# Context Gateway installs with a piped shell script and builds a dashboard nobody builds

> A Go proxy from Compresr that sits between an agent and the LLM API to compact conversation history in the background. The README explains the product in 182 words and hands off every build detail to files that contradict each other.

**Compresr-ai/Context-Gateway** — Context Gateway is an agentic proxy that enhances any AI agent workflow with instant history compaction and context optimization tools

- Repository: https://github.com/Compresr-ai/Context-Gateway
- Stars: 644 · Forks: 51
- Language: Go
- License: Apache-2.0
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/compresr-ai-context-gateway

## The documented install pipes a remote script into a shell

The entire quick start is two steps. The first downloads and executes an installer from the vendor's own site; the second runs the installed binary, which opens an interactive TUI wizard.

```
# Install gateway binary
curl -fsSL https://compresr.ai/api/install | sh

# Then select an agent (opens interactive TUI wizard)
context-gateway
```

Neither step names a version, and the page never mentions the release tags that exist, v0.5.1, v0.5.2 and v0.5.3, all published in March 2026. What the binary reports as its version comes from a Makefile variable that shells out to `git describe --tags --always --dirty` and falls back to the literal string `dev`, so a build made outside a tagged checkout identifies itself that way. The repository also ships a Makefile and a Dockerfile, which the wizard path bypasses entirely.

## The module path and the image binary use two different names

go.mod declares the module as `github.com/compresr/context-gateway`, all lowercase, while the repository it lives in is `Compresr-ai/Context-Gateway`. A `go get` or an import written against the repository's own casing will not line up with what go.mod says. Naming drifts a second time in the build: the Makefile sets `BINARY_NAME=context-gateway`, the README tells you to run `context-gateway`, and the image builds the same program as `gateway`.

```
module github.com/compresr/context-gateway

go 1.24.0
```

The dependency list also reaches past a local proxy: the AWS SDK for Go v2 is required at the top level, alongside tiktoken-go for token counting, the pure-Go modernc.org/sqlite, zerolog, gjson and sjson, and the coder websocket library. Disabling cgo is why SQLite is the modernc flavour, and the image build sets `CGO_ENABLED=0` accordingly.

## The image build skips the step the Makefile calls required

The Makefile is explicit that the React dashboard has to be compiled before the Go binary, because the assets are embedded into it. Its `build` target depends on two prerequisites, `embed-prep` and `build-dashboard`, and the latter runs npm inside `web/dashboard` to fill `cmd/dashboard_dist/`.

```
RUN make embed-prep && \
    CGO_ENABLED=0 GOOS=linux go build -ldflags="-s -w" -o gateway ./cmd
```

The Dockerfile does not do that. It installs only make on top of the Go alpine image, runs `make embed-prep`, and compiles straight away. There is no Node in the builder stage and no `build-dashboard` call, so whatever the embed step finds in `cmd/dashboard_dist/` is what ships in the image, and the dashboard the Makefile insists on is the part the container does not build.

## One Makefile target names three different config files

Configuration is where the files disagree most. A variable at the top of the Makefile sets a default of `configs/preemptive_summarization.yaml` and is never referenced by any target in the visible recipe set. The `run-config` target carries a usage comment pointing at `configs/tool_output_passthrough.yaml`, and a `CONFIG` variable set to that same path, then runs something else entirely.

```
	$(GORUN) $(MAIN_PATH) serve --config configs/config.yaml
```

So the variable a user is told to override has no effect on the command that runs, and the config actually loaded is a third filename. The TUI wizard adds a fourth place configuration lives, since it offers to create or edit a summarizer model and API key, optional Slack notifications, and a trigger threshold, without naming a file path or a format for any of it.

## The default summarizer points at a hosted endpoint

The sample environment file is where the architecture becomes visible. It sets an admin key for the compression service with the comment that it authenticates requests from the API, and a default base URL on the vendor's domain. Two more hosted services appear, one for a Qwen Coder strategy and one labelled for SWE-bench datasets and evaluation, followed by provider keys for Anthropic, OpenAI, Gemini and Cursor.

```
COMPRESR_BASE_URL=https://api.compresr.ai
COMPRESR_API_KEY=your_compresr_api_key_here
```

The README describes compaction as background work inside your own agent workflow and never says that summarization is a network call, what text is sent with it, or what the admin key authorizes. Anyone treating this as a local context manager should read that file before the first summarization runs.

## The only observability surface is one JSONL path

When something needs checking, the page offers a single instruction: look at `logs/history_compaction.jsonl`. No entry format, no retention rule, no rotation, and no way to raise the log level appears anywhere in the documentation. The Docker image creates and chowns `/app/logs` for the non-root gateway user, which confirms the path is the real one, and nothing else describes what a healthy run looks like. The repository carries far more surface than the page accounts for: a `web/` directory holding the React dashboard, plus `internal/`, `docs/`, `docker/`, `distribution/`, `external/`, `scripts/`, `tests/`, a golangci lint config, a ChangeLog, a CONTRIBUTING file and two shell entry points named `start_agent.sh` and `test_ci_local.sh`. None of them is referenced by the README.

## The 75% trigger is the only default that carries a number

The wizard collects three things, and one of them is quantified. The trigger threshold for compression defaults to 75%, which the page never defines: not the share of the context window, not a token count, not a share of any model's limit. Four agent profiles are offered, `claude_code`, `cursor`, `openclaw`, described as an open-source alternative to Claude Code, and `custom` for bringing your own configuration, whose format is documented nowhere in the repository's own files. The container also reveals an operating surface the README skips entirely: it listens on port 18081 and starts with the `serve` subcommand and a `--no-banner` flag.

```
EXPOSE 18081
ENTRYPOINT ["./gateway"]
CMD ["serve", "--no-banner"]
```

## Conclusion

The compression idea is documented well enough to understand and thin enough to distrust as documentation: one install line, one threshold, one log path, and no version named anywhere. Before trusting it, check what the summarizer call sends to api.compresr.ai, since the sample environment points the default summarizer at a hosted endpoint and the page never mentions the traffic. Then check whether the binary you installed matches the tag you meant, since the install path is a piped script while the releases stop at v0.5.3 in March 2026 and the last commit is dated 2 August 2026. It fits someone who already has an agent and wants history compaction without waiting. It does not fit someone who needs a pinned build, a documented config file, or an offline path, because none of the three is written down.

## FAQ

### What is context rot in an LLM, and does Context Gateway address it?

The page never uses the phrase. What it describes is conversation length as the trigger: the gateway watches the history, and once it passes the configured threshold, default 75%, it swaps in a summary that was computed ahead of time in the background. Whether that is the same problem the term names is not something the documentation takes up.

### What is a context engine, in the sense Context Gateway uses?

Context Gateway calls itself an agentic proxy that sits between an AI agent such as Claude Code or Cursor and the LLM API. Its documented jobs are history compaction, a TUI wizard for agent selection and summarizer settings, optional Slack notifications, and a JSONL log of compaction events. No component inside the repository is named a context engine.

### What is context AI used for in Context Gateway?

For this project the scope is agent conversation history. The gateway compresses that history so a long session does not block on compaction, and the README claims the summary is already computed when the threshold is hit, which is why the wait disappears. Related infrastructure in the repository includes a SWE-bench labelled service and a Qwen Coder strategy endpoint in the sample environment.

## Sources

- [Compresr-ai/Context-Gateway on GitHub](https://github.com/Compresr-ai/Context-Gateway)
- [Issues](https://github.com/Compresr-ai/Context-Gateway/issues)
- [License: Apache-2.0](https://github.com/Compresr-ai/Context-Gateway/blob/main/LICENSE)
- [README](https://github.com/Compresr-ai/Context-Gateway/blob/main/README.md)
- [Releases](https://github.com/Compresr-ai/Context-Gateway/releases)

---

Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/compresr-ai-context-gateway