Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I need a webhook-based lead enrichment pipeline that enriches incoming leads, stores research notes, and asks a human before outreach. Recommend an orchestration architecture with tradeoffs.

Retrieved evidence:
[S1] tool=n8n
title=n8n: Return more than one data item (deprecated)
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/core-nodes/n8n-nodes-base.respondtowebhook.md
keywords=Workflow, Web, webhook-triggers
excerpt=/// note | Deprecated in 1.22.0 n8n 1.22.0 added support for returning all data items using the **All Incoming Items** option. n8n recommends upgrading to the latest version of n8n, instead of using the workarounds described in this section. /// The Respond to Webhook node runs once, using the first incoming data item. This includes when using expressions. You can't force looping using the Loop node: the workflow wi...

[S2] tool=Chroma
title=Chroma: How Chroma Sync Works
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/overview.mdx
keywords=Pipeline Architecture, Hybrid Search, Search, API, vector-database, api-automation, semantic-search
excerpt=Sync runs the same pipeline regardless of source: - **Managed ingestion.** Connect a source once; every invocation runs through Chroma's queue-based pipeline with automatic retries, rate-limit awareness, and error recovery. Monitor invocations in the dashboard or through the Sync API. - **High throughput.** The pipeline is designed to maximize throughput without dropping work, whether you're syncing a handful of fil...

[S3] tool=Weaviate
title=Weaviate: 4.3 Schema FSM — `cluster/schema/` + `cluster/proto/api/`
url=https://github.com/weaviate/weaviate/blob/main/docs/runtime-reindex.md
keywords=API, orchestration, api-automation, stateful-workflow
excerpt=Three changes here serve the reindex feature: **BucketGeneration counter on Property.** Bumped on every reindex that touches a property. Lets unrelated machinery (notably backup) tell "this is the same bucket I saw before" apart from "this property has been rebuilt since I last looked." **UpdateProperty fieldmask.** applyPerPropertySchemaUpdate passes a fields []string mask all the way down to the FSM apply path (Me...

[S4] tool=pgvector
title=pgvector: HNSW
url=https://github.com/pgvector/pgvector/blob/master/README.md
keywords=Memory, vector-database, agent-memory
excerpt=An HNSW index creates a multilayer graph. It has better query performance than IVFFlat (in terms of speed-recall tradeoff), but has slower build times and uses more memory. Also, an index can be created without any data in the table since there isn’t a training step like IVFFlat. Add an index for each distance function you want to use. L2 distance Note: Use halfvec_l2_ops for halfvec and sparsevec_l2_ops for sparsev...

Chroma: How Chroma Sync Works

Pipeline Architecture
Hybrid Search
Search
API
vector-database
api-automation
semantic-search

API orchestration api-automation stateful-workflow

pgvector: HNSW


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Compare n8n and LangGraph for a long-running document review workflow that needs retries, human approval, and durable state.

Retrieved evidence:
[S1] tool=LangGraph
title=LangGraph: Why use LangGraph?
url=https://github.com/langchain-ai/langgraph/blob/main/README.md
keywords=Stateful Orchestration, Human-in-the-Loop, Workflow, Memory, Agent, Scalable, tool-selection, stateful-workflow
excerpt=LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent: - **Durable execution** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. - **Human-in-the-loop** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. - **Comprehensiv...

[S2] tool=n8n
title=n8n: How the MCP Server Trigger node works
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger.md
keywords=Workflow, tool-selection
excerpt=The MCP Server Trigger node acts as an entry point into n8n for MCP clients. It operates by exposing a URL that MCP clients can interact with to access n8n tools. Unlike conventional trigger nodes, which respond to events and pass their output to the next connected node, the MCP Server Trigger node only connects to and executes tool nodes. Clients can list the available tools and call individual tools to perform wor...

[S3] tool=CrewAI
title=CrewAI: How CrewAI Compares
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Workflow, Orchestration, Evaluation, Agent, stateful-workflow
excerpt=**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control. - **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant bo...

[S4] tool=GitHub MCP Server
title=GitHub MCP Server: Tools
url=https://github.com/github/github-mcp-server/blob/main/README.md
keywords=orchestration, tool-selection, stateful-workflow
excerpt=Pull Requests - **add_comment_to_pending_review** - Add review comment to the requester's latest pending pull request review - **Required OAuth Scopes**: repo - body: The text of the review comment (string, required) - line: The line of the blob in the pull request diff that the comment applies to. For multi-line comments, the last line of the range (number, optional) - owner: Repository owner (string, required) - p...

LangGraph: Why use LangGraph?

Stateful Orchestration
Human-in-the-Loop
Workflow
Memory
Agent
Scalable
tool-selection
stateful-workflow

CrewAI: How CrewAI Compares

Workflow Orchestration Evaluation Agent stateful-workflow

GitHub MCP Server: Tools


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Design a local-first personal knowledge assistant over a markdown vault. It needs citations, semantic search, and optional local model inference.

Retrieved evidence:
[S1] tool=Ollama
title=Ollama: Desktop
url=https://github.com/ollama/ollama/blob/main/README.md
keywords=Memory, Search, Mobile, Desktop, self-hosted, agent-memory, semantic-search
excerpt=- Dify.AI - LLM app development platform - AnythingLLM - All-in-one AI app for Mac, Windows, and Linux - Maid - Cross-platform mobile and desktop client - Witsy - AI desktop app for Mac, Windows, and Linux - Cherry Studio - Multi-provider desktop client - Ollama App - Multi-platform client for desktop and mobile - PyGPT - AI desktop assistant for Linux, Windows, and Mac - Alpaca - GTK4 client for Linux and macOS - S...

[S2] tool=Weaviate
title=Weaviate: Weaviate <img alt='Weaviate logo' src='https://weaviate.io/img/site/weaviate-logo-light.pn
url=https://github.com/weaviate/weaviate/blob/main/README.md
keywords=Search, Recommendation, Docker, vector-database, rag, self-hosted, semantic-search, deployment
excerpt=**Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate support...

[S3] tool=Chroma
title=Chroma: How Chroma Sync Works
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/overview.mdx
keywords=Pipeline Architecture, Hybrid Search, Search, API, vector-database, api-automation, semantic-search
excerpt=Sync runs the same pipeline regardless of source: - **Managed ingestion.** Connect a source once; every invocation runs through Chroma's queue-based pipeline with automatic retries, rate-limit awareness, and error recovery. Monitor invocations in the dashboard or through the Sync API. - **High throughput.** The pipeline is designed to maximize throughput without dropping work, whether you're syncing a handful of fil...

[S4] tool=vLLM
title=vLLM: Welcome to vLLM
url=https://github.com/vllm-project/vllm/blob/main/docs/README.md
keywords=Pipeline Architecture, Integration, Search, API, orchestration, api-automation, tool-selection, stateful-workflow
excerpt=vLLM is flexible and easy to use with: - Seamless integration with popular Hugging Face models - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more - Tensor, pipeline, data, expert, and context parallelism for distributed inference - Streaming outputs - Generation of structured outputs using xgrammar or guidance - Tool calling and reasoning parsers - Open...

Ollama: Desktop

Memory Search Mobile Desktop self-hosted agent-memory semantic-search

Search Recommendation Docker vector-database rag self-hosted semantic-search deployment

Chroma: How Chroma Sync Works

vLLM: Welcome to vLLM

Pipeline Architecture
Integration
Search
API
orchestration
api-automation
tool-selection
stateful-workflow


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I want model routing across local and cloud LLMs based on latency, cost, privacy, and context length. What should the orchestration layer look like?

Retrieved evidence:
[S1] tool=LiteLLM
title=LiteLLM: Get Started
url=https://github.com/BerriAI/litellm/blob/litellm_internal_staging/README.md
keywords=Observability, Monitoring, Integration, Azure, Docker, API, orchestration, api-automation
excerpt=You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs: LiteLLM AI Gateway LiteLLM Python SDK Use Case Central service (LLM Gateway) to access multiple LLMs Use LiteLLM directly in your Python code Who Uses It? Gen AI Enablement / ML Platform Teams Developers building LLM projects Key Featur...

[S2] tool=vLLM
title=vLLM: Welcome to vLLM
url=https://github.com/vllm-project/vllm/blob/main/docs/README.md
keywords=Pipeline Architecture, Integration, Search, API, orchestration, api-automation, tool-selection, stateful-workflow
excerpt=vLLM is flexible and easy to use with: - Seamless integration with popular Hugging Face models - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more - Tensor, pipeline, data, expert, and context parallelism for distributed inference - Streaming outputs - Generation of structured outputs using xgrammar or guidance - Tool calling and reasoning parsers - Open...

[S3] tool=llama.cpp
title=llama.cpp: Then, pick the card that you want to use (here for e.g. /dev/dri/card0).
url=https://github.com/ggml-org/llama.cpp/blob/master/docs/docker.md
keywords=Docker, Web, self-hosted
excerpt=docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:full-intel -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 99 docker run --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 -v /path/to/models:/models local/llama.cpp:light-in...

[S4] tool=Ollama
title=Ollama: LLM libraries
url=https://github.com/ollama/ollama/blob/main/docs/troubleshooting.mdx
keywords=vector-database
excerpt=Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. cpu_avx2 will perform the best, followed by cpu_avx an the slowest but most compatible is cpu. Ros...

LiteLLM: Get Started

Observability Monitoring Integration Azure Docker API orchestration api-automation

vLLM: Welcome to vLLM

Ollama: LLM libraries


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Build a GitHub issue triage assistant that labels issues, drafts replies, and escalates security-sensitive reports to a human.

Retrieved evidence:
[S1] tool=GitHub MCP Server
title=GitHub MCP Server: Use Cases
url=https://github.com/github/github-mcp-server/blob/main/README.md
keywords=Pipeline Architecture, Workflow, Automation, Security, Search, Agent, tool-selection, semantic-search
excerpt=- Repository Management: Browse and query code, search files, analyze commits, and understand project structure across any repository you have access to. - Issue & PR Automation: Create, update, and manage issues and pull requests. Let AI help triage bugs, review code changes, and maintain project boards. - CI/CD & Workflow Intelligence: Monitor GitHub Actions workflow runs, analyze build failures, manage releases,...

[S2] tool=Chroma
title=Chroma: Getting Started
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/docs/overview/oss.mdx
keywords=Search, self-hosted, semantic-search
excerpt=Here are some helpful links to get you started with contributing to Chroma - The Chroma codebase is hosted on Github - Issues are tracked on Github Issues. Please report any issues you find there making sure to fill out the correct form for the type of issue you are reporting. - In order to run Chroma locally you can follow the Development Instructions. - If you want to contribute and aren't sure where to get starte...

[S3] tool=Langfuse
title=Langfuse: 💭 Support
url=https://github.com/langfuse/langfuse/blob/main/README.md
keywords=documentation
excerpt=Finding an answer to your question: - Our documentation is the best place to start looking for answers. It is comprehensive, and we invest significant time into maintaining it. You can also suggest edits to the docs via GitHub. - Langfuse FAQs where the most common questions are answered. - Use "Ask AI" to get instant answers to your questions. Support Channels: - **Ask any question in our public Q&A on GitHub Discu...

[S4] tool=Promptfoo
title=Promptfoo: Event Types
url=https://github.com/promptfoo/promptfoo/blob/main/site/docs/enterprise/webhooks.md
keywords=Security, Web, webhook-triggers
excerpt=The following webhook event types are available: - issue.created: Triggered when a new security vulnerability is detected and created - issue.updated: Triggered when a vulnerability is updated (such as when multiple attributes change at once) - issue.status_changed: Triggered when a vulnerability's status changes (e.g., from open to fixed) - issue.severity_changed: Triggered when a vulnerability's severity level cha...

GitHub MCP Server: Use Cases

Pipeline Architecture
Workflow
Automation
Security
Search
Agent
tool-selection
semantic-search

Chroma: Getting Started

Langfuse: 💭 Support

Promptfoo: Event Types


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
What orchestration pattern fits a multi-agent product research workflow with researcher, analyst, and strategist roles, but strict quality gates?

Retrieved evidence:
[S1] tool=CrewAI
title=CrewAI: Define structured state for precise control
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Search, Recommendation, Routing, Agent, stateful-workflow, semantic-search
excerpt=class MarketState(BaseModel): sentiment: str = "neutral" confidence: float = 0.0 recommendations: list = [] class AdvancedAnalysisFlow(Flow[MarketState]): @start() def fetch_market_data(self): # Demonstrate low-level control with structured state self.state.sentiment = "analyzing" return {"sector": "tech", "timeframe": "1W"} # These parameters match the task description template @listen(fetch_market_data) def analyz...

[S2] tool=LangGraph
title=LangGraph: LangGraph ecosystem
url=https://github.com/langchain-ai/langgraph/blob/main/README.md
keywords=Observability, Stateful Orchestration, Workflow, Integration, Agent, rag, tool-selection, stateful-workflow
excerpt=While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks. - LangChain – Provides integrations and composable components to streamline LLM applicatio...

[S3] tool=AutoGen
title=AutoGen: Why AutoGen?
url=https://github.com/microsoft/autogen/blob/main/README.md
keywords=Event-Driven Architecture, Orchestration, Search, Agent, Azure, API, self-hosted, api-automation
excerpt=Pioneered in Microsoft Research, AutoGen opened the door to experimental multi-agent orchestration patterns that inspired the community. While AutoGen is now in maintenance mode, existing users can continue to use the framework with the architecture described below. **For new projects, we recommend Microsoft Agent Framework**, which builds on the lessons learned from AutoGen with enterprise-grade support. The autoge...

[S4] tool=Semantic Kernel
title=Semantic Kernel: **Design - Abstractions**
url=https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0032-agents.md
keywords=Optimization, Agent, self-hosted
excerpt=Here the detailed class definitions from the high-level pattern from the previous section are enumerated. Also shown are entities defined as part of the _ChatHistory_ optimization: IChatHistoryHandler, ChatHistoryKernelAgent, and ChatHistoryChannel. These _ChatHistory_ entities eliminates the requirement for _Agents_ that act on a locally managed ChatHistory instance (as opposed to agents managed via remotely hosted...

Search Recommendation Routing Agent stateful-workflow semantic-search

LangGraph: LangGraph ecosystem

Observability
Stateful Orchestration
Workflow
Integration
Agent
rag
tool-selection
stateful-workflow

AutoGen: Why AutoGen?

Event-Driven Architecture
Orchestration
Search
Agent
Azure
API
self-hosted
api-automation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I need an AI workflow observability layer that records prompts, outputs, latency, token cost, errors, and user feedback.

Retrieved evidence:
[S1] tool=Langfuse
title=Langfuse: ✨ Core Features
url=https://github.com/langfuse/langfuse/blob/main/README.md
keywords=Pipeline Architecture, Observability, Feedback Loop, Workflow, Integration, Evaluation, Data Pipeline, Agent
excerpt=- LLM Application Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Try the interactive demo to see this in action. - Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks...

[S2] tool=Promptfoo
title=Promptfoo: Workflow and philosophy
url=https://github.com/promptfoo/promptfoo/blob/main/site/docs/intro.md
keywords=Feedback Loop, Workflow, Evaluation, API, Web, api-automation, tool-selection
excerpt=Test-driven prompt engineering is much more effective than trial-and-error. Serious LLM development requires a systematic approach to prompt engineering. Promptfoo streamlines the process of evaluating and improving language model performance. - **Define test cases**: Identify core use cases and failure modes. Prepare a set of prompts and test cases that represent these scenarios. - **Configure evaluation**: Set up...

[S3] tool=LiteLLM
title=LiteLLM: Get Started
url=https://github.com/BerriAI/litellm/blob/litellm_internal_staging/README.md
keywords=Observability, Monitoring, Integration, Azure, Docker, API, orchestration, api-automation
excerpt=You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs: LiteLLM AI Gateway LiteLLM Python SDK Use Case Central service (LLM Gateway) to access multiple LLMs Use LiteLLM directly in your Python code Who Uses It? Gen AI Enablement / ML Platform Teams Developers building LLM projects Key Featur...

[S4] tool=Ragas
title=Ragas: Prompt Evaluation
url=https://github.com/explodinggradients/ragas/blob/main/docs/tutorials/prompt.md
keywords=Pipeline Architecture, Evaluation, API, rag, api-automation
excerpt=In this tutorial, we will write a simple evaluation pipeline to evaluate a prompt that is part of an AI system, here a movie review sentiment classifier. At the end of this tutorial you’ll learn how to evaluate and iterate on a single prompt using evaluation driven development. We will start by testing a simple prompt that classifies movie reviews as positive or negative. First, make sure you have installed ragas ex...

Langfuse: ✨ Core Features

Pipeline Architecture
Observability
Feedback Loop
Workflow
Integration
Evaluation
Data Pipeline
Agent

Promptfoo: Workflow and philosophy

Feedback Loop
Workflow
Evaluation
API
Web
api-automation
tool-selection

LiteLLM: Get Started

Ragas: Prompt Evaluation

Pipeline Architecture
Evaluation
API
rag
api-automation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Recommend a safe architecture for a browser automation agent that can fill forms but must never submit or send messages without approval.

Retrieved evidence:
[S1] tool=Playwright MCP Server
title=Playwright MCP Server: Tools
url=https://github.com/microsoft/playwright-mcp/blob/main/README.md
keywords=Browser, orchestration, tool-selection
excerpt=- **browser_evaluate** - Title: Evaluate JavaScript - Description: Evaluate JavaScript expression on page or element - Parameters: - element (string, optional): Human-readable element description used to obtain permission to interact with the element - target (string, optional): Exact target element reference from the page snapshot, or a unique element selector - function (string): () => { /* code */ } or (element)...

[S2] tool=LangGraph
title=LangGraph: 🤔 What is this?
url=https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/README.md
keywords=Stateful Orchestration, Human-in-the-Loop, Workflow, Orchestration, Integration, Memory, Agent, API
excerpt=LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. LangGraph provides the infrastructure for durable execution, streaming, human-in-the-loop, persistence, memory, and more. We recommend you use LangGraph when you have advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latenc...

[S3] tool=CrewAI
title=CrewAI: Fast and Flexible Multi-Agent Automation Framework
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Event-Driven Architecture, Orchestration, Automation, Agent, API, api-automation
excerpt=> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**. > It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario. - **CrewAI Crews**: Optimize for autonomy and collaborative intelligence. - **CrewAI Flows**: The **enterprise and pro...

[S4] tool=FastMCP
title=FastMCP: Overview
url=https://github.com/jlowin/fastmcp/blob/main/docs/updates.mdx
keywords=Security, Integration, Search, Browser, Edge, API, REST, documentation
excerpt=The Apps release. Your tools can return interactive UIs — charts, dashboards, forms, maps — rendered right inside the conversation. 🎨 **FastMCPApp** — separate the tools the LLM sees (@app.ui()) from the backend tools the UI calls (@app.tool()), built on Prefab. 🧩 **Built-in providers** — FileUpload, Approval, Choice, FormInput, and GenerativeUI. 🖥 **Dev server** — fastmcp dev apps previews app tools in the browser...

Playwright MCP Server: Tools

LangGraph: 🤔 What is this?

Stateful Orchestration
Human-in-the-Loop
Workflow
Orchestration
Integration
Memory
Agent
API

Event-Driven Architecture
Orchestration
Automation
Agent
API
api-automation

FastMCP: Overview

Security Integration Search Browser Edge API REST documentation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I need an orchestration design for a lead enrichment pipeline that ingests webhook data, enriches it with public APIs, stores the results in a vector store, and triggers notifications when a lead is ready for outreach.

Retrieved evidence:
[S1] tool=Qdrant
title=Qdrant: Overview
url=https://github.com/qdrant/qdrant/blob/master/README.md
keywords=Integration, Search, Agent, Edge, API, vector-database, api-automation, semantic-search
excerpt=Vector Search Engine for the next generation of AI applications **Qdrant** (read: _quadrant_) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, facete...

[S2] tool=Weaviate
title=Weaviate: Weaviate <img alt='Weaviate logo' src='https://weaviate.io/img/site/weaviate-logo-light.pn
url=https://github.com/weaviate/weaviate/blob/main/README.md
keywords=Search, Recommendation, Docker, vector-database, rag, self-hosted, semantic-search, deployment
excerpt=**Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate support...

[S3] tool=n8n
title=n8n: Webhook node
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/core-nodes/n8n-nodes-base.webhook/index.md
keywords=Workflow, API, Web, webhook-triggers, api-automation
excerpt=Use the Webhook node to create webhooks, which can receive data from apps and services when an event occurs. It's a trigger node, which means it can start an n8n workflow. This allows services to connect to n8n and run a workflow. You can use the Webhook node as a trigger for a workflow when you want to receive data and run a workflow based on the data. The Webhook node also supports returning the data generated at...

[S4] tool=Chroma
title=Chroma: How Chroma Sync Works
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/overview.mdx
keywords=Pipeline Architecture, Hybrid Search, Search, API, vector-database, api-automation, semantic-search
excerpt=Sync runs the same pipeline regardless of source: - **Managed ingestion.** Connect a source once; every invocation runs through Chroma's queue-based pipeline with automatic retries, rate-limit awareness, and error recovery. Monitor invocations in the dashboard or through the Sync API. - **High throughput.** The pipeline is designed to maximize throughput without dropping work, whether you're syncing a handful of fil...

Qdrant: Overview

Integration Search Agent Edge API vector-database api-automation semantic-search

n8n: Webhook node

Workflow API Web webhook-triggers api-automation

Chroma: How Chroma Sync Works

Pipeline Architecture
Hybrid Search
Search
API
vector-database
api-automation
semantic-search


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Design a cost-conscious local workflow to build an AI developer assistant that can answer questions about my tool stack and recommend integrations.

Retrieved evidence:
[S1] tool=LiteLLM
title=LiteLLM: What does CodeLlama Server do
url=https://github.com/BerriAI/litellm/blob/litellm_internal_staging/cookbook/codellama-server/README.MD
keywords=Integration, Memory, Azure, documentation, agent-memory
excerpt=- Uses Together AI's CodeLlama to answer coding questions, with GPT-4 + Claude-2 as backups (you can easily switch this to any model from Huggingface, Replicate, Cohere, AI21, Azure, OpenAI, etc.) - Sets default system prompt for guardrails system_prompt = "Only respond to questions about code. Say 'I don't know' to anything outside of that." - Integrates with Promptlayer for model + prompt tracking - Example output...

[S2] tool=Langfuse
title=Langfuse: 💭 Support
url=https://github.com/langfuse/langfuse/blob/main/README.md
keywords=documentation
excerpt=Finding an answer to your question: - Our documentation is the best place to start looking for answers. It is comprehensive, and we invest significant time into maintaining it. You can also suggest edits to the docs via GitHub. - Langfuse FAQs where the most common questions are answered. - Use "Ask AI" to get instant answers to your questions. Support Channels: - **Ask any question in our public Q&A on GitHub Discu...

[S3] tool=AutoGen
title=AutoGen: Why AutoGen?
url=https://github.com/microsoft/autogen/blob/main/README.md
keywords=Event-Driven Architecture, Orchestration, Search, Agent, Azure, API, self-hosted, api-automation
excerpt=Pioneered in Microsoft Research, AutoGen opened the door to experimental multi-agent orchestration patterns that inspired the community. While AutoGen is now in maintenance mode, existing users can continue to use the framework with the architecture described below. **For new projects, we recommend Microsoft Agent Framework**, which builds on the lessons learned from AutoGen with enterprise-grade support. The autoge...

[S4] tool=LangGraph
title=LangGraph: LangGraph ecosystem
url=https://github.com/langchain-ai/langgraph/blob/main/README.md
keywords=Observability, Stateful Orchestration, Workflow, Integration, Agent, rag, tool-selection, stateful-workflow
excerpt=While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks. - LangChain – Provides integrations and composable components to streamline LLM applicatio...

LiteLLM: What does CodeLlama Server do

Integration Memory Azure documentation agent-memory

Langfuse: 💭 Support

AutoGen: Why AutoGen?

LangGraph: LangGraph ecosystem


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Compare an n8n-based workflow versus a graph-agent approach for automating document review, extraction, and summary generation.

Retrieved evidence:
[S1] tool=n8n
title=n8n: Webhook node
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/core-nodes/n8n-nodes-base.webhook/index.md
keywords=Workflow, API, Web, webhook-triggers, api-automation
excerpt=Use the Webhook node to create webhooks, which can receive data from apps and services when an event occurs. It's a trigger node, which means it can start an n8n workflow. This allows services to connect to n8n and run a workflow. You can use the Webhook node as a trigger for a workflow when you want to receive data and run a workflow based on the data. The Webhook node also supports returning the data generated at...

[S2] tool=CrewAI
title=CrewAI: How CrewAI Compares
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Workflow, Orchestration, Evaluation, Agent, stateful-workflow
excerpt=**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control. - **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant bo...

[S3] tool=LangGraph
title=LangGraph: Development
url=https://github.com/langchain-ai/langgraph/blob/main/libs/cli/js-examples/README.md
keywords=Agent, self-hosted, stateful-workflow
excerpt=While iterating on your graph, you can edit past state and rerun your app from previous states to debug specific nodes. Local changes will be automatically applied via hot reload. Try experimenting with: - Modifying the system prompt to give your chatbot a unique personality. - Adding new nodes to the graph for more complex conversation flows. - Implementing conditional logic to handle different types of user inputs...

[S4] tool=LlamaIndex
title=LlamaIndex: LlamaParse (document agent platform)
url=https://github.com/run-llama/llama_index/blob/main/README.md
keywords=Pipeline Architecture, Workflow, Data Pipeline, Agent, API, rag, documentation, api-automation
excerpt=**LlamaParse** is its own platform—focused on document agents and agentic OCR. It includes **Parse** (parsing), **LlamaAgents** (deployed document agents), **Extract** (structured extraction), and **Index** (ingest and RAG). You can use it with the LlamaIndex framework or standalone. - **Sign up for LlamaParse** — Create an account and get your API key. - **Parse** — Agentic OCR and document parsing (130+ formats)....

n8n: Webhook node

CrewAI: How CrewAI Compares

Workflow Orchestration Evaluation Agent stateful-workflow

LangGraph: Development

Pipeline Architecture
Workflow
Data Pipeline
Agent
API
rag
documentation
api-automation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I want to route different tasks to different models based on latency and cost; how should I implement model routing for a mixed local/cloud orchestration pipeline?

Retrieved evidence:
[S1] tool=vLLM
title=vLLM: Frequently Asked Questions
url=https://github.com/vllm-project/vllm/blob/main/docs/usage/faq.md
keywords=API, orchestration, api-automation, stateful-workflow
excerpt=> Q: How can I serve multiple models on a single port using the OpenAI API? A: Assuming that you're referring to using OpenAI compatible server to serve multiple models at once, that is not currently supported, you can run multiple instances of the server (each serving a different model) at the same time, and have another layer to route the incoming request to the correct server accordingly. --- > Q: Which model to...

[S2] tool=Ollama
title=Ollama: Importing a fine tuned adapter from Safetensors weights
url=https://github.com/ollama/ollama/blob/main/docs/import.mdx
keywords=orchestration, tool-selection
excerpt=First, create a Modelfile with a FROM command pointing at the base model you used for fine tuning, and an ADAPTER command which points to the directory with your Safetensors adapter: Make sure that you use the same base model in the FROM command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLo...

[S3] tool=LiteLLM
title=LiteLLM: LiteLLM on GCP (Cloud Run)
url=https://github.com/BerriAI/litellm/blob/litellm_internal_staging/terraform/litellm/gcp/README.md
keywords=Serverless, Memory, Routing, GCP, REST, agent-memory
excerpt=The button above opens the DeployStack installer in Cloud Shell, walks you through TUTORIAL.md, and runs terraform apply once you've answered the prompts. The rest of this README is the manual / advanced path. Deploys the componentized LiteLLM proxy on GCP: - **VPC** + Private Services Access range + a Serverless VPC Access connector so Cloud Run can reach private IPs - **Cloud SQL for PostgreSQL** — primary instanc...

[S4] tool=llama.cpp
title=llama.cpp: Building Docker locally
url=https://github.com/ggml-org/llama.cpp/blob/master/docs/docker.md
keywords=Docker, Containerized, self-hosted, tool-selection
excerpt=You may want to pass in some different ARGS, depending on the SYCL environment supported by your container host, as well as the GPU architecture. Refer to .devops/intel.Dockerfile for the available ARGS and their defaults. The resulting images, are essentially the same as the non-SYCL images: - local/llama.cpp:full-intel: This image includes both the llama-cli and llama-completion executables and the tools to conver...

vLLM: Frequently Asked Questions

API orchestration api-automation stateful-workflow

LiteLLM: LiteLLM on GCP (Cloud Run)

Serverless Memory Routing GCP REST agent-memory

llama.cpp: Building Docker locally

Docker Containerized self-hosted tool-selection


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Build a pipeline for a multi-agent finance research assistant that keeps long-term memory, creates summaries, and follows GDPR-safe data handling.

Retrieved evidence:
[S1] tool=LangGraph
title=LangGraph: Why use LangGraph?
url=https://github.com/langchain-ai/langgraph/blob/main/README.md
keywords=Stateful Orchestration, Human-in-the-Loop, Workflow, Memory, Agent, Scalable, tool-selection, stateful-workflow
excerpt=LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent: - **Durable execution** — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. - **Human-in-the-loop** — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. - **Comprehensiv...

[S2] tool=CrewAI
title=CrewAI: Create a StdioServerParameters object
url=https://github.com/crewAIInc/crewAI/blob/main/docs/en/mcp/stdio.mdx
keywords=Search, Agent, rag, self-hosted, tool-selection, semantic-search
excerpt=server_params=StdioServerParameters( command="python3", args=["servers/your_stdio_server.py"], env={"UV_PYTHON": "3.12", **os.environ}, ) with MCPServerAdapter(server_params) as tools: print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}") # Example: Using the tools from the Stdio MCP server in a CrewAI Agent research_agent = Agent( role="Local Data Processor", goal="Process data using a lo...

[S3] tool=Semantic Kernel
title=Semantic Kernel: Semantic Kernel
url=https://github.com/microsoft/semantic-kernel/blob/main/README.md
keywords=Orchestration, Agent, API, api-automation
excerpt=> [!IMPORTANT] > Semantic Kernel is now Microsoft Agent Framework! Microsoft Agent Framework (MAF) is the enterprise‑ready successor to Semantic Kernel. Microsoft Agent Framework is now available at version 1.0 as a production-ready release: stable APIs, and a commitment to long-term support. Whether you're building a single assistant or orchestrating a fleet of specialized agents, Microsoft Agent Framework 1.0 give...

[S4] tool=AutoGen
title=AutoGen: AutoGen [![Maintenance Mode](https://img.shields.io/badge/status-maintenance%20mode-orange
url=https://github.com/microsoft/autogen/blob/main/README.md
keywords=Orchestration, Agent, API, rag, api-automation
excerpt=**AutoGen** is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans. > [!CAUTION] > **⚠ Maintenance Mode** > > AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward. > > New users should start with Microsoft Agent Framework. Existing users are encouraged to migrate using the AutoGen → Microsoft A...

LangGraph: Why use LangGraph?

Stateful Orchestration
Human-in-the-Loop
Workflow
Memory
Agent
Scalable
tool-selection
stateful-workflow

Search Agent rag self-hosted tool-selection semantic-search

Semantic Kernel: Semantic Kernel

Orchestration Agent API rag api-automation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
How should I combine a vector database, an LLM, and a webhook-based trigger system to automate customer support ticket categorization and escalation?

Retrieved evidence:
[S1] tool=Weaviate
title=Weaviate: Weaviate <img alt='Weaviate logo' src='https://weaviate.io/img/site/weaviate-logo-light.pn
url=https://github.com/weaviate/weaviate/blob/main/README.md
keywords=Search, Recommendation, Docker, vector-database, rag, self-hosted, semantic-search, deployment
excerpt=**Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate support...

[S2] tool=Qdrant
title=Qdrant: Overview
url=https://github.com/qdrant/qdrant/blob/master/README.md
keywords=Integration, Search, Agent, Edge, API, vector-database, api-automation, semantic-search
excerpt=Vector Search Engine for the next generation of AI applications **Qdrant** (read: _quadrant_) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, facete...

[S3] tool=Chroma
title=Chroma: Reads
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/pricing.mdx
keywords=Search, vector-database, semantic-search
excerpt=Read costs are based on both the amount of data queried and the volume of data returned: - **$0.0075 per TiB queried** - **$0.09 per GiB returned** **How queries are counted:** - A single vector similarity query counts as one query. - Each metadata or full-text predicate in a query counts as an additional query. - Full-text and regex filters are billed as *(N - 2)* queries, where *N* is the number of characters in t...

[S4] tool=n8n
title=n8n: Webhook node
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/core-nodes/n8n-nodes-base.webhook/index.md
keywords=Workflow, API, Web, webhook-triggers, api-automation
excerpt=Use the Webhook node to create webhooks, which can receive data from apps and services when an event occurs. It's a trigger node, which means it can start an n8n workflow. This allows services to connect to n8n and run a workflow. You can use the Webhook node as a trigger for a workflow when you want to receive data and run a workflow based on the data. The Webhook node also supports returning the data generated at...

Search Recommendation Docker vector-database rag self-hosted semantic-search deployment

Qdrant: Overview

Chroma: Reads

n8n: Webhook node


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Recommend the best architecture for a self-hosted automation platform that integrates LangChain, Qdrant, and local model inference for internal engineering knowledge retrieval.

Retrieved evidence:
[S1] tool=Qdrant
title=Qdrant: Qdrant Edge
url=https://github.com/qdrant/qdrant/blob/master/README.md
keywords=Search, Edge, REST, vector-database, self-hosted, semantic-search
excerpt=Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint,...

[S2] tool=LangGraph
title=LangGraph: 🤔 What is this?
url=https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/README.md
keywords=Stateful Orchestration, Human-in-the-Loop, Workflow, Orchestration, Integration, Memory, Agent, API
excerpt=LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. LangGraph provides the infrastructure for durable execution, streaming, human-in-the-loop, persistence, memory, and more. We recommend you use LangGraph when you have advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latenc...

[S3] tool=LangSmith
title=LangSmith: LangSmith Client SDK
url=https://github.com/langchain-ai/langsmith-sdk/blob/main/js/README.md
keywords=Workflow, Integration, Agent
excerpt=This package contains the TypeScript client for interacting with the LangSmith platform. To install: LangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM Application and provides seamless integration with LangChain, a widely recognized open-source framework that simplifies the process for developers to create powerful language model applications...

[S4] tool=Ollama
title=Ollama: Web
url=https://github.com/ollama/ollama/blob/main/README.md
keywords=Search, Self-Hosted, On-Prem, Desktop, Edge, Web, rag, documentation
excerpt=- Open WebUI - Extensible, self-hosted AI interface - Onyx - Connected AI workspace - LibreChat - Enhanced ChatGPT clone with multi-provider support - Lobe Chat - Modern chat framework with plugin ecosystem (docs) - NextChat - Cross-platform ChatGPT UI (docs) - Perplexica - AI-powered search engine, open-source Perplexity alternative - big-AGI - AI suite for professionals - Lollms WebUI - Multi-model web interface -...

Qdrant: Qdrant Edge

Search Edge REST vector-database self-hosted semantic-search

LangGraph: 🤔 What is this?

Stateful Orchestration
Human-in-the-Loop
Workflow
Orchestration
Integration
Memory
Agent
API

LangSmith: LangSmith Client SDK

Ollama: Web

Search Self-Hosted On-Prem Desktop Edge Web rag documentation


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I need a high-reliability orchestration system for a production agent workflow with retries, observability, and failure recovery.

Retrieved evidence:
[S1] tool=Langfuse
title=Langfuse: ✨ Core Features
url=https://github.com/langfuse/langfuse/blob/main/README.md
keywords=Pipeline Architecture, Observability, Feedback Loop, Workflow, Integration, Evaluation, Data Pipeline, Agent
excerpt=- LLM Application Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Try the interactive demo to see this in action. - Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks...

[S2] tool=LangGraph
title=LangGraph: LangGraph ecosystem
url=https://github.com/langchain-ai/langgraph/blob/main/README.md
keywords=Observability, Stateful Orchestration, Workflow, Integration, Agent, rag, tool-selection, stateful-workflow
excerpt=While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks. - LangChain – Provides integrations and composable components to streamline LLM applicatio...

[S3] tool=CrewAI
title=CrewAI: How CrewAI Compares
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Workflow, Orchestration, Evaluation, Agent, stateful-workflow
excerpt=**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control. - **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant bo...

[S4] tool=Semantic Kernel
title=Semantic Kernel: Key Features
url=https://github.com/microsoft/semantic-kernel/blob/main/README.md
keywords=Observability, Workflow, Security, Integration, Planning, Memory, Search, Agent
excerpt=- **Model Flexibility**: Connect to any LLM with built-in support for OpenAI, Azure OpenAI, Hugging Face, NVidia and more - **Agent Framework**: Build modular AI agents with access to tools/plugins, memory, and planning capabilities - **Multi-Agent Systems**: Orchestrate complex workflows with collaborating specialist agents - **Plugin Ecosystem**: Extend with native code functions, prompt templates, OpenAPI specs,...

Langfuse: ✨ Core Features

Pipeline Architecture
Observability
Feedback Loop
Workflow
Integration
Evaluation
Data Pipeline
Agent

LangGraph: LangGraph ecosystem

Observability
Stateful Orchestration
Workflow
Integration
Agent
rag
tool-selection
stateful-workflow

CrewAI: How CrewAI Compares

Semantic Kernel: Key Features

Observability Workflow Security Integration Planning Memory Search Agent


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
What is the simplest way to connect Hugging Face model inference, Discord webhooks, and a vector memory layer for a community support bot?

Retrieved evidence:
[S1] tool=PEFT
title=PEFT: PEFT configuration and model
url=https://github.com/huggingface/peft/blob/main/docs/source/quicktour.md
keywords=Memory, Search, agent-memory, semantic-search
excerpt=Wrapping means that PEFT replaces the targeted layers (here: all q_proj layers) with the adapter-specific layer for the target layer's type. Since we're dealing with linear layers, it will be, in this case, a lora.Linear layer. Note that these changes are done in-place to save memory, so your base model is now modified. Note that we've only specified q_proj but in actuality we are targeting all model.layers[:].self_...

[S2] tool=TRL
title=TRL: Running the environments
url=https://github.com/huggingface/trl/blob/main/docs/source/openenv.md
keywords=Docker, Web, Containerized, self-hosted
excerpt=When using environment_factory, the trainer connects to the environment server automatically. You just need the server to be running. There are three ways to run an OpenEnv environment server: **Connect to a remote Hugging Face Space** *(simplest)* Most example scripts default to a hosted Space (no setup needed): > [!WARNING] > For training, **duplicate the Space to your own account** to avoid concurrency issues. Th...

[S3] tool=vLLM
title=vLLM: About
url=https://github.com/vllm-project/vllm/blob/main/README.md
keywords=Pipeline Architecture, Integration, Search, API, orchestration, api-automation, tool-selection, stateful-workflow
excerpt=vLLM is flexible and easy to use with: - Seamless integration with popular Hugging Face models - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more - Tensor, pipeline, data, expert, and context parallelism for distributed inference - Streaming outputs - Generation of structured outputs using xgrammar or guidance - Tool calling and reasoning parsers - Open...

[S4] tool=llama.cpp
title=llama.cpp: Hot topics
url=https://github.com/ggml-org/llama.cpp/blob/master/README.md
keywords=Browser, Web, orchestration, tool-selection
excerpt=- **Hugging Face cache migration: models downloaded with -hf are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.** - **guide : using the new WebUI of llama.cpp** - guide : running gpt-oss with llama.cpp - [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313) - Support for the gpt-oss model...

PEFT: PEFT configuration and model

Memory Search agent-memory semantic-search

TRL: Running the environments

vLLM: About

Pipeline Architecture
Integration
Search
API
orchestration
api-automation
tool-selection
stateful-workflow

llama.cpp: Hot topics

Browser Web orchestration tool-selection


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Design an orchestration plan for a tool that monitors GitHub repos, identifies new release notes, and generates update summaries automatically.

Retrieved evidence:
[S1] tool=GitHub MCP Server
title=GitHub MCP Server: Tools
url=https://github.com/github/github-mcp-server/blob/main/README.md
keywords=orchestration, tool-selection
excerpt=Labels - **get_label** - Get a specific label from a repository - **Required OAuth Scopes**: repo - name: Label name. (string, required) - owner: Repository owner (username or organization name) (string, required) - repo: Repository name (string, required) - **label_write** - Write operations on repository labels - **Required OAuth Scopes**: repo - color: Label color as 6-character hex code without '#' prefix (e.g.,...

[S2] tool=Chroma
title=Chroma: Direct Sync
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/github.mdx
keywords=orchestration, branching-logic
excerpt=Direct Sync is the default syncing method, which uses the Chroma Cloud GitHub app. To use your own custom GitHub app, use Platform Sync. - **Prerequisites** This walkthrough assumes that you have a GitHub account with at least one repository. - **New database setup** If you do not already have a Chroma Cloud account, you will need to create one at trychroma.com. After creating an account, you can create a database b...

[S3] tool=n8n
title=n8n: Events
url=https://github.com/n8n-io/n8n-docs/blob/main/docs/integrations/builtin/trigger-nodes/n8n-nodes-base.postgrestrigger.md
keywords=Workflow
excerpt=You can configure how the node listens for events. - Select **Listen and Create Trigger Rule**, then choose the events to listen for: - Insert - Update - Delete - Select **Listen to Channel**, then enter a channel name that the node should monitor. /// note | Postgres event listener and required database permissions - To listen for trigger events, n8n automatically creates a Postgres trigger on the target table. Thi...

[S4] tool=AutoGen
title=AutoGen: GitHubOptions
url=https://github.com/microsoft/autogen/blob/main/dotnet/samples/dev-team/docs/github-flow-getting-started.md
keywords=Web, orchestration, webhook-triggers
excerpt=For the GitHubOptions section, you'll need to fill in the following values: - **AppKey (PrivateKey)**: this is a key generated while creating a GitHub App. If you haven't saved it during creation, you'll need to generate a new one. Go to the settings of your GitHub app, scroll down to "Private keys" and click on "Generate a new private key". It will download a .pem file that contains your App Key. Then copy and past...

GitHub MCP Server: Tools

Chroma: Direct Sync

n8n: Events

AutoGen: GitHubOptions


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
How can I create a hybrid architecture that uses local models for sensitive data and cloud models for expensive reasoning tasks?

Retrieved evidence:
[S1] tool=vLLM
title=vLLM: Welcome to vLLM
url=https://github.com/vllm-project/vllm/blob/main/docs/README.md
keywords=Pipeline Architecture, Integration, Search, API, orchestration, api-automation, tool-selection, stateful-workflow
excerpt=vLLM is flexible and easy to use with: - Seamless integration with popular Hugging Face models - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more - Tensor, pipeline, data, expert, and context parallelism for distributed inference - Streaming outputs - Generation of structured outputs using xgrammar or guidance - Tool calling and reasoning parsers - Open...

[S2] tool=Ollama
title=Ollama: How can I set the quantization type for the K/V cache?
url=https://github.com/ollama/ollama/blob/main/docs/faq.mdx
keywords=Memory, agent-memory
excerpt=The K/V context cache can be quantized to significantly reduce memory usage when Flash Attention is enabled. To use quantized K/V cache with Ollama you can set the following environment variable: - OLLAMA_KV_CACHE_TYPE - The quantization type for the K/V cache. Default is f16. Currently this is a global option - meaning all models will run with the specified quantization type. The currently available K/V cache quant...

[S3] tool=llama.cpp
title=llama.cpp: Description
url=https://github.com/ggml-org/llama.cpp/blob/master/README.md
keywords=Memory, self-hosted, stateful-workflow, agent-memory
excerpt=The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2, AVX512 and AMX support for x86 architectures - RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE suppo...

[S4] tool=Ragas
title=Ragas: **📚 Blog & Insights**
url=https://github.com/explodinggradients/ragas/blob/main/docs/community/index.md
keywords=Pipeline Architecture, Hybrid Search, Evaluation, Search, Data Pipeline, Azure, rag, tool-selection
excerpt=Explore insightful articles, tutorials, and stories written by and for our community members. - Luka Panić shares his work on - Ragas Evaluation: In-Depth Insights | PIXION Blog: A detailed explanation of the metrics and how they are calculated. - RAG in practice - Test Set Generation | PIXION Blog: A tutorial on how to generate a test set using Ragas. - Shanthi Vardhan shares how his team at Atomicwork uses ragas t...

vLLM: Welcome to vLLM

llama.cpp: Description

Memory self-hosted stateful-workflow agent-memory

Ragas: **📚 Blog & Insights**

Pipeline Architecture
Hybrid Search
Evaluation
Search
Data Pipeline
Azure
rag
tool-selection


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Suggest the best pattern for building an automation pipeline that quickly prototypes new tools while being easy to transition into production.

Retrieved evidence:
[S1] tool=Ragas
title=Ragas: 1. Introduction
url=https://github.com/explodinggradients/ragas/blob/main/docs/howtos/observability.md
keywords=Pipeline Architecture, Workflow, Evaluation, Data Pipeline, API, rag, api-automation, tool-selection
excerpt=Building a baseline for a RAG pipeline is not usually difficult, but enhancing it to make it suitable for production and ensuring the quality of your responses is almost always hard. Choosing the right tools and parameters for RAG can itself be challenging when there is an abundance of options available. This tutorial shares a robust workflow for making the right choices while building your RAG and ensuring its qual...

[S2] tool=Semantic Kernel
title=Semantic Kernel: **Decision Drivers**
url=https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0032-agents.md
keywords=Agent, API, api-automation, tool-selection
excerpt=- _Agent Framework_ shall provide sufficient abstraction to enable the construction of agents that could utilize potentially any LLM API. - _Agent Framework_ shall provide sufficient abstraction and building blocks for the most frequent types of agent collaboration. It should be easy to add new blocks as new collaboration methods emerge. - _Agent Framework_ shall provide building blocks to modify agent input and out...

[S3] tool=LlamaIndex
title=LlamaIndex: What is context augmentation?
url=https://github.com/run-llama/llama_index/blob/main/docs/src/content/docs/framework/index.md
keywords=Workflow, API, rag, api-automation, tool-selection
excerpt=LLMs offer a natural language interface between humans and data. LLMs come pre-trained on huge amounts of publicly available data, but they are not trained on **your** data. Your data may be private or specific to the problem you're trying to solve. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks. Context augmentation makes your data available to the LLM to solve the problem at hand. LlamaInde...

[S4] tool=FastMCP
title=FastMCP: Overview
url=https://github.com/jlowin/fastmcp/blob/main/docs/v2/updates.mdx
keywords=Pipeline Architecture, Workflow, Compliance, Memory, Agent, API, api-automation, tool-selection
excerpt=--- title: "FastMCP Updates" sidebarTitle: "Updates" icon: "sparkles" tag: NEW --- A 2.x backport of the fakeredis pin: fakeredis 2.35.0 renamed a connection class that pydocket's memory:// backend relied on, crashing fastmcp[tasks] installs at startup. Caps fakeredis v2.14.4 backported dereference_refs() but never wired it into the tool schema pipeline — $ref and $defs were still sent to MCP clients. Now fixed: sch...

Ragas: 1. Introduction

Pipeline Architecture
Workflow
Evaluation
Data Pipeline
API
rag
api-automation
tool-selection

Semantic Kernel: **Decision Drivers**

Workflow API rag api-automation tool-selection

FastMCP: Overview

Pipeline Architecture
Workflow
Compliance
Memory
Agent
API
api-automation
tool-selection


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
I want to build an AI orchestration advisor for internal teams; what should the data flow look like from user request to recommended tool chain?

Retrieved evidence:
[S1] tool=PEFT
title=PEFT: Writing source documentation
url=https://github.com/huggingface/peft/blob/main/docs/README.md
keywords=orchestration, tool-selection
excerpt=Values that should be put in code should either be surrounded by backticks: \like so\. Note that argument names and objects like True, None, or any strings should usually be put in code. When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool adds a link to its documentation with this syntax: \[\XXXClass\\] or \[\function\\]. This requires the class or fu...

[S2] tool=llama.cpp
title=llama.cpp: Build CLI on Android using Termux
url=https://github.com/ggml-org/llama.cpp/blob/master/docs/android.md
keywords=Memory, agent-memory
excerpt=Termux is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid. With Termux, you can install and run llama.cpp as if the environment were Linux. Once in the Termux shell: Then, follow the build instructions, specifically for CMake. Once the binar...

[S3] tool=Chroma
title=Chroma: Feature Requests
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/docs/overview/oss.mdx
keywords=orchestration, stateful-workflow
excerpt=Feature requests and proposals for large changes to Chroma should be submitted using GitHub Issues. If you want to suggest a new feature or a major change, please open an issue and select the relevant template. This allows the core Chroma team and the community to discuss and provide feedback directly in the issue. For smaller changes like bug fixes or documentation updates, you can submit an issue or open a pull re...

[S4] tool=GitHub MCP Server
title=GitHub MCP Server: Use Cases
url=https://github.com/github/github-mcp-server/blob/main/README.md
keywords=Pipeline Architecture, Workflow, Automation, Security, Search, Agent, tool-selection, semantic-search
excerpt=- Repository Management: Browse and query code, search files, analyze commits, and understand project structure across any repository you have access to. - Issue & PR Automation: Create, update, and manage issues and pull requests. Let AI help triage bugs, review code changes, and maintain project boards. - CI/CD & Workflow Intelligence: Monitor GitHub Actions workflow runs, analyze build failures, manage releases,...

PEFT: Writing source documentation

Chroma: Feature Requests

GitHub MCP Server: Use Cases

Pipeline Architecture
Workflow
Automation
Security
Search
Agent
tool-selection
semantic-search


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Compare using LangGraph versus a custom agent framework for a process that requires stateful decision-making across multiple steps.

Retrieved evidence:
[S1] tool=LangGraph
title=LangGraph: 🤔 What is this?
url=https://github.com/langchain-ai/langgraph/blob/main/libs/langgraph/README.md
keywords=Stateful Orchestration, Human-in-the-Loop, Workflow, Orchestration, Integration, Memory, Agent, API
excerpt=LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. LangGraph provides the infrastructure for durable execution, streaming, human-in-the-loop, persistence, memory, and more. We recommend you use LangGraph when you have advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latenc...

[S2] tool=CrewAI
title=CrewAI: How CrewAI Compares
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Workflow, Orchestration, Evaluation, Agent, stateful-workflow
excerpt=**CrewAI's Advantage**: CrewAI combines autonomous agent intelligence with precise workflow control through its unique Crews and Flows architecture. The framework excels at both high-level orchestration and low-level customization, enabling complex, production-grade systems with granular control. - **LangGraph**: While LangGraph provides a foundation for building agent workflows, its approach requires significant bo...

[S3] tool=Semantic Kernel
title=Semantic Kernel: **Context and Problem Statement**
url=https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0032-agents.md
keywords=Agent, API, api-automation, stateful-workflow
excerpt=Support for the OpenAI Assistant API was published in an experimental *.Assistants package that was later renamed to *.Agents with the aspiration of pivoting to a more general agent framework. The initial Assistants work was never intended to evolve into a general _Agent Framework_. This ADR defines that general _Agent Framework_. An agent is expected to be able to support two interaction patterns: - **Direct Invoca...

[S4] tool=Ollama
title=Ollama: Vulkan GPU Support
url=https://github.com/ollama/ollama/blob/main/docs/gpu.mdx
keywords=rag, documentation
excerpt=Additional GPU support on Windows and Linux is provided via Vulkan. Vulkan is enabled by default when the backend is installed. On Windows most GPU vendors drivers come bundled with Vulkan support and require no additional setup steps. Most Linux distributions require installing additional components, and you may have multiple options for Vulkan drivers between Mesa and GPU Vendor specific packages - Linux Intel GPU...

LangGraph: 🤔 What is this?

CrewAI: How CrewAI Compares

Workflow Orchestration Evaluation Agent stateful-workflow

Agent API api-automation stateful-workflow

Ollama: Vulkan GPU Support


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Describe a federated orchestration approach where different teams can plug in their own microservices and local tools under a shared workflow layer.

Retrieved evidence:
[S1] tool=Chroma
title=Chroma: Multi-Tenant Buckets
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/s3.mdx
keywords=Routing, REST, orchestration
excerpt=S3 Sync supports multi-tenant setups where a single bucket serves multiple tenants. **Path prefixes** restrict which S3 keys a source can sync. When a path_prefix is configured, only objects whose key starts with that prefix can be synced — invocations for keys outside the prefix will be rejected. Create one source per tenant with a distinct prefix (e.g. tenant-a/, tenant-b/) to enforce isolation within a shared buc...

[S2] tool=Weaviate
title=Weaviate: What goes through RAFT vs. what is local-only
url=https://github.com/weaviate/weaviate/blob/main/docs/runtime-reindex.md
keywords=Memory, self-hosted, stateful-workflow, agent-memory
excerpt=What does NOT go through RAFT (local-only): - **PREP** (FlushAndSwitch, Prepend, Shutdown the reindex bucket). - **OVERLAY SET** (install per-shard tokenization resolver). - **ATOMIC SWAP** (SwapBucketPointer per prop, in microseconds). - **Post-atomic inline tidy** (Shutdown old main, rename old → backup). - **OnMigrationComplete** per-strategy hook (in-memory shard-local state mutations). All five of those are pur...

[S3] tool=Semantic Kernel
title=Semantic Kernel: Embeddings
url=https://github.com/microsoft/semantic-kernel/blob/main/docs/EMBEDDINGS.md
keywords=Knowledge Graph, DSL, Memory, Edge, vector-database, tool-selection, agent-memory
excerpt=**Semantic Memory** is similar to how the human brain stores and retrieves knowledge about the world. Embeddings are used to create a semantic memory by **representing concepts or entities as vectors in a high-dimensional space**. This approach allows the model to learn relationships between concepts and make inferences based on similarity or distance between vector representations. For example, the Semantic Memory...

[S4] tool=AutoGen
title=AutoGen: Legal Notices
url=https://github.com/microsoft/autogen/blob/main/README.md
keywords=Azure, orchestration, stateful-workflow
excerpt=Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file. Microsoft, Windows, Microsoft Azure, and/or other Microsoft products and services referenced in the d...

Chroma: Multi-Tenant Buckets

Semantic Kernel: Embeddings

Knowledge Graph
DSL
Memory
Edge
vector-database
tool-selection
agent-memory

AutoGen: Legal Notices


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Propose an architecture for a workflow that ingests Slack messages, performs semantic search over team docs, and answers follow-up questions.

Retrieved evidence:
[S1] tool=pgvector
title=pgvector: IVFFlat
url=https://github.com/pgvector/pgvector/blob/master/README.md
keywords=Memory, Search, vector-database, agent-memory, semantic-search
excerpt=An IVFFlat index divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff). Three keys to achieving good recall are: - Create the index *after* the table has some data - Choose an appropriate number of lists - a good place to start is rows / 1...

[S2] tool=Weaviate
title=Weaviate: Weaviate features
url=https://github.com/weaviate/weaviate/blob/main/README.md
keywords=Memory, Search, API, Scalable, vector-database, rag, api-automation, tool-selection
excerpt=These features enable you to build AI-powered applications: - **⚡ Fast Search Performance**: Perform complex semantic searches over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our ANN benchmarks for more info. - **🔌 Flexible Vectorization**: Seamlessly vectorize data at import tim...

[S3] tool=LlamaIndex
title=LlamaIndex: LlamaParse (document agent platform)
url=https://github.com/run-llama/llama_index/blob/main/README.md
keywords=Pipeline Architecture, Workflow, Data Pipeline, Agent, API, rag, documentation, api-automation
excerpt=**LlamaParse** is its own platform—focused on document agents and agentic OCR. It includes **Parse** (parsing), **LlamaAgents** (deployed document agents), **Extract** (structured extraction), and **Index** (ingest and RAG). You can use it with the LlamaIndex framework or standalone. - **Sign up for LlamaParse** — Create an account and get your API key. - **Parse** — Agentic OCR and document parsing (130+ formats)....

[S4] tool=Qdrant
title=Qdrant: Qdrant Edge
url=https://github.com/qdrant/qdrant/blob/master/README.md
keywords=Search, Edge, REST, vector-database, self-hosted, semantic-search
excerpt=Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint,...

pgvector: IVFFlat

Memory Search vector-database agent-memory semantic-search

Weaviate: Weaviate features

Memory Search API Scalable vector-database rag api-automation tool-selection

Pipeline Architecture
Workflow
Data Pipeline
Agent
API
rag
documentation
api-automation

Qdrant: Qdrant Edge

Search Edge REST vector-database self-hosted semantic-search


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
How should I design a data pipeline that keeps the vector knowledge base current with new tool releases and changelog updates?

Retrieved evidence:
[S1] tool=Qdrant
title=Qdrant: Qdrant 2024 Roadmap
url=https://github.com/qdrant/qdrant/blob/master/docs/roadmap/roadmap-2024.md
keywords=Hybrid Search, Search, vector-database, rag, tool-selection, semantic-search, deployment
excerpt=Hi! This document is our plan for Qdrant development in 2024. Previous year roadmap is available here: - Roadmap 2023 - Roadmap 2022 Goals of the release: - **Maintain easy upgrades** - we plan to keep backward compatibility for at least one minor version back (this stays the same in 2024). - That means that you can upgrade Qdrant without any downtime and without any changes in your client code within one minor vers...

[S2] tool=Chroma
title=Chroma: How Chroma Sync Works
url=https://github.com/chroma-core/chroma/blob/main/docs/mintlify/cloud/sync/overview.mdx
keywords=Pipeline Architecture, Hybrid Search, Search, API, vector-database, api-automation, semantic-search
excerpt=Sync runs the same pipeline regardless of source: - **Managed ingestion.** Connect a source once; every invocation runs through Chroma's queue-based pipeline with automatic retries, rate-limit awareness, and error recovery. Monitor invocations in the dashboard or through the Sync API. - **High throughput.** The pipeline is designed to maximize throughput without dropping work, whether you're syncing a handful of fil...

[S3] tool=FastMCP
title=FastMCP: Overview
url=https://github.com/jlowin/fastmcp/blob/main/docs/v2/updates.mdx
keywords=Pipeline Architecture, Workflow, Compliance, Memory, Agent, API, api-automation, tool-selection
excerpt=--- title: "FastMCP Updates" sidebarTitle: "Updates" icon: "sparkles" tag: NEW --- A 2.x backport of the fakeredis pin: fakeredis 2.35.0 renamed a connection class that pydocket's memory:// backend relied on, crashing fastmcp[tasks] installs at startup. Caps fakeredis v2.14.4 backported dereference_refs() but never wired it into the tool schema pipeline — $ref and $defs were still sent to MCP clients. Now fixed: sch...

[S4] tool=Langfuse
title=Langfuse: Requirements
url=https://github.com/langfuse/langfuse/blob/main/worker/src/backgroundMigrations/README.md
keywords=orchestration, stateful-workflow
excerpt=- The background migration must be recoverable at all times, i.e. it can be interrupted and must be resumed at any stage of the operation. We can achieve this by making them either idempotent for cross-system migrations or by making each change atomic if it's in a single database. - Only one background migration can run at a time. This is not a technical limitation, but makes reasoning about them easier. - We must h...

Qdrant: Qdrant 2024 Roadmap

Hybrid Search
Search
vector-database
rag
tool-selection
semantic-search
deployment

Chroma: How Chroma Sync Works

Pipeline Architecture
Hybrid Search
Search
API
vector-database
api-automation
semantic-search

FastMCP: Overview

Langfuse: Requirements


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
What is the best orchestration stack for a desktop agent that can launch local processes, access files, and report status through a simple UI?

Retrieved evidence:
[S1] tool=CrewAI
title=CrewAI: 3. Running Your Crew
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Planning, API, orchestration, api-automation, agent-planning
excerpt=Before running your crew, make sure you have the following keys set as environment variables in your .env file: - An OpenAI API key (or other LLM API key): OPENAI_API_KEY=sk-... - A Serper.dev API key: SERPER_API_KEY=YOUR_KEY_HERE Lock the dependencies and install them by using the CLI command but first, navigate to your project directory: To run your crew, execute the following command in the root of your project:...

[S2] tool=GitHub MCP Server
title=GitHub MCP Server: Install in Other MCP Hosts
url=https://github.com/github/github-mcp-server/blob/main/README.md
keywords=Integration, Agent, Desktop, self-hosted
excerpt=For other MCP host applications, please refer to our installation guides: - **Copilot CLI** - Installation guide for GitHub Copilot CLI - **GitHub Copilot in other IDEs** - Installation for JetBrains, Visual Studio, Eclipse, and Xcode with GitHub Copilot - **Claude Code & Claude Desktop** - Installation guide for Claude Code and Claude Desktop - **Cursor** - Installation guide for Cursor IDE - **Google Gemini CLI**...

[S3] tool=Weaviate
title=Weaviate: What goes through RAFT vs. what is local-only
url=https://github.com/weaviate/weaviate/blob/main/docs/runtime-reindex.md
keywords=Memory, self-hosted, stateful-workflow, agent-memory
excerpt=What does NOT go through RAFT (local-only): - **PREP** (FlushAndSwitch, Prepend, Shutdown the reindex bucket). - **OVERLAY SET** (install per-shard tokenization resolver). - **ATOMIC SWAP** (SwapBucketPointer per prop, in microseconds). - **Post-atomic inline tidy** (Shutdown old main, rename old → backup). - **OnMigrationComplete** per-strategy hook (in-memory shard-local state mutations). All five of those are pur...

[S4] tool=LiteLLM
title=LiteLLM: Hardened / Offline Testing
url=https://github.com/BerriAI/litellm/blob/litellm_internal_staging/docker/README.md
keywords=Docker, REST, self-hosted, tool-selection
excerpt=To ensure changes are safe for non-root, read-only root filesystems and restricted egress, always validate with the hardened compose file: This setup: - Builds from docker/Dockerfile.non_root with Prisma engines and Node toolchain baked into the image. - Runs the proxy as a non-root user with a read-only rootfs and only writable tmpfs mounts: - /app/cache (Prisma/NPM cache; backing PRISMA_BINARY_CACHE_DIR, NPM_CONFI...

CrewAI: 3. Running Your Crew

Planning API orchestration api-automation agent-planning

LiteLLM: Hardened / Offline Testing


Prompt:
You are ORCA, an orchestration advisor.

Answer the user using only the retrieved evidence below.

Rules:
- Cite specific tool claims with provided source IDs like [S1].
- Do not cite or link to sources that are not in the evidence packet.
- If evidence is thin, say what is uncertain instead of filling the gap.
- Label tradeoffs as "Inference" when they are reasoned from metadata or excerpts rather than explicitly stated.
- Do not claim a tool supports a feature unless the source excerpt or metadata supports it.
- Include exactly these sections: Recommendation, Architecture, Tradeoffs, Next steps.

User request:
Recommend an orchestration strategy for a tool that automates API-based SaaS workflows with conditional branching and error handling.

Retrieved evidence:
[S1] tool=CrewAI
title=CrewAI: Define structured state for precise control
url=https://github.com/crewAIInc/crewAI/blob/main/README.md
keywords=Workflow, Recommendation, Agent, stateful-workflow, branching-logic
excerpt=@listen("high_confidence") def execute_strategy(self): # Demonstrate complex decision making strategy_crew = Crew( agents=[ Agent(role="Strategy Expert", goal="Develop optimal market strategy") ], tasks=[ Task(description="Create detailed strategy based on analysis", expected_output="Step-by-step action plan") ] ) return strategy_crew.kickoff() @listen(or_("medium_confidence", "low_confidence")) def request_addition...

[S2] tool=TRL
title=TRL: Tips for environment classes
url=https://github.com/huggingface/trl/blob/main/docs/source/openenv.md
keywords=orchestration, tool-selection, stateful-workflow
excerpt=- **State for reward**: You can store any state you want on the environment instance (e.g., self.reward, self.done, etc.) and access it in your reward function via the environments parameter. Refer to the Quick Start guide for an example of this pattern. - **Error handling**: If a tool method raises an exception (e.g., ValueError("Game over.")), the trainer catches it and feeds the error message back to the model as...

[S3] tool=Semantic Kernel
title=Semantic Kernel: **Design - Analysis**
url=https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0032-agents.md
keywords=Agent, API, api-automation, stateful-workflow
excerpt=Agents participate in a conversation, often in response to user or environmental input. In addition to Agent, two fundamental concepts are identified from this pattern: - Conversation - Context for sequence of agent interactions. - Channel: ("Communication Path" from diagram) - The associated state and protocol with which the agent interacts with a single conversation. > Agents of different modalities must be free t...

[S4] tool=FastMCP
title=FastMCP: Overview
url=https://github.com/jlowin/fastmcp/blob/main/docs/v2/updates.mdx
keywords=AWS, Azure, API, Containerized, rag, api-automation
excerpt=🏢 **Enterprise Authentication** expands with AWS Cognito and Descope providers, broadening the authentication ecosystem. 🛠 **Improved Reliability** through enhanced URL handling, persistent OAuth storage, and numerous parser fixes based on community feedback. FastMCP 2.12.3 focuses on performance and developer experience improvements. This release includes optimized auth provider imports that reduce server startup...

Workflow Recommendation Agent stateful-workflow branching-logic

TRL: Tips for environment classes

Semantic Kernel: **Design - Analysis**

FastMCP: Overview

AWS Azure API Containerized rag api-automation