deepagents-thread-inspector

by langchain-ai

Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for…

npx skills add https://github.com/langchain-ai/deepagents --skill deepagents-thread-inspector
Download ZIPGitHub
27.8k

Deep Agents Thread Inspector

If LangSmith tooling is available for a traced thread, prefer it. Otherwise, use scripts/inspect_sessions.py instead of manually decoding database blobs. It opens the database read-only and deserializes the root message channel with LangGraph's strict MsgPack loader — reading the materialized messages from the latest checkpoint, or replaying writes in checkpoint order when that fast path is unavailable — and emits JSON.

Inspect local state

Resolve SKILL_DIR to the directory containing this SKILL.md; do not assume a user, project, or installation-specific location. Start with the smallest useful view:

python3 "$SKILL_DIR/scripts/inspect_sessions.py" THREAD_ID --mode latest-turn

A unique thread-ID prefix is accepted. Select another view when needed:

python3 "$SKILL_DIR/scripts/inspect_sessions.py" THREAD_ID --mode summary
python3 "$SKILL_DIR/scripts/inspect_sessions.py" THREAD_ID --mode transcript

Use --include-metadata only when run, repository, model, checkpoint, or LangGraph metadata matters. Use --max-content N to raise or lower the default 4,000-character limit per message, tool result, or tool-call argument.

If the user does not know the ID, list recent threads first:

python3 "$SKILL_DIR/scripts/inspect_sessions.py" --list 20

Pass --db PATH only for a non-default session store. The default is ~/.deepagents/.state/sessions.db; DEEPAGENTS_SESSIONS_DB can override it.

Explain the result

Synthesize the JSON rather than pasting it verbatim.

  • State the user's request, the assistant's conclusion, and significant tool actions or failures.
  • Distinguish stored facts from your interpretation.
  • For the latest turn, describe only the final user message and subsequent activity unless earlier context is required to make it understandable.
  • Mention truncation when a relevant record has content_truncated or args_truncated set.
  • Surface reconstruction problems when the result includes a top-level warnings array (for example, a corrupt checkpoint, a skipped write, or malformed metadata) so conclusions are appropriately hedged.
  • Do not expose unrelated credentials, tokens, personal data, or hidden reasoning that may appear in local records.

Safety

Keep inspection read-only. Do not deserialize an untrusted database: checkpoint deserialization is intended for trusted local Deep Agents state. Do not mutate or delete session rows unless the user separately and explicitly requests it.

More skills from langchain-ai

langgraph-docs
langchain-ai
Access LangGraph documentation to build stateful agents and multi-agent workflows. Fetches official LangGraph Python docs covering state machines, graph-based agent design, and human-in-the-loop patterns Prioritizes relevant documentation by query type: implementation guides for how-to questions, concept pages for theory, tutorials for end-to-end examples, and API references for technical details Automatically selects 2–4 most relevant documentation URLs and retrieves their content to answer...
official
langgraph-human-in-the-loop
langchain-ai
Pause graph execution for human review, approval, or validation, then resume with their input. Requires three components: a checkpointer (InMemorySaver or PostgresSaver), a thread ID in config, and JSON-serializable interrupt payloads interrupt(value) pauses and surfaces data; Command(resume=value) resumes and returns that value to the paused node All code before interrupt() re-executes on resume, so side effects must be idempotent (use upsert, not insert) Supports approval workflows,...
official
web-research
langchain-ai
Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research
official
langchain-oss-primer
langchain-ai
ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any…
official
skill-creator
langchain-ai
Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user…
official
social-media
langchain-ai
Drafts platform-specific social media posts with research-backed content and generated companion images. Supports LinkedIn posts (1,300 characters with professional tone) and Twitter/X threads (280 characters per tweet with 1/🧵 format) Requires delegating research to a subagent before writing, then reading findings to ensure accuracy and relevance Generates eye-catching social images automatically using generate_social_image tool with bold, high-contrast compositions optimized for small...
official
deep-agents-memory
langchain-ai
Pluggable memory and file backends for Deep Agents with ephemeral, persistent, and hybrid routing options. Four backend types: StateBackend (thread-scoped, ephemeral), StoreBackend (cross-session persistent), FilesystemBackend (real disk access for local dev), and CompositeBackend (route different paths to different backends) FilesystemMiddleware provides six file operation tools: ls , read_file , write_file , edit_file , glob , grep CompositeBackend uses longest-prefix matching to route...
official
deep-agents-orchestration
langchain-ai
Orchestrate subagents, plan multi-step tasks, and require human approval for sensitive operations. Delegate work to specialized subagents via the task tool; custom subagents support isolated tool sets and system prompts, while the default "general-purpose" subagent inherits main agent configuration Plan and track complex workflows with write_todos , organizing tasks across pending, in-progress, and completed states; requires a thread_id for persistence across invocations Implement...
official