LangChain

First-party Compresr middleware, document compressor, and tool wrappers for LangChain 1.0+ agents and retrievers.

compresr.integrations.langchain (Python) and @compresr/sdk/integrations/langchain (TypeScript) cover the four places LangChain users typically burn tokens: tool outputs in an agent loop, long chat history, the final outbound prompt, and retrieved documents in a ContextualCompressionRetriever. Every entry point makes the same Compresr call as the SDK — same auth, same models, same parameter semantics.

Python is class-based, TypeScript is factory-based

In Python the three middlewares are classes (CompresrToolMiddleware(...)); in TypeScript they are factory functions (compresrToolMiddleware({...})). The document compressor CompresrExtractor is a class in both. Snake_case kwargs in Python; camelCase options in TypeScript.

1. Install

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LangChain 1.0+ is required for the create_agent / createAgent middleware mechanism; the document compressor (CompresrExtractor) works on any version that ships BaseDocumentCompressor. LangChain deps ship in the base install — pip install compresr[langchain] still resolves for back-compat but adds nothing (extras are no-ops since 2.6.0).

2. Compress tool outputs in an agent

CompresrToolMiddleware plugs into create_agent. Each time a tool returns, the middleware compresses the result against the user's question before it lands in agent state, so the LLM's next reasoning step sees a shorter ToolMessage. api_key= can be omitted when COMPRESR_API_KEY is set or you've run compresr-sdk login — see Authentication.

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3. Replace old history with a compressed summary

CompresrSummarizationMiddleware is a KV-cache-friendly alternative to LangChain's SummarizationMiddleware. When the conversation crosses a token threshold, it compresses everything older than the last N messages into a single [Earlier conversation summary] ... HumanMessage and leaves recent messages untouched. No LLM round-trip: token-level compression. It detects its own prior summary via the [Earlier conversation summary] prefix and refuses to re-summarize an already-summarized prefix, so it's safe to run on every turn.

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4. Cap the outbound prompt with `CompresrPromptMiddleware`

The last-mile budget cap. CompresrPromptMiddleware runs in wrap_model_call and walks the messages largest-first, compressing just enough to fit under max_tokens. It mutates the model request, not agent state; the next turn still sees the original messages.

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The three middlewares cover orthogonal token sources and compose in the same create_agent / createAgent call:

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5. Middleware options

All keyword-only (Python) / options-object fields (TypeScript). The compression knobs share their semantics with the agent client's compression policy. Shared by all three middlewares:

Python	TypeScript	Default	Notes
`api_key`	`apiKey`	n/a	Required unless `client` is passed.
`client`	`client`	n/a	Pre-built `CompressionClient`: bypasses `api_key`/`base_url`.
`base_url`	`baseUrl`	`https://api.compresr.ai`	Override for self-hosted.
`compression_model`	`compressionModel`	`"latte_v1"`	Query-aware, requires a query. Pass `"latte_v2"` to opt into the newer model.
`target_compression_ratio`	`targetCompressionRatio`	`0.5`	`0 < r ≤ 1` removes that fraction; `r > 1` is Nx target.
`coarse`	`coarse`	server default	Paragraph-level vs token-level.
`query`	`query`	n/a	Static query overriding everything else.
`query_extractor`	`queryExtractor`	n/a	Custom resolution: `(tool_call, messages) -> str` for the tool middleware, `(messages) -> str` for the other two.
`on_error`	`onError`	`"passthrough"`	`"raise"` to fail fast in tests. The type is exported as `ErrorPolicy` for typed configs.

CompresrToolMiddleware only:

Python	TypeScript	Default	Notes
`min_tokens`	`minTokens`	`200`	Skip tool outputs shorter than this.
`allow_tools`	`allowTools`	`None`	Whitelist of tool names. Pass `allow_tools` OR `ignore_tools`, not both.
`ignore_tools`	`ignoreTools`	`None`	Blacklist.
`query_arg`	`queryArg`	n/a	Pull the query directly from a named tool arg.

CompresrSummarizationMiddleware only:

Python	TypeScript	Default	Notes
`max_tokens_before_summary` (alias `trigger`)	`maxTokensBeforeSummary` (alias `trigger`)	`4000`	Token threshold that triggers a summary. Aliases match LangChain's own `SummarizationMiddleware` naming.
`messages_to_keep` (alias `keep`)	`messagesToKeep` (alias `keep`)	`20`	Recent messages preserved verbatim.
`token_counter`	`tokenCounter`	SDK `estimate_tokens`	char/4 heuristic, upgrades to tiktoken `cl100k_base` when available.

CompresrPromptMiddleware only:

Python	TypeScript	Default	Notes
`max_tokens`	`maxTokens`	n/a	Required. Hard ceiling for the outbound prompt.
`min_tokens`	`minTokens`	`200`	Don't touch messages smaller than this.
`token_counter`	`tokenCounter`	SDK `estimate_tokens`	Same char/4 heuristic with tiktoken upgrade.

6. Compress retrieved documents

CompresrExtractor is a BaseDocumentCompressor: a drop-in replacement for LLMChainExtractor inside a ContextualCompressionRetriever. Batches all eligible documents into a single Compresr call (up to 100 per batch) — pairs well with high-recall retrieval (k=20+). See the RAG guide for the surrounding pipeline.

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The extractor sets metadata["compresr"] = True on every document it touched and leaves documents below min_tokens unchanged (or filters them out if drop_below_min=True).

7. Wrap a single tool

If you only need compression on one tool, without an agent middleware, wrap it directly. wrap_tool_with_compression / wrapToolWithCompression returns a new StructuredTool preserving name, description, args_schema, return_direct, and error handlers.

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Python raises TypeError if the input isn't a StructuredTool; wrap a raw function with @tool first. TypeScript is more permissive: wrapToolWithCompression accepts anything exposing name plus func, _call, or invoke.

For the case where you own the tool's source, there is also a decorator form with the same options, compress_tool_output / compressToolOutput, applied on top of @tool:

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LangGraph: same middlewares applied inside StateGraph, plus node-level helpers, lossy checkpoint serializer, lossy store wrapper, and multi-agent handoff.
Models: latte_v2 parameter semantics (target_compression_ratio, coarse, and friends).
RAG guide: the underlying retrieve → compress → answer pipeline.