# Extension points — langchain-ai/langchain

> Canonical page: https://codevetter.com/unpack/langchain/extension-points

# Extension points — langchain-ai/langchain

Scanned commit 9e4ea7a3495992c02a79de6fa5788e028b03df11 (2026-09-22). Generated by codevetter codevetter 1.12.0 on 2026-09-22.

The framework is built around extension. Agents are extended via AgentMiddleware subclasses (six hook points + state_schema + transformers), custom tools (BaseTool/@tool/dict provider tools), custom state/context schemas, structured-output strategies, checkpointers/stores/caches from LangGraph, and stream transformers. Models and embeddings plug in through partner packages implementing core abstractions plus registry entries in _BUILTIN_PROVIDERS, validated by the langchain-tests conformance suite. Shell execution is extensible via BaseExecutionPolicy.
- AgentMiddleware is the primary extension point: subclasses override any of before_agent/before_model/after_model/after_agent/wrap_model_call/wrap_tool_call (sync and/or async variants); decorators @before_agent/@before_model/@after_agent/@after_model/@dynamic_prompt/@wrap_model_call/@wrap_tool_call offer a functional alternative. — [libs/langchain_v1/langchain/agents/middleware/types.py#L42-66](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/middleware/types.py#L42-L66) [libs/langchain_v1/langchain/agents/middleware/__init__.py#L90-100](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/middleware/__init__.py#L90-L100)
- Middleware can declare `state_schema` (merged into agent state), `tools` (registered on the agent), `transformers` (stream post-processors), `trace_policy`, and `can_jump_to` per hook — factory.py inspects which hooks are overridden to decide which graph nodes to add. — [libs/langchain_v1/langchain/agents/factory.py#L1174-1182](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L1174-L1182) [libs/langchain_v1/langchain/agents/factory.py#L1550-1645](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L1550-L1645)
- New chat-model providers are added by implementing BaseChatModel in a partner package and adding a (module_path, class_name, creator_func) entry to `_BUILTIN_PROVIDERS`; custom creator lambdas handle non-standard constructors (e.g. huggingface's from_model_id, ibm's model_id kwarg). — [libs/langchain_v1/langchain/chat_models/base.py#L56-97](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/chat_models/base.py#L56-L97)
- New embeddings providers follow the identical registry pattern in embeddings/base.py `_BUILTIN_PROVIDERS`. — [libs/langchain_v1/langchain/embeddings/base.py#L15-34](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/embeddings/base.py#L15-L34)
- Partner packages prove conformance by subclassing langchain-tests suites (unit_tests/chat_models.py, integration_tests/vectorstores.py, etc.) rather than writing their own contract tests. — [libs/standard-tests/langchain_tests/unit_tests/chat_models.py](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/standard-tests/langchain_tests/unit_tests/chat_models.py) [libs/standard-tests/langchain_tests/integration_tests](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/standard-tests/langchain_tests/integration_tests)
- Tools plug in as BaseTool subclasses, @tool-decorated callables, or raw dicts (provider built-in tools); tools can inject runtime/state/store via ToolRuntime/InjectedState/InjectedStore annotations. — [libs/langchain_v1/langchain/tools/__init__.py](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/tools/__init__.py) [libs/langchain_v1/langchain/agents/factory.py#L879-882](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L879-L882)
- Agent state is extensible two ways: a custom `state_schema` TypedDict passed to create_agent (wins field conflicts over middleware schemas since it's merged last), or middleware-declared state_schema attributes. — [libs/langchain_v1/langchain/agents/factory.py#L916-923](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L916-L923) [libs/langchain_v1/langchain/agents/factory.py#L1174-1182](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L1174-L1182)
- Structured output strategies are pluggable ResponseFormat implementations (ToolStrategy, ProviderStrategy, AutoStrategy); callers can also pass raw Pydantic/dataclass/TypedDict/JSON-schema which get wrapped in AutoStrategy. — [libs/langchain_v1/langchain/agents/structured_output.py#L29-33](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/structured_output.py#L29-L33) [libs/langchain_v1/langchain/agents/factory.py#L1347-1353](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L1347-L1353)
- Persistence/streaming extension points come straight from LangGraph: create_agent accepts checkpointer (Checkpointer), store (BaseStore), cache (BaseCache), interrupt_before/after node names, and user `transformers` factories appended to the default pipeline. — [libs/langchain_v1/langchain/agents/factory.py#L849-858](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L849-L858) [libs/langchain_v1/langchain/agents/factory.py#L1841-1857](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L1841-L1857)
- Shell execution policy is pluggable via BaseExecutionPolicy subclasses — built-ins are HostExecutionPolicy, DockerExecutionPolicy, CodexSandboxExecutionPolicy, each implementing process launch and post-spawn limits. — [libs/langchain_v1/langchain/agents/middleware/_execution.py#L92-323](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/middleware/_execution.py#L92-L323)
- Multi-agent composition is supported by naming an agent (`name=` becomes a subgraph node name) and by SubagentTransformer, which is always registered on the compiled graph to reshape nested-agent stream events. — [libs/langchain_v1/langchain/agents/factory.py#L946-950](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L946-L950) [libs/langchain_v1/langchain/agents/_subagent_transformer.py](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/_subagent_transformer.py)
- Observability is extensible per-middleware via TracePolicy and process-wide via configure_trace_policy; all wrap_* hooks are traced through LangSmith `traceable` with scrubbed inputs. — [libs/langchain_v1/langchain/agents/middleware/_trace_policy.py](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/middleware/_trace_policy.py) [libs/langchain_v1/langchain/agents/factory.py#L158-171](https://github.com/langchain-ai/langchain/blob/9e4ea7a3495992c02a79de6fa5788e028b03df11/libs/langchain_v1/langchain/agents/factory.py#L158-L171)

Every claim links to its cited files at the pinned commit. Citations identify source evidence; they do not establish runtime correctness. Independent CodeVetter scan — not reviewed by upstream maintainers.

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