weaver-kernel 0.9.0

Capability-based security kernel for AI agents operating in large tool ecosystems

agent-kernel

A capability-based security kernel for AI agents operating in large tool ecosystems (MCP, A2A, 1000+ tools).

30-second pitch

Modern AI agents face three hard problems when given access to hundreds or thousands of tools:

1. Context blowup — raw tool output floods the LLM context window.
2. Tool-space interference — agents accidentally invoke the wrong tool or escalate privileges.
3. No audit trail — there's no record of what ran, when, and why.

agent-kernel solves all three with a thin, composable layer that sits above your tool execution layer:

• Capability Tokens — HMAC-signed, time-bounded, principal-scoped. No token → no execution.
• Policy Engine — READ/WRITE/DESTRUCTIVE safety classes + PII/PCI sensitivity handling.
• Context Firewall — raw driver output is never returned to the LLM; always a bounded Frame.
• Audit Trail — every invocation creates an ActionTrace retrievable via kernel.explain().

Architecture

graph LR
    LLM["LLM / Agent"] -->|goal| K["Kernel"]
    K -->|search| REG["Registry"]
    K -->|evaluate| POL["Policy Engine"]
    K -->|sign| TOK["HMAC Token"]
    K -->|route| DRV["Driver (MCP/HTTP/Memory)"]
    DRV -->|RawResult| FW["Context Firewall"]
    FW -->|Frame| LLM
    K -->|record| AUD["Audit Trace"]

Quickstart

pip install weaver-kernel

Note: The PyPI package is weaver-kernel (Weaver ecosystem), but the Python import remains agent_kernel.

New here? docs/tutorial.md walks through register → grant → invoke → expand → explain in five minutes.

import asyncio, os
os.environ["AGENT_KERNEL_SECRET"] = "my-secret"

from agent_kernel import (
    Capability, CapabilityRegistry,
    InMemoryDriver, Kernel, Principal, SafetyClass, StaticRouter,
)
from agent_kernel.models import CapabilityRequest

# 1. Register a capability
registry = CapabilityRegistry()
registry.register(Capability(
    capability_id="tasks.list",
    name="List Tasks",
    description="List all tasks",
    safety_class=SafetyClass.READ,
    tags=["tasks", "list"],
))

# 2. Wire up a driver
driver = InMemoryDriver()
driver.register_handler("tasks.list", lambda ctx: [{"id": 1, "title": "Buy milk"}])

# 3. Build the kernel
kernel = Kernel(registry=registry, router=StaticRouter(routes={"tasks.list": ["memory"]}))
kernel.register_driver(driver)

async def main():
    principal = Principal(principal_id="alice", roles=["reader"])

    # 4. Discover → grant → invoke → expand → explain
    token = kernel.get_token(
        CapabilityRequest(capability_id="tasks.list", goal="list tasks"),
        principal, justification="",
    )
    frame = await kernel.invoke(token, principal=principal, args={})
    print(frame.facts)           # ['Total rows: 1', 'Top keys: id, title', ...]
    print(frame.handle)          # Handle(handle_id='...', ...)

    # `principal` is required: the handle is bound to the granting principal,
    # so an omitted principal raises HandleConstraintViolation.
    expanded = kernel.expand(
        frame.handle, query={"limit": 1, "fields": ["title"]}, principal=principal
    )
    print(expanded.table_preview)  # [{'title': 'Buy milk'}]

    trace = kernel.explain(frame.action_id)
    print(trace.driver_id)       # 'memory'

asyncio.run(main())

This snippet is extracted and executed by CI (tests/test_readme_quickstart.py), and a standalone runnable mirror lives at examples/readme_quickstart.py (run by make example). CI fails if either stops producing the documented output, so this quickstart cannot silently drift from the working API.

Where it fits

┌─────────────────────────────────────────────┐
│             LLM / Agent loop                │
├─────────────────────────────────────────────┤
│  agent-kernel  ← you are here               │
│  (registry · policy · tokens · firewall)    │
├────────────────┬────────────────────────────┤
│  contextweaver │  tool execution layer       │
│  (context      │  (MCP · HTTP · A2A ·        │
│   compilation) │   internal APIs)            │
└────────────────┴────────────────────────────┘

agent-kernel sits above contextweaver (context compilation) and above raw tool execution. It provides the authorization, execution, and audit layer.

How this relates to neighboring projects

agent-kernel is the embeddable runtime layer of the Weaver ecosystem. The projects below solve adjacent problems and are designed to compose, not to overlap.

Project	Role	Where it runs	Use it when…
agent-kernel (this repo)	Embeddable library/runtime: capability registry, policy, HMAC tokens, context firewall, audit trace.	In-process inside your agent host.	You need authorization, redaction, and audit between an LLM loop and a large tool ecosystem.
AgentFence	External CLI / local proxy that intercepts tool calls and applies a policy gate.	Out-of-process, alongside your agent.	You want a policy boundary without changing your agent code, or you need to gate a third-party agent host you can't modify.
contextweaver	Library that selects and compiles the context an LLM receives.	In-process, before the LLM call.	You need to assemble relevant context for a prompt. It sits under the LLM loop; agent-kernel sits between the LLM and tools.
ChainWeaver	Orchestrator for deterministic tool chains.	In-process or as a separate service.	You need to run a multi-step deterministic flow rather than free-form LLM tool use.
weaver-spec	Specification: invariants, capability/token/frame contracts, conformance suite.	Not a runtime — it's docs + a contract test suite.	You're building another Weaver-compatible implementation, or you want to verify an existing one.

A minimal architecture using agent-kernel as the central runtime:

LLM / agent loop
       │
       ▼
contextweaver  ─►  agent-kernel  ─►  driver  ─►  MCP / HTTP / A2A / internal API
                       │
                       ▼
                  ActionTrace

When not to use this

• You only need a process-level policy gate around an existing agent host — reach for AgentFence instead.
• You only need to compile context for a prompt — use contextweaver.
• You want a deterministic, scripted workflow with no LLM in the inner loop — use ChainWeaver.
• You're writing a static analyzer or one-shot CLI scanner with no per-invocation runtime — agent-kernel would be overkill.

See docs/tutorial.md for an end-to-end "secure your first MCP tool in 5 minutes" walkthrough.

Weaver Spec Compatibility: v0.1.0

agent-kernel is a compliant implementation of weaver-spec v0.1.0. The following invariants are satisfied:

Invariant	Description	How agent-kernel satisfies it
I-01	LLM never sees raw tool output by default	Context Firewall always transforms RawResult → Frame; raw driver output is not returned by default, and non-admin principals cannot obtain raw response mode
I-02	Every execution is authorized and auditable	PolicyEngine authorizes at grant time; a valid CapabilityToken (HMAC-verified on every invoke()) carries the authorization decision; TraceStore records every ActionTrace
I-06	CapabilityTokens are scoped	Tokens bind principal_id + capability_id + constraints with an explicit TTL; revoke(token_id) / revoke_all(principal_id) are supported

See docs/agent-context/invariants.md for the full internal invariant list and weaver-spec INVARIANTS.md for the specification.

Security disclaimers

v0.1 is not production-hardened for real authentication.

• HMAC tokens are tamper-evident (SHA-256) but not encrypted. Do not put sensitive data in token fields.
• Set AGENT_KERNEL_SECRET to a strong random value in production. If unset, a random dev secret is generated per-process with a warning.
• PII redaction is heuristic (regex). It is not a substitute for proper data governance.
• See docs/security.md for the full threat model.

Documentation

• Architecture
• Security model
• Integrations (MCP, HTTPDriver)
• Designing capabilities
• Context Firewall

Development

git clone https://github.com/dgenio/agent-kernel
cd agent-kernel
pip install -e ".[dev]"
make ci      # fmt-check + lint + type + test + examples

License

Apache-2.0 — see LICENSE.