jetflow 1.0.75

Lightweight, production-ready framework for building agentic workflows with LLMs

Jetflow

Build agents that actually work in production.

LLM agents fail in predictable ways: they hallucinate tool calls, bloat context until they forget instructions, and cost 10x what you budgeted. Jetflow fixes this with typed actions, deterministic exits, and full cost visibility.

from jetflow import Agent, action
from jetflow.clients.openai import OpenAIClient
from jetflow.actions.serper_web_search import SerperWebSearch
from pydantic import BaseModel

class Report(BaseModel):
    """Final research report with citations"""
    headline: str
    findings: list[str]
    sources: list[str]

@action(schema=Report, exit=True)
def publish(r: Report) -> str:
    findings = "\n".join(f"• {f}" for f in r.findings)
    refs = "\n".join(f"[{i+1}] {s}" for i, s in enumerate(r.sources))
    return f"# {r.headline}\n\n{findings}\n\n---\nSources:\n{refs}"

agent = Agent(
    client=OpenAIClient(model="gpt-4o"),
    actions=[SerperWebSearch(), publish],
    system_prompt="Research thoroughly. Cite every claim. Publish when done.",
    require_action=True  # Must exit via publish()
)

resp = agent.run("What are the pricing implications of OpenAI's recent enterprise deals?")
print(resp.content)
print(f"Cost: ${resp.usage.estimated_cost:.4f}")

30 seconds to a research agent with web search, citations, and cost tracking.

---

Why Jetflow

Multi-agent without the complexity.

You want to build sophisticated agent systems. You've looked at:

• LangGraph — State machines, nodes, edges, conditional routing... just to call two models in sequence
• CrewAI — "Crews", "tasks", "processes"... a whole new vocabulary for what should be function calls
• Custom code — Works, but you're reinventing message handling, cost tracking, streaming every time

Jetflow gives you one mental model: agents are functions.

# An agent IS a function: input → output
resp = agent.run("Research NVIDIA earnings")

# Wrap it as a tool for another agent
@action(schema=ResearchQuery)
def research(q: ResearchQuery) -> str:
    return researcher.run(q.query).content

# Now the parent agent can call it
analyst = Agent(actions=[research, done])

That's it. No graphs. No crews. No orchestration layer. Just composition.

What you get:

• Agents as tools — Any agent can call any other agent. Nest them, chain them, fan them out.
• Deterministic exits — require_action=True + exit action = guaranteed structured output
• Full visibility — Every message, every tool call, every cost in resp.messages and resp.usage
• Provider-agnostic — OpenAI, Anthropic, Gemini, Grok, Groq with identical APIs

---

Install

pip install jetflow[openai]      # OpenAI
pip install jetflow[anthropic]   # Anthropic
pip install jetflow[gemini]      # Google Gemini
pip install jetflow[e2b]         # Cloud code execution
pip install jetflow[all]         # Everything

export OPENAI_API_KEY=sk-...
export SERPER_API_KEY=...        # For web search
export E2B_API_KEY=...           # For cloud execution

---

Real Examples

Research Agent with Citations

Search the web, synthesize findings, cite sources:

from jetflow import Agent, action
from jetflow.clients.openai import OpenAIClient
from jetflow.actions.serper_web_search import SerperWebSearch
from pydantic import BaseModel

class ResearchReport(BaseModel):
    """Structured research output"""
    summary: str
    key_findings: list[str]
    sources: list[str]

@action(schema=ResearchReport, exit=True)
def finished(r: ResearchReport) -> str:
    findings = "\n".join(f"• {f}" for f in r.key_findings)
    refs = "\n".join(f"[{i+1}] {s}" for i, s in enumerate(r.sources))
    return f"{r.summary}\n\n{findings}\n\nSources:\n{refs}"

agent = Agent(
    client=OpenAIClient(model="gpt-4o"),
    actions=[SerperWebSearch(), finished],
    system_prompt="Search for current information. Cite every claim.",
    require_action=True
)

resp = agent.run("What's the current state of AI regulation in the EU?")

Data Analysis with Cloud Execution

Run Python in a sandboxed cloud environment with pre-loaded data:

from jetflow import Agent
from jetflow.clients.openai import OpenAIClient
from jetflow.actions.e2b_python_exec import E2BPythonExec, S3Storage

agent = Agent(
    client=OpenAIClient(model="gpt-4o"),
    actions=[E2BPythonExec(
        storage=S3Storage(
            bucket="market-data",
            access_key_id="AKIA...",
            secret_access_key="..."
        ),
        embeddable_charts=True
    )],
    system_prompt="You are a quantitative analyst. Data is in /home/user/bucket/"
)

resp = agent.run("Load returns.parquet and plot risk-adjusted performance for 2024")
# Charts automatically extracted, code preserved in transcript

Multi-Agent: Fast Scout + Deep Analyst

Use cheap models to gather data, expensive models to reason:

from jetflow import Agent, action
from jetflow.clients.openai import OpenAIClient
from jetflow.actions.serper_web_search import SerperWebSearch
from pydantic import BaseModel, Field

# --- Scout Agent (fast, cheap) ---
class ScoutFindings(BaseModel):
    facts: list[str]
    sources: list[str]

@action(schema=ScoutFindings, exit=True)
def scout_done(f: ScoutFindings) -> str:
    return "\n".join(f.facts) + "\n\nSources: " + ", ".join(f.sources)

scout = Agent(
    client=OpenAIClient(model="gpt-4o-mini"),  # Fast and cheap
    actions=[SerperWebSearch(), scout_done],
    system_prompt="Gather facts quickly. Don't analyze, just collect.",
    require_action=True
)

# --- Wrap scout as a tool for the analyst ---
class Research(BaseModel):
    """Gather information on a topic"""
    query: str = Field(description="What to research")

@action(schema=Research)
def research(r: Research) -> str:
    scout.reset()
    return scout.run(r.query).content

# --- Analyst Agent (powerful, thorough) ---
class Analysis(BaseModel):
    headline: str
    insights: list[str]
    recommendation: str

@action(schema=Analysis, exit=True)
def analysis_done(a: Analysis) -> str:
    return f"# {a.headline}\n\n" + "\n".join(f"• {i}" for i in a.insights) + f"\n\n**Recommendation:** {a.recommendation}"

analyst = Agent(
    client=OpenAIClient(model="gpt-4o"),  # Powerful reasoning
    actions=[research, analysis_done],
    system_prompt="Use research tool to gather data. Synthesize insights. Be precise.",
    require_action=True
)

resp = analyst.run("Compare AWS vs GCP pricing for GPU instances in 2024")
print(resp.content)
print(f"Total cost: ${resp.usage.estimated_cost:.4f}")

Sequential Chain: Shared Context Pipeline

When agents need to build on each other's work:

from jetflow import Agent, Chain
from jetflow.clients.openai import OpenAIClient
from jetflow.actions.serper_web_search import SerperWebSearch

# Agent 1: Research
researcher = Agent(
    client=OpenAIClient(model="gpt-4o-mini"),
    actions=[SerperWebSearch()],
    system_prompt="Find relevant information. Be thorough."
)

# Agent 2: Analyze (sees researcher's output)
analyst = Agent(
    client=OpenAIClient(model="gpt-4o"),
    actions=[],  # Pure reasoning
    system_prompt="Analyze the research above. Identify patterns and insights."
)

# Agent 3: Write (sees both previous outputs)
writer = Agent(
    client=OpenAIClient(model="gpt-4o"),
    actions=[],
    system_prompt="Write a clear, concise summary for executives."
)

chain = Chain([researcher, analyst, writer])
resp = chain.run("What's driving the recent surge in AI infrastructure spending?")

---

Core Concepts

Actions = Typed Tools

Every action has a Pydantic schema. The LLM can't hallucinate invalid parameters:

from pydantic import BaseModel, Field
from jetflow import action

class SearchQuery(BaseModel):
    """Search the web for information"""
    query: str = Field(description="Search query")
    max_results: int = Field(default=5, ge=1, le=20)

@action(schema=SearchQuery)
def search(params: SearchQuery) -> str:
    # Your implementation
    return results

Exit Actions = Deterministic Completion

Use exit=True to force structured output:

class FinalAnswer(BaseModel):
    answer: str
    confidence: float
    reasoning: str

@action(schema=FinalAnswer, exit=True)
def done(f: FinalAnswer) -> str:
    return f.answer

agent = Agent(
    client=client,
    actions=[search, done],
    require_action=True  # MUST call an exit action
)

Cost Tracking

Every response includes usage:

resp = agent.run("...")
print(f"Prompt tokens: {resp.usage.prompt_tokens}")
print(f"Completion tokens: {resp.usage.completion_tokens}")
print(f"Estimated cost: ${resp.usage.estimated_cost:.4f}")

Streaming

Real-time updates for UI:

for event in agent.stream("Analyze this data"):
    if isinstance(event, ContentDelta):
        print(event.delta, end="", flush=True)
    elif isinstance(event, ActionExecuted):
        print(f"\n[Executed: {event.name}]")

---

Built-in Actions

Action	What It Does
SerperWebSearch()	Web search with citation tracking
E2BPythonExec()	Cloud Python sandbox with chart extraction
LocalPythonExec()	Local sandboxed Python execution

---

Supported Providers

from jetflow.clients.openai import OpenAIClient
from jetflow.clients.anthropic import AnthropicClient
from jetflow.clients.gemini import GeminiClient
from jetflow.clients.grok import GrokClient
from jetflow.clients.groq import GroqClient

All clients support streaming with consistent event semantics.

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Docs

• Quickstart — 5-minute tutorial
• E2B Code Execution — Cloud Python sandbox
• API Reference — Full API docs

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License

MIT © 2025 Lucas Astorian