agent-utilities 0.13.2

Agent Utilities for Pydantic AI Agents

Agent Utilities - AGI Harness

Version: 0.13.2

Table of Contents

• Overview
• Key Features
• Intelligence Graph
• First Principles Architecture
• Concept Map
• Architecture & Orchestration
• Multi-Model Config & Secret Storage
• Installation
• Quick Start
• Creating an Agent
• Building MCP Servers
• API Documentation
• Documentation
• Contributing
• License

🌌 Mission & Future State: Distributed Evolution

The core vision for agent-utilities transcends being just an execution harness—it is the bedrock for Distributed Agentic Evolution.

As autonomous agents leverage this ecosystem to solve complex problems, they continuously learn, adapt, and refine their own capabilities. Our future state envisions a community of independent, self-improving agents that not only run on this harness but dynamically contribute their localized evolutionary breakthroughs—new skills, optimized TeamConfigs, refined prompts, and advanced reasoning traces—back to the open-source collective.

By tying our unified Knowledge Graph, capability auto-activation, and cross-agent communication protocols together, agent-utilities becomes an interconnected hive mind where the evolution of one agent elevates the intelligence of all. The harness is not just a way to run an agent; it is the heartbeat of a distributed, self-evolving intelligence network.

Key Features

• Multi-Domain Expert System: Scale across finance, medical, and scientific domains using Vectorized Topological Memory and specialized MCP tools.
• Unified Intelligence Graph: A topological pipeline combining in-memory NetworkX analysis with persistent Cypher (LadybugDB/Neo4j/Falkor) backends.
• Spec-Driven Development (SDD): High-fidelity orchestration that decomposes goals into structured specs, implementation plans, and parallel tasks.
• Emergent Architecture: Dynamic AgentCapability auto-activation, TeamConfig coalition promotion, and evolutionary skill refinement via self-models.
• Agent OS & Safety: Built-in Universal Tool Guards, structural vulnerability scanning, and transparent process lifecycle management.

📖 View the Comprehensive Feature List & Architecture Deep Dives

🗺 Concept Map

Consolidated from 169 tags into 34 canonical concepts across 5 Core Pillars.

→ Full Concept Map: docs/concept_map.md — canonical concept registry (single source of truth). → Concept Index: docs/overview.md — all pillars with descriptions and code paths.

Pillar	ID Range	Count	Focus
ORCH-1 Graph Orchestration	ORCH-1.0 – 1.6	7	Intelligence graph, HTN planning, routing, execution safety, DSTDD
KG-2 Knowledge Graph	KG-2.0 – 2.8	9	Active KG, memory, ontology, retrieval, research, finance, enterprise
AHE-3 Agentic Harness	AHE-3.0 – 3.6	7	Evaluation, evolution, teams, heavy thinking, backtest
ECO-4 Ecosystem	ECO-4.0 – 4.5	6	MCP, A2A, telemetry, connectors, KG server, terminal agent launcher
OS-5 Agent OS	OS-5.0 – 5.4	5	Kernel, security, scheduling, guardrails, observability

🏗️ Architecture & Pillar Reference

The detailed architectural diagrams and deep-dive documentation for agent-utilities have been moved to their respective Pillar documentation pages in /docs.

• 1. Graph Orchestration & Planning
  • Contains: First Principles Architecture, SDD Lifecycle, Execution Flow (Dynamic Multi-Layer Parallelism).
• 2. Epistemic Knowledge Graph
  • Contains: Graph-OS Native Ingestion Pipeline, MAGMA Reasoning Views, Persistent Task Tracking.
• 3. Agentic Harness Engineering
  • Contains: Self-Models, Evolution, Evaluation.
• 4. Ecosystem Peripherals
  • Contains: graph-os MCP Tools, Server Endpoints, MCP Loading & Registry Architecture.
• 5. Agent OS Infrastructure
  • Contains: Human-in-the-Loop Tool Approval, Process Lifecycle, Auth/Security.
• C4 Architecture Diagrams
  • Contains: Ecosystem Dependency Graph, C4 Container Diagram, Cross-Pillar Data Flows.
• Memory Architecture
  • Contains: Multi-Timescale Memory, Memento Context Management, Observational Memory Bridge.

External Agent Discovery (mcp_config.json)

Register the Knowledge Graph in your IDE's mcp_config.json using the standard CLI pattern:

{
  "mcpServers": {
    "graph-os": {
      "command": "uv",
      "args": ["run", "graph-os"],
      "env": {
        "AGENT_ID": "local-developer",
        "WORKSPACE_PATH": "${workspaceFolder}"
      }
    }
  }
}

Note: Model selection, routing logic, and system configurations are centralized in your XDG ~/.config/agent-utilities/config.json. Only local workspace paths, local agent IDs, or environment overrides remain in the environment.

Multi-Model Config & Secret Storage

All LLM providers, model registries, safety guardrails, and scheduler policies are managed centrally via the XDG-compliant configuration file at ~/.config/agent-utilities/config.json.

Every field in the config.json has a 1-to-1 environment variable override. The environment variables (detailed in .env.example) act as secondary overrides for all settings.

Centralized config.json Template

Here is a fully-populated and production-ready config.json file representing the absolute source of truth for the agent-utilities Pydantic AgentConfig schema:

{
  "default_agent_name": "Agent",
  "agent_description": "AI Agent",
  "agent_system_prompt": null,

  "host": "0.0.0.0",
  "port": 9000,
  "debug": false,
  "enable_web_ui": false,
  "enable_terminal_ui": false,
  "enable_web_logs": true,
  "enable_acp": false,
  "acp_port": 8001,
  "acp_session_root": ".acp-sessions",
  "mcp_config": null,
  "max_upload_size": 10485760,

  "agent_api_key": null,
  "enable_api_auth": false,
  "auth_jwt_jwks_uri": null,
  "auth_jwt_issuer": null,
  "auth_jwt_audience": null,
  "allowed_origins": null,
  "allowed_hosts": null,
  "tool_guard_mode": "strict",
  "sensitive_tool_patterns": [
    ".*delete.*",
    ".*remove.*",
    ".*rm_.*",
    ".*prune.*",
    ".*kill.*",
    ".*exec.*",
    ".*run_command.*"
  ],

  "secrets_backend": "inmemory",
  "secrets_sqlite_path": null,
  "secrets_vault_url": null,
  "secrets_vault_mount": "secret",

  "routing_strategy": "hybrid",
  "graph_persistence_type": "file",
  "graph_persistence_path": "~/.local/share/agent-utilities/graph_state",
  "enable_llm_validation": false,
  "graph_router_timeout": 300.0,
  "graph_verifier_timeout": 300.0,
  "graph_direct_execution": true,
  "min_confidence": 0.4,
  "validation_mode": false,
  "approval_timeout": 0.0,

  "enable_kg_embeddings": true,
  "kg_backups": 3,
  "knowledge_graph_sync_background": true,

  "enable_otel": true,
  "otel_exporter_otlp_endpoint": "http://langfuse.arpa/api/public/otel",
  "otel_exporter_otlp_headers": null,
  "otel_exporter_otlp_public_key": "lf_pk_...",
  "otel_exporter_otlp_secret_key": "lf_sk_...",
  "otel_exporter_otlp_protocol": "http/protobuf",
  "langfuse_host": "http://langfuse.arpa",
  "langfuse_public_key": "lf_pk_...",
  "langfuse_secret_key": "lf_sk_...",
  "langfuse_dataset_capture_threshold": 0.0,

  "a2a_broker": "in-memory",
  "a2a_broker_url": null,
  "a2a_storage": "in-memory",
  "a2a_storage_url": null,
  "a2a_config": null,
  "a2a_refresh_interval": 300,

  "max_tokens": 16384,
  "temperature": 0.7,
  "top_p": 1.0,
  "timeout": 32400.0,
  "tool_timeout": 32400.0,
  "parallel_tool_calls": true,
  "seed": null,
  "presence_penalty": 0.0,
  "frequency_penalty": 0.0,
  "logit_bias": null,
  "stop_sequences": null,
  "extra_headers": null,
  "extra_body": null,

  "cognitive_scheduler_enabled": true,
  "max_concurrent_agents": 5,
  "agent_token_quota": 100000,
  "preemption_threshold_pct": 0.85,
  "agent_policies_path": null,
  "permissions_signing_key": null,
  "specialist_registry_path": null,
  "homeostatic_downgrade_enabled": true,
  "adversarial_verification": false,
  "maintenance_token_budget": 0,
  "maintenance_priority": "LOW",
  "watchdog_patterns": [
    "pyproject.toml",
    "mcp_config.json",
    "requirements*.txt"
  ],

  "custom_skills_directory": null,
  "skill_types": null,

  "chat_models": [
    {
      "id": "qwen/qwen3.5-9b",
      "provider": "openai",
      "base_url": "http://10.0.0.18:1234/v1",
      "supports_json": false,
      "vision": true,
      "reasoning": true,
      "tools_enabled": true,
      "parallel_instances": 3,
      "context_window": 256000,
      "intelligence_level": "normal",
      "can_route": true,
      "can_kg": true
    }
  ],
  "embedding_models": [
    {
      "id": "text-embedding-nomic-embed-text-v2-moe",
      "provider": "openai",
      "base_url": "http://10.0.0.18:1234/v1",
      "parallel_instances": 4,
      "chunk_size": 768
    }
  ],

  "workspace_path": "/home/apps/workspace",
  "agent_utilities_config_dir": "~/.config/agent-utilities"
}

Note: JSON does not support comments. The JSON key names correspond exactly to their uppercase environment variable overrides (e.g. default_agent_name → DEFAULT_AGENT_NAME).

For comprehensive definitions and capabilities of specific variables, see the Configuration Guide and Local Secret Storage Guide.

Installation

Install via pip:

pip install agent-utilities

To install with all optional dependencies (including MCP servers, UI, and external graph backends):

pip install "agent-utilities[all]"

For more details, see the Installation Guide.

Quick Start

You can quickly launch the graph-os MCP server:

uv run graph-os

Or start the standalone agent from your code:

from agent_utilities.core.config import config
from agent_utilities.agent.factory import AgentFactory

# Configuration is automatically loaded from config.json
agent = AgentFactory.create_agent("MyAgent")
response = agent.run_sync("Analyze the knowledge graph for recent updates.")
print(response.data)

For a comprehensive walkthrough, see the Quick Start Guide.

📚 Guides & Tutorials

For detailed tutorials, installation options, and configuration guides, refer to the docs/guides/ directory:

• Quick Start
• Installation Guide
  • Bare-metal, pip packages, Docker
• Configuration & Environment Variables
  • Multi-tiered LLM setup, Models Config
• Local Secret Storage (Vault & SQLite)
• Creating an Agent
• Building MCP Servers & API Wrappers
• API Documentation & Swagger

Documentation

Comprehensive system documentation is available in the docs/ directory:

New to the project? Start with the Concept Overview Map to get oriented.

Core References

Guide	Description
Overview Map	The Concept Galaxy — 34 canonical concepts, query lifecycle, concept index
Concept Map	Canonical concept registry (single source of truth)
C4 Architecture	System context, container, and component diagrams
Evolution Pipeline	Assimilation governance, wire-or-discard heuristic, 4-phase pipeline

Pillar Deep-Dives

Pillar	Guide
Graph Orchestration	docs/pillars/1_graph_orchestration.md
Epistemic Knowledge Graph	docs/pillars/2_epistemic_knowledge_graph.md
Agentic Harness Engineering	docs/pillars/3_agentic_harness_engineering.md
Ecosystem & Peripherals	docs/pillars/4_ecosystem_peripherals.md
Agent OS Infrastructure	docs/pillars/5_agent_os_infrastructure.md

Contributing

Contributions are welcome. Please follow these guidelines:

1. Fork the repository and create a feature branch.
2. Write tests for new functionality — all tests must include assertions.
3. Follow existing patterns — use the established Pydantic models, structured prompts, and concept markers.
4. Run the test suite before submitting: uv run pytest tests/ -q.

   Note: All tests are strictly bounded by a 60-second timeout via pytest-timeout. Any test that sleeps or hangs indefinitely will fail automatically. Ensure you don't use time.sleep without bounds.

5. Update documentation in docs/ if your changes affect public APIs.

See AGENTS.md for project-specific conventions and architecture rules.

License

This project is licensed under the terms specified in the LICENSE file.