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Jon Saad-FalconandClaude Opus 4.6
880e3f67f2
Closes the remaining feature gaps vs Qwen-Agent, CoPaw, Google ADK, Apple FM SDK, and LFM2: SDK: - Add ask_stream() and ask_full_stream() async generator methods to Jarvis class Orchestrator: - Parallel tool execution via ThreadPoolExecutor (parallel_tools=True by default) Engine: - ResponseFormat dataclass for structured output / JSON mode - OpenAI, Anthropic, Google, and Ollama structured output support Tools: - DockerCodeInterpreterTool (code_interpreter_docker) with sandboxed execution - sandbox-docker optional dependency Rust: - Session checkpointing with checkpoint(), rewind(), list_checkpoints() Examples (5 hero demo apps): - browser_assistant — web browsing agent - security_scanner — project security auditor - daily_digest — morning news briefing - doc_qa — document Q&A with memory indexing - multi_model_router — cost-optimized model routing Docs: - 10 copy-paste code snippets page - "What You Can Build" quickstart section Tests: 275 Python tests pass, 392 Rust tests pass, lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
3.1 KiB
3.1 KiB
title, description
| title | description |
|---|---|
| Code Snippets | Copy-paste patterns for common OpenJarvis tasks |
Code Snippets
Ready-to-use patterns for the most common OpenJarvis tasks. Each snippet is self-contained and copy-pasteable.
Ask a Question (3 lines)
from openjarvis import Jarvis
with Jarvis() as j:
print(j.ask("What is the capital of France?"))
Stream Tokens (4 lines)
import asyncio
from openjarvis import Jarvis
async def main():
with Jarvis() as j:
async for token in j.ask_stream("Tell me a story"):
print(token, end="", flush=True)
asyncio.run(main())
Agent with Tools (5 lines)
from openjarvis import Jarvis
with Jarvis() as j:
result = j.ask_full(
"Search the web for the latest Python release",
agent="orchestrator",
tools=["web_search", "think"],
)
print(result["content"])
Memory: Index + Search (6 lines)
from openjarvis import Jarvis
with Jarvis() as j:
j.memory.index("./docs/", chunk_size=512)
results = j.memory.search("deployment options")
for r in results:
print(f"[{r['score']:.3f}] {r['content'][:100]}")
Recipe TOML (4 lines)
Define an agent pipeline in TOML — no code required:
[recipe]
name = "research_assistant"
agent = "orchestrator"
tools = ["web_search", "think", "file_read"]
prompt = "Research the given topic and write a summary."
Run with: jarvis compose run research_assistant "quantum computing advances"
API Server (1 command)
jarvis serve --port 8000 --engine ollama --model qwen3:8b
Any OpenAI-compatible client works against this endpoint.
Docker Deployment (2 commands)
docker build -t openjarvis .
docker run -p 8000:8000 openjarvis serve --host 0.0.0.0
Custom Tool (10 lines)
from openjarvis.core.registry import ToolRegistry
from openjarvis.core.types import ToolResult
from openjarvis.tools._stubs import BaseTool, ToolSpec
@ToolRegistry.register("my_tool")
class MyTool(BaseTool):
tool_id = "my_tool"
@property
def spec(self):
return ToolSpec(name="my_tool", description="My custom tool",
parameters={"type": "object", "properties": {"input": {"type": "string"}}})
def execute(self, **params):
return ToolResult(tool_name="my_tool", content=f"Processed: {params.get('input', '')}", success=True)
Multi-Model Routing (5 lines)
from openjarvis import Jarvis
j = Jarvis()
# Router automatically selects the best model per query
simple = j.ask("What is 2+2?") # routes to fast/cheap model
complex = j.ask("Analyze this research paper...") # routes to capable model
j.close()
Human-in-the-Loop Confirmation (6 lines)
from openjarvis import Jarvis
with Jarvis() as j:
result = j.ask_full(
"Delete old log files in /tmp",
agent="orchestrator",
tools=["shell_exec", "file_read"],
)
print(f"Agent took {result['turns']} turns")
print(result["content"])
Tools like shell_exec can be configured with requires_confirmation: true in TOML for interactive approval.