Mini toolsAgentic Parallelism
Core concepts - where to apply parallelism in agentic solution
README
๐ค Agentic Parallelism: A Practical Guide ๐
Conceptual based implementations of 14 core patterns for building faster, smarter, and more reliable AI agentic systems using LangChain and LangGraph.
๐ Want to learn every concept in detail?
Read the full, in-depth article that defines and explains all 14 core concepts of Agentic Parallelism.
โจ Introduction
Welcome to the future of AI agent development! This repository is a comprehensive, hands-on collection of 14 industrial-grade notebooks demonstrating the core principles of Agentic Parallelism.
In the world of AI agents, speed, quality, and reliability are not just featuresโthey are requirements. A single, sequential agent can be slow, prone to errors, and limited in its problem-solving ability. The solution is to think in parallel: designing systems where multiple agents, processes, or tasks execute concurrently to achieve a common goal.
This repository is your practical guide to building faster, smarter, and more robust agentic systems using LangChain and LangGraph. Each notebook tackles a specific parallelism pattern, complete with real-world examples, detailed instrumentation, and state analysis.
โก The Core Problem: Why Parallelism Matters
Large-scale agentic systems are often bottlenecked by two things:
- I/O Latency: Waiting for networks, databases, and external API calls.
- Quality & Reliability: A single line of reasoning can lead to suboptimal or incorrect outcomes.
Agentic Parallelism addresses these challenges head-on by overlapping wait times, exploring multiple solution paths, and creating resilient, self-correcting systems.
๐ Table of Concepts
This repository is structured into four key areas of agentic design. Each notebook is self-contained and ready to run. Simply click on a notebook link to explore its implementation.
๐ Core Agent Patterns
Fundamental techniques for enhancing a single agent's capabilities.
| Concept | Description | Key Benefit | Notebook |
|---|---|---|---|
| 1. Parallel Tool Use | An agent calls multiple tools (e.g., a stock API and a news search) simultaneously instead of sequentially. | ๐ Reduces I/O Latency | 01_parallel_tool_use.ipynb |
| 2. Parallel Hypothesis | An agent generates multiple strategies or "thoughts" in parallel, explores them all, and synthesizes the best outcome. | ๐ก Improves Solution Quality | 02_parallel_hypothesis.ipynb |
| 3. Parallel Evaluation | A panel of specialized "critic" agents reviews a piece of content simultaneously, each from a unique perspective (e.g., brand voice, fact-checking). | โ Robust AI Governance | 03_parallel_evaluation.ipynb |
| 4. Speculative Execution | The system anticipates the agent's most likely next action (e.g., a tool call) and begins executing it while the agent is still "thinking". | ๐โโ๏ธ Hides Latency | 04_speculative_execution.ipynb |
๐งโ๐คโ๐ง Multi-Agent Architectures
Designing digital organizations where multiple agents collaborate to solve complex problems.
| Concept | Description | Key Benefit | Notebook |
|---|---|---|---|
| 5. Hierarchical Teams | A "manager" agent decomposes a complex task and delegates sub-tasks to a team of "worker" agents who execute in parallel. | ๐ Scalability & Specialization | 05_hierarchical_agent_teams.ipynb |
| 6. Competitive Ensembles | A diverse team of agents tackles the same problem independently. A "judge" agent then selects the best solution from the competing outputs. | ๐ Robustness & Creativity | 06_competitive_agent_ensembles.ipynb |
| 7. Agent Assembly Line | A sequence of specialized agents processes a continuous stream of tasks in a pipeline, maximizing overall system throughput. | ๐ญ High-Throughput Processing | 07_agent_assembly_line.ipynb |
| 8. Decentralized Blackboard | Independent agents collaborate by reading from and writing to a shared data space, enabling emergent, opportunistic problem-solving. | ๐ง Adaptive Collaboration | 08_decentralized_blackboard.ipynb |
๐ก๏ธ System Reliability Patterns
Building resilient systems that can handle real-world failures and unpredictability.
| Concept | Description | Key Benefit | Notebook |
|---|---|---|---|
| 9. Redundant Execution | For a critical but unreliable task, execute two identical agents in parallel. The system uses the result from the first one to finish. | โ๏ธ Fault Tolerance & Consistency | 09_redundant_execution.ipynb |
๐ Advanced RAG Patterns
Applying parallelism to build state-of-the-art Retrieval-Augmented Generation systems.
| Concept | Description | Key Benefit | Notebook |
|---|---|---|---|
| 10. Parallel Query Expansion | Transform a user's query into multiple, diverse search queries (e.g., sub-questions, hypothetical documents) and execute them all at once. | ๐ฏ Maximizes Retrieval Recall | 10_parallel_query_expansion.ipynb |
| 11. Sharded Retrieval | Partition a massive knowledge base into smaller "shards" and search them all in parallel, enabling low-latency retrieval over enterprise-scale data. | ๐๏ธ Scalability & Performance | 11_sharded_retrieval.ipynb |
| 12. Hybrid Search Fusion | Run vector (semantic) search and keyword (lexical) search in parallel, then fuse their results to get the best of both worlds. | ๐งฌ High-Fidelity Retrieval | 12_hybrid_search_fusion.ipynb |
| 13. Context Pre-processing | After retrieval, use parallel LLM calls to distill a large, noisy context into a smaller, denser, and more relevant context before final generation. | ๐ง Improves Accuracy & Reduces Cost | 13_parallel_context_preprocessing.ipynb |
| 14. Multi-Hop Retrieval | Decompose a complex query into sub-questions, answer each with its own parallel RAG process, and then synthesize a final, comprehensive answer. | ๐ง Solves Complex Questions | 14_parallel_multi_hop_retrieval.ipynb |
๐ ๏ธ Technical Stack
This project leverages a modern, powerful stack for building agentic systems:
- Orchestration: ๐ฆ๐ LangGraph
- Core Framework: ๐ฆ LangChain
- LLMs: ๐ฆ Meta Llama 3, โ๏ธ Claude 3 Sonnet (on Vertex AI)
- Models & Inference: ๐ค Hugging Face Transformers
- Tools: ๐ Tavily Search, ๐น yfinance
- Vector Stores: ๐พ FAISS
- Observability: ฮนฯ LangSmith
๐ Getting Started
-
Clone the repository:
git clone https://github.com/FareedKhan-dev/agentic-parallelism.git cd agentic-parallelism -
Install dependencies:
Each notebook lists its specific dependencies at the top. To install all common dependencies at once:pip install -U langchain langgraph langsmith langchain-huggingface transformers accelerate bitsandbytes torch tavily-python yfinance langchain-google-vertexai sentence-transformers faiss-cpu scikit-learn tiktoken -
Set up your API Keys:
The notebooks will prompt you to enter the necessary API keys. At a minimum, you will need:LANGCHAIN_API_KEY(for LangSmith tracing)HUGGING_FACE_HUB_TOKEN(to download Llama 3)TAVILY_API_KEY(for search-enabled agents)- Google Cloud Authentication (for notebook
06_competitive_agent_ensembles)
Now you're ready to run the notebooks and explore the power of agentic parallelism!
๐ค Contributing
Contributions are welcome! If you have ideas for new parallelism patterns, improvements to existing notebooks, or bug fixes, please open an issue or submit a pull request.
๐ License
This project is licensed under the MIT License. See the LICENSE file for details.
