Welcome to Runeval 👋
Practical AI research for developers choosing tools and building reliable systems.
Expect useful guides, clear tradeoffs, and implementation pitfalls drawn from documentation and research.
Start with our practical guide to agent memory, checkpoints, and durable workflows. Then explore the tool reports that match your work.
Browse the library: https://runeval.dev/?utm_source=telegram&utm_medium=social&utm_campaign=channel_welcome&utm_content=pinned
By alokrm
Practical AI research for developers choosing tools and building reliable systems.
Expect useful guides, clear tradeoffs, and implementation pitfalls drawn from documentation and research.
Start with our practical guide to agent memory, checkpoints, and durable workflows. Then explore the tool reports that match your work.
Browse the library: https://runeval.dev/?utm_source=telegram&utm_medium=social&utm_campaign=channel_welcome&utm_content=pinned
By alokrm
Runeval
Runeval — Technical AI Intelligence
Evidence-led technical research, implementation notes, and comparisons for emerging AI tools and models.
PRACTICAL GUIDE · Agent reliability
Agent memory, checkpoints, and durable workflows: choosing what to persist
A checkpoint can preserve progress without proving that an external action happened only once. If a worker loses a refund receipt, recovery must resolve the provider’s outcome before issuing another action.
For developers designing agents that pause, resume, or call external services. Includes a worked refund scenario, two diagrams, comparison tables, and recovery checks.
15 min read · By alokrm
https://runeval.dev/guides/agent-memory-checkpoints-durable-workflows?utm_source=telegram&utm_medium=social&utm_campaign=agent-memory-checkpoints-durable-workflows&utm_content=initial
Agent memory, checkpoints, and durable workflows: choosing what to persist
A checkpoint can preserve progress without proving that an external action happened only once. If a worker loses a refund receipt, recovery must resolve the provider’s outcome before issuing another action.
For developers designing agents that pause, resume, or call external services. Includes a worked refund scenario, two diagrams, comparison tables, and recovery checks.
15 min read · By alokrm
https://runeval.dev/guides/agent-memory-checkpoints-durable-workflows?utm_source=telegram&utm_medium=social&utm_campaign=agent-memory-checkpoints-durable-workflows&utm_content=initial
runeval.dev
Agent memory, checkpoints, and durable workflows: choosing what to persist
Choose what must survive a conversation, a worker crash, and an external action, with a worked refund scenario and concrete recovery checks.
AGENT STATE · LangGraph
When does an agent need persistent state?
Conversation history, execution progress, and long-term memory have different jobs. Treating them as one growing transcript makes recovery and retrieval harder to reason about.
For developers whose agents span interruptions, approvals, or multiple sessions. This report explains the adoption threshold and the recovery questions to answer first.
3 min read · By alokrm
https://runeval.dev/tools/langchain-ai-langgraph?utm_source=telegram&utm_medium=social&utm_campaign=langchain-ai-langgraph&utm_content=initial
When does an agent need persistent state?
Conversation history, execution progress, and long-term memory have different jobs. Treating them as one growing transcript makes recovery and retrieval harder to reason about.
For developers whose agents span interruptions, approvals, or multiple sessions. This report explains the adoption threshold and the recovery questions to answer first.
3 min read · By alokrm
https://runeval.dev/tools/langchain-ai-langgraph?utm_source=telegram&utm_medium=social&utm_campaign=langchain-ai-langgraph&utm_content=initial
Runeval
LangGraph: deciding when an agent needs persistent state
Understand LangGraph's stateful orchestration, memory, and intervention points, including the recovery questions its introductory docs leave open.
DURABLE JOBS · Vercel Workflow
When should a TypeScript job become a workflow?
Start with the failure it must survive. Saved progress and retries help a job continue after interruptions, while external writes still need their own duplicate-request protections.
For TypeScript developers building jobs with long waits or several stages. The report covers workflow and step boundaries, backend ownership, and why waiting without compute is not a complete cost model.
3 min read · By alokrm
https://runeval.dev/tools/vercel-workflow?utm_source=telegram&utm_medium=social&utm_campaign=vercel-workflow&utm_content=initial
When should a TypeScript job become a workflow?
Start with the failure it must survive. Saved progress and retries help a job continue after interruptions, while external writes still need their own duplicate-request protections.
For TypeScript developers building jobs with long waits or several stages. The report covers workflow and step boundaries, backend ownership, and why waiting without compute is not a complete cost model.
3 min read · By alokrm
https://runeval.dev/tools/vercel-workflow?utm_source=telegram&utm_medium=social&utm_campaign=vercel-workflow&utm_content=initial
Runeval
Vercel Workflow: when a TypeScript job needs durable execution
Learn what Vercel Workflow persists, why step boundaries matter, and how managed or self-hosted backends change the operational responsibility.
EVALUATION · Promptfoo
Build checks that catch real regressions
A higher aggregate score can hide a serious failure. Define the release decision first, then keep output-shape checks, semantic criteria, and known regression cases visible as separate requirements.
For developers comparing prompts or models and adding evaluation to CI. The report explains useful comparison boundaries, model-judge limitations, and how regression gates differ from red teaming.
3 min read · By alokrm
https://runeval.dev/tools/promptfoo-promptfoo?utm_source=telegram&utm_medium=social&utm_campaign=promptfoo-promptfoo&utm_content=initial
Build checks that catch real regressions
A higher aggregate score can hide a serious failure. Define the release decision first, then keep output-shape checks, semantic criteria, and known regression cases visible as separate requirements.
For developers comparing prompts or models and adding evaluation to CI. The report explains useful comparison boundaries, model-judge limitations, and how regression gates differ from red teaming.
3 min read · By alokrm
https://runeval.dev/tools/promptfoo-promptfoo?utm_source=telegram&utm_medium=social&utm_campaign=promptfoo-promptfoo&utm_content=initial
Runeval
Promptfoo: building evaluation checks that catch real regressions
Use Promptfoo's comparisons and automated checks to evaluate meaningful changes, while separating test coverage, model judgments, and data boundaries.
AI agent observability
MLflow for AI Agents: Tracing, Evaluation Metrics, and Gateway Architecture
MLflow treats an agent run as a hierarchy of OpenTelemetry spans, so teams can inspect tool calls, retrieval context, latency, exceptions, and final output in one trace, then evaluate those traces for correctness, relevance, safety, and other failure modes. The trade-off is operational: high-volume traces increase storage and serialization load, while LLM judges are usually better run asynchronously on sampled batches than on every production span.
Useful for platform and ML engineering teams deciding whether they need one observability and evaluation layer across agent systems and traditional ML workloads.
5 min read · By alokrm
Read the article →
MLflow for AI Agents: Tracing, Evaluation Metrics, and Gateway Architecture
MLflow treats an agent run as a hierarchy of OpenTelemetry spans, so teams can inspect tool calls, retrieval context, latency, exceptions, and final output in one trace, then evaluate those traces for correctness, relevance, safety, and other failure modes. The trade-off is operational: high-volume traces increase storage and serialization load, while LLM judges are usually better run asynchronously on sampled batches than on every production span.
Useful for platform and ML engineering teams deciding whether they need one observability and evaluation layer across agent systems and traditional ML workloads.
5 min read · By alokrm
Read the article →
Runeval
MLflow for AI Agents: Tracing, Evaluation Metrics, and Gateway Architecture
Explore how MLflow leverages OpenTelemetry tracing, 50+ evaluation metrics, and prompt lifecycle governance for production agent and LLM applications.
Distributed vector search infrastructure
Milvus Vector Database: How Compute-Storage Separation Scales ANN Search
Milvus separates query execution from ingestion, so read-heavy traffic can scale Query Nodes without scaling the write path, while bulk ingestion can expand Data Nodes independently. That flexibility comes with Kubernetes and multi-service operational overhead in a distributed deployment; Milvus Lite offers a file-backed Python path for local prototypes.
Useful for platform and ML engineers evaluating vector databases for high-scale RAG or independently scalable read and write workloads.
4 min read · By alokrm
Read the article →
Milvus Vector Database: How Compute-Storage Separation Scales ANN Search
Milvus separates query execution from ingestion, so read-heavy traffic can scale Query Nodes without scaling the write path, while bulk ingestion can expand Data Nodes independently. That flexibility comes with Kubernetes and multi-service operational overhead in a distributed deployment; Milvus Lite offers a file-backed Python path for local prototypes.
Useful for platform and ML engineers evaluating vector databases for high-scale RAG or independently scalable read and write workloads.
4 min read · By alokrm
Read the article →
Runeval
Milvus Vector Database: How Compute-Storage Separation Scales ANN Search
An evidence-based technical analysis of Milvus architecture, evaluating compute-storage separation, query versus data nodes, index options, and deployment tradeoffs.
Durable TypeScript AI agents
How Trigger.dev Sustains Long-Running AI Agents via Checkpointing and Zero-Idle Waits
A long-running agent does not need to keep a worker occupied while it waits. Trigger.dev checkpoints the run at a delay, webhook callback, or human approval, frees the compute, then restores the saved execution state when work resumes.
Useful for TypeScript developers and platform engineers designing approval-gated agents, durable background jobs, or workflows that wait on external events.
4 min read · By alokrm
Read the article →
How Trigger.dev Sustains Long-Running AI Agents via Checkpointing and Zero-Idle Waits
A long-running agent does not need to keep a worker occupied while it waits. Trigger.dev checkpoints the run at a delay, webhook callback, or human approval, frees the compute, then restores the saved execution state when work resumes.
Useful for TypeScript developers and platform engineers designing approval-gated agents, durable background jobs, or workflows that wait on external events.
4 min read · By alokrm
Read the article →
Runeval
How Trigger.dev Sustains Long-Running AI Agents via Checkpointing and Zero-Idle Waits
An architectural analysis of Trigger.dev's durable execution engine for AI agents, covering checkpointing, zero-idle waits, and TypeScript orchestration.
Parallel AI coding agent orchestration
Orchestrating Parallel AI Coding Agents with amux's Rust Control Plane
A shared task board can avoid duplicate agent work by claiming each task with an atomic compare-and-swap operation. amux pairs that ownership rule with separate done and verified gates, so implementation and peer or CI validation remain distinct instead of being collapsed into one status.
Useful for engineering teams evaluating local-first orchestration for parallel AI coding agents.
5 min read · By alokrm
Read the article →
Orchestrating Parallel AI Coding Agents with amux's Rust Control Plane
A shared task board can avoid duplicate agent work by claiming each task with an atomic compare-and-swap operation. amux pairs that ownership rule with separate done and verified gates, so implementation and peer or CI validation remain distinct instead of being collapsed into one status.
Useful for engineering teams evaluating local-first orchestration for parallel AI coding agents.
5 min read · By alokrm
Read the article →
Runeval
Orchestrating Parallel AI Coding Agents with amux's Rust Control Plane
Explore amux, a single-binary Rust control plane for parallel AI coding agents featuring atomic SQLite task claiming, self-healing watchdogs, and .mdai DAG workflows.
Unified graph and vector storage
Can HelixDB Unify Vector Search and Graph Traversals on Object Storage?
HelixDB Cloud pairs object-storage-backed persistence with one writer and auto-scaling readers. That lets teams expand read capacity for retrieval workloads, but write-heavy ingestion remains bounded by the single-writer topology—a key tradeoff to test before replacing separate graph, vector, and application databases.
Useful for platform and AI engineers evaluating a unified data layer for RAG, agent memory, or knowledge-graph applications.
5 min read · By alokrm
Read the article →
Can HelixDB Unify Vector Search and Graph Traversals on Object Storage?
HelixDB Cloud pairs object-storage-backed persistence with one writer and auto-scaling readers. That lets teams expand read capacity for retrieval workloads, but write-heavy ingestion remains bounded by the single-writer topology—a key tradeoff to test before replacing separate graph, vector, and application databases.
Useful for platform and AI engineers evaluating a unified data layer for RAG, agent memory, or knowledge-graph applications.
5 min read · By alokrm
Read the article →
Runeval
Can HelixDB Unify Vector Search and Graph Traversals on Object Storage?
An evidence-led analysis of HelixDB: how its Rust-based engine combines OLTP graph traversal, vector search, and object storage persistence for AI retrieval.
AI application performance
Why AI responses are slow: tracing retrieval, tools and model generation
Measure two waits separately: user submit to the first visible answer, and user submit to a complete, usable result. A fast first protocol chunk may contain only metadata, while a prompt first answer can still be followed by a slow completion. Trace one request across application queues, retrieval, every tool attempt, every model call and display so you optimize the measured interval that actually delays the user milestone—and verify the end-to-end result improves without more errors or timeouts.
For engineers and technical leads diagnosing latency in retrieval-augmented, tool-using or streaming AI applications.
13 min read · By alokrm
Read the practical guide →
Why AI responses are slow: tracing retrieval, tools and model generation
Measure two waits separately: user submit to the first visible answer, and user submit to a complete, usable result. A fast first protocol chunk may contain only metadata, while a prompt first answer can still be followed by a slow completion. Trace one request across application queues, retrieval, every tool attempt, every model call and display so you optimize the measured interval that actually delays the user milestone—and verify the end-to-end result improves without more errors or timeouts.
For engineers and technical leads diagnosing latency in retrieval-augmented, tool-using or streaming AI applications.
13 min read · By alokrm
Read the practical guide →
runeval.dev
Why AI responses are slow: tracing retrieval, tools and model generation
Separate first-visible-answer delay from completion time, map the request's required work, and choose the next measurement for retrieval, tools, queueing or generation.
Open-source LLM observability
Opik: Technical Architecture for Open-Source LLM Observability
Opik can keep one evaluation workflow across development and production: teams can compare prompts or models against datasets, enforce PyTest checks in CI, and run asynchronous online evaluation rules on production traces. The self-hosted Apache-2.0 stack adds infrastructure responsibility—Docker Compose locally or Kubernetes and Helm at larger scale—while its managed cloud option trades that control for lower operational overhead.
Useful for ML and platform engineers choosing between self-hosted and managed tracing, evaluation, and monitoring for LLM applications and agents.
3 min read · By alokrm
Read the article →
Opik: Technical Architecture for Open-Source LLM Observability
Opik can keep one evaluation workflow across development and production: teams can compare prompts or models against datasets, enforce PyTest checks in CI, and run asynchronous online evaluation rules on production traces. The self-hosted Apache-2.0 stack adds infrastructure responsibility—Docker Compose locally or Kubernetes and Helm at larger scale—while its managed cloud option trades that control for lower operational overhead.
Useful for ML and platform engineers choosing between self-hosted and managed tracing, evaluation, and monitoring for LLM applications and agents.
3 min read · By alokrm
Read the article →
Runeval
Opik: Technical Architecture for Open-Source LLM Observability
An architectural breakdown of Opik, covering open-source LLM tracing, online evaluation rules, Docker/Kubernetes deployment options, and OpenTelemetry integration.