glia-agents 0.1.0

A glass-box, minimal library for building LLM agents — modern techniques as opt-in primitives, no hidden control flow.

glia

A glass-box, minimal library for building LLM agents. Every model call, tool call, and state transition is a plain object you can log, snapshot, and replay. No hidden control flow. The whole loop fits in one file you can read in an afternoon.

glia (n.): the cells that support and connect neurons. This is the connective tissue for LLM agents — not a framework you submit to, a small library you build on.

 Python 3.10+  ·  MIT  ·  zero required dependencies  ·  typed

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Why another agent library?

The 2026 agent-framework field is crowded, and the loudest, most consistent complaint about the incumbents is the same: too much abstraction, hidden control flow, painful to debug. Developers keep stripping the framework out to call the model API directly, just to see what's happening.

glia is the opposite bet. It ships the modern techniques — tools, structured outputs, context compaction, durable checkpoints, guardrails, subagents, evals-as-tests — as opt-in primitives you can read, not a monolith you must trust. The design goal is understandability and control, not feature count.

If you want a graph engine, use LangGraph. If you want role-play crews, use CrewAI. If you want a small, transparent loop you fully understand — glia.

See docs/STRATEGY.md for the full market analysis.

Install

pip install glia-agents               # core — no dependencies
pip install "glia-agents[anthropic]"  # + the Claude provider

The distribution is glia-agents (the bare name glia was taken on PyPI); the import stays import glia. For development: git clone then pip install -e ".[anthropic,dev]".

30-second tour

import asyncio
from glia import Agent, tool
from glia.providers import ClaudeLLM

@tool
async def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return {"Paris": "18°C, cloudy"}.get(city, "unknown")

async def main():
    agent = Agent(ClaudeLLM(), tools=[get_weather], system="Be concise.")
    result = await agent.run("What's the weather in Paris?")
    print(result.output)          # the answer
    print(result.usage)           # what it cost

asyncio.run(main())

No API key handy? Every example runs offline with the deterministic EchoLLM provider — same code, no network:

from glia.providers import EchoLLM, call
llm = EchoLLM([call("get_weather", {"city": "Paris"}), "It's 18°C and cloudy."])

See the whole glass box

Because the loop emits an event for everything it does, you can watch it work:

async for event in agent.run_events("What's the weather in Paris?"):
    print(event.kind)
# run_started → model_call → model_response → tool_called → tool_returned → model_call → ... → run_finished

And the entire run state is one serialisable object:

from glia.checkpoint import save, load
save(result.trajectory, "run.json")     # durable execution: it's just JSON
resumed = load("run.json")
await agent.run("follow-up question", trajectory=resumed)   # pick up where you left off

What's in the box

Primitive	What it gives you
Transparent loop	agent.run() / agent.run_events() — no hidden control flow
Typed tools	@tool on a plain function; JSON schema derived from type hints
Provider boundary	one ~40-line LLM protocol; Claude + offline adapters
Trajectory	the full, JSON-serialisable run state and event log
Structured output	generate_structured(...) → a dataclass / Pydantic model / dict
Context engineering	SummarizingCompactor, TrimmingCompactor
Durable execution	checkpoint & resume — a run is a JSON file
Guardrails	(text) -> None validators for input and output
Subagents	agent.as_tool(...) — any agent becomes a tool
Evals-as-tests	a pytest-style regression harness for agent behaviour

Examples

Runnable, offline, no API key needed:

python examples/01_hello_agent.py       # basic agent + event stream
python examples/02_tools.py             # tools
python examples/03_structured_output.py # typed output
python examples/04_subagents.py         # subagent as a tool
python examples/05_checkpoint_resume.py # durable execution
python examples/06_evals.py             # eval suite

Design in one picture

Agent.run() → [ call model → (tools? run them, loop) : done ]
                     │              │
                every step emits an Event you can see, log, and replay
                     │
              all state lives in one serialisable Trajectory

Full details in docs/ARCHITECTURE.md.

Project docs

• Strategy & market analysis — why glia, and who it competes with
• Architecture — how the whole thing works
• Roadmap — where it's going
• Contributing

Status

v0.1 — alpha. The core thesis is proven end-to-end with a full test suite and green CI. APIs may still change before 1.0. Feedback and issues welcome.

License

MIT — see LICENSE.