This is a huge backend change that essentially started with rewriting
the concurrency handling for processes and blew up to a refactor of the
entire application. In short these are the improvements:
**Better state and life cycle management:**
Life cycle management of processes has always been the trickiest part of
the code. Juggling mutex locks between multiple locations to reduce race
conditions was complex. Too complex for my feeble brain to build a
simple mental model around as llama-swap gained more features. All of
that has been refactored. Most of the locks are gone, replaced with a
single run() that owns all state changes. There is one place to start
from now to understand and extend routing logic.
The improved life cycle management makes it easier to implement more
complex swap optimization strategies in the future like #727.
**Collation of requests:**
llama-swap previously handled requests and swapping in the order they
came in. For example requests for models in this order ABCABC would
result in 5 swaps. Now those requests are handled in this order AABBCC.
The result is less time waiting for swap under a high churn request
queue. This fixes#588#612.
A possible future enhancement is to support a starvation parameter so
swap can be forced when models have been waiting too long.
**Shared base implementation for groups and swap matrix:**
During the refactor it became clear that much of the swapping logic was
shared between these two implementations. That is not surprising
considering the swap matrix was added many moons after groups. Now they
share a common base and their specific swap strategies are implemented
into the swapPlanner interface.
Requests for bespoke or specific swapping scenarios is a common theme in
the issues. Now users can implement whatever bespoke and weird swapping
strategy they want in their own fork. Just ask your agent of choice to
implement swapPlanner. I'll still remaining more conservative on what
actually lands in core llama-swap and will continue to evaluate PRs if
the changes is good for everyone or just one specific use case.
**AI / Agentic Disclosure:**
I paid very close attention to the low level swap concurrency design and
implementation. It's important to keep that essential part reliable,
boring and no surprises. Backwards compatibility was also maintained,
even the one way non-exclusive group model loading behaviour that people
have rightly pointed out be a weird design decision.
With the underlying swap core done the web server, api and UI sitting on
top were largely ported over with Claude Code and Opus 4.7 in multiple
phases. If you're curious I kept the changes in docs/newrouter-todo.md.
I did several passes to make sure things weren't left behind.
However, even frontier LLMs at the time of this PR still make small
decisions that don't make a lot of sense. They get shit wrong all the
time, just in small subtle way.
That said, there's likely to be some new bugs introduced with this
massive refactor. I'm fairly confident that there's no major
architectural flaws that would cause goal seeking agents to make dumb,
ugly code decisions.
For a little while the legacy llama-swap will be available under
cmd/legacy/llama-swap. The plan is to eventually delete that entry point
as well as the proxy package.
On a bit of a personal note, this PR is exciting and a bit sad for me. I
hand wrote much of the original code and this PR ultimately replaces
much of it. While the old code served as a good reference for the agent
to implement the new stuff it still a bit sad to eventually delete it
all.
This improves the support for activity logging from the v1/responses and
v1/messages endpoints.
- add chat endpoint selection to Playground > Chat > Settings
- improve metrics extraction for streaming v1/messages and v1/responses
endpoints (tested with llama-server)
Fixes#742
- inference handles to store an activity record for all inference endpoints
- add path, status code, and content type to Activities page
- toggle on/off columns no Activities page
- add configurable capture level for inference endpoints so large binary blobs are not stored in memory
- store captures in compressed binary format
The previous captures were saved uncompressed in memory. In agentic
workflows there can be many turns with each request containing the
previous context in the body with a lot of redundant data. Use zstd to
compress the request and response data before keeping a copy of memory.
Results:
- Average Percentage Saved: 73.19%
- Average Compression Factor: ~6.77:1
Keep request duration from being underreported when upstream timings
only cover part of the full request lifecycle.
- compare wall-clock and upstream timing durations
- keep token and throughput values from timings
- add regression coverage for underreported timings
fixes#602
Add saving request and response headers and bodies that go through
llama-swap in memory.
- captureBuffer added to configuration. Captures are enabled by default.
- 5MB of memory is allocated for req/response captures in a ring buffer.
Setting captureBuffer to 0 will disable captures.
- UI elements to view captured data added to Activity page. Includes
some
QOL features like json formatting and recombining SSE chat streams
- capture saving is done at the byte level and has minimal impact on
llama-swap performance
Fixes#464
Ref #503
This PR allows a single llama-swap to be the central proxy for models served by other inference servers. The peer servers can be another llama-swap or any API that supports the /v1/* inference endpoint.
Updates: #433, #299Closes: #296
* proxy: refactor metrics recording
- remove metrics_middleware.go as this wrapper is no longer needed. This
also eliminiates double body parsing for the modelID
- move metrics parsing to be part of MetricsMonitor
- refactor how metrics are recording in ProxyManager
- add MetricsMonitor tests
- improve mem efficiency of processStreamingResponse
- add benchmarks for MetricsMonitor.addMetrics
- proxy: refactor MetricsMonitor to be more safe handling errors
Benson Wong
Benson Wong
Leoy