lllm-core 0.2.1

Protocol and service layer for reusable agentic tactics.

Low-Level Language Models (LLLM)

Low-Level Language Models (LLLM) is a small protocol and service layer for reusable agentic tactics.

The center model is Tactic: a typed, runtime-agnostic unit that does one thing well and can be called locally, exposed through FastAPI, described for a PsiHub package, and composed later through refs and local config.

Install

pip install lllm-core

The PyPI distribution is lllm-core; the import package remains lllm:

import lllm

For local development:

pip install -e ".[dev]"

Smallest Tactic

from pydantic import BaseModel
from lllm import Tactic


class EchoInput(BaseModel):
    text: str


class EchoOutput(BaseModel):
    text: str


class EchoTactic(Tactic[EchoInput, EchoOutput]):
    name = "echo"
    input_type = EchoInput
    output_type = EchoOutput

    def _run(self, input_value, *, context=None):
        return EchoOutput(text=input_value.text.upper())


assert EchoTactic().run({"text": "hello"}).text == "HELLO"

Serve It

from lllm.services import create_tactic_app

app = create_tactic_app(EchoTactic())

uvicorn app:app --reload
curl -X POST http://127.0.0.1:8000/run \
  -H 'content-type: application/json' \
  -d '{"input":{"text":"hello"}}'

Remote clients normalize base service URLs into /run and /stream endpoints. RemoteTactic.arun() calls the JSON run endpoint, while RemoteTactic.astream() consumes the service-sent event stream and yields the same raw data items as local Tactic.astream(). Use RemoteTactic.aevents() when you need the full TacticEvent envelopes. RemoteTactic.fetch_info() and RemoteTactic.afetch_info() retrieve the service-advertised TacticInfo from /info without making local info() do network I/O.

Pydantic AI

Pydantic AI remains the runtime owner. Configure the agent normally, then wrap it:

from lllm.runtimes import PydanticAITactic

tactic = PydanticAITactic(agent, input_type=str, output_type=str)

LLLM forwards request metadata where the agent run method accepts metadata. examples/pydantic_ai_tactic/structured_agent.py shows structured input/output, streaming, and tool wrapping with an offline fake agent. examples/pydantic_ai_tactic/surrounding_features.py shows that normal runtime-owned kwargs such as model settings, deps, eval hooks, durable IDs, graph/workflow state, and tool approval pass through the wrapper.

Live provider credentials can be smoke-checked without sending prompts:

if [ -f .env ]; then
  set -a
  source .env
  set +a
fi
LLLM_LIVE_PROVIDER_TESTS=1 pytest tests/test_live_providers.py

Those opt-in tests list models using whichever credentials are available: OPENAI_API_KEY, ANTHROPIC_API_KEY, and TOGETHER_API_KEY. Together is included as an expected-soft-failure check because some networks return an edge-level 403 error code: 1010 before API-key validation.

Parsers

Shared parser utilities live outside runtime adapters:

from lllm.parsers import DefaultTagParser

parser = DefaultTagParser(required_xml_tags=["answer"])
parsed = parser.parse("<answer>Hello</answer>")

Native prompts can use the same parser objects, and plain Python or Pydantic AI wrappers can call them directly around tactic output.

Proxies

Proxy utilities live at the Tactic boundary, so they can wrap any runtime:

from lllm import InMemoryProxyLog, ProxyTactic

log = InMemoryProxyLog()
proxy = ProxyTactic(EchoTactic(), sink=log.append)
assert proxy.run({"text": "hello"}).text == "HELLO"

Use proxy hooks for small call-boundary transforms, observability, or local guardrails. Payload capture is opt-in with capture_inputs and capture_outputs. Proxies mirror wrapped tactic capabilities, including streaming, and record captured stream chunks after the stream is consumed.

Sandboxes

Sandbox utilities provide application-level guardrails around a tactic:

from lllm import SandboxPolicy, SandboxedTactic

sandboxed = SandboxedTactic(
    EchoTactic(),
    policy=SandboxPolicy(max_input_bytes=4096, timeout_seconds=2.0),
)

Use them for payload budgets, request-metadata allowlists, and async/service deadlines. Metadata allowlist keys must be plain string tokens, without whitespace, percent escapes, or path separators. They are not OS-level isolation for untrusted code.

Native Prompt/Dialog Core

The native namespace preserves prompt and dialog primitives without letting them shape the Tactic protocol:

from lllm.runtimes.native import Dialog, Prompt, Role

system = Prompt(path="agent/system", prompt="You are a {style} assistant.")
dialog = Dialog(owner="agent")
dialog.put_prompt(system, prompt_args={"style": "careful"}, role=Role.SYSTEM)
dialog.put_text("Draft the next checkpoint.")

retry = dialog.fork(last_n=1, first_k=1)

Use these pieces for native runtime transcripts, prompt templates, tool schemas, and forked histories. Wrap executable native agents with NativeTacticAdapter when they need to cross the reusable tactic boundary. examples/native_service/ shows an offline native prompt/dialog workflow served through the same FastAPI API as ordinary tactics.

Create A Project

Generate a runnable tactic/service project:

lllm create plain my-tactic
cd my-tactic
pip install -e ".[dev,server]"
pytest
uvicorn app:app --reload

Or serve the generated tactic entrypoint directly:

lllm serve my_tactic.tactics:build_tactic --port 8000

Templates:

• plain: typed Python Tactic.
• pydantic-ai: a Pydantic AI-style agent wrapped as a tactic.
• native: a native-style object wrapped behind the tactic boundary.

Add package metadata later with psihub init.

Boundaries

• LLLM owns the Tactic protocol and service adapter.
• PsiHub owns psi.toml, package validation, package cards, local hub storage, downloads, and config templates.
• Native runtime ideas live under lllm.runtimes.native and do not shape the protocol layer.

Compose Tactics

One tactic can call another directly or through an HTTP service. LLLM keeps this as ref resolution, not service launching:

from lllm import TacticResolver

resolver = TacticResolver()
resolver.register("psi://demo/echo/tactics/echo", EchoTactic())

result = resolver.run(
    "psi://demo/echo/tactics/echo",
    {"text": "hello"},
)

Local config can bind the same ref to a running service:

[refs."psi://demo/echo/tactics/echo"]
url = "http://127.0.0.1:8000/tactics/echo"

[refs."psi://demo/echo/tactics/echo".metadata]
policy_url = "http://127.0.0.1:9000"

resolver = TacticResolver.from_config(".")
tactic = resolver.resolve("psi://demo/echo/tactics/echo")

Tactic refs are strict package resource identifiers: psi://org/package/tactics/name with no semicolon params, query string, fragment, or path-control separators in ref segments. Shared config may include non-tactic refs from known PSI resource sections, including schemas, services, channels, snapshots, runs, configs, docs, examples, and assets, but malformed refs and unknown resource sections fail validation. TacticResolver.from_config() preserves [refs."...".metadata] on tactic URL bindings; legacy top-level extras still work, and explicit metadata table values win on duplicate keys. Tactic URL bindings must not also declare a store, path, or object target. Tactic refs with a concrete target must use url; store, path, and serialized object targets belong to other layers or direct in-process registration. URL bindings must not include URL params, query strings, fragments, embedded credentials, percent escapes, backslashes, colons, empty segments, or dot segments in URL paths, and binding metadata must not include raw secret-shaped keys such as api_key/apiKey/apikey, tokens, accessToken/accesstoken, passwords, cookies, authorization, or credentials. Use local credential refs such as api_key_ref/apiKeyRef/apikeyref or auth hooks instead. Metadata maps must use string keys; direct Python metadata with non-string keys is rejected before Pydantic can coerce keys into text.

Remote service failures raise RemoteTacticError with status_code, error_type, message, tactic, endpoint, request_id, and raw detail fields parsed from the service envelope. Protocol and schema errors use HTTP 400; unexpected tactic runtime failures use HTTP 500.

Package Metadata Helpers

LLLM does not own psi.toml, but it can export tactic metadata for PsiHub:

from lllm.integrations import tactic_resource

resource = tactic_resource(EchoTactic())

Custom endpoint decorators and tactic examples are included in that metadata so package cards can show domain routes and concrete calls alongside the portable /run interface. Use @endpoint.get, @endpoint.post, @endpoint.put, @endpoint.patch, or @endpoint.delete to declare typed service routes without changing the tactic protocol. Public service info and PsiHub metadata exports filter raw secret-shaped keys from examples and user metadata, including api_key/apiKey/apikey, tokens, accessToken/accesstoken, passwords, cookies, authorization, and credentials, while preserving local refs such as api_key_ref, apiKeyRef, and apikeyref. SSE stream event metadata goes through the same public filter before it is written to the response; event data is left as the tactic output. Runtime adapters such as as_tactic, PydanticAITactic, and NativeTacticAdapter accept package refs, service refs, descriptions, examples, and metadata so wrapper-created tactics can keep the same package-facing contract as subclassed tactics. Those metadata maps must use string keys, including nested maps.