actor-ai 0.3.1

Multi-provider AI agents built on pykka.

actor-ai

Multi-provider AI agents built on pykka.

actor-ai extends pykka — a Python actor-model framework created by Stein Magnus Jodal — so each agent runs in its own thread, processes messages safely from a FIFO queue, and exposes a clean proxy API. On top of that foundation, actor-ai adds natural-language instructions, tool calling, conversation sessions, long-term memory, token accounting, traffic monitoring, multi-agent Chorus coordination, and state-machine Workflow orchestration — all with a provider-agnostic API.

Installation

pip install actor-ai          # or: uv add actor-ai

Requires Python ≥ 3.14 and one (or more) provider API keys.

Quick start

from actor_ai import make_agent, Claude

Assistant = make_agent(
    "Assistant",
    "You are a helpful assistant.",
    Claude(),          # requires ANTHROPIC_API_KEY
)

with Assistant.get_proxy() as proxy:
    reply = proxy.instruct("What is the capital of France?").get()
print(reply)   # → "The capital of France is Paris."

make_agent() returns a ready-to-use AIActor subclass without any boilerplate. Agents can delegate to other agents via sub_agents, which are auto-wired as @tool methods the LLM can call:

from actor_ai import make_agent, Claude

Researcher = make_agent("Researcher", "You research topics thoroughly.", Claude())
Writer     = make_agent("Writer",     "You write clear summaries.",      Claude())

Orchestrator = make_agent(
    "Orchestrator",
    "Coordinate research and writing. Use your tools.",
    Claude(),
    sub_agents={"researcher": Researcher, "writer": Writer},
)

with Orchestrator.get_proxy() as proxy:
    report = proxy.instruct("Write a report on the actor model.").get()

For agents that need lifecycle hooks or stateful tools, the class-based approach is still available:

from actor_ai import AIActor, Claude

class Assistant(AIActor):
    system_prompt = "You are a helpful assistant."
    provider = Claude()

with Assistant.get_proxy() as proxy:
    reply = proxy.instruct("What is the capital of France?").get()

AIActor can also run without a provider — it behaves as a plain pykka actor (memory, session, and message-passing still work; instruct() raises RuntimeError if called without a provider):

DataNode = make_agent("DataNode", "Pure coordination actor.")  # no provider

Providers

Class	Backend	Environment variable
Claude	Anthropic	ANTHROPIC_API_KEY
GPT	OpenAI	OPENAI_API_KEY
Gemini	Google AI (OpenAI-compat)	GOOGLE_API_KEY
Mistral	Mistral AI	MISTRAL_API_KEY
DeepSeek	DeepSeek	DEEPSEEK_API_KEY
Copilot	GitHub Copilot (OpenAI-compat)	GITHUB_TOKEN
LiteLLM	Any (100+ models)	depends on model

Copilot routes requests through GitHub Copilot's OpenAI-compatible endpoint and supports multiple underlying models from a single token:

from actor_ai import AIActor, Copilot

class Assistant(AIActor):
    system_prompt = "You are a helpful coding assistant."
    provider = Copilot()                    # gpt-4o (default)
    provider = Copilot("claude-sonnet-4-5") # Claude via Copilot
    provider = Copilot("gemini-2.0-flash")  # Gemini via Copilot

print(Copilot.MODELS)  # frozenset of valid model strings

Valid models: gpt-4o, gpt-4o-mini, o1, o1-mini, o3-mini, claude-sonnet-4-5, gemini-2.0-flash. Passing any other string raises ValueError at construction time.

The token is resolved in order: api_key argument → GITHUB_TOKEN env var → gh auth token CLI → OS keyring (GitHub CLI / VS Code).

Chorus

Chorus groups named actors and lets you coordinate them at three levels. Members can be AIActor instances, other Chorus instances, or any plain pykka actor that exposes an instruct() method.

from actor_ai import Chorus, ChorusType

# Create a typed chorus
team = Chorus.start(type="team", agents={"writer": writer_ref, "editor": editor_ref})

# Single-agent instruct
reply = team.proxy().instruct("writer", "Draft a proposal.").get()

# Broadcast to all members in parallel
replies = team.proxy().broadcast("Introduce yourself.").get()

# Pipeline: chain output of each member to the next
final = team.proxy().pipeline(["writer", "editor"], "Write a report.").get()

# Actors can join and leave at runtime
team.proxy().join("reviewer", reviewer_ref).get()
team.proxy().leave("editor").get()

ChorusType is a Literal["system", "project", "team", "department", "custom"] — set at construction and readable via proxy.

Nested choruses: a Chorus can be a member of another Chorus. broadcast() and memory propagation cascade correctly through nesting levels.

Workflow

Workflow implements a state-machine that orchestrates Chorus and actor instances. Transitions fire either when a reply matches a guard predicate or when a named event is dispatched. States and transitions can be added or replaced at runtime on a live workflow.

from actor_ai import Workflow, WorkflowState, WorkflowTransition

wf = Workflow.start(
    states={
        "draft":  WorkflowState(draft_chorus,  instruction="{input}"),
        "review": WorkflowState(review_chorus, instruction="Review this:\n{output}"),
        # Parallel actors — fire simultaneously, replies combined:
        "analyse": WorkflowState(
            chorus={"researcher": r_ref, "critic": c_ref},
            instruction="Analyse: {input}",
        ),
    },
    transitions=[
        WorkflowTransition("draft",  "review",  guard=lambda r: "ready" in r),
        WorkflowTransition("review", "draft",   on_event="reject"),
    ],
    initial_state="draft",
)

# Blocking run — follows guard transitions until terminal:
output = wf.proxy().run("Draft a proposal.").get()

# Fire a named event:
wf.proxy().event("reject").get()

# Non-blocking run — actor mailbox stays free for events during execution:
wf.proxy().run_detached(
    "Draft again.",
    on_complete=lambda out: print("Done:", out),
).get()

# Add a state at runtime:
wf.proxy().add_state("approve", WorkflowState(approve_chorus, "{output}")).get()
wf.proxy().add_transition(WorkflowTransition("review", "approve", on_event="approve")).get()

Key features

• Agent factory — make_agent() creates agents in one call; sub_agents auto-wires delegation tools
• Multi-turn sessions — rolling conversation history, configurable window (max_history)
• Long-term memory — remember(key, value) / forget(key) facts injected into every system prompt
• Tool calling — decorate methods with @tool to expose them to the LLM
• Chorus — group actors as a named team with broadcast, pipeline, join/leave, typed (ChorusType), nestable
• Workflow — state-machine orchestration with guard and event transitions, parallel actor states, runtime modification, non-blocking run_detached()
• Accounting — track token usage and cost per actor, model, and session (Ledger, Rates)
• Monitoring — forward per-call metadata to LiteLLM callbacks (Langfuse, Helicone, custom)

Examples

The examples/ directory contains self-contained, runnable scripts. Each example uses a fake scripted provider so no API key is required.

examples/
  01_hello_world.py         — One-turn interaction
  02_session.py             — Session history, max_history, clear_session
  03_memory.py              — remember() / forget()
  04_tools.py               — @tool decorator and tool calling
  05_providers.py           — All provider configurations
  06_chorus.py              — Chorus: instruct, broadcast, pipeline, memory
  07_accounting.py          — Ledger and Rates
  08_monitoring.py          — LiteLLM monitoring
  09_messages.py            — Instruct / Remember / Forget message objects
  10_advanced.py            — Full pipeline combining all features
  11_copilot.py             — Copilot provider with token auto-resolution
  12_chorus_advanced.py     — ChorusType, join/leave, nested choruses, non-AI actors
  13_workflow.py            — Workflow state machine: run, step, event, runtime modification
  14_workflow_parallel.py   — Parallel actor states and run_detached()
  15_make_agent.py          — make_agent() factory: simple agents, tools, sub-agents

Run any example:

uv run python examples/01_hello_world.py

User Manual

See MANUAL.md for the complete reference.

License

MIT