hermes-okf 0.1.0

Universal OKF-based memory system for Hermes agent — structured, persistent, agent-readable knowledge storage.

Hermes OKF — Universal Memory for AI Agents

The first open-source memory system built on Google's Open Knowledge Format (OKF) for the Hermes agent ecosystem.

Hermes OKF gives your AI agent a persistent, structured, version-controlled memory — no database, no lock-in, just markdown + YAML on your filesystem. Every decision, observation, and project context lives in a human-readable knowledge graph that your agent can read, write, and traverse programmatically.

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Why Hermes OKF?

Feature	What You Get
🧠 Agent Memory	Persistent decisions, observations, and tool-call history across sessions
🔗 Knowledge Graph	Implicit graph from markdown links — no RDF, no Cypher
📁 Filesystem-First	Plain .md + YAML. cat it, grep it, Git it.
⚡ Zero-DB Core	Single dependency: pyyaml. Optional RAG via LangChain/ChromaDB.
🎁 Hermes-Ready	Drop-in decorators: @memorize_decision, @memorize_tool
📦 Portable	Clone a bundle to another machine — the agent resumes instantly.

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Quick Start

pip install hermes-okf

from hermes_okf.bundle import OKFBundle

# Create a knowledge bundle
bundle = OKFBundle("./my_knowledge")

# Store a project concept
bundle.write_concept(
    "projects/my_project",
    body="# My Project\n\nDescribe your project here.",
    type="Project",
    title="My Project",
    tags=["ml", "data", "gpu"],
    resource="https://github.com/YOUR_USERNAME/my-project",
)

# Log a decision
bundle.append_log("Switched from TensorFlow to PyTorch for better ecosystem support", category="Decision")

# Search by tag
for concept in bundle.search_by_tag("gpu"):
    print(concept.title)

# Inspect the graph
for edge in bundle.get_graph_edges():
    print(f"{edge['source']} -> {edge['target']}")

---

Agent Integration (Memory Mixin)

from hermes_okf.agent import HermesMemoryMixin

class MyAgent(HermesMemoryMixin):
    def __init__(self):
        super().__init__("./agent_knowledge", agent_id="my-agent-v1")
        self.start_session()

    @HermesMemoryMixin.memorize_decision
    def choose_model(self, task: str) -> str:
        if "code" in task.lower():
            return "anthropic/claude-3.5-sonnet"
        return "openai/gpt-4o"

    @HermesMemoryMixin.memorize_tool
    def scrape_data(self, url: str) -> dict:
        return {"url": url, "items": 42}

# Run it
agent = MyAgent()
agent.choose_model("Write a Python script")
agent.scrape_data("https://example.com")

# Recall relevant context
context = agent.with_context("python script", top_k=3)

---

CLI

# Initialise a new OKF bundle
hermes-okf init ./knowledge

# Validate conformance
hermes-okf validate --path ./knowledge

# List concepts
hermes-okf list --path ./knowledge

# Show a concept
hermes-okf show --path ./knowledge projects/my_project

# Search
hermes-okf search --path ./knowledge "ffmpeg GPU"

# View log
hermes-okf log --path ./knowledge

# Append to log
hermes-okf log-append --path ./knowledge "New decision made" --category Decision

# Graph inspection
hermes-okf graph-edges --path ./knowledge
hermes-okf graph-neighbors --path ./knowledge projects/my_project

---

RAG Integration (Optional)

pip install hermes-okf[rag]

Feed your OKF bundle into LangChain + ChromaDB for vector retrieval:

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import MarkdownHeaderTextSplitter
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

from hermes_okf.bundle import OKFBundle

bundle = OKFBundle("./my_knowledge")
loader = DirectoryLoader(
    str(bundle.root),
    glob="**/*.md",
    loader_cls=TextLoader,
    loader_kwargs={"encoding": "utf-8"},
)
docs = loader.load()

splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=[("#", "Header 1"), ("##", "Header 2")]
)
splits = [chunk for doc in docs for chunk in splitter.split_text(doc.page_content)]

vectorstore = Chroma.from_documents(
    documents=splits,
    embedding=OpenAIEmbeddings(),
    persist_directory="./chroma_okf_db",
)

# Query
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
results = retriever.invoke("What GPU decisions did we make?")

See examples/rag_integration.py for a complete example.

---

Architecture

┌─────────────────────────────────────┐
│  CLI (hermes-okf)                   │
├─────────────────────────────────────┤
│  HermesMemory / MemoryMixin         │  ← Agent integration
├─────────────────────────────────────┤
│  OKFBundle                          │  ← Core read/write API
│  ├── Concept (dataclass)            │
│  ├── GraphExtractor                 │
│  ├── SearchIndex                    │
│  └── OKFValidator                   │
├─────────────────────────────────────┤
│  Filesystem (markdown + YAML)       │  ← Persistent storage
└─────────────────────────────────────┘

Read the full architecture in docs/ARCHITECTURE.md.

---

OKF Format

This library implements the Google Open Knowledge Format (OKF) v0.1 spec:

• Every concept is a .md file with YAML frontmatter
• Frontmatter must include a type field
• Reserved files: index.md (directory), log.md (chronology)
• Markdown links create implicit directed edges
• Directory structure provides containment hierarchy
• Tags create cross-cutting clusters

"If you can cat a file, you can read OKF."

---

Project Structure

hermes-okf/
├── src/hermes_okf/          # Core library
│   ├── bundle.py             # OKFBundle read/write
│   ├── concept.py            # Concept dataclass
│   ├── graph.py              # Graph extraction & traversal
│   ├── search.py             # Full-text search & indexing
│   ├── validators.py         # OKF conformance checking
│   ├── memory.py             # Agent memory layer
│   ├── agent.py              # Drop-in decorators
│   └── cli.py                # CLI entry point
├── tests/                    # pytest suite
├── examples/                 # Usage examples
├── docs/                     # Architecture docs
└── .github/workflows/        # CI/CD

---

Development

git clone https://github.com/EliaszDev/hermes-okf.git
cd hermes-okf
pip install -e ".[dev]"
pytest

See CONTRIBUTING.md for full guidelines.

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Roadmap

• Async I/O support for high-throughput agents
• Multi-agent bundle merging and conflict resolution
• Git-backed history with automatic diff summaries
• Web viewer for knowledge graph exploration
• Plugin system for custom concept types and validators
• Integration with Hermes agent orchestration layer

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License

MIT — see LICENSE.

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Acknowledgements

Built for the Hermes agent ecosystem and inspired by the Google Cloud Knowledge Catalog team's OKF draft specification. If you use Hermes or OKF, this library is designed to be your memory backbone.

⭐ Star this repo if you're building agent memory systems — let's make Hermes the best agent framework out there.