synth-agent-sdk 0.9.8

Autonomous agents, engineered. A Python SDK for building production-grade AI agents and multi-agent systems.

Synth

Autonomous agents, engineered.

Latest Version: 0.9.8 | PyPI | Changelog

A Python SDK for building production-grade AI agents and multi-agent systems. From a 3-line single agent to complex, stateful, resumable multi-agent graphs — with model-agnostic provider support, streaming, observability, evaluation, and guardrails out of the box.

---

Table of Contents

1. What is Synth?
2. Installation
3. Quick Start
4. Core Concepts
5. Creating an Agent
6. Tools
7. Running Your Agent
8. Streaming
9. Model Providers
10. Memory
11. Guards
12. Structured Output
13. Pipelines
14. Graphs
15. Human-in-the-Loop
16. Agent Teams
17. Tracing
18. Checkpointing
19. Evaluation
20. CLI Commands
21. Testing Dashboard
22. Deploying to AWS AgentCore
23. AgentCore Evaluations
24. Error Handling
25. Environment Variables
26. FAQ

---

What is Synth?

Synth is a Python library for building AI-powered agents. An agent uses a large language model (Claude, GPT, Gemini, etc.) to understand instructions, make decisions, and take actions — calling functions, searching databases, generating reports, or coordinating with other agents.

Synth handles the plumbing (provider communication, conversation management, retries, cost tracking) so you focus on what your agent actually does.

---

Installation

Requires Python 3.10+.

pip install synth-agent-sdk[anthropic]     # Anthropic Claude (recommended)

Other options:

pip install synth-agent-sdk[quickstart]    # Claude + GPT (tutorials/demos)
pip install synth-agent-sdk[openai]        # OpenAI GPT
pip install synth-agent-sdk[google]        # Google Gemini
pip install synth-agent-sdk[ollama]        # Local Ollama models
pip install synth-agent-sdk[bedrock]       # AWS Bedrock
pip install synth-agent-sdk[agentcore]     # AWS AgentCore deployment
pip install synth-agent-sdk[ui]            # Browser testing dashboard
pip install synth-agent-sdk[all]           # All providers

Important: The package name is synth-agent-sdk, not synth. Running pip install synth installs an unrelated C++ template engine that will fail to build. Always use synth-agent-sdk.

Recommended: Install in a Virtual Environment

# macOS / Linux
python3 -m venv .venv
source .venv/bin/activate

# Windows
python -m venv .venv
.venv\Scripts\activate

Then install:

pip install synth-agent-sdk[anthropic]

macOS Notes

Apple Silicon (M1/M2/M3/M4): If you install the bedrock or agentcore extras, the botocore[crt] dependency pulls in awscrt, a compiled C extension. If the build fails:

1. Make sure Xcode Command Line Tools are installed:

   xcode-select --install
   

2. If using pyenv, ensure your Python was built with the correct architecture:

   python3 -c "import platform; print(platform.machine())"
   # Should print "arm64" on Apple Silicon
   

3. If the awscrt wheel still fails, install without CRT (slightly slower S3 transfers but fully functional):

   pip install botocore boto3
   pip install synth-agent-sdk[agentcore] --no-deps
   pip install synth-agent-sdk
   

Homebrew Python: If you use Homebrew's Python, create a venv first — installing packages globally into Homebrew Python is externally managed and will be rejected by pip.

Global Install with pipx

If you want the synth CLI available globally without activating a venv each time, use pipx:

# Install pipx if you don't have it
# macOS
brew install pipx
pipx ensurepath

# Linux / Windows
pip install --user pipx
pipx ensurepath

Then install Synth:

pipx install synth-agent-sdk[anthropic]

To add extra providers to an existing pipx install:

pipx inject synth-agent-sdk anthropic openai       # add provider SDKs
pipx inject synth-agent-sdk boto3 'botocore[crt]'   # add Bedrock/AWS support

This gives you the synth CLI globally (synth init, synth dev, synth doctor, etc.) while keeping dependencies isolated. For project work that imports from synth import Agent, you'll still want a venv with pip install synth-agent-sdk so your project can access the library.

Set your API key:

export ANTHROPIC_API_KEY="your-key-here"   # Claude
export OPENAI_API_KEY="your-key-here"      # GPT
export GOOGLE_API_KEY="your-key-here"      # Gemini
# AWS Bedrock uses standard IAM credentials — no Synth-specific key needed

Verify your setup:

synth doctor

---

Quick Start

The fastest way to get going is synth init, which scaffolds a complete project interactively:

mkdir my-agent && cd my-agent
synth init

This walks you through provider selection, model choice, tools, and features — then generates a ready-to-run project:

  SYNTH INIT
  Interactive project setup

  Project type (single, multi) [single]:
  Project name [my-agent]:
  Description [An AI agent built with SynthAgentSDK]:

  Available providers:
    anthropic              Anthropic Claude
    openai                 OpenAI GPT
    google                 Google Gemini
    ollama                 Local Ollama
    bedrock                AWS Bedrock
    agentcore              AWS AgentCore

  Provider [anthropic]:
  Model [claude-sonnet-4-5]:
  Agent instructions [You are a helpful assistant.]:

  ...tool wizard, MCP wizard, feature toggles...

  Summary:
    Name:         my-agent
    Provider:     Anthropic Claude
    Model:        claude-sonnet-4-5
    Features:     memory, guards
    Files:        agent.py, README.md, synth.toml

  Create project? [Y/n]:

  How would you like to test?
    ui                     Launch the browser-based testing dashboard
    cli                    Open the interactive CLI shell

  Testing mode [cli]:

Once generated, run your agent:

synth dev agent.py          # Interactive REPL with streaming + trace UI
synth run agent.py "Hello"  # One-shot execution

For multi-agent projects, select multi at the project type prompt to configure multiple agents with orchestration (Pipeline, Graph, AgentTeam, or Human-in-the-Loop).

Or skip the wizard and write an agent directly:

from synth import Agent

agent = Agent(model="claude-sonnet-4-5", instructions="You are a helpful assistant.")
result = agent.run("What is the capital of France?")
print(result.text)
# => "The capital of France is Paris."

---

Core Concepts

Concept	What It Is
Agent	The main building block. Wraps an AI model with tools, memory, and guards.
Tool	A Python function your agent can call.
ToolKit	A bundle of related tools.
RunResult	Returned by agent.run() — text, token usage, cost, latency, trace.
Memory	Lets your agent remember previous conversations.
Guard	A safety rule applied to input or output.
Pipeline	Chains agents sequentially.
Graph	A workflow with branching, loops, and conditional logic.
AgentTeam	Multiple agents coordinated by an orchestrator.
Trace	A detailed record of everything that happened during a run.
Checkpoint	A saved snapshot of a run's state for resumption.

---

Creating an Agent

from synth import Agent, Guard, Memory

agent = Agent(
    model="claude-sonnet-4-5",        # AI model to use
    instructions="You are helpful.",   # System prompt
    tools=[my_tool, my_toolkit],      # Optional tools
    memory=Memory.thread(),           # Optional memory
    guards=[Guard.no_pii_output()],   # Optional safety rules
    output_schema=MyModel,            # Optional Pydantic schema
    max_retries=3,                    # Retry on transient errors
    retry_backoff=1.0,                # Base delay between retries (seconds)
)

All parameters except model are optional. Default model is claude-sonnet-4-5.

---

Tools

Tools are Python functions your agent can call. Mark them with @tool — Synth auto-generates JSON schemas from type hints and docstrings.

from synth import tool

@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny, 72°F."

agent = Agent(
    model="claude-sonnet-4-5",
    instructions="You are a weather assistant.",
    tools=[get_weather],
)

Rules: every parameter needs a type annotation, and the function needs a docstring. Missing either raises ToolDefinitionError immediately.

Group related tools with ToolKit:

from synth import ToolKit

math_tools = ToolKit([add, multiply, divide])
agent = Agent(model="gpt-4o", tools=[math_tools, get_weather])

Inspect tool calls after a run:

for tc in result.tool_calls:
    print(f"{tc.name}({tc.args}) → {tc.result}  [{tc.latency_ms:.1f}ms]")

---

Running Your Agent

Synchronous:

result = agent.run("Explain quantum computing in simple terms.")
print(result.text)        # Response text
print(result.tokens)      # TokenUsage(input, output, total)
print(result.cost)        # Estimated cost in USD
print(result.latency_ms)  # Latency in milliseconds
print(result.tool_calls)  # Tools that were called
print(result.trace)       # Full execution trace
print(result.output)      # Parsed structured output (if output_schema set)

Asynchronous:

import asyncio

async def main():
    result = await agent.arun("What is 2 + 2?")
    print(result.text)

asyncio.run(main())

---

Streaming

from synth import TokenEvent, ToolCallEvent, ToolResultEvent, DoneEvent, ErrorEvent

for event in agent.stream("Write a short poem about coding."):
    if isinstance(event, TokenEvent):
        print(event.text, end="", flush=True)
    elif isinstance(event, ToolCallEvent):
        print(f"\n[Calling: {event.name}]")
    elif isinstance(event, DoneEvent):
        print(f"\n\nTokens: {event.result.tokens.total_tokens}")

Async streaming:

async for event in agent.astream("Write a haiku."):
    if isinstance(event, TokenEvent):
        print(event.text, end="", flush=True)

Event	When
TokenEvent	Model produced a text token
ToolCallEvent	Model decided to call a tool
ToolResultEvent	Tool finished executing
ThinkingEvent	Model produced a reasoning token
DoneEvent	Stream completed — contains full RunResult
ErrorEvent	Something went wrong

---

Model Providers

Switch providers by changing the model string — no other code changes needed.

Provider	Model String Examples	Extra	API Key
Anthropic	"claude-sonnet-4-5", "claude-haiku-3-5"	synth[anthropic]	ANTHROPIC_API_KEY
OpenAI	"gpt-4o", "gpt-4o-mini"	synth[openai]	OPENAI_API_KEY
Google	"gemini-2.0-flash"	synth[google]	GOOGLE_API_KEY
Ollama	"ollama/llama3", "ollama/mistral"	synth[ollama]	None (local)
AWS Bedrock	"bedrock/claude-sonnet-4-5"	synth[bedrock]	AWS IAM

Custom endpoint:

agent = Agent(model="my-model", base_url="https://my-proxy.example.com/v1")

---

Memory

By default each run() is stateless. Add memory to persist conversations.

Thread memory (in-process, fast):

agent = Agent(model="claude-sonnet-4-5", memory=Memory.thread())

agent.run("My name is Alice.", thread_id="user-123")
result = agent.run("What's my name?", thread_id="user-123")
print(result.text)  # "Your name is Alice."

Persistent memory (Redis, survives restarts):

agent = Agent(model="gpt-4o", memory=Memory.persistent("redis://localhost:6379"))

Semantic memory (vector embeddings, retrieves most relevant context):

agent = Agent(model="gemini-2.0-flash", memory=Memory.semantic(embedder=my_embedder_fn))

---

Guards

Declarative safety rules applied automatically to every run.

from synth import Guard

agent = Agent(
    model="claude-sonnet-4-5",
    guards=[
        Guard.no_pii_output(),             # Block PII in responses
        Guard.max_cost(dollars=0.50),       # Stop if cost exceeds $0.50
        Guard.no_tool_calls(["delete_*"]), # Block tools matching glob
        Guard.custom(my_check_fn),          # Your own check function
    ],
)

Guards run in order. First failure stops execution and raises GuardViolationError.

---

Structured Output

Get typed Pydantic objects back instead of raw text:

from pydantic import BaseModel

class MovieReview(BaseModel):
    title: str
    rating: float
    summary: str
    recommended: bool

agent = Agent(
    model="claude-sonnet-4-5",
    instructions="You are a movie critic.",
    output_schema=MovieReview,
)

result = agent.run("Review the movie Inception.")
review = result.output  # MovieReview instance

print(review.title)        # "Inception"
print(review.rating)       # 9.2
print(review.recommended)  # True

If parsing fails, Synth retries with a corrective prompt up to max_retries times.

---

Pipelines

Chain agents sequentially — output of each becomes input of the next:

from synth import Pipeline

researcher = Agent(model="claude-sonnet-4-5", instructions="You research topics.")
writer = Agent(model="claude-sonnet-4-5", instructions="You write clear articles.")
editor = Agent(model="claude-sonnet-4-5", instructions="You edit for clarity.")

pipeline = Pipeline([researcher, writer, editor])
result = pipeline.run("The history of the internet")

Run stages in parallel with ParallelGroup:

from synth.orchestration.pipeline import ParallelGroup

pipeline = Pipeline([
    writer,
    ParallelGroup([fact_checker, style_checker]),  # Run concurrently
    editor,
])

Stream with stage labels:

for stage_event in pipeline.stream("Write about AI"):
    print(f"[{stage_event.stage_name}] {stage_event.event}")

---

Graphs

Directed-graph workflows with branching, loops, and conditional logic:

from synth import Graph, node

graph = Graph()

@node(graph)
def classify(state):
    state["priority"] = "high" if "urgent" in state["text"].lower() else "low"
    return state

@node(graph)
def handle_urgent(state):
    state["response"] = "Escalating immediately."
    return state

@node(graph)
def handle_normal(state):
    state["response"] = "We'll respond within 24 hours."
    return state

graph.set_entry("classify")
graph.add_edge("classify", "handle_urgent", when=lambda s: s["priority"] == "high")
graph.add_edge("classify", "handle_normal", when=lambda s: s["priority"] == "low")
graph.add_edge("handle_urgent", Graph.END)
graph.add_edge("handle_normal", Graph.END)

result = graph.run({"text": "This is urgent! Server is down!"})
print(result.output["response"])

Loops are supported. Synth enforces max_iterations=100 by default to prevent infinite loops.

Visualize your graph:

print(graph.visualise())  # Outputs a Mermaid diagram

---

Human-in-the-Loop

Pause a graph at specific nodes for human review before continuing:

graph.with_human_in_the_loop(pause_at=["draft_email"], timeout=3600)
graph.with_checkpointing()

result = graph.run({"customer": "Alice"}, run_id="email-001")
# result is a PausedRun — inspect result.state["draft"] here

final = graph.resume("email-001", human_input="Looks good, send it.")

---

Agent Teams

Coordinate multiple specialized agents under an orchestrator:

from synth import AgentTeam

team = AgentTeam(
    orchestrator="claude-sonnet-4-5",
    agents=[researcher, writer, analyst],
    strategy="auto",   # orchestrator decides who does what
)

result = team.run("Write a report on renewable energy trends.")
print(result.answer)
print(result.contributions)   # Each agent's individual contribution
print(result.total_cost)

Use strategy="parallel" to run all agents concurrently.

---

Tracing

Every run automatically records a detailed trace:

result = agent.run("Summarize this document.")
trace = result.trace

print(f"Tokens: {trace.total_tokens}")
print(f"Cost: ${trace.total_cost:.4f}")
print(f"Latency: {trace.total_latency_ms:.1f}ms")

result.trace.show()                    # Open visual timeline in browser
path = result.trace.export()           # Export as OpenTelemetry JSON

Auto-forward all traces to an OTel collector:

export SYNTH_TRACE_ENDPOINT="https://my-otel-collector.example.com/v1/traces"

---

Checkpointing

Save and resume graph execution state:

graph.with_checkpointing()
result = graph.run(initial_state, run_id="my-run-001")

# Later, even in a different process
result = graph.resume("my-run-001")

Redis backend for distributed systems:

from synth.checkpointing.redis import RedisCheckpointStore

graph.with_checkpointing(store=RedisCheckpointStore("redis://localhost:6379"))

---

Evaluation

Run structured tests against your agent:

from synth import Eval

evaluation = Eval(agent=agent)
evaluation.add_case(input="Capital of France?", expected="Paris")
evaluation.add_case(input="Capital of Japan?", expected="Tokyo")

report = evaluation.run()
print(f"Score: {report.overall_score}")

for case in report.cases:
    status = "PASS" if case.passed else "FAIL"
    print(f"  [{status}] {case.input} → {case.actual}")

Custom checker:

def contains_keyword(output: str, expected: str) -> float:
    return 1.0 if expected.lower() in output.lower() else 0.0

evaluation.add_case(input="Explain photosynthesis.", expected="chlorophyll", checker=contains_keyword)

---

CLI Commands

Run synth with no arguments to launch the interactive shell:

synth

synth> run agent.py "Hello"
synth> create agent my-bot
synth> doctor
synth> exit

All commands also work directly:

synth init                                  # Interactive project setup wizard
synth create agent my-bot                   # Scaffold an agent project
synth create agent my-bot -p openai         # Skip prompt, use OpenAI
synth create agentcore my-service           # AWS AgentCore project
synth create team my-team                   # Multi-agent team + pipeline
synth create tool my-tools                  # Standalone tools file
synth create mcp my-server                  # MCP server with FastMCP
synth create ui my-ui                       # Local browser testing dashboard
synth dev my_agent.py                       # Rich terminal UI with hot-reload
synth run my_agent.py "prompt"              # Execute agent, print result
synth bench my_agent.py "prompt" --runs 20  # Benchmark latency/cost
synth eval my_agent.py --dataset cases.json # Run evaluation suite
synth trace <run_id>                        # Open trace in browser
synth deploy --target agentcore             # Deploy to AWS AgentCore
synth deploy --target agentcore --dry-run   # Validate without deploying
synth edit agent agent.py                   # Modify existing agent config
synth doctor                                # Check env, credentials, deps
synth info --extra anthropic                # Show package info
synth help                                  # Quick reference card

synth init

The fastest way to start a new project. Walks you through:

1. Project type — single agent or multi-agent
2. Project name and description
3. Provider selection (anthropic, openai, google, ollama, bedrock, agentcore)
4. Model selection (region-aware for AgentCore with Bedrock model catalog)
5. Agent instructions
6. Tool Wizard — pick pre-built tools or scaffold custom @tool stubs
7. MCP Wizard — pick pre-built MCP servers or scaffold custom @mcp.tool() stubs
8. Feature toggles (memory, guards, structured output, eval, deploy)
9. Credential check (AgentCore only)
10. Summary and confirmation
11. Project generation
12. Optional "Deploy now?" prompt (AgentCore only)
13. Testing mode — launch the browser UI dashboard or the interactive CLI

Multi-Agent Projects

When you select multi at the project type prompt, the wizard guides you through:

• Agent count (minimum 2) with per-agent configuration (name, description, provider, model, instructions, tools, MCP)
• Orchestration pattern selection with descriptions:
  • Pipeline — linear sequential chaining, each agent receives the previous agent's output
  • Graph — directed graph with conditional edges, branching, and loops
  • AgentTeam — orchestrator routes tasks to specialized agents (auto or parallel strategy)
  • Human-in-the-Loop — graph with pause/resume checkpoints for human review
• Pattern-specific configuration (execution order, edges, strategy, pause nodes, etc.)
• Feature selection, summary, and project generation

Generated multi-agent project structure:

my-project/
├── agent_researcher.py    # Individual agent files
├── agent_writer.py
├── main.py                # Orchestration wiring (Pipeline/Graph/Team/HITL)
├── tools_researcher.py    # Per-agent tool files (if configured)
├── README.md
├── synth.toml
└── ui/                    # Testing dashboard (if UI mode selected)
    ├── server.py
    └── static/

Single-Agent Projects

Generated project structure:

my-agent/
├── agent.py           # Your agent with selected provider, tools, and features
├── README.md          # Project-specific docs with run instructions
├── synth.toml         # Project configuration
├── tools.py           # Custom tool stubs (if tools selected)
├── mcp_server.py      # MCP server stubs (if MCP selected)
├── eval_dataset.json  # Evaluation cases (if eval selected)
├── eval_config.json   # AgentCore Evaluations config (AgentCore + eval only)
├── agentcore.yaml     # AWS config (AgentCore projects only)
└── .env.template      # Environment variable template (AgentCore only)

The testing UI (if selected) is scaffolded at the workspace root, shared across all agents:

workspace/
├── my-agent/          # Agent project
├── another-agent/     # Another agent project
└── ui/                # Shared testing dashboard
    ├── server.py
    └── static/

For AgentCore projects, synth init also:

• Auto-detects AWS credentials (env vars → ~/.aws/credentials → AWS Toolkit profiles)
• Prompts for target AWS region (default: us-east-1)
• Shows Bedrock models available in that region
• Writes aws_region, model_id, cris_enabled, and aws_profile to agentcore.yaml

Common patterns:

synth init                          # Full interactive wizard
synth init && cd my-agent && synth dev agent.py   # Init + start developing

synth dev

Rich terminal UI for interactive development:

synth dev my_agent.py

When run without a file argument, synth dev scans the workspace for agent files and presents an interactive picker. For agents with an agentcore.yaml, it checks live deployment status against the AWS account and shows color-coded badges (active, creating, failed). If the selected agent isn't deployed yet, you'll be prompted to deploy before opening the REPL.

Features: streaming token-by-token output, tool call visualization, slash commands (/tools, /reload, /trace, /export, /clear, /cost, /quit), markdown rendering, status bar with live cost/token tracking.

synth create ui

Scaffold a full-featured browser-based testing dashboard:

synth create ui my-dashboard
cd my-dashboard
pip install uvicorn fastapi
python server.py
# Open http://localhost:8420

The dashboard includes:

• Streaming chat with SSE, thinking block support, and markdown rendering
• Conversation management with persistence, multiple threads, and export
• Telemetry panel with per-response and session-level tokens, cost, latency, and cost-per-turn sparkline
• Tool playground to test individual tools with custom arguments
• Prompt library with versioning, notes, and variable injection ({{variable}} syntax)
• A/B testing to compare two prompt variants side-by-side with diff view
• Eval runner with keyword scoring, LLM judge, golden baselines, and regression detection
• Session replay with timeline view, token usage heatmap, and anomaly detection (slow, expensive, or short responses)
• Scenario builder for scripted multi-turn conversations
• AgentCore Evaluations panel showing evaluator scores, config status, and on-demand evaluation (when configured)
• Hot-reload to pick up agent changes without restarting the server

The UI is also scaffolded automatically when you choose ui as the testing mode during synth init. The UI is created once at the workspace root and shared across all agents — subsequent synth init runs detect the existing UI and reuse it. If the server is already running, you'll just see the URL. UI dependencies (uvicorn, fastapi) are auto-installed if missing.

synth deploy

Guided six-stage deployment wizard:

synth deploy --target agentcore my_agent.py
synth deploy --target agentcore --dry-run my_agent.py  # Stages 1–4 only

Stages: credential validation → dependency check → file validation → manifest generation → artifact packaging → deployment readiness → AgentCore API submission. Each prints [ OK ] or [FAIL] with a corrective suggestion on failure.

The readiness stage reports on auth method, memory backend, guards, tools, search API keys, and target region/model — with warnings for any missing components.

synth edit agent

Interactively modify an existing agent without editing files manually:

synth edit agent agent.py

Menu options: (a) instructions, (b) model, (c) tools, (d) MCP servers. Shows a diff before writing. Uses atomic temp-file rename to prevent corruption.

synth doctor

synth doctor

Checks: Python version, core dependencies, provider API keys, SYNTH_TRACE_ENDPOINT format, optional provider packages, and (when agentcore.yaml is present) AgentCore config fields (aws_region, model_id, cris_enabled, aws_profile).

synth bench

synth bench my_agent.py "Hello" --runs 20 --warmup 2

Reports p50/p95/p99 latency, average tokens, cost per run, and success rate.

---

Deploying to AWS AgentCore

Prerequisites

Install the AgentCore extra:

pip install synth-agent-sdk[agentcore]

You also need working AWS credentials on your machine. Set them up using one of these methods:

Option A — AWS CLI (recommended for most users):

# Install the AWS CLI
# macOS
brew install awscli

# Windows
winget install Amazon.AWSCLI

# Linux
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip && sudo ./aws/install

# Then configure your credentials
aws configure
# Enter your Access Key ID, Secret Access Key, default region, and output format

Option B — AWS IAM Identity Center (SSO):

aws configure sso
# Follow the prompts to set up SSO with your organization's identity provider
aws sso login --profile your-profile

Option C — AWS Toolkit for VS Code / JetBrains:

If you use an IDE with the AWS Toolkit extension, it manages credentials through its own auth flow (Builder ID or IAM Identity Center). Synth picks up these credentials automatically via the shared AWS credential chain.

Verify your credentials:

aws sts get-caller-identity
# Should print your account ID, user ARN, and user ID

synth doctor
# Checks AWS credentials and AgentCore config

For AgentCore deployments, your IAM role needs permissions for Bedrock model invocation and AgentCore API access. Check with your AWS administrator if synth deploy fails with access denied errors.

Wrapping Your Agent

from synth import Agent
from synth.deploy.agentcore import agentcore_handler

agent = Agent(
    model="bedrock/claude-sonnet-4-5",
    instructions="You are a customer support agent.",
    tools=[lookup_order, check_inventory],
)

app = agentcore_handler(agent)

Deploy

synth deploy --target agentcore --dry-run   # Validate first
synth deploy --target agentcore             # Deploy

The packager automatically excludes .env files, credential files, and .synth/checkpoints/ from the artifact. It also scans agentcore.yaml for accidental credential patterns and aborts if any are found.

Secure User Identity

from synth.deploy.agentcore import extract_user_id

user_id = extract_user_id(context)  # Extracts from signed JWT in RequestContext

Gateway MCP Client

from synth.deploy.agentcore import create_gateway_client

client = create_gateway_client(
    gateway_url="https://my-gateway.example.com",
    client_id_param="/myapp/gateway/client_id",
    client_secret_param="/myapp/gateway/client_secret",
)
mcp_client = client.as_mcp_client()

Code Interpreter

from synth.deploy.agentcore import CodeInterpreterTools

ci = CodeInterpreterTools()
result = ci.execute_python("import math; print(math.sqrt(144))")
print(result)  # "12.0"

Browser Tool

Search the web and navigate pages using AgentCore's managed Chrome browser — no third-party API keys needed:

from synth.deploy.agentcore import BrowserTools
from synth import tool

browser = BrowserTools(region="us-west-2")

@tool
def search_web(query: str) -> str:
    """Search the web for information."""
    return browser.search(query)

@tool
def browse_page(url: str) -> str:
    """Navigate to a URL and extract its content."""
    return browser.navigate(url)

agent = Agent(model="bedrock/claude-sonnet-4-5", tools=[search_web, browse_page])

Built-in Web Search (API-based)

For lighter-weight search without a browser session, use the built-in web_search tool with a search API key:

from synth.tools import web_search

agent = Agent(model="claude-sonnet-4-5", tools=[web_search])

Supports BRAVE_API_KEY, SERPAPI_API_KEY, or TAVILY_API_KEY — auto-detects whichever is set.

AgentCore Memory

Memory is automatically configured when deploying to AgentCore. The adapter wraps your agent with AgentCoreMemory, which stores and retrieves conversation history via the AgentCore events API. No manual setup required — just ensure AGENTCORE_MEMORY_ENDPOINT and AGENTCORE_MEMORY_ID are set in your deployment environment.

# Memory works automatically in AgentCore deployments.
# For explicit configuration:
from synth.deploy.agentcore import AgentCoreMemory

agent = Agent(
    model="bedrock/claude-sonnet-4-5",
    memory=AgentCoreMemory(memory_id="mem-abc123"),
)

SSM Config

from synth.deploy.agentcore import get_ssm_parameter

db_url = get_ssm_parameter("/myapp/prod/db_url")
api_key = get_ssm_parameter("/myapp/prod/api_key", decrypt=True)

---

AgentCore Evaluations

Synth integrates with AgentCore's Evaluations service for continuous agent quality monitoring. When you run synth init with the AgentCore provider and enable the "eval" feature, the wizard generates everything you need.

What Gets Generated

• eval_config.json — Online evaluation configuration with three built-in evaluators (Helpfulness, Correctness, GoalSuccessRate) at a 1.0 sampling rate
• agentcore.yaml — Updated with an evaluations section and the required IAM permissions
• eval_dataset.json — Local evaluation dataset (also available for non-AgentCore providers)
• Agent code comment referencing the eval config

Built-in Evaluators

Evaluator	Level	What It Measures
Builtin.Helpfulness	TRACE	Whether the agent's response is helpful and relevant
Builtin.Correctness	TRACE	Factual accuracy of the agent's response
Builtin.GoalSuccessRate	SESSION	Whether the agent achieved the user's goal

Dashboard Integration

When evaluations are configured, the Dashboard's AgentCore tab shows an Evaluations sub-section with:

• Summary table of most recent evaluator scores (scores below 0.5 are flagged)
• Online evaluation config status (active/disabled, sampling rate, evaluator list)
• "Run Evaluation" button for on-demand evaluation against the most recent session

API Endpoints

Endpoint	Method	Description
/api/agentcore/evaluations	GET	Fetch evaluation scores
/api/agentcore/evaluations/run	POST	Trigger on-demand evaluation
/api/agentcore/evaluations/config	GET	Get evaluation config status

All evaluation endpoints apply credential scrubbing to response data.

---

Error Handling

All Synth errors inherit from SynthError and include component and suggestion fields.

Error	When
SynthConfigError	Missing API key, invalid model, missing provider package
ToolDefinitionError	@tool missing type annotations or docstring
ToolExecutionError	Tool function raised an exception
GuardViolationError	A guard check failed
CostLimitError	Cost guard limit exceeded
SynthParseError	Structured output couldn't be parsed after retries
GraphRoutingError	No edge condition matched at a graph node
GraphLoopError	Graph exceeded max_iterations
RunNotFoundError	No checkpoint found for the given run_id
PipelineError	A pipeline stage failed

from synth.errors import SynthConfigError, ToolExecutionError, GuardViolationError

try:
    result = agent.run("Do something risky.")
except GuardViolationError as e:
    print(f"Guard '{e.guard_name}' blocked: {e.remediation}")
except ToolExecutionError as e:
    print(f"Tool '{e.tool_name}' failed: {e.original_error}")
except SynthConfigError as e:
    print(f"Config issue in {e.component}: {e.suggestion}")

---

Environment Variables

Variable	Purpose	Required?
ANTHROPIC_API_KEY	Anthropic Claude API key	Only for claude-* models
OPENAI_API_KEY	OpenAI GPT API key	Only for gpt-* models
GOOGLE_API_KEY	Google Gemini API key	Only for gemini-* models
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY	AWS credentials for Bedrock	Only for bedrock/* (or use IAM)
SYNTH_TRACE_ENDPOINT	HTTPS URL of an OTel collector	No
SYNTH_NO_BANNER	Set to 1 to skip the boot sequence	No
NO_COLOR	Disable colored terminal output	No

---

FAQ

Do I need an API key? Yes, for cloud models. Ollama runs locally and needs no key.

Can I use Synth in Jupyter? Yes. Synth detects an existing event loop and handles it automatically.

How do I switch models? Change the model string. Install the matching extra and set the API key.

What if the provider is down? Synth retries on HTTP 429 and 5xx with exponential backoff. Configure with max_retries and retry_backoff.

Can I use multiple models in one app? Yes. Each Agent has its own model. Use synth init with the multi project type to scaffold a multi-agent project with orchestration built in.

How do I test my agent in a browser? Run synth create ui my-dashboard or choose ui as the testing mode during synth init. This gives you a full dashboard with streaming chat, telemetry, prompt library, A/B testing, evals, session replay, and scenario builder at http://localhost:8420.

How do I debug what my agent is doing? Use result.trace.show() for a visual timeline, or synth dev my_agent.py for an interactive terminal UI with /trace command.

Is my data secure? Synth never logs or serializes API keys. Guards run before side-effecting operations. Checkpoints use JSON only. All provider calls use HTTPS.

What are the core dependencies? pydantic, httpx, click, typing-extensions, rich, prompt-toolkit. Provider SDKs are optional extras.

---

License

MIT