agnt5 0.7.20

AGNT5 Python SDK - Build durable, resilient agent-first applications

AGNT5 Python SDK

Build durable AI agents and workflows with automatic retries, checkpointing, and state management.

Documentation

See the online documentation for example applications, user guides, and detailed API reference.

Installation

pip install agnt5

Quick Start

from agnt5 import function, Context

@function
async def analyze_document(ctx: Context, document_id: str) -> dict:
    """Analyze document with automatic retries."""
    ctx.logger.info(f"Analyzing document: {document_id}")

    # Checkpoint expensive operations
    embeddings = await ctx.step("embeddings", lambda: generate_embeddings(document_id))

    # State management
    ctx.set("embedding_id", embeddings["id"])

    return {"document_id": document_id, "status": "completed"}

Functions automatically retry on failure. Use ctx.step() to checkpoint progress.

Client Authentication

The AGNT5 client supports two authentication methods:

Subdomain URLs (Recommended)

Access your deployment directly via its unique subdomain:

from agnt5 import Client

# Production environment
client = Client("https://myproject-prod.run.agnt5.com")

# Staging environment
client = Client("https://myproject-stg.run.agnt5.com")

# Run a workflow
result = client.run("my_workflow", {"input": "data"})

API Keys

For programmatic access, use service keys:

from agnt5 import Client

# Option 1: Pass api_key directly
client = Client(
    gateway_url="https://api.agnt5.com",
    api_key="agnt5_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
)

# Option 2: Use AGNT5_API_KEY environment variable
# export AGNT5_API_KEY=agnt5_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
client = Client(gateway_url="https://api.agnt5.com")

result = client.run("my_workflow", {"input": "data"})

See the Authentication Guide for more details.

Core Components

Functions

Stateless operations with automatic retry and checkpointing.

from agnt5 import function, RetryPolicy, BackoffPolicy, BackoffType

@function(
    retries=RetryPolicy(max_attempts=5, initial_interval_ms=1000),
    backoff=BackoffPolicy(type=BackoffType.EXPONENTIAL, multiplier=2.0)
)
async def generate_summary(ctx: Context, text: str) -> dict:
    return await llm.generate(prompt=f"Summarize: {text}")

Entities

Stateful components where each instance maintains its own state.

from agnt5 import entity

@entity
class Conversation:
    pass

@Conversation.method
async def add_message(ctx: Context, role: str, content: str) -> list:
    messages = ctx.get("messages", [])
    messages.append({"role": role, "content": content})
    ctx.set("messages", messages)
    return messages

# Each conversation has independent message history
convo = Conversation.instance("user-123")
await convo.add_message(ctx, role="user", content="Hello!")

Workflows

Orchestrate multi-step processes with parallel execution and coordination.

from agnt5 import workflow

@workflow
async def research_workflow(ctx: Context, topic: str) -> dict:
    # Run searches in parallel
    results = await ctx.parallel(
        search_papers(ctx, topic),
        search_web(ctx, topic)
    )

    # Wait for human review
    reviewed = await ctx.signal("review_complete", timeout_ms=60000)

    return {"topic": topic, "results": results, "reviewed": reviewed}

Tools

Functions callable by LLMs with automatic schema generation.

from agnt5 import tool

@tool
async def search_knowledge_base(ctx: Context, query: str, limit: int = 10) -> list:
    """Search the knowledge base using semantic search.

    Args:
        query: Search query
        limit: Max results to return
    """
    embeddings = await generate_embeddings(query)
    return await vector_db.search(embeddings, limit=limit)

# Schema generated from type hints and docstring
schema = search_knowledge_base.get_schema()

Agents

LLM-powered agents with tool orchestration.

from agnt5 import Agent

agent = Agent(
    name="research_assistant",
    instructions="You are a research assistant. Use tools to find information.",
    tools=[search_knowledge_base, search_papers, summarize_text],
    model_name="gpt-4o"
)

result = await agent.run("Find recent papers on transformer architectures")

Context API

@function
async def example(ctx: Context, data: dict) -> dict:
    # Metadata
    ctx.run_id, ctx.attempt, ctx.logger

    # State management
    ctx.set("key", value)
    ctx.get("key", default=None)
    ctx.delete("key")

    # Checkpointing
    result = await ctx.step("step_name", lambda: expensive_op())

    # Coordination
    await ctx.sleep(5)
    await ctx.timer(delay_ms=1000)
    await ctx.signal("name", timeout_ms=5000)

    # Parallel execution
    await ctx.parallel(task1(), task2())
    await ctx.gather(db=fetch_db(), api=fetch_api())

    return {"result": result}

Error Handling

from agnt5.exceptions import RetryError, StateError

@function
async def robust_function(ctx: Context, data: str) -> dict:
    try:
        return await risky_operation(data)
    except RetryError as e:
        ctx.logger.error(f"Failed after {e.attempts} attempts")
        return {"error": "max_retries_exceeded"}

Examples

See examples/ directory for working examples.

License

Apache 2.0