ambivo-agents 2.1.1

Lightweight AI agent framework for research synthesis

Ambivo Agents

A lightweight AI agent framework for research synthesis with quality-gated multi-source knowledge gathering.

Why v2.x

v2 is a focused rewrite. We cut commodity agents and their heavy dependencies (Docker, Redis, Playwright) to focus on what's actually unique: quality-gated knowledge synthesis — iteratively consulting multiple sources, assessing response quality, and refining until a threshold is met.

Metric	v1.x	v2.x
Agents	14	7
Core dependencies	12	9
Requires Redis	Yes	No (in-memory default)
Requires Docker	Yes	No
Requires Playwright	Yes (~400 MB browsers)	No (API-based scraping)
Install size	~500 MB	~15 MB

Quick Start

pip install ambivo-agents

import asyncio
from ambivo_agents import AssistantAgent

async def main():
    agent = AssistantAgent.create_simple(user_id="demo")
    response = await agent.chat("Explain quantum computing in 3 sentences.")
    print(response)
    await agent.cleanup_session()

asyncio.run(main())

No Redis. No Docker. No config file. Just works.

Available Agents

Agent	Purpose
AssistantAgent	General conversation and explanations
ModeratorAgent	Intelligent query routing to specialized agents
KnowledgeSynthesisAgent	Multi-source research with quality assessment loops
WebSearchAgent	Web search via Brave/AVES APIs
WebScraperAgent	Content extraction via Jina Reader / Firecrawl / requests+bs4
KnowledgeBaseAgent	Document ingestion and semantic search (Qdrant)
GatherAgent	Conversational form filling with conditional logic

Minimal Configuration

The only required variable is one LLM provider API key. Everything else has sensible defaults.

# Required — at least one
export AMBIVO_AGENTS_OPENAI_API_KEY="sk-..."
export AMBIVO_AGENTS_ANTHROPIC_API_KEY="sk-ant-..."

That's it. You can now create any agent and call .chat().

Optional: Enable web search and scraping

# Web search (optional — enables WebSearchAgent)
export AMBIVO_AGENTS_BRAVE_API_KEY="..."
export AMBIVO_AGENTS_ENABLE_WEB_SEARCH="true"

# Web scraping (optional — Jina Reader works without a key)
export AMBIVO_AGENTS_ENABLE_WEB_SCRAPING="true"
export AMBIVO_AGENTS_JINA_API_KEY="..."        # optional, higher rate limits
export AMBIVO_AGENTS_FIRECRAWL_API_KEY="..."   # optional, premium scraping

Optional: Knowledge base (requires Qdrant)

pip install ambivo-agents[knowledge]

export AMBIVO_AGENTS_ENABLE_KNOWLEDGE_BASE="true"
export AMBIVO_AGENTS_QDRANT_URL="https://your-cluster.qdrant.tech:6333"
export AMBIVO_AGENTS_QDRANT_API_KEY="..."

Optional: Distributed memory via Redis

pip install ambivo-agents[redis]

export AMBIVO_AGENTS_REDIS_HOST="localhost"
export AMBIVO_AGENTS_REDIS_PORT="6379"
export AMBIVO_AGENTS_REDIS_PASSWORD=""

By default the framework uses an in-memory store. Set Redis variables only for multi-process or distributed deployments.

YAML config alternative (agent_config.yaml)

llm:
  preferred_provider: anthropic
  anthropic_api_key: sk-ant-...
  openai_api_key: sk-...

agent_capabilities:
  enable_web_search: true
  enable_web_scraping: true
  enable_knowledge_base: false

web_search:
  brave_api_key: ...

web_scraping:
  scraping:
    provider: jina   # jina | firecrawl | requests

Core Differentiator: Quality-Gated Synthesis

from ambivo_agents import KnowledgeSynthesisAgent

agent = KnowledgeSynthesisAgent.create_simple(user_id="researcher")
response = await agent.chat("What are the latest advances in quantum error correction?")

The synthesis pipeline:

1. Analyzes the query to select a search strategy
2. Searches the web (Brave) and scrapes relevant pages (Jina Reader)
3. Queries the knowledge base if available
4. Assesses response quality (POOR → FAIR → GOOD → EXCELLENT)
5. If below threshold, gathers more sources and refines
6. Returns a synthesized answer with a confidence score

No other mainstream agent framework ships this out of the box.

Agent Creation Patterns

# Simple — recommended
agent = AssistantAgent.create_simple(user_id="user123")
response = await agent.chat("Hello!")
await agent.cleanup_session()

# With explicit context
agent, context = AssistantAgent.create(user_id="user123")
print(f"Session: {context.session_id}")

# ModeratorAgent auto-routes to the best agent
moderator = ModeratorAgent.create_simple(user_id="user123")
response = await moderator.chat("Search for AI news")          # → WebSearchAgent
response = await moderator.chat("Scrape https://example.com")  # → WebScraperAgent

Web Scraping (API-Based)

No browsers, no Docker. Just HTTP APIs with SSRF protection:

Provider	Cost	JS rendering	Default
Jina Reader	Free tier	Yes	Yes
Firecrawl	Paid	Yes	No
requests+bs4	Free	No	Offline fallback

from ambivo_agents import WebScraperAgent

scraper = WebScraperAgent.create_simple(user_id="demo")
response = await scraper.chat("scrape https://example.com")

All scraped URLs are validated to block SSRF attacks against private IPs, loopback addresses, and cloud metadata services.

Optional Extras

pip install ambivo-agents[redis]       # Distributed memory (Redis + lz4)
pip install ambivo-agents[aws]         # AWS Bedrock LLM support
pip install ambivo-agents[knowledge]   # Knowledge base (Qdrant + LlamaIndex, Python 3.11-3.12)
pip install ambivo-agents[documents]   # Document processing (PDF, DOCX, PPTX)
pip install ambivo-agents[async]       # Async utilities (aiohttp, aiofiles)
pip install ambivo-agents[full]        # All runtime extras
pip install ambivo-agents[all-ml]      # Everything including knowledge base

All Environment Variables

Only set what you need:

Variable	Purpose
AMBIVO_AGENTS_OPENAI_API_KEY	OpenAI API key
AMBIVO_AGENTS_ANTHROPIC_API_KEY	Anthropic API key
AMBIVO_AGENTS_AWS_ACCESS_KEY_ID	AWS Bedrock access key
AMBIVO_AGENTS_AWS_SECRET_ACCESS_KEY	AWS Bedrock secret
AMBIVO_AGENTS_AWS_REGION	AWS region (default: us-east-1)
AMBIVO_AGENTS_LLM_PREFERRED_PROVIDER	openai, anthropic, or bedrock
AMBIVO_AGENTS_LLM_TEMPERATURE	LLM temperature (default: 0.5)
AMBIVO_AGENTS_ENABLE_WEB_SEARCH	Enable WebSearchAgent
AMBIVO_AGENTS_ENABLE_WEB_SCRAPING	Enable WebScraperAgent
AMBIVO_AGENTS_ENABLE_KNOWLEDGE_BASE	Enable KnowledgeBaseAgent
AMBIVO_AGENTS_BRAVE_API_KEY	Brave Search API key
AMBIVO_AGENTS_AVESAPI_API_KEY	AVES Search API key
AMBIVO_AGENTS_SCRAPING_PROVIDER	jina (default), firecrawl, or requests
AMBIVO_AGENTS_JINA_API_KEY	Jina Reader API key (optional)
AMBIVO_AGENTS_FIRECRAWL_API_KEY	Firecrawl API key (optional)
AMBIVO_AGENTS_QDRANT_URL	Qdrant vector DB URL
AMBIVO_AGENTS_QDRANT_API_KEY	Qdrant API key
AMBIVO_AGENTS_REDIS_HOST	Redis host (optional)
AMBIVO_AGENTS_REDIS_PORT	Redis port (default: 6379)
AMBIVO_AGENTS_REDIS_PASSWORD	Redis password
AMBIVO_AGENTS_LOG_LEVEL	Log level (default: INFO)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Format code
black ambivo_agents/ --line-length=100
isort ambivo_agents/ --profile black --line-length=100

Maintainers

• Arnav Gosain — @agosain1

Contributors

• Arnav Gosain (@agosain1)

See the full contributors list on GitHub.

License

MIT License — see LICENSE file.