# Langchain Knowledge Patch > LangChain - Skill: `nevaberry/langchain-knowledge-patch-2` (Agent Skill, multi-file: 9 files) - Install (CLI): `npx skillmds@latest add nevaberry/langchain-knowledge-patch-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nevaberry/langchain-knowledge-patch-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: nevaberry (https://skillmd.com/u/nevaberry) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nevaberry/langchain-knowledge-patch-2 --- # LangChain Knowledge Patch Use this skill when implementing or migrating LangChain agents, tools, provider integrations, MCP clients, Deep Agents, or LangGraph workflows. ## Reference index Read the reference that matches the code being changed. Several may apply to an agent that combines provider tools, MCP, and a LangGraph runtime. | Reference | Topics | | --- | --- | | [references/v1-migration.md](references/v1-migration.md) | Package moves, runtime floor, `create_agent`, middleware, structured output, content blocks, and model profiles | | [references/agents-and-tools.md](references/agents-and-tools.md) | Tool runtime injection, dynamic tools, custom state, middleware, routing, limits, and tracing | | [references/langgraph-workflows.md](references/langgraph-workflows.md) | Typed streams and results, fault tolerance, draining, state schemas, caching, deferred nodes, and resumable streams | | [references/mcp.md](references/mcp.md) | Session lifetime, structured results, resources, prompts, interceptors, callbacks, and elicitation | | [references/deep-agents.md](references/deep-agents.md) | Harness tools, filesystem backends, subagents, sandboxes, profiles, memory, context, and CLI use | | [references/openai.md](references/openai.md) | `ChatOpenAI`, Responses routing and tools, Azure endpoints, conversation state, compaction, caching, and token counting | | [references/anthropic.md](references/anthropic.md) | `ChatAnthropic`, schemas, tool streaming and discovery, caching, compaction, code execution, files, and remote MCP | ## Migration essentials ### Build agents with `create_agent` Use `langchain.agents.create_agent` for the LangGraph-backed model/tool loop. It supersedes the deprecated `create_react_agent` from `langgraph.prebuilt`. ```python from langchain.agents import create_agent agent = create_agent( model="openai:gpt-5", tools=[get_weather], system_prompt="Fetch the weather.", ) ``` The `system_prompt` may also be a `SystemMessage`, including one with advanced content blocks or cache-control blocks. ### Move legacy APIs to the classic package The main Python package is limited to core abstractions. Install `langchain-classic` for legacy APIs. Python 3.9 is unsupported; use Python 3.10 or newer. ```shell uv pip install --upgrade langchain uv pip install langchain-classic ``` In JavaScript, legacy chains, retrievers, indexing APIs, and community exports move to `@langchain/classic`; for example, replace `langchain/chains` with `@langchain/classic/chains`. ```shell npm install langchain @langchain/core @langchain/classic ``` ### Use middleware at the agent-loop boundary Python custom middleware subclasses `AgentMiddleware`; JavaScript uses `createMiddleware`. The lifecycle hook pairs are: | Python | JavaScript | | --- | --- | | `before_agent` | `beforeAgent` | | `before_model` | `beforeModel` | | `wrap_model_call` | `wrapModelCall` | | `wrap_tool_call` | `wrapToolCall` | | `after_model` | `afterModel` | | `after_agent` | `afterAgent` | Bundled policies include PII redaction or blocking, summarization triggers, human decisions of `approve`, `edit`, or `reject`, model retries with exponential backoff, and OpenAI content moderation across user, model, and tool boundaries. JavaScript `dynamicSystemPromptMiddleware` returns are additive: strings and `SystemMessage` objects extend existing system messages rather than replacing them. ## Agent state and tools ### Inject `ToolRuntime` A tool may declare a model-hidden `runtime: ToolRuntime` parameter. It exposes short-term `state`, immutable typed `context`, the long-term `store`, `stream_writer`, `config`, and `tool_call_id`. Both `config` and `runtime` are reserved argument names. Return a `Command` to mutate state. When the model needs the result, include a `ToolMessage` correlated with `runtime.tool_call_id`. State fields written by parallel tools need reducers. ### Install runtime-discovered tools in both paths Filtering tools already registered on `create_agent` only requires `wrap_model_call`. A tool discovered at runtime must also be installed on the `ToolCallRequest` in `wrap_tool_call`; showing the tool to the model does not make it executable. ### Define custom state as `TypedDict` Custom agent state must extend `AgentState` as a `TypedDict`; Pydantic models and dataclasses are not accepted. Prefer middleware-owned `state_schema` when the middleware's hooks or tools use the fields. The `create_agent` argument remains a backwards-compatible shortcut for tool-only state. ### Set the `ToolNode` execution-error boundary explicitly `ToolNode` catches invocation errors by default but re-raises errors from tool execution. Set `handle_tool_errors` to `True`, a model-visible error string, an exception-handling callable, or a tuple of exception types to catch execution failures. ## Structured output and message content Structured output participates in the agent's main model/tool loop. `response_format` selects tool-based or provider-native generation. Wrap a Pydantic schema in `ToolStrategy` for tool-based generation; use its `handle_errors` option (`handleErrors` in JavaScript) to control schema-parse failures and multiple structured-output tool calls. Provider-native structured output can infer `ProviderStrategy` from the model's `.profile`. A profile declares capabilities such as structured output, function calling, and JSON mode; summarization middleware also consults it. Messages expose provider-neutral `.content_blocks` for typed reasoning, citations, tool calls, and server-side tool calls. Check the migration reference before assuming every integration implements content blocks. When middleware dynamically changes models for an agent using structured output, replacement models must not be pre-bound with `bind_tools`. ## LangGraph execution Use `stream_mode="messages"` to receive `(message_chunk, metadata)` tokens for model calls anywhere in a graph, even when node code calls `model.invoke()`. Filter by metadata such as `langgraph_node` or model tags. The opt-in Python `version="v2"` API returns typed stream parts and wraps invoke results in `GraphOutput`; event streaming `version="v3"` exposes typed run and chat-model projections. Read the workflow reference before changing a consumer because older modes remain unchanged. For async-node limits and recovery, configure `timeout=` and `error_handler=` on `add_node`. A timeout discards that attempt's writes and enters retry handling. For cooperative shutdown, `RunControl.request_drain()` saves a resumable checkpoint after the current superstep and raises `GraphDrained`. ## Provider and protocol boundaries `ChatOpenAI` follows official API schemas and does not preserve non-standard fields from compatible third-party endpoints. Use the endpoint-specific integration when those fields matter. Anthropic client-side bash, computer-use, text-editor, and memory specifications describe calls but do not execute them. Supply executors or the provided middleware implementations and return correlated tool results. `MultiServerMCPClient` is stateless by default: every tool call gets a fresh session. Open `client.session()` explicitly when server context must persist across tools, resources, or prompts. ## Deep Agents essentials `create_deep_agent` produces a LangGraph-backed runnable with caller-supplied tools plus `write_todos` for adaptive planning and `task` for context-isolated subagent delegation. Filesystem behavior is backend-sensitive. Unsupported deletion hides the `delete` tool; `write_file` overwrites existing paths; `edit_file` remains the targeted-edit tool. Use `FilesystemMiddleware(tools=...)` to allowlist exposed filesystem tools. Python `StateBackend()` and `StoreBackend()` are directly constructible; the factory forms are deprecated. In JavaScript, `BackendProtocolV2` returns structured result objects with an `error` field, and `adaptBackendProtocol` bridges deprecated v1 implementations. ## Final checks - Follow moved package and import boundaries before adapting APIs. - Keep custom state, runtime tool installation, and structured-output routing within their documented constraints. - Preserve provider-returned compaction blocks when the relevant provider reference requires them. - Distinguish server-executed tools from client-side tool specifications that require an application executor. - Check session lifetime before relying on MCP server state.