oTTomator Agents
github.com/coleam00/ottomator-agents- Category
- AI Agents
- Rank
- No. 984Tools index
- Pricing
- Open Source
- Type
- AGENT
- Builder
- coleam00
- GitHub
- 5.8k stars
- Date
About
Cole Medin's open-source collection of AI agents hosted on the oTTomator Live Agent Studio — production-grade reference implementations.
What it does
A code-first gallery of task-specific assistants spanning document search, web research, file chat, social drafting, video summaries, and tabletop support. Most accept a prompt through an HTTP endpoint or terminal session, call a language model plus external tools, and persist conversation data when needed.
Why it's ranked here
Worth attention as a practical pattern library, especially for comparing several agent architectures in one place. The examples cover FastAPI, Flask, command-line chat, retrieval, MCP-backed search, and agent-to-agent requests. Quality and maturity vary, so treat them as instructive starting points rather than interchangeable finished services.
What's good
The collection shows concrete end-to-end plumbing. Examples authenticate requests, preserve conversation history, stream terminal output, cite retrieved documents, ingest file context, and connect models to search or vector databases. Several agents separate dependencies from prompts and tool logic, making their execution model easier to understand and adapt.
Tradeoffs
Running these agents requires substantial external setup, including model keys, search credentials, Supabase, PostgreSQL, ChromaDB, YouTube services, or Reddit credentials depending on the example. Hosted use usually costs tokens, and the studio is described as beta with slower responses under load. Security posture varies, with multiple services allowing all CORS origins.
How to use it well
Best for engineers learning agent integration patterns or assembling a prototype from a close example. Pick one use case, trace its request, model, tool, and persistence flow, then replace its credentials and storage assumptions deliberately. It does not provide a single uniform framework, managed deployment layer, or dependency-free local experience.
Technical notes+
Most supplied implementations are Python services. TinyDM-agent/main.py, file-agent/file_agent.py, python-local-ai-agent/main.py, and tweet-generator-agent/main.py expose FastAPI endpoints with bearer authentication and Supabase-backed messages. google-a2a-agent/server.py uses Flask, serves /.well-known/agent.json, accepts /tasks/send, and launches Brave Search through MCPServerStdio. docling-rag-agent/cli.py combines PydanticAI with asyncpg and a PostgreSQL match_chunks query. crawl4AI-agent-v2/rag_agent.py and crawl4AI-agent-v2/utils.py implement retrieval over persistent ChromaDB with SentenceTransformer embeddings. pydantic-github-agent/cli.py and ai-agent-fundamentals/agent.py provide terminal interfaces. ag-ui-rag-agent/next.config.ts indicates at least one Next.js component.
Observed
- Primary language
- Python dominates the supplied agent implementations; one supplied configuration file uses TypeScript.
- Interfaces
- The repository includes HTTP APIs, interactive command-line interfaces, an A2A task endpoint, and an MCP-backed agent.
- Web frameworks
- Several services use FastAPI, while the A2A search agent uses Flask.
- Persistence
- Examples use Supabase, PostgreSQL with vector matching, and persistent ChromaDB.
- Model surface
- Supplied agents call OpenAI-compatible models, Google Gemini, OpenRouter, and a local OpenAI-compatible endpoint.
- License signal
- The README calls all agents open source, while its footer states all rights reserved; no license file was included in the supplied text.
Read from README.md, TinyDM-agent/main.py, docling-rag-agent/cli.py, TinyDM-agent/database.py, file-agent/file_agent.py, google-a2a-agent/server.py, crawl4AI-agent-v2/utils.py, pydantic-github-agent/cli.py, ask-reddit-agent/ai_agent.py, python-local-ai-agent/main.py, tweet-generator-agent/main.py, youtube-summary-agent/main.py, ag-ui-rag-agent/next.config.ts, ai-agent-fundamentals/agent.py, crawl4AI-agent-v2/rag_agent.py.
What it can do
Deploy AI agents to production environment
AI agent configuration and code → Running AI agent instance
Host multiple AI agents simultaneously
Multiple agent definitions → Live agent studio with running agents
Provide reference implementations for AI agents
Agent requirements and specifications → Production-grade agent code examples
Execute automated tasks through AI agents
Task parameters and data → Completed automated workflow results
Manage agent lifecycle and monitoring
Agent performance metrics and status → Agent health reports and management controls
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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.