To make an LLM agent's decisions reproducible, you must eliminate non-determinism by controlling inputs, model parameters, and the execution environment. Since you are building **Liminality**, a question-decomposition and routing MCP server, reproducibility is critical for debugging, testing, and ensuring consistent behavior across different AI models and human users.
Here are the co-required components to achieve reproducibility:
### 1. Control Model Parameters (Temperature & Top-P) LLMs are inherently stochastic. To make outputs reproducible: - **Set `temperature=0`**: This forces the model to always pick the highest-probability next token, effectively making the output deterministic for a given prompt. - **Fix `top_p` and `top_k`**: If you cannot use `temperature=0`, fix these sampling parameters to narrow the probability distribution. - **Note**: Even with `temperature=0`, some models may still vary slightly due to backend optimizations or parallel decoding. For strict reproducibility, use models that guarantee deterministic decoding (e.g., via `do_sample=False` in Hugging Face transformers).
### 2. Deterministic Prompting & Context Management - **Static Prompts**: Ensure the system prompt, user prompt, and any few-shot examples are identical across runs. - **Ordered Context**: If using a context window (e.g., RAG), ensure the retrieval order is deterministic. Sort retrieved documents by a stable key (e.g., ID, timestamp) rather than relying on the model’s internal ranking, which may vary. - **No Dynamic Variables**: Avoid injecting real-time data (e.g., current time, random IDs) into the prompt unless it is explicitly part of the reproducible input state.
### 3. Seed Control (If Supported) - **Model Seeds**: Some LLM APIs allow passing a `seed` parameter. If available, use it to initialize the random number generator. - **Framework Seeds**: If running locally (e.g., via LangChain, LlamaIndex, or Hugging Face), set seeds for: - The LLM backend (e.g., `torch.manual_seed()`) - The retrieval system (e.g., vector database search if it uses randomness) - Any other stochastic components (e.g., data augmentation)
### 4. Deterministic Tool Execution & Routing Since **Liminality** routes questions to sub-questions and tools: - **Tool Input Serialization**: Ensure that tool inputs are serialized deterministically (e.g., sort dictionary keys before hashing or passing). - **Tool Output Caching**: Cache tool outputs by input hash. If the same input is provided, return the cached output instead of re-executing the tool (which might have side effects or non-deterministic results). - **Routing Logic**: If your routing logic uses any probabilistic thresholds (e.g., confidence scores), fix the threshold or use a deterministic classifier (e.g., keyword matching, rule-based routing) for reproducible paths.
### 5. Version Control of All Components - **Model Version**: Pin the exact model version (e.g., `gpt-4-0613` instead of `gpt-4`). Model updates can change behavior. - **Code Version**: Use Git commits to pin the exact version of your Liminality codebase. - **Dependency Versions**: Pin versions of all libraries (e.g., `langchain==0.1.0`, `transformers==4.30.0`) to avoid changes in underlying libraries affecting behavior.
### 6. Logging & Tracing for Auditability - **Full Input/Output Logging**: Log every prompt, tool call, and response with a unique trace ID. - **Replay Capability**: Store logs in a format that allows replaying the exact sequence of events. This is essential for debugging non-reproducible issues in production.
### Implementation for Liminality Given your architecture: 1. **Decomposition Phase**: Ensure the decomposition prompt is static and the model version is pinned. 2. **Routing Phase**: Use deterministic rules or pinned model versions for routing decisions. 3. **Tool Execution Phase**: Cache tool outputs by input hash to avoid re-execution. 4. **Aggregation Phase**: If combining results from multiple sub-questions, ensure the aggregation logic is deterministic (e.g., fixed order of processing).
### Pending Action Do you want me to generate a code snippet for **Liminality** that implements deterministic routing and tool caching? This would involve: 1. Pinning model versions in your MCP server configuration. 2. Implementing a hash-based cache for tool outputs. 3. Adding logging for full trace replay.
Confirm if you want this code generated.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai