fastaiagent 0.1.0a4

Build, debug, evaluate, and operate AI agents. The only SDK with fork-and-rerun Agent Replay.

FastAIAgent SDK

Build, debug, evaluate, and operate AI agents. The only SDK with Agent Replay — fork-and-rerun debugging for AI agents.

Works standalone or connected to the FastAIAgent Platform for visual editing, production monitoring, and team collaboration.

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Debug a failing agent in 30 seconds

from fastaiagent.trace import Replay

# Load a trace from a production failure
replay = Replay.load("trace_abc123")

# Step through to find the problem
replay.step_through()
# Step 3: LLM hallucinated the refund policy ← found it

# Fork at the failing step, fix, rerun
forked = replay.fork_at(step=3)
forked.modify_prompt("Always cite the exact policy section...")
result = forked.rerun()

No other SDK can do this.

Evaluate agents systematically

from fastaiagent.eval import evaluate

results = evaluate(
    agent_fn=my_agent.run,
    dataset="test_cases.jsonl",
    scorers=["correctness", "relevance"]
)
print(results.summary())
# correctness: 92% | relevance: 88%

Trace any agent — yours or LangChain/CrewAI

import fastaiagent
fastaiagent.integrations.langchain.enable()

# Your existing LangChain agent, now with full tracing
result = langchain_agent.invoke({"input": "..."})
# → Traces stored locally or pushed to FastAIAgent Platform

Build agents with guardrails and cyclic workflows

from fastaiagent import Agent, Chain, LLMClient, Guardrail
from fastaiagent.guardrail import no_pii, json_valid

agent = Agent(
    name="support-bot",
    system_prompt="You are a helpful support agent...",
    llm=LLMClient(provider="openai", model="gpt-4o"),
    tools=[search_tool, refund_tool],
    guardrails=[no_pii(), json_valid()]
)

# Chains with loops (retry until quality is good enough)
chain = Chain("support-pipeline", state_schema=SupportState)
chain.add_node("research", agent=researcher)
chain.add_node("evaluate", agent=evaluator)
chain.add_node("respond", agent=responder)
chain.connect("research", "evaluate")
chain.connect("evaluate", "research", max_iterations=3, exit_condition="quality >= 0.8")
chain.connect("evaluate", "respond", condition="quality >= 0.8")

result = chain.execute({"message": "My order is late"}, trace=True)

Connect to FastAIAgent Platform (optional)

from fastaiagent import FastAI

fa = FastAI(api_key="sk-...", project="customer-support")

# Push your agent to the platform — see it in the visual editor
fa.push(chain)

# Traces appear in the platform dashboard
# Prompts sync between code and platform
# Eval results visible in the platform

SDK works standalone. Platform adds: visual chain editor, production monitoring, advanced KB intelligence, prompt optimization, team collaboration, HITL approval workflows.

Free tier available →

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Install

pip install fastaiagent

With optional integrations:

pip install "fastaiagent[openai]"       # OpenAI auto-tracing
pip install "fastaiagent[langchain]"    # LangChain auto-tracing
pip install "fastaiagent[kb]"           # Local knowledge base
pip install "fastaiagent[all]"          # Everything

Documentation

• Getting Started
• Agent Replay Guide
• Building Chains with Cycles
• Guardrails
• Evaluation
• API Reference

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

Apache 2.0 — see LICENSE.