SKILL.md
OpenViking Context Database
Skill by ara.so — Daily 2026 Skills collection.
OpenViking is an open-source context database for AI Agents that replaces fragmented vector stores with a unified filesystem paradigm. It manages agent memory, resources, and skills in a tiered L0/L1/L2 structure, enabling hierarchical context delivery, observable retrieval trajectories, and self-evolving session memory.
Installation
Python Package
pip install openviking --upgrade --force-reinstallOptional Rust CLI
# Install via script
curl -fsSL https://raw.githubusercontent.com/volcengine/OpenViking/main/crates/ov_cli/install.sh | bash
# Or build from source (requires Rust toolchain)
cargo install --git https://github.com/volcengine/OpenViking ov_cliPrerequisites
- Python 3.10+
- Go 1.22+ (for AGFS components)
- GCC 9+ or Clang 11+ (for core extensions)
Configuration
Create ~/.openviking/ov.conf:
{
"storage": {
"workspace": "/home/user/openviking_workspace"
},
"log": {
"level": "INFO",
"output": "stdout"
},
"embedding": {
"dense": {
"api_base": "https://api.openai.com/v1",
"api_key": "$OPENAI_API_KEY",
"provider": "openai",
"dimension": 1536,
"model": "text-embedding-3-large"
},
"max_concurrent": 10
},
"vlm": {
"api_base": "https://api.openai.com/v1",
"api_key": "$OPENAI_API_KEY",
"provider": "openai",
"model": "gpt-4o",
"max_concurrent": 100
}
}Note: OpenViking reads api_key values as strings; use environment variable injection at startup rather than literal secrets.
Provider Options
Role
Provider Value
Example Model
VLM
openai
gpt-4o
VLM
volcengine
doubao-seed-2-0-pro-260215
VLM
litellm
claude-3-5-sonnet-20240620, ollama/llama3.1
Embedding
openai
text-embedding-3-large
Embedding
volcengine
doubao-embedding-vision-250615
Embedding
jina
jina-embeddings-v3
LiteLLM VLM Examples
{
"vlm": {
"provider": "litellm",
"model": "claude-3-5-sonnet-20240620",
"api_key": "$ANTHROPIC_API_KEY"
}
}{
"vlm": {
"provider": "litellm",
"model": "ollama/llama3.1",
"api_base": "http://localhost:11434"
}
}{
"vlm": {
"provider": "litellm",
"model": "deepseek-chat",
"api_key": "$DEEPSEEK_API_KEY"
}
}Core Concepts
Filesystem Paradigm
OpenViking organizes agent context like a filesystem:
workspace/
├── memories/ # Long-term agent memories (L0 always loaded)
│ ├── user_prefs/
│ └── task_history/
├── resources/ # External knowledge, documents (L1 on demand)
│ ├── codebase/
│ └── docs/
└── skills/ # Reusable agent capabilities (L2 retrieved)
├── coding/
└── analysis/Tiered Context Loading (L0/L1/L2)
- L0: Always loaded — core identity, persistent preferences
- L1: Loaded on demand — relevant resources fetched per task
- L2: Semantically retrieved — skills pulled by similarity search
This tiered approach minimizes token consumption while maximizing context relevance.
Python API Usage
Basic Setup
import os
from openviking import OpenViking
# Initialize with config file
ov = OpenViking(config_path="~/.openviking/ov.conf")
# Or initialize programmatically
ov = OpenViking(
workspace="/home/user/openviking_workspace",
vlm_provider="openai",
vlm_model="gpt-4o",
vlm_api_key=os.environ["OPENAI_API_KEY"],
embedding_provider="openai",
embedding_model="text-embedding-3-large",
embedding_api_key=os.environ["OPENAI_API_KEY"],
embedding_dimension=1536,
)Managing a Context Namespace (Agent Brain)
# Create or open a namespace (like a filesystem root for one agent)
brain = ov.namespace("my_agent")
# Add a memory file
brain.write("memories/user_prefs.md", """
# User Preferences
- Language: Python
- Code style: PEP8
- Preferred framework: FastAPI
""")
# Add a resource document
brain.write("resources/api_docs/stripe.md", open("stripe_docs.md").read())
# Add a skill
brain.write("skills/coding/write_tests.md", """
# Skill: Write Unit Tests
When asked to write tests, use pytest with fixtures.
Always mock external API calls. Aim for 80%+ coverage.
""")Querying Context
# Semantic search across the namespace
results = brain.search("how does the user prefer code to be formatted?")
for result in results:
print(result.path, result.score, result.content[:200])
# Directory-scoped retrieval (recursive)
skill_results = brain.search(
query="write unit tests for a FastAPI endpoint",
directory="skills/",
top_k=3,
)
# Direct path read (L0 always available)
prefs = brain.read("memories/user_prefs.md")
print(prefs.content)Session Memory & Auto-Compression
# Start a session — OpenViking tracks turns and auto-compresses
session = brain.session("task_build_api")
# Add conversation turns
session.add_turn(role="user", content="Build me a REST API for todo items")
session.add_turn(role="assistant", content="I'll create a FastAPI app with CRUD operations...")
# After many turns, trigger compression to extract long-term memory
summary = session.compress()
# Compressed insights are automatically written to memories/
# End session — persists extracted memories
session.close()Retrieval Trajectory (Observable RAG)
# Enable trajectory tracking to observe retrieval decisions
with brain.observe() as tracker:
results = brain.search("authentication best practices")
trajectory = tracker.trajectory()
for step in trajectory.steps:
print(f"[{step.level}] {step.path} → score={step.score:.3f}")
# Output:
# [L0] memories/user_prefs.md → score=0.82
# [L1] resources/security/auth.md → score=0.91
# [L2] skills/coding/jwt_auth.md → score=0.88Common Patterns
Pattern 1: Agent with Persistent Memory
import os
from openviking import OpenViking
ov = OpenViking(config_path="~/.openviking/ov.conf")
brain = ov.namespace("coding_agent")
def agent_respond(user_message: str, conversation_history: list) -> str:
# Retrieve relevant context
context_results = brain.search(user_message, top_k=5)
context_text = "\n\n".join(r.content for r in context_results)
# Build prompt with retrieved context
system_prompt = f"""You are a coding assistant.
## Relevant Context
{context_text}
"""
# ... call your LLM here with system_prompt + conversation_history
response = call_llm(system_prompt, conversation_history, user_message)
# Store interaction for future memory
brain.session("current").add_turn("user", user_message)
brain.session("current").add_turn("assistant", response)
return responsePattern 2: Hierarchical Skill Loading
# Register skills from a directory structure
import pathlib
skills_dir = pathlib.Path("./agent_skills")
for skill_file in skills_dir.rglob("*.md"):
relative = skill_file.relative_to(skills_dir)
brain.write(f"skills/{relative}", skill_file.read_text())
# At runtime, retrieve only relevant skills
def get_relevant_skills(task: str) -> list[str]:
results = brain.search(task, directory="skills/", top_k=3)
return [r.content for r in results]
task = "Refactor this class to use dependency injection"
skills = get_relevant_skills(task)
# Returns only DI-related skills, not all registered skillsPattern 3: RAG over Codebase
import subprocess
import pathlib
brain = ov.namespace("codebase_agent")
# Index a codebase
def index_codebase(repo_path: str):
for f in pathlib.Path(repo_path).rglob("*.py"):
content = f.read_text(errors="ignore")
# Store with relative path as key
rel = f.relative_to(repo_path)
brain.write(f"resources/codebase/{rel}", content)
index_codebase("/home/user/myproject")
# Query with directory scoping
def find_relevant_code(query: str) -> list:
return brain.search(
query=query,
directory="resources/codebase/",
top_k=5,
)
hits = find_relevant_code("database connection pooling")
for h in hits:
print(h.path, "\n", h.content[:300])Pattern 4: Multi-Agent Shared Context
# Agent 1 writes discoveries
agent1_brain = ov.namespace("researcher_agent")
agent1_brain.write("memories/findings/api_rate_limits.md", """
# API Rate Limits Discovered
- Stripe: 100 req/s in live mode
- SendGrid: 600 req/min
""")
# Agent 2 reads shared workspace findings
agent2_brain = ov.namespace("coder_agent")
# Cross-namespace read (if permitted)
shared = ov.namespace("shared_knowledge")
rate_limits = shared.read("memories/findings/api_rate_limits.md")CLI Commands (ov_cli)
# Check version
ov --version
# List namespaces
ov namespace list
# Create a namespace
ov namespace create my_agent
# Write context file
ov write my_agent/memories/prefs.md --file ./prefs.md
# Read a file
ov read my_agent/memories/prefs.md
# Search context
ov search my_agent "how to handle authentication" --top-k 5
# Show retrieval trajectory for a query
ov search my_agent "database migrations" --trace
# Compress a session
ov session compress my_agent/task_build_api
# List files in namespace
ov ls my_agent/skills/
# Delete a context file
ov rm my_agent/resources/outdated_docs.md
# Export namespace to local directory
ov export my_agent ./exported_brain/
# Import from local directory
ov import ./exported_brain/ my_agent_restoredTroubleshooting
Config Not Found
# Verify config location
ls -la ~/.openviking/ov.conf
# OpenViking also checks OV_CONFIG env var
export OV_CONFIG=/path/to/custom/ov.confEmbedding Dimension Mismatch
If you switch embedding models, the stored vector dimensions will conflict:
# Check current dimension setting vs stored index
# Solution: re-index after model change
brain.reindex(force=True)Workspace Permission Errors
# Ensure workspace directory is writable
chmod -R 755 /home/user/openviking_workspace
# Check disk space (embedding indexes can be large)
df -h /home/user/openviking_workspaceLiteLLM Provider Not Detected
# Use explicit prefix for ambiguous models
{
"vlm": {
"provider": "litellm",
"model": "openrouter/anthropic/claude-3-5-sonnet", # full prefix required
"api_key": "$OPENROUTER_API_KEY",
"api_base": "https://openrouter.ai/api/v1"
}
}High Token Usage
Enable tiered loading to reduce L1/L2 fetches:
# Scope searches tightly to avoid over-fetching
results = brain.search(
query=user_message,
directory="skills/relevant_domain/", # narrow scope
top_k=2, # fewer results
min_score=0.75, # quality threshold
)Slow Indexing on Large Codebases
# Increase concurrency in config
{
"embedding": {
"max_concurrent": 20 # increase from default 10
},
"vlm": {
"max_concurrent": 50
}
}
# Or batch-write with async
import asyncio
async def index_async(files):
tasks = [brain.awrite(f"resources/{p}", c) for p, c in files]
await asyncio.gather(*tasks)Environment Variables Reference
Variable
Purpose
OV_CONFIG
Path to ov.conf override
OPENAI_API_KEY
OpenAI API key for VLM/embedding
ANTHROPIC_API_KEY
Anthropic Claude via LiteLLM
DEEPSEEK_API_KEY
DeepSeek via LiteLLM
GEMINI_API_KEY
Google Gemini via LiteLLM
OV_LOG_LEVEL
Override log level (DEBUG, INFO, WARN)
OV_WORKSPACE
Override workspace path
Resources
- Website: https://openviking.ai
- LiteLLM Providers: https://docs.litellm.ai/docs/providers