🦦 OtterFlow

Build production-ready AI agents in 10 lines of Python.

OtterFlow is a minimal, composable framework for building multi-step AI agents powered by Claude. No bloat. No magic. Just agents that work.

from otterflow import Agent
from otterflow.tools import web_search, calculator

agent = Agent(
    name="FinanceBot",
    role="You are a financial research assistant.",
    tools=[web_search, calculator],
)

print(agent.run("What's NVIDIA's latest revenue? Calculate 15% YoY growth."))

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Why OtterFlow?

Most agent frameworks are either toy demos or enterprise nightmares. otterflow is neither.

	OtterFlow	LangChain	AutoGen
Lines to a working agent	~10	~50	~40
Multi-agent orchestration	✅	✅	✅
Built-in business agents	✅	❌	❌
Zero magic / full control	✅	❌	❌
Persistent memory	✅	✅	✅
Custom tools in 3 lines	✅	❌	❌

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Install

pip install otterflow

Then set your Anthropic API key. The easiest way is a .env file in your project root:

cp .env.example .env
# then open .env and paste your key

Or export it directly in your shell:

export ANTHROPIC_API_KEY="your-key-here"

Get a key at console.anthropic.com.

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New to AI agents?

→ Read LEARN.md — a beginner's guide that explains agents, tools, memory, and orchestration in plain English. No jargon. No experience required. Goes from zero to your first working agent in about 20 minutes.

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Core Concepts

1. Agent — the building block

from otterflow import Agent

agent = Agent(
    name="Analyst",
    role="You are a senior business analyst. Be concise and data-driven.",
    verbose=True,   # streams tool call logs to stdout
)

result = agent.run("Summarize the current state of the AI chip market.")
print(result)

2. Streaming — real-time output

agent = Agent("Writer", "You are a sharp business writer.")

for chunk in agent.stream("Write a 3-paragraph brief on the AI chip market."):
    print(chunk, end="", flush=True)

print()  # newline after stream ends

Works with tools too — tool-call steps run silently between turns, and only the final text response is streamed to the caller.

3. Async — non-blocking, production-ready

import asyncio
from otterflow import Agent

agent = Agent("Bot", "You are a research assistant.", tools=[web_search])

# Single async call
result = await agent.arun("What are the top AI infrastructure startups?")

# Run multiple agents concurrently — both fire at the same time
results = await asyncio.gather(
    agent1.arun("Research the US EV market."),
    agent2.arun("Research the EU EV market."),
)

4. Pipe operator — chain agents

from otterflow import Agent
from otterflow.agents import ResearchAgent

researcher = ResearchAgent()
writer = Agent("Writer", "Turn research into a sharp, structured report.")
editor = Agent("Editor", "Tighten copy. Cut filler. Keep it punchy.")

# Build a pipeline with |
pipeline = researcher | writer | editor

# Each agent's output becomes the next agent's input
result = pipeline.run("What's the state of the B2B AI market in 2025?")
print(result)

Async pipelines work the same way:

result = await pipeline.arun("What's the state of the B2B AI market in 2025?")

5. Token tracking — know your costs

agent = Agent("Researcher", "You research topics.", tools=[web_search])
agent.run("Summarize the latest AI chip news.")

print(agent.usage)
# Usage(input=3,241, output=892, ~$0.0231)

print(agent.usage.estimated_cost_usd)   # 0.0231
print(agent.usage.total_tokens)         # 4133

Usage accumulates across all .run() / .arun() / .stream() calls on the same agent instance.

6. Tools — give your agent superpowers

otterflow ships with 7 built-in tools:

from otterflow.tools import (
    web_search,     # searches the web for current info
    read_file,      # reads local files
    write_file,     # writes output to disk (system paths are blocked)
    run_python,     # executes Python snippets in an isolated subprocess
    calculator,     # evaluates math expressions safely
    memory_store,   # stores key-value pairs
    memory_recall,  # recalls stored values
)

agent = Agent(
    name="ResearchBot",
    role="You research topics and save reports.",
    tools=[web_search, write_file],
)

agent.run("Research the top 3 AI coding assistants and save a report to report.md")

7. Custom tools — 3 lines

from otterflow.tools import tool

@tool(description="Fetch the current price of a stock ticker.")
def get_stock_price(ticker: str) -> str:
    import yfinance as yf
    return str(yf.Ticker(ticker).fast_info.last_price)

agent = Agent("Trader", "You track stocks.", tools=[get_stock_price])
agent.run("What's Apple trading at?")

8. Memory — agents that remember

from otterflow import Agent, Memory

memory = Memory(max_turns=20)
memory.remember("client_name", "Acme Corp")
memory.remember("deal_value", "$120k")

agent = Agent("SalesBot", "You are a sales assistant.", memory=memory)

agent.run("Help me prep for tomorrow's renewal call.")
agent.run("What objections should I prepare for given the deal size?")
# ↑ Agent remembers the deal value from the previous turn

9. Multi-agent orchestration

Spawn sub-agents and let a parent orchestrator delegate tasks:

from otterflow import Agent
from otterflow.tools import web_search, write_file

# Specialist agents
researcher = Agent("Researcher", "Deep research specialist.", tools=[web_search])
writer = Agent("Writer", "Turn research into polished reports.", tools=[write_file])

# Orchestrator spawns both as tools it can call
orchestrator = Agent("Boss", "Delegate and synthesize.")
orchestrator.spawn(researcher)
orchestrator.spawn(writer)

# Single call routes through both agents automatically
orchestrator.run(
    "Research the EV market and write a 1-page executive brief to ev_brief.md"
)

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Pre-built Business Agents

Drop these into your app immediately:

from otterflow.agents import (
    ResearchAgent,          # Deep web research + structured reports
    EmailAgent,             # Draft, rewrite, and improve emails
    DataAnalystAgent,       # Reads files, runs Python, surfaces insights
    CompetitiveIntelAgent,  # Competitor battle cards
    ContentCreatorAgent,    # Platform-specific content coaching + rewrites
    BusinessIntelPipeline,  # All of the above, orchestrated
)

ResearchAgent

researcher = ResearchAgent(verbose=True)
report = researcher.run(
    "What are the top 5 B2B SaaS trends for 2025? Focus on AI and pricing."
)

EmailAgent

email_bot = EmailAgent(tone="assertive")
draft = email_bot.run(
    "Write a follow-up to a prospect who went cold after last week's demo."
)

ContentCreatorAgent

coach = ContentCreatorAgent(platform="LinkedIn")

feedback = coach.run("""
    Analyze this post:

    Excited to share that I've been thinking a lot about leadership lately.
    Great leaders listen to their teams. What do you think? Like and follow!
""")

# In the next turn, the agent remembers what it already critiqued
rewrite = coach.run("Now rewrite it using what you just taught me.")

Supports "LinkedIn", "Twitter/X", and "Newsletter" — each with platform-specific rules for hook quality, formatting, engagement triggers, and CTAs.

BusinessIntelPipeline

pipeline = BusinessIntelPipeline(verbose=True)
analysis = pipeline.run(
    "Should I build a B2B AI sales coaching product in 2025? "
    "Analyze market size, competitors, and give me a go/no-go."
)

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Real-world example: content coach with memory

OtterFlow agents remember context across turns — so feedback from turn one informs the rewrite in turn two, just like working with a real editor.

from otterflow import Memory
from otterflow.agents import ContentCreatorAgent

# Load user context into memory
memory = Memory()
memory.remember("author", "Jordan")
memory.remember("niche", "B2B SaaS founders")
memory.remember("goal", "grow LinkedIn to 10k followers in 90 days")

coach = ContentCreatorAgent(platform="LinkedIn", memory=memory)

# First turn: critique
with open("draft_post.txt") as f:
    draft = f.read()

feedback = coach.run(f"Analyze this post for Jordan:\n{draft}")
print(feedback)

# Second turn: agent remembers the draft, the critique, and Jordan's goal
rewrite = coach.run("Now rewrite it.")
print(rewrite)

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Architecture

otterflow/
├── agent.py          # Core Agent class — tool loop, sub-agent spawning
├── memory.py         # Memory — sliding window, persistent facts
├── tools.py          # Tool class, @tool decorator, built-in tools
└── agents/
    └── __init__.py   # Pre-built business agents

The execution loop is deliberately transparent:

run(prompt)
  │
  ├─ inject memory + history into messages
  ├─ call Claude with tool specs
  │
  └─ loop:
      ├─ if stop_reason == "end_turn" → return text
      └─ else → execute tool calls → append results → repeat

No hidden chains. No mysterious abstractions. You can read the entire core in < 200 lines.

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Contributing

PRs welcome. High-value contributions:

• New built-in tools (Slack, email, calendar, Postgres)
• Async support (agent.arun())
• Streaming output
• Token usage tracking
• More pre-built agents

git clone https://github.com/tabato/otterflow
cd otterflow
pip install -e ".[dev]"
pytest tests/

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License

MIT. Use it, fork it, build your startup on it.

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Built with Claude by Anthropic.