terminalskills OpenViking
Overview
Manage AI agent context using a file-system paradigm — context is organized as files and directories that agents can read, write, and navigate. Inspired by ByteDance's OpenViking, this approach treats context like a filesystem: hierarchical, scoped, persistent, and self-evolving. Agents don't just consume context — they organize and update it.
Core Concepts
context/
├── project/
│ ├── README.md # Project overview (always loaded)
│ ├── architecture.md # System design context
│ └── decisions/ # Architecture decision records
├── task/
│ ├── current.md # Active task context
│ └── history/completed/ # Past task context for reference
├── memory/
│ ├── facts.md # Known facts about the project
│ ├── lessons.md # Lessons learned from mistakes
│ └── preferences.md # User preferences and patterns
└── skills/
├── coding-style.md # Code conventions
└── tools.md # Available tools and how to use them
Key idea: Context is not a flat prompt. It's a tree with scoping rules — agents see context relevant to their current scope, not everything at once.
Instructions
When a user asks to build agent memory, persistent context, or hierarchical context systems:
- Design the context tree — Map out what context exists and how it's organized
- Define scoping rules — What context loads at each level (project, task, subtask)
- Implement CRUD — Agents need to read, create, update, and delete context files
- Add self-evolution — Agents update context based on outcomes and learnings
Context Manager Implementation
"""File-system based context manager for AI agents."""
import os, json
from pathlib import Path
from datetime import datetime, timezone
from typing import Optional
class ContextManager:
"""Manages hierarchical context for AI agents."""
def __init__(self, root: str = "./context"):
self.root = Path(root)
self.root.mkdir(parents=True, exist_ok=True)
def read(self, path: str) -> Optional[str]:
full = self.root / path
return full.read_text() if full.is_file() else None
def write(self, path: str, content: str, metadata: Optional[dict] = None):
full = self.root / path
full.parent.mkdir(parents=True, exist_ok=True)
header = ""
if metadata:
meta = {**metadata, "updated": datetime.now(timezone.utc).isoformat()}
header = f"<!-- meta: {json.dumps(meta)} -->\n\n"
full.write_text(header + content)
def list(self, path: str = "") -> list[str]:
full = self.root / path
if not full.is_dir():
return []
return [str(p.relative_to(self.root)) for p in sorted(full.rglob("*")) if p.is_file()]
def delete(self, path: str):
full = self.root / path
if full.is_file():
trash = self.root / ".trash" / path
trash.parent.mkdir(parents=True, exist_ok=True)
full.rename(trash)
def search(self, query: str, path: str = "") -> list[tuple[str, str]]:
results = []
for filepath in self.list(path):
content = self.read(filepath)
if content and query.lower() in content.lower():
idx = content.lower().index(query.lower())
snippet = content[max(0, idx - 50):idx + len(query) + 50]
results.append((filepath, snippet))
return results
Hierarchical Context Delivery
class ScopedContext:
"""Delivers context based on the agent's current scope."""
SCOPE_RULES = {
"project": ["project/README.md", "memory/facts.md", "memory/preferences.md", "skills/coding-style.md"],
"task": ["task/current.md"],
"subtask": [],
}
def __init__(self, ctx: ContextManager):
self.ctx = ctx
def get_context(self, scope: str = "task", subtask_id: Optional[str] = None) -> str:
parts = []
for path in self.SCOPE_RULES["project"]:
content = self.ctx.read(path)
if content:
parts.append(f"## {path}\n{content}")
if scope in ("task", "subtask"):
for path in self.SCOPE_RULES["task"]:
content = self.ctx.read(path)
if content:
parts.append(f"## {path}\n{content}")
if scope == "subtask" and subtask_id:
content = self.ctx.read(f"task/subtasks/{subtask_id}.md")
if content:
parts.append(f"## Subtask: {subtask_id}\n{content}")
lessons = self.ctx.read("memory/lessons.md")
if lessons:
parts.append(f"## Lessons Learned\n{lessons}")
return "\n\n---\n\n".join(parts)
Self-Evolving Context
Agents don't just read context — they update it based on what they learn:
class EvolvingAgent:
def __init__(self, ctx: ContextManager, llm):
self.ctx = ctx
self.llm = llm
async def complete_task(self, task: str, result: str, success: bool):
timestamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M")
current = self.ctx.read("task/current.md")
if current:
self.ctx.write(f"task/history/{timestamp}.md", current)
if not success:
lesson = await self.llm.invoke(
f"Task: {task}\nResult: {result}\n\nWhat went wrong? Extract a concise lesson."
)
existing = self.ctx.read("memory/lessons.md") or ""
self.ctx.write("memory/lessons.md", f"{existing}\n\n### {timestamp}\n{lesson.content}")
new_facts = await self.llm.invoke(
f"Task: {task}\nResult: {result}\n\nAny new facts discovered? List them or say NONE."
)
if "NONE" not in new_facts.content:
existing = self.ctx.read("memory/facts.md") or ""
self.ctx.write("memory/facts.md", f"{existing}\n\n### Discovered {timestamp}\n{new_facts.content}")
Examples
Example 1: Setting Up Agent Memory for a Web App Project
ctx = ContextManager("./my-project-context")
# Initialize project context
ctx.write("project/README.md", "# E-commerce Platform\nNext.js + Postgres + Stripe")
ctx.write("memory/facts.md", "- Database: PostgreSQL 16\n- Auth: NextAuth with Google OAuth")
ctx.write("memory/preferences.md", "- Use TypeScript strict mode\n- Prefer server components")
ctx.write("skills/coding-style.md", "- camelCase variables\n- Zod for validation")
# Agent reads scoped context for a task
scoped = ScopedContext(ctx)
context = scoped.get_context(scope="task")
# Returns: project README + facts + preferences + coding style + current task
Example 2: LangChain Tool Integration
from langchain_core.tools import tool
ctx = ContextManager("./agent-context")
@tool
def read_context(path: str) -> str:
"""Read a context file to recall project info, decisions, or lessons."""
return ctx.read(path) or f"No context at {path}"
@tool
def write_context(path: str, content: str) -> str:
"""Save learnings, decisions, or facts to context."""
ctx.write(path, content, metadata={"source": "agent"})
return f"Written to {path}"
@tool
def search_context(query: str) -> str:
"""Search all context files for relevant information."""
results = ctx.search(query)
return "\n".join(f"[{p}] ...{s}..." for p, s in results[:5]) or "No matches."
tools = [read_context, write_context, search_context]
Guidelines
- Scope aggressively — Don't load all context every time. Use hierarchical scoping to keep prompts focused
- Metadata headers — Add timestamps and source info to context files for auditability
- Soft delete — Move to
.trashinstead of deleting. Context that seems useless now may matter later - Token budgeting — Set a max token budget per scope level. Compact if exceeded
- Version context — Use git or timestamps to track how context evolves over time
- Separate facts from opinions — Keep factual knowledge separate from preferences and lessons
- Periodic cleanup — Run compaction weekly. Archive context not accessed in 30 days
Dependencies
pip install langchain-core langchain-openai # For LangChain integration
# No external deps needed for core ContextManager — it's pure Python + filesystem