agent-utilities
Agent Utilities for Pydantic AI Agents
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive MIT license, and solves a real problem—bootstrapping Pydantic-AI agents with orchestration and knowledge graphs without external infrastructure. It's suitable for both rapid prototyping (tiny profile) and production deployments (enterprise profile). No known security vulnerabilities. The main gotcha is the Python 3.11+ requirement and the need to configure a model provider API key.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- A model provider API key (e.g., OPENAI_API_KEY) or local endpoint (vLLM/Ollama) must be configured via environment or .env file.
- Low install friction with a pure-Python wheel and 15 runtime dependencies.
License · maintenance · safety
MIT (permissive) — MIT license is permissive—you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-07-13 (32 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 74,931 downloads/mo, #14,766 on PyPI
Alternatives
Verify before relying
pip install agent-utilities
from agent_utilities import create_agent
agent, toolsets = create_agent(name="assistant", skill_types=["universal", "graphs"])
print(agent.run_sync("What can you do?").output)- Whether the in-process knowledge graph persists across restarts or requires explicit serialization
- Performance characteristics and scalability limits of the embedded epistemic-graph engine
- Whether all 50+ connectors mentioned for enterprise profiles are included in the base package or require separate installation
What it is and what it does
Agent Utilities is a framework for building AI agents on top of Pydantic-AI that bundles a knowledge graph, tool orchestration, memory, and skill management into a single harness. The zero-infrastructure default runs the knowledge graph in-process using the epistemic-graph Rust engine, with no external databases or services required to start. It exposes three consumption models: a Python library for standalone agents, an MCP server for integration with existing tools like Claude Code and Cursor, and a REST gateway for sharing a knowledge graph backend across multiple clients.
The package ships with configuration management via Pydantic Settings and environment variables, support for multiple deployment profiles (tiny for homelabs, single-node-prod, and enterprise), and a skill toolkit that auto-loads into agent tools. It handles secrets, multi-model configuration, and optional durable persistence to backends like Postgres or Neo4j. The core authority—the epistemic-graph engine—manages compute, caching, semantics, and persistence, while the gateway layer handles identity, action policies, and metrics.
Use it for
- Build a standalone Python agent with built-in knowledge graph, memory, and tools without setting up external infrastructure.
- Integrate a knowledge graph and skill toolkit into Claude Code, Cursor, or other IDE-embedded AI tools via the MCP server.
- Deploy a shared knowledge graph backend across multiple agent clients or containers using the REST gateway.
- Set up an enterprise multi-host agent fleet with Vault, SSO, DNS, and observability using the enterprise profile.
- Ingest, query, and reason over domain knowledge using the in-process graph without a separate database.
- Evolve agent behavior by capturing learned skills and refined prompts back into a shared knowledge graph.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive MIT license, and solves a real problem—bootstrapping Pydantic-AI agents with orchestration and knowledge graphs without external infrastructure. It's suitable for both rapid prototyping (tiny profile) and production deployments (enterprise profile). No known security vulnerabilities. The main gotcha is the Python 3.11+ requirement and the need to configure a model provider API key.
Install
agent-utilities on PyPI
Before you install
Low install friction with a pure-Python wheel and 15 runtime dependencies. Active maintenance with a release 32 days ago. Requires Python 3.11 or later but supports current versions.
Requires Python 3.11 or later. A model provider API key (e.g., OPENAI_API_KEY) or local endpoint (vLLM/Ollama) must be configured via environment or .env file.
License in practice
MIT license is permissive—you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install agent-utilities
from agent_utilities import create_agent
agent, toolsets = create_agent(name="assistant", skill_types=["universal", "graphs"])
print(agent.run_sync("What can you do?").output)Verify before relying
- Whether the in-process knowledge graph persists across restarts or requires explicit serialization
- Performance characteristics and scalability limits of the embedded epistemic-graph engine
- Whether all 50+ connectors mentioned for enterprise profiles are included in the base package or require separate installation
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagespydanticpydantic-settingsPyYAMLpython-dotenvrequestsurllib3idnacryptographypathspecplatformdirspackagingpsutilfilelockdefusedxmlepistemic-graph |
| Maintenance | Actively maintained 32 days since the last release |
| First released | |
| Downloads | 74,931 / month, #14,766 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleLicense :: OSI Approved :: MIT LicenseOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 |
Evidence: agent_utilities-1.26.4-py3-none-any.whl
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See also pydantic-deep · cognee · pydantic-ai-harness · graphiti-core · deepagents · nvidia-nat · pydantic-ai · aip-agents-binary · fast-agent-mcp · nvidia-nat-core