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LangChain Forum - Topics tagged python-help

Llama-server process has terminated: invalid argument --load-mode with Ollama client 0.32.6 and langchain-ollama 1.1.0 Langchain Certified Agent Engineer Exam - Exam link not received and no response Null-drift: A bare-metal O(1) Memory Store for continuous LangGraph agents Clarification needed: Assistant config vs context and graph initialization Proposal: a small local helper for readable run traces via PR Proxy Authentication Required 407 What is the right way to dynamically create and run a graph? Re-Implement claude code's dynamic workflow using langchian & deepagents How to define a correct state for multi-agent system Response Format Groq Model Pydantic I hope to get some recommendations for practical skills Interrupt does not work correctly in LangGraph The Qwen3.6b model in fireworks through initchatmodel reporting hugely inflated tokens For parallel execution in Node, should i use the functional API? Potential Enhancement: Django-Managed PostgresSaver Pre-interrupt() code re-runs on resume — anti-pattern, or is there a sanctioned way to detect resume? Interrupt parallel branch execution Best practices for self-hosting LangGraph Server OSS without LangGraph keys Dynamically Enabling/Disabling Graphs in a LangGraph Server at Runtime LangGraph thread copy can take 12+ minutes: recommended production pattern? Will DeltaChannel be the default for AgentState.messages, or expected to stay opt-in? Proposal: additional docs for implementing custom DB checkpointers or a guide on generic base checkpointer Prompt_cache_retention: '24h' supported in langchain agents and where to provide it, inside invoke or while creating client? Could RAG pipelines realistically cause deployment timeouts, is Render suitable for first-time RAG deployments? How do I use langchain_postgres' init_vectorstore_table correctly? Proposal: Graph-wide default error handler for StateGraph (fallback for nodes without error_handler) Support timedelta for CachePolicy.ttl, consistent with TimeoutPolicy Anyone confirms this issue that deepagent ui streaming is disturb by update in deepagent or bug issue Best Stack for Building AI Applications Seeking help regarding the connection between Websocket and tool calls
Tiny LangGraph -> Assay evidence sample from tasks v2
Rul1an · 2026-04-20 · via LangChain Forum - Topics tagged python-help
Hi all — we put together a very small sample on our side to test one honest external-evidence seam from LangGraph, and I wanted to share that rather than open with a broad integration pitch. The sample is here: github.com assay/examples/langgraph-task-evidence at main · Rul1an/assay main/examples/langgraph-task-evidence Trust compiler for agent systems Deterministic MCP policy enforcement, verifiable evidence bundles, and reviewable trust artifacts. - Rul1an/assay It stays deliberately narrow. We used graph.stream(..., stream_mode="tasks", version="v2") as the current best first seam hypothesis, kept the checkpointer only as an enabling dependency, and froze one success artifact, one failure artifact, and one malformed case. Those get mapped into Assay-shaped placeholder evidence without treating LangGraph task outcomes, orchestration behavior, or graph routing as Assay truth. One small nuance: if a run aborts before LangGraph emits a terminal task-result part, the exporter adds a tiny terminal stream_error record so the broken run is still visible in the sample. That record is exporter-added after reading failed task state from the required checkpointer; it is not claimed as a native tasks stream part. We are not asking Assay to inherit LangGraph task outcomes, runtime judgments, or trust semantics as truth. The goal here is just to keep the seam small and see whether there is a clean external-consumer handoff at all. If this is roughly the right direction, the main thing I’d love to understand is whether tasks in stream v2 is also how you’d point an external evidence consumer at the smallest stable OSS-native surface, or whether there is a thinner exported seam you’d rather steer people toward. Happy to keep this small and follow your lead on the right boundary. 1 post - 1 participant Read full topic