Hermes Agent Framework - Setup Guide¶

Source: reason-machines/ai-agent-skills (176+ installs) Category: Hermes Agent / Core Framework Quality Tier: 🟡 Beta

Expert knowledge for working with Hermes Agent, the open-source AI Agent framework by Nous Research featuring built-in self-improving learning loops, three-layer memory system (episodic, semantic, procedural), and automatic Skill creation and evolution. Differs from traditional agents (OpenClaw/Claude Code) by implementing learning-as-a-first-class-feature.

Installation¶

npx skills add reason-machines/ai-agent-skills --skill hermes-agent-framework

Core Hermes Agent Setup¶

git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
pip install -r requirements.txt
cp .env.example .env
# Edit .env with your API keys

Prerequisites¶

Requirement	Details
Python	3.10+
API Key	OpenAI, Anthropic, or compatible provider
Memory Path	`~/.hermes/memory` (auto-created)
Skills Path	`~/.hermes/skills` (auto-created)

Key Capabilities¶

Three-Layer Memory System¶

Episodic: Conversation history - stores user/agent interactions with retrieval
Semantic: Knowledge base - searchable vector store for facts, patterns, and domain knowledge
Procedural: Skills - reusable procedures that auto-evolve based on usage and feedback

Self-Improving Learning Loop¶

Configure automatic learning: reflection interval (every N interactions), skill creation threshold (after N similar tasks), and feedback sensitivity. Agent detects patterns, creates Skills for repeated tasks, and improves existing Skills based on human feedback.

Automatic Skill Creation¶

Skills are generated from repeated interaction patterns. Define via Python SDK or YAML files. Includes versioning, usage tracking, and auto-improvement toggles.

Multi-Agent Orchestration¶

AgentOrchestrator supports sequential, parallel, and hierarchical coordination strategies. Create specialized agents (code expert, reviewer, doc writer) and dispatch complex workflows.

Extensible Tool System¶

Built-in tools: WebSearch, CodeExecutor, FileSystem, APICaller. Custom tools defined via Tool base class with parameter validation.

Quick Start¶

from hermes_agent import HermesAgent, MemoryConfig, LearningConfig

agent = HermesAgent(
    model="gpt-4-turbo",
    memory_config=MemoryConfig(
        episodic_enabled=True,
        semantic_enabled=True,
        procedural_enabled=True,
        memory_path="~/.hermes/memory"
    ),
    learning_config=LearningConfig(
        enable_auto_learning=True,
        reflection_interval=5,
        skill_creation_threshold=3
    )
)

response = agent.chat("Help me analyze this Python code")

CLI Usage¶

hermes chat "What's the weather today?"
hermes chat --skill code-reviewer "Review my Python script"
hermes memory list
hermes skills list

Verification¶

# Verify installation
python -c "from hermes_agent import HermesAgent; print('OK')"

# Start interactive mode
python -m hermes_agent

# Check memory stats
python -c "from hermes_agent import HermesAgent; a = HermesAgent(model='gpt-4-turbo'); print(a.memory.stats())"

Notes¶

Start with conservative learning settings - higher thresholds prevent over-generalization
Provide feedback regularly - the learning loop improves proportionally with human input quality
Review auto-generated Skills before heavy use - inspect and refine before production reliance
Monitor token usage - learning loops can increase API calls
Version control your Skills in git for reproducibility
Separate agents by role using orchestration for complex workflows

Integration with CorpusIQ¶

CorpusIQ is the connector layer that lets AI assistants read the systems a business already runs on - accounting, payments, CRM, email, files and analytics - and answer with the source named. It is read-only by design: it retrieves data and never writes to, or acts in, a connected system, which is what makes it safe to point at live accounts.

This page is part of the CorpusIQ directory, the reference we maintain for the MCP server and agent-skill ecosystem. Run alongside CorpusIQ, a third-party server like this one can be combined with a company's own business data in a single answer.

See what CorpusIQ connects