lazybridge 0.9.0

Zero-boilerplate multi-provider LLM agent framework

LazyBridge

Status: alpha (0.9.x). API is intentionally fluid pre-1.0 — see CHANGELOG for breaking changes between minor releases . Pin a minor (lazybridge>=0.9,<0.10) in production until 1.0.

Zero-boilerplate, multi-provider Python framework for LLM agents. One Agent class, swappable engines (LLM / Plan / Human / Supervisor), and one tool contract — plain Python functions, other Agents, MCP servers, and full pipelines all compose through tools=[...]. Parallelism is automatic when the engine emits N tool calls in a turn; deterministic when you declare it.

from lazybridge import Agent, LLMEngine

agent = Agent(
    engine=LLMEngine("claude-opus-4-7"),
)
result = agent("hello")
print(result.text())

That's the whole surface when you start. It grows only when your problem grows.

• Documentation: https://lazybridge.com
• Recipes: https://lazybridge.com/recipes/
• For LLM assistants (Claude Skill, llms.txt): https://lazybridge.com/for-llms/

The mental model

Every Agent is the composition Engine + Tools + State:

• Engine — what decides next. LLMEngine is the common case; swap for Plan (deterministic DAG), HumanEngine (approval gate), or SupervisorEngine (REPL).
• Tools — anything the agent can invoke. Functions, other Agents, Plan-backed pipelines, MCP servers, and provider-native tools live in the same tools=[...] list.
• State — Memory (in-prompt history), Store (durable blackboard), Session (event bus + observability).

The same Agent(engine=..., tools=..., ...) shape supports a one-shot helper, a hierarchical multi-agent system, and a checkpointed production pipeline — only the engine= argument changes. See Concepts → Mental model.

Pick your tier

LazyBridge grows with you — every tier is additive.

Tier	For	Key imports
Basic	one-shot or tool-calling agents	Agent · LLMEngine · Tool · NativeTool · Envelope
Mid	real apps with memory, tracing, guardrails, composition	Memory · Store · Session · Guard* · verify= · MCP · HumanEngine · EvalSuite
Full	production pipelines: typed hand-offs, routing, resume, OTel	Plan · Step · sentinels · SupervisorEngine · checkpoint · exporters
Advanced	extending the framework	BaseProvider · Plan.to_dict · custom engines · OpenTelemetry · Visualizer

See Decisions → Pick your tier for a flowchart.

Install

PyPI version note. Older releases on PyPI (0.4.x) expose the legacy LazyAgent / LazyTool / LazySession API and do not match this README. Pin lazybridge>=0.9,<0.10, or install from source:

pip install "git+https://github.com/selvaz/LazyBridge.git#egg=lazybridge[anthropic]"

pip install "lazybridge[anthropic]"
# or [openai], [google], [deepseek], [litellm], [otel], [encryption], [all]
# Concrete tools (MCP, Gmail, Telegram, gateways, doc readers) ship in the
# sibling lazytoolkit package: pip install "lazytoolkit[mcp]"  (see https://lazybridge.com/)

Confirm you're on the modern API:

import lazybridge
assert lazybridge.__version__.startswith(("0.7", "0.8", "0.9")), (
    f"LazyBridge {lazybridge.__version__} predates the modern API — this "
    f"README requires >=0.7.9.  See https://github.com/selvaz/LazyBridge."
)

Set an API key for your provider of choice (ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, DEEPSEEK_API_KEY).

Worked examples

1 · Function becomes a tool, auto-schema

from lazybridge import Agent, LLMEngine, Tool


def get_weather(city: str) -> str:
    """Return current temperature and conditions for ``city``."""
    return f"{city}: 22°C, sunny"


agent = Agent(
    engine=LLMEngine("claude-opus-4-7"),
    tools=[Tool.wrap(get_weather, name="get_weather")],
)
result = agent("what's the weather in Rome and Paris?")
print(result.text())

No decorators, no JSON schemas. Type hints + docstring become the tool's LLM-facing schema automatically. The explicit Tool.wrap(fn, name=...) factory pins the LLM-visible name so refactors don't break tool-maps or plan references; the bare-callable form tools=[get_weather] works too (backward-compatible auto-wrap). See Guides → Basic → Tool.

2 · Native tools (no code at all)

from lazybridge import Agent, LLMEngine, NativeTool

agent = Agent(
    engine=LLMEngine("claude-opus-4-7"),
    native_tools=[NativeTool.WEB_SEARCH],
)
agent("AI news this week")

WEB_SEARCH · CODE_EXECUTION · FILE_SEARCH · COMPUTER_USE · GOOGLE_SEARCH · GOOGLE_MAPS (each supported by a subset of providers). CODE_EXECUTION and COMPUTER_USE require allow_dangerous_native_tools=True — they execute code or click your screen.

3 · Tool-is-tool — agents wrap agents

from lazybridge import Agent, LLMEngine

researcher = Agent(
    engine=LLMEngine("claude-opus-4-7"),
    tools=[search],
    name="research",
)
editor = Agent(
    engine=LLMEngine("claude-opus-4-7"),
    tools=[researcher],
    name="editor",
)
result = editor("summarise AI trends April 2026")
print(result.text())

Parallelism is emergent: when editor decides to call two tools in the same turn, they run concurrently via asyncio.gather. No flag, no config, no "parallel mode".

4 · MCP servers as tool catalogues

from lazybridge import Agent, LLMEngine
from lazytools.connectors.mcp import MCP

fs = MCP.stdio(
    "fs",
    command="npx",
    args=["-y", "@modelcontextprotocol/server-filesystem", "/tmp/project"],
    allow=["fs.read_*", "fs.list_*"],   # required since 0.7.9 (deny-by-default)
    cache_tools_ttl=60.0,
)
agent = Agent(
    engine=LLMEngine("claude-opus-4-7"),
    tools=[fs],
)
agent("Read README.md and summarise the install steps")

The MCP server expands into one LazyBridge Tool per remote tool — no separate engine, no graph wrappers. See LazyTools → MCP.

5 · Declared typed pipeline with crash resume

from lazybridge import Agent, LLMEngine, Plan, Step, Store, from_prev, from_step

store = Store(db="pipeline.sqlite")

researcher = Agent(engine=LLMEngine("claude-opus-4-7"), name="search")
ranker     = Agent(engine=LLMEngine("claude-opus-4-7"), name="rank")
writer     = Agent(engine=LLMEngine("gpt-5.4-mini"),          name="write")

pipeline = Agent(
    engine=Plan(
        Step("search", writes="hits"),
        Step("rank",
             task="Rank these search hits by relevance; return the top 5.",
             context=from_prev),
        Step("write",
             task="Write a 200-word brief from the ranked items.",
             context=from_step("rank")),
        store=store,
        checkpoint_key="research",
        resume=True,
    ),
    tools=[researcher, ranker, writer],
)
pipeline("AI trends April 2026")

If a step fails mid-plan, the next run with resume=True retries from the failing step only. Concurrent runs on the same checkpoint_key are serialised via compare_and_swap — first writer wins, second raises ConcurrentPlanRunError. Pass on_concurrent="fork" for fan-out workflows. See Guides → Full → Checkpoint & resume.

6 · Human-in-the-loop with a full REPL

from lazybridge import Agent, LLMEngine
from lazybridge.ext.hil import supervisor_agent

sup = supervisor_agent(
    tools=[search],
    agents=[researcher],   # human can `retry research: <feedback>`
)
result = sup("publish a policy brief")
print(result.text())

REPL commands: continue, retry <agent>: <feedback>, store <key>, <tool>(<args>). For approval-only flows use the lighter human_agent(...) or HumanEngine — see Decisions → HumanEngine vs SupervisorEngine.

What makes LazyBridge different

1. Tool-is-Tool. Functions, Agents, Agents-of-Agents, Plan-backed pipelines, and tool providers (MCP servers, external HTTP gateways) all plug into tools=[...] with the same contract.
2. Compile-time plan validation. PlanCompileError at construction catches broken DAGs — duplicate names, forward references, broken from_step / from_parallel sentinels — before any LLM call.
3. CAS-protected crash resume. Plan checkpoints to Store via compare_and_swap. Two concurrent runs on the same checkpoint_key deterministically converge instead of silently overwriting.
4. Parallelism as capability. When the engine emits N tool calls in one turn, they run concurrently via asyncio.gather. No flag, no tool_choice="parallel" knob.
5. Transitive cost roll-up. Envelope.metadata.nested_* aggregates token / cost telemetry across an Agent-of-Agents tree — the outer envelope reports total pipeline spend without double-counting.
6. OTel GenAI conventions out of the box. OTelExporter ships gen_ai.* attributes and proper parent-child spans; existing GenAI dashboards render LazyBridge traces unchanged.
7. First-class LLM-assistant artifact. A signature-first Claude Skill ships with the library at lazybridge/skill/, loadable by any LLM coding assistant. See For LLM assistants.

Documentation

The full docs live at https://lazybridge.com. Highlights:

• Concepts — the mental model, "everything is a tool", progressive complexity, and canonical-vs-sugar.
• Guides — one focused page per public concept, all following the same Signature → Synopsis → When to use / NOT → Example → Pitfalls → See also template.
• Recipes — runnable examples from examples/, embedded verbatim.
• Decisions — "which one do I use?" trees for tier, return type, state layer, composition, parallelism, HumanEngine vs SupervisorEngine, verify= placement, checkpointing.
• Errors — cause → diagnosis → fix table for every framework exception.
• For LLM assistants — Claude Skill install, /llms.txt index, /llms-full.txt corpus.

Contributing

Issues and PRs welcome at https://github.com/selvaz/LazyBridge. Run the test suite with pip install -e ".[test,all]" then pytest. See SECURITY.md for the disclosure policy.

Licence

Apache 2.0 — see LICENSE.