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Merged
lbedner merged 1 commit into from
Jul 21, 2026
Merged

Agents #896

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6 changes: 6 additions & 0 deletions .gitignore
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Expand Up @@ -150,3 +150,9 @@ demo-*/
# Generated-project artifact if aegis init ever runs at repo root
/.copier-answers.yml
test-project/

# Planning docs (working notes, not published site content)
docs/plans/

# Editor workspace files
*.code-workspace
12 changes: 12 additions & 0 deletions aegis/commands/add_service.py
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Expand Up @@ -533,6 +533,18 @@ def add_service_command(
if not migration_success:
brand.warn(t("add_service.migration_failed"))

# Seed the AI fixtures (LLM catalog + default agent) when the
# ai service just landed with a persistence backend; mirrors
# the init path so an added ai service starts with the same
# seeded registry a fresh project gets.
ai_added = any(
service_base_map[s] == AnswerKeys.SERVICE_AI for s in services_to_add
)
if migration_success and ai_added and ai_needs_migrations:
from ..core.post_gen_tasks import seed_ai_fixtures

seed_ai_fixtures(target_path)

brand.success(f"\n{t('add_service.success')}")

# Show project map with newly added services + auto-added components highlighted
Expand Down
260 changes: 260 additions & 0 deletions aegis/core/migration_generator.py
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Expand Up @@ -536,6 +536,242 @@ class ServiceMigrationSpec:
],
)

AGENTS_MIGRATION = ServiceMigrationSpec(
service_name="ai_agents",
description="AI agent registry tables (agents, tools, agent-tool links)",
tables=[
# Agent - the DB-driven agent definition. model_id is a plain
# indexed string, NOT an FK to large_language_model: the catalog
# is ETL-synced and rows churn, so agents stay decoupled from
# catalog lifecycle exactly like llm_usage. NULL model_id means
# "use the service's active model".
TableSpec(
name="agent",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("slug", "sa.String()", nullable=False),
ColumnSpec("name", "sa.String()", nullable=False),
ColumnSpec("description", "sa.String()", nullable=True),
ColumnSpec("category", "sa.String()", nullable=True),
ColumnSpec("model_id", "sa.String()", nullable=True),
ColumnSpec("system_prompt", "sa.String()", nullable=False),
ColumnSpec("temperature", "sa.Float()", nullable=False, default="0.7"),
ColumnSpec(
"max_tokens", "sa.Integer()", nullable=False, default="1000"
),
ColumnSpec("memory_modules", "sa.JSON()", nullable=False, default="[]"),
ColumnSpec(
"knowledge_base_ids", "sa.JSON()", nullable=False, default="[]"
),
ColumnSpec("is_active", "sa.Boolean()", nullable=False, default="True"),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
ColumnSpec("updated_at", "sa.DateTime()", nullable=False),
],
indexes=[
IndexSpec("ix_agent_slug", ["slug"], unique=True),
IndexSpec("ix_agent_model_id", ["model_id"]),
],
),
# Tool - registry rows keyed by name into the Python tool registry.
TableSpec(
name="tool",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("name", "sa.String()", nullable=False),
ColumnSpec("description", "sa.String()", nullable=True),
ColumnSpec("is_active", "sa.Boolean()", nullable=False, default="True"),
],
indexes=[IndexSpec("ix_tool_name", ["name"], unique=True)],
),
# AgentTool - join rows are agent-owned; CASCADE both sides so
# deleting an agent or a tool cleans its links.
TableSpec(
name="agent_tool",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("agent_id", "sa.Integer()", nullable=False),
ColumnSpec("tool_id", "sa.Integer()", nullable=False),
],
indexes=[
IndexSpec("uq_agent_tool_pair", ["agent_id", "tool_id"], unique=True),
IndexSpec("ix_agent_tool_tool_id", ["tool_id"]),
],
foreign_keys=[
ForeignKeySpec(["agent_id"], "agent", ["id"], ondelete="CASCADE"),
ForeignKeySpec(["tool_id"], "tool", ["id"], ondelete="CASCADE"),
],
),
# MemoryModule - reusable prompt-context blocks agents opt into
# via Agent.memory_modules. Hybrid by design: a row may carry
# static prompt_content, a dynamic fetch_function, or both;
# assembly is column-driven (there is deliberately no "kind").
TableSpec(
name="memory_module",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("slug", "sa.String()", nullable=False),
ColumnSpec("name", "sa.String()", nullable=False),
ColumnSpec("description", "sa.String()", nullable=True),
ColumnSpec("category", "sa.String()", nullable=True),
ColumnSpec("prompt_content", "sa.String()", nullable=True),
ColumnSpec("fetch_function", "sa.String()", nullable=True),
ColumnSpec("context_key", "sa.String()", nullable=False),
ColumnSpec(
"supports_days_back",
"sa.Boolean()",
nullable=False,
default="False",
),
ColumnSpec("default_days_back", "sa.Integer()", nullable=True),
ColumnSpec("priority", "sa.Integer()", nullable=False, default="100"),
ColumnSpec(
"token_estimate", "sa.Integer()", nullable=False, default="0"
),
ColumnSpec("is_active", "sa.Boolean()", nullable=False, default="True"),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
ColumnSpec("updated_at", "sa.DateTime()", nullable=False),
],
indexes=[IndexSpec("ix_memory_module_slug", ["slug"], unique=True)],
),
# AgentUserMemory - one JSON memory document per user, written by
# the built-in save_memory tool and injected (guarded) into chat
# context. Same gate as the registry, so it rides the same spec.
TableSpec(
name="agent_user_memory",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("user_id", "sa.String()", nullable=False),
ColumnSpec("memory", "sa.JSON()", nullable=False, default="{}"),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
ColumnSpec("updated_at", "sa.DateTime()", nullable=False),
],
indexes=[
IndexSpec("ix_agent_user_memory_user_id", ["user_id"], unique=True),
],
),
],
)

KNOWLEDGE_MIGRATION = ServiceMigrationSpec(
service_name="ai_knowledge",
description="Knowledge base metadata (agent-scoped RAG collections)",
tables=[
# A knowledge base maps 1:1 onto a Chroma collection by name;
# agents scope retrieval via Agent.knowledge_base_ids holding
# these names.
TableSpec(
name="knowledge_base",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("name", "sa.String()", nullable=False),
ColumnSpec("description", "sa.String()", nullable=True),
ColumnSpec("category", "sa.String()", nullable=True),
ColumnSpec("meta_data", "sa.JSON()", nullable=False, default="{}"),
ColumnSpec("is_active", "sa.Boolean()", nullable=False, default="True"),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
ColumnSpec("updated_at", "sa.DateTime()", nullable=False),
],
indexes=[IndexSpec("ix_knowledge_base_name", ["name"], unique=True)],
),
# A source document within a KB; ``loaded`` gates ingestion state
# and ``chunking_strategy`` picks the chunker preset per source.
TableSpec(
name="knowledge_base_source",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("knowledge_base_id", "sa.Integer()", nullable=False),
ColumnSpec("name", "sa.String()", nullable=False),
ColumnSpec("file_path", "sa.String()", nullable=True),
ColumnSpec("content_type", "sa.String()", nullable=True),
ColumnSpec(
"chunking_strategy",
"sa.String()",
nullable=False,
default="'paragraph'",
),
ColumnSpec("loaded", "sa.Boolean()", nullable=False, default="False"),
ColumnSpec("meta_data", "sa.JSON()", nullable=False, default="{}"),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
ColumnSpec("updated_at", "sa.DateTime()", nullable=False),
],
indexes=[
IndexSpec(
"ix_knowledge_base_source_knowledge_base_id",
["knowledge_base_id"],
),
],
foreign_keys=[
ForeignKeySpec(
["knowledge_base_id"],
"knowledge_base",
["id"],
ondelete="CASCADE",
),
],
check_constraints=[
CheckConstraintSpec(
name="ck_knowledge_base_source_chunking_strategy",
sqltext=(
"chunking_strategy IN "
"('paragraph', 'sentence', 'fixed', 'code')"
),
),
],
),
],
)

SENTIMENT_MIGRATION = ServiceMigrationSpec(
service_name="ai_sentiment",
description="Conversation sentiment analysis results",
tables=[
# One verdict per conversation (unique conversation_id enforces
# score-once); rows die with their conversation via CASCADE.
TableSpec(
name="sentiment_analysis",
columns=[
ColumnSpec("id", "sa.Integer()", nullable=False, primary_key=True),
ColumnSpec("conversation_id", "sa.String()", nullable=False),
ColumnSpec("overall_sentiment", "sa.String()", nullable=False),
ColumnSpec("overall_score", "sa.Float()", nullable=False),
ColumnSpec("assistant_performance", "sa.String()", nullable=False),
ColumnSpec("issues", "sa.JSON()", nullable=False, default="[]"),
ColumnSpec("summary", "sa.String()", nullable=True),
ColumnSpec("model_id", "sa.String()", nullable=True),
ColumnSpec("created_at", "sa.DateTime()", nullable=False),
],
indexes=[
IndexSpec(
"ix_sentiment_analysis_conversation_id",
["conversation_id"],
unique=True,
),
],
foreign_keys=[
ForeignKeySpec(
["conversation_id"],
"conversation",
["id"],
ondelete="CASCADE",
),
],
check_constraints=[
CheckConstraintSpec(
name="ck_sentiment_analysis_overall_sentiment",
sqltext=(
"overall_sentiment IN "
"('positive', 'neutral', 'negative', 'frustrated')"
),
),
CheckConstraintSpec(
name="ck_sentiment_analysis_assistant_performance",
sqltext=("assistant_performance IN ('good', 'acceptable', 'poor')"),
),
],
),
],
)

AUTH_TOKENS_MIGRATION = ServiceMigrationSpec(
service_name="auth_tokens",
description="Auth token tables (password reset, email verification, refresh)",
Expand Down Expand Up @@ -4059,6 +4295,30 @@ def get_services_needing_migrations(context: dict[str, Any]) -> list[str]:
) and ai_backend != StorageBackends.MEMORY:
services.append("ai")

# AI agent registry - rides the exact same gate as the ai catalog
# tables: agents are the service's default architecture, and the DB
# config source exists whenever there is a persistence backend.
if (
include_ai == "yes" or include_ai is True
) and ai_backend != StorageBackends.MEMORY:
services.append("ai_agents")

# KB metadata (only with AI persistence AND the rag flag)
ai_rag = context.get(AnswerKeys.AI_RAG)
if (
(include_ai == "yes" or include_ai is True)
and ai_backend != StorageBackends.MEMORY
and (ai_rag == "yes" or ai_rag is True)
):
services.append("ai_knowledge")

# Sentiment analysis (with AI persistence; the conversation table is
# its FK target). The job that populates it is settings-gated off.
if (
include_ai == "yes" or include_ai is True
) and ai_backend != StorageBackends.MEMORY:
services.append("ai_sentiment")

# AI Voice service (only if AI with persistence and voice enabled)
ai_voice = context.get(AnswerKeys.AI_VOICE)
if (
Expand Down
52 changes: 45 additions & 7 deletions aegis/core/post_gen_tasks.py
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Expand Up @@ -362,6 +362,43 @@ def is_enabled(key: str) -> bool:
# Remove LLM tracking models (only needed with persistence)
# Keep app/services/ai/models/__init__.py - contains core types (AIProvider, ProviderConfig)
remove_dir(project_path, "app/services/ai/models/llm")
# Agent registry models ride the same persistence gate (the memory
# backend resolves the default agent from code, not DB rows). The
# tools.py registry itself stays: it is DB-free and the code-config
# path still resolves tools through it.
remove_dir(project_path, "app/services/ai/models/agents")
remove_file(project_path, "tests/services/ai/test_agent_models.py")
# Per-user memory needs the agent_user_memory table + async DB.
remove_file(project_path, "app/services/ai/user_memory.py")
remove_file(project_path, "tests/services/ai/test_user_memory.py")
# Memory modules are DB rows (the memory_module table).
remove_file(project_path, "app/services/ai/memory_modules.py")
remove_file(project_path, "tests/services/ai/test_memory_modules.py")
# Reference fetchers query the conversation tables (removed above).
# The fetcher registry itself (fetchers.py) stays: it is DB-free.
remove_file(project_path, "app/services/ai/builtin_fetchers.py")
remove_file(project_path, "tests/services/ai/test_builtin_fetchers.py")
# Module context assembly reads memory_module rows.
remove_file(project_path, "app/services/ai/module_context.py")
remove_file(project_path, "tests/services/ai/test_module_context.py")
# Sentiment scoring reads/writes conversation + sentiment tables.
remove_file(project_path, "app/services/ai/sentiment.py")
remove_file(project_path, "app/services/ai/models/sentiment.py")
remove_file(project_path, "tests/services/ai/test_sentiment.py")
# Agent registry CLI inspects DB rows.
remove_file(project_path, "app/cli/agents.py")
remove_file(project_path, "tests/cli/test_agents_cli.py")
# Agent registry admin surface (API service + dashboard tab).
remove_file(project_path, "app/services/ai/agent_registry.py")
remove_file(
project_path,
"app/components/frontend/dashboard/modals/agents_tab.py",
)
remove_file(project_path, "tests/services/ai/test_agent_registry.py")
# KB metadata models are DB-backed; the rag service itself stays
# (Chroma is file-based and works without a database).
remove_file(project_path, "app/services/rag/models/knowledge.py")
remove_file(project_path, "tests/services/rag/test_knowledge_models.py")
# chat_kit + usage_recording ledger the LLM catalog + conversation
# tables removed above, so they only work with a persistent backend.
remove_dir(project_path, "app/services/ai/chat_kit")
Expand Down Expand Up @@ -872,12 +909,13 @@ class DependencyInstallationError(Exception):
pass


def seed_llm_fixtures(project_path: Path, python_version: str | None = None) -> bool:
def seed_ai_fixtures(project_path: Path, python_version: str | None = None) -> bool:
"""
Seed LLM fixtures (vendors, models, pricing) into the database.
Seed the AI fixtures into the database.

This runs the load_all_llm_fixtures function from the generated project
to populate the database with initial LLM data.
Runs the generated project's ``load_all_ai_fixtures``: the LLM catalog
(vendors, models, deployments, pricing) plus the agent registry's
default ``assistant`` row.

Args:
project_path: Path to the project directory
Expand All @@ -904,8 +942,8 @@ def seed_llm_fixtures(project_path: Path, python_version: str | None = None) ->
"python",
"-c",
"from app.core.db import SessionLocal; "
"from app.services.ai.fixtures import load_all_llm_fixtures; "
"load_all_llm_fixtures(SessionLocal())",
"from app.services.ai.fixtures import load_all_ai_fixtures; "
"load_all_ai_fixtures(SessionLocal())",
]
)

Expand Down Expand Up @@ -1082,7 +1120,7 @@ def run_post_generation_tasks(
slug = project_slug or project_path.name

# Always seed static fixtures first as baseline data
fixtures_loaded = seed_llm_fixtures(project_path, python_version)
fixtures_loaded = seed_ai_fixtures(project_path, python_version)

if skip_llm_sync:
brand.accent(t("postgen.llm_sync_skipped"))
Expand Down
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