tests - `tests/test_mma_prompts.py` — Verifies persona-derived prompts are constructed correctly - `tests/test_bias_efficacy.py` — Verifies bias profile integration ### Test Pattern ```python def test_persona_scope_overrides(tmp_path): # Global persona global_path = tmp_path / "global_personas.toml" global_path.write_text(""" [personas.coder] system_prompt = "Global: code only." """) # Project persona (override) project_path = tmp_path / "personas.toml" project_path.write_text(""" [personas.coder] system_prompt = "Project: code only, focus on our domain." """) manager = PersonaManager(project_root=tmp_path, ...) # Patch paths module to return the test paths with patch("src.paths.get_global_personas_path", return_value=global_path), \ patch("src.paths.get_project_personas_path", return_value=project_path): all_personas = manager.load_all() assert "coder" in all_personas assert all_personas["coder"].system_prompt == "Project: code only, focus on our domain." ``` --- ## Limitations 1. **No Versioning**: Personas are stored in TOML. Changes are not versioned unless the project uses git (which most do, but the persona changes are buried in diffs). Consider a "persona history" feature for future. 2. **No Inheritance Chains**: A persona cannot reference another persona as a base. If a project wants to share settings across multiple personas, it must duplicate them. 3. **No Validation of Referenced Names**: A persona can name a `tool_preset` that doesn't exist. The error surfaces only when the worker tries to apply the persona. 4. **No Live Reload**: Changing a persona TOML file does not take effect until the application is restarted (or `PersonaManager.load_all()` is called again). 5. **No Conflict Resolution UI**: If a project and global define different personas with the same name, the project wins silently. The user must check the editor modal to see the actual definition. --- ## Future Work - **Persona Composition** — Allow a persona to reference another as a base, with override semantics. - **Live Reload** — Watch the persona TOML files and reload on change. - **Persona History** — Track changes over time, allow rollback. - **Conflict Resolution UI** — When global and project both define a persona, show both in the editor with a "use global" / "use project" / "merge" choice. - **Persona Templates** — Pre-built personas for common roles (code reviewer, test writer, doc writer) that users can clone. See [guide_mma.md#persona-application](guide_mma.md#persona-application) for the worker-side integration.