deepstrike 0.1.7

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DeepStrike Python SDK

Agent framework built on a Rust kernel. The kernel handles loop control, context compression, skill routing, governance, signal prioritization — the SDK handles all I/O.

Install

pip install deepstrike

Requires Python 3.10+. The Rust kernel is distributed as a pre-built wheel (deepstrike._kernel).

---

Quick start

import asyncio
from deepstrike import Agent, OpenAIProvider, tool

@tool
async def add(x: int, y: int) -> str:
    """Add two numbers."""
    return str(x + y)

agent = Agent(OpenAIProvider(api_key="sk-...", model="gpt-5-mini"), max_tokens=4096, max_turns=25)
agent.register(add)

asyncio.run(agent.run("What is 17 + 28?"))
# => "45"

Streaming:

async for event in agent.run_streaming("Summarize README.md"):
    if isinstance(event, TextDelta):
        print(event.delta, end="", flush=True)
    elif isinstance(event, ToolCallEvent):
        print(f"\n[→ {event.name}]")
    elif isinstance(event, DoneEvent):
        print(f"\ndone in {event.iterations} turns ({event.status})")

---

Providers

Class	Backend	Notes
OpenAIProvider	OpenAI API	SSE tool-call accumulation
AnthropicProvider	Anthropic API	Native SSE, ThinkingDelta support
QwenProvider	DashScope	enable_thinking via extensions
DeepSeekProvider	DeepSeek API	Reasoner models strip tools automatically
MiniMaxProvider	MiniMax API	M1 reasoning via expose_reasoning
OllamaProvider	Local Ollama	http://localhost:11434 default
KimiProvider	Moonshot API

All providers accept RetryConfig for exponential backoff and share a CircuitBreaker.

---

Agent options

agent = Agent(
    provider,
    max_tokens=4096,            # context window size
    max_turns=25,               # max turns (default 25)
    timeout_ms=60_000,          # timeout in ms (None = no limit)
    extensions={"temperature": 0.1},
    skill_dir="./skills",       # skill .md files directory
    knowledge_source=my_ks,     # KnowledgeSource implementation
    governance=gov,             # kernel Governance instance
    signal_source=gateway,      # SignalGateway or any SignalSource
    dream_store=my_store,       # DreamStore for long-term memory
    agent_id="my-agent",        # required with dream_store for memory meta-tool
)

---

Tools

from deepstrike import tool, read_file

agent.register(tool(name="search", description="Search.", parameters=schema)(my_fn))
agent.register(read_file)
agent.unregister("search")
agent.block_tool("bash")

---

Skills

Set skill_dir — the kernel auto-injects a skill meta-tool, and the LLM loads skills by name on demand.

agent = Agent(provider, max_tokens=4096, max_turns=25, skill_dir="./skills")

---
name: summarize
description: Summarize text into 2-3 concise bullet points
when_to_use: When you need to condense long text
effort: 1
---
1. Identify the 2-3 most important points
2. Express each as a concise bullet

---

Knowledge

Implement KnowledgeSource — the kernel injects a knowledge meta-tool.

from deepstrike import KnowledgeSource

class VectorSearch(KnowledgeSource):
    async def retrieve(self, query: str, top_k: int = 5) -> list[str]:
        return await vector_db.search(query, top_k)

agent = Agent(provider, max_tokens=4096, max_turns=25, knowledge_source=VectorSearch())

---

Memory

WorkingMemory (in-session scratch pad)

from deepstrike import WorkingMemory

mem = WorkingMemory()
mem.set("step", 1)
mem.get("step")  # 1
mem.clear()

DreamStore (long-term memory + dreaming pipeline)

from deepstrike import DreamStore

class MyStore(DreamStore):
    async def load_sessions(self, agent_id): ...
    async def load_memories(self, agent_id): ...
    async def commit(self, agent_id, result, existing): ...
    async def search(self, agent_id, query, top_k): ...

agent = Agent(provider, max_tokens=4096, max_turns=25,
              dream_store=MyStore(), agent_id="my-agent")

# In-session: LLM calls memory(query) → DreamStore.search()
# Post-session: trigger memory consolidation
result = await agent.dream("my-agent", now_ms=int(time.time() * 1000))

---

Governance

SDK PermissionManager

from deepstrike import PermissionManager, PermissionMode

pm = PermissionManager(PermissionMode.DEFAULT)
pm.grant("fs", "read")
pm.revoke("db", "drop")
pm.evaluate("fs", "read")  # PermissionDecision(allowed=True, ...)

Kernel Governance (full pipeline)

from deepstrike import Governance

gov = Governance("allow")
gov.add_permission_rule("danger.*", "deny")
gov.block_tool("rm_rf")
gov.set_rate_limit("api_call", max_calls=10, window_ms=60_000)

agent = Agent(provider, max_tokens=4096, max_turns=25, governance=gov)
# Every tool call: Permission → Veto → RateLimit → Constraint → Audit

---

Signals

from deepstrike import SignalGateway, ScheduledPrompt, RuntimeSignal

gw = SignalGateway()

gw.schedule(ScheduledPrompt(goal="standup", run_at_ms=target_time))
gw.ingest(RuntimeSignal(kind="interrupt", payload={}, urgency="critical"))

agent = Agent(provider, max_tokens=4096, max_turns=25, signal_source=gw)

agent.interrupt()  # direct interrupt
gw.destroy()

---

Harness (evaluation framework)

from deepstrike import SinglePassHarness, EvalLoopHarness, HarnessLoop, HarnessRequest

# 1. SinglePass — run once, always passes
outcome = await SinglePassHarness(agent).run(HarnessRequest(goal="Say hello"))

# 2. EvalLoop — retry until QualityGate passes
class ContainsHello(QualityGate):
    async def evaluate(self, request, outcome) -> bool:
        return "hello" in outcome.result.lower()

outcome = await EvalLoopHarness(agent, gate=ContainsHello(), max_attempts=3).run(req)

# 3. HarnessLoop — LLM-as-judge with feedback injection + skill extraction
loop = HarnessLoop(agent, eval_provider=eval_provider, max_attempts=3, skill_dir="./skills")
outcome = await loop.run(HarnessRequest(goal="Write a haiku", criteria=["Must be 3 lines"]))
print(outcome.passed, outcome.feedback)

---

Stream events

Class	Key fields
TextDelta	delta
ThinkingDelta	delta
ToolCallEvent	id, name, arguments
ToolResultEvent	call_id, content, is_error
DoneEvent	iterations, total_tokens, status
ErrorEvent	message

status: completed · max_turns · token_budget · timeout · user_abort · error