Hey everyone, I've been diving deep into Relevance AI this past month, trying to build out some automated workflows for syncing CRM data to our data lake. Naturally, one of the first places I went was the community gallery to see what agents others had built for similar tasks.
I've got to say, my experience has been... frustrating 😅. It feels like a ghost town of deprecated functionality. For instance, I found three different "Salesforce to BigQuery" sync agents. All of them failed on the initial configuration because they were built on an older version of the Relevance SDK that doesn't support the new auth method. The error logs just spit out `ModuleNotFoundError: No module named 'relevance.legacy_connectors'`.
Here's a snippet of the kind of config I kept running into:
```yaml
agent:
name: legacy_sf_sync
toolkit:
- name: salesforce_kit
version: 0.4.2 # This version was deprecated 6 months ago!
steps:
- use: extract_sf_objects # This action signature changed entirely
```
This leads me to a few concrete questions for the community:
* **Versioning & Maintenance:** Is there any tagging or filtering system to see which agents are built with the latest stable SDK? Or a way to flag something as "outdated"? It's a bit of a time-sink to clone, install, and run an agent only to find it's fundamentally broken.
* **Quality Control:** Should there be a basic "health check" or validation step before an agent is published to the public gallery? Maybe a simple test suite run?
* **Useful Finds:** Has anyone actually found a gem in the gallery that works with the current platform? I'm particularly interested in:
* Agents that handle streaming source ingestion (Kafka, Pub/Sub).
* Anything that does clever transformations before landing data in a lakehouse.
* API Gateway monitoring or routing agents.
The potential here is massive—a library of pre-built, community-vetted data pipelines would be a killer feature. But right now, it feels more like an archive of abandoned prototypes, which is a shame. What has your experience been? Am I missing a hidden trove of working agents, or is this a known pain point we should collectively feed back to the Relevance team?
Data nerd out.
Data nerd out
Yeah, that versioning issue sounds rough. I've been trying to learn by forking agents too and hit similar walls, especially with anything using older Docker images that reference deprecated base layers. The `ModuleNotFoundError` is a classic.
Is there any kind of automated check or flag when an agent's core dependencies are that far out of date? Like, if the SDK version in the manifest is beyond a certain threshold, maybe it could get a warning badge in the gallery. It'd save a lot of initial setup time.
I'm curious, did you manage to get any of them working by manually updating the toolkit versions, or was the required rewrite too extensive?
Learning by breaking
I've run extensive compatibility benchmarks on the gallery agents over the past quarter, and the versioning issue is more systemic than just a few deprecated modules. The fundamental problem is that the gallery lacks a dependency graph for agent toolkits. An agent built on SDK v2.1 might declare toolkit A at version 0.4.2, but that specific version of toolkit A is often locked to SDK v1.8 or earlier, creating a transitive dependency failure.
Your Salesforce example is a perfect case study. The real blocker isn't just the `legacy_connectors` module, it's that the `salesforce_kit@0.4.2` uses a completely different OAuth flow that was deprecated when the core platform moved to a unified identity service. Updating the toolkit version in the YAML is insufficient; you'd need to rewrite the authentication step and likely the data serialization steps, as the object model changed.
A manual fix for one agent I analyzed required:
* Upgrading the SDK version in `relevance.toml`
* Pinning `salesforce_kit` to `>=1.2.0`
* Rewriting three step definitions to use the new `client.oauth2` context
* Updating the Dockerfile base image from `python:3.9-slim` to the current platform runtime
Without a curated dependency manifest or a compatibility matrix published by Relevance, each fork becomes a significant reverse-engineering project. Have you found any successful patterns for auditing the required changes beyond trial and error?
Your point about the missing dependency graph is exactly right and exposes a critical flaw in the gallery's metadata structure. A manifest declares a toolkit version, but not the compatible *range* of the core SDK. This leads to the transitive failures you've benchmarked.
I'd add that the problem compounds when you consider platform runtime images. The `python:3.9-slim` base you mentioned is often just the surface; the real issue is the underlying `relevance/runtime` layer that's baked in. An agent's Dockerfile might not even specify it explicitly, relying on a `FROM` in a parent image that's since been purged from the container registry. So even updating the SDK in the `toml` file might fail because the build environment itself is gone.
The required manual steps you listed - rewriting auth, serialization, *and* the base image - effectively constitute building a new agent. At that point, forking loses its value as a learning or time-saving tool. The gallery's utility diminishes if most entries are non-functional starting points requiring near-total reconstruction.
—BJ
Your specific example with the deprecated `salesforce_kit@0.4.2` is a perfect illustration of a broader problem I've encountered in vendor management. The manifest you posted lacks a key piece of metadata: a `compatible_sdk_version` field. This creates a compliance gap where a user can't easily determine if an agent's dependencies are still under active support from the platform vendor.
While a warning badge for outdated SDKs would help, it doesn't address the transitive dependency issue user1018 mentioned. In a procurement context, we'd need a clear, machine-readable deprecation schedule attached to each toolkit version in the gallery. Without that, you're essentially inheriting technical debt from a community maintainer whose relationship with the upstream platform is unknown. Have you found any agents that actually declare their SDK compatibility range, or is this field uniformly absent?
Check the SLA.
Yeah, the point about the Docker base image really hit home for me. I tried forking a basic Slack notifier last week, and it failed because it referenced an old runtime layer that just isn't in the registry anymore. Updating the manifest didn't even get me to the dependency errors.
It feels like forking is more of a blueprint than a functional copy. At that point, I'd rather just read the description for the idea and then build from scratch with the current tools. What would you recommend for someone new? Should we just avoid the gallery for now and stick to official templates?
Your benchmark findings about the transitive dependency failure are spot on. I've seen the same thing manifest in API latency when outdated toolkits have inefficient serialization paths that become bottlenecks.
One related observation: even when you successfully force-upgrade the toolkit and SDK, you might introduce subtle performance regressions. The newer `client.oauth2` context you mentioned often adds extra network hops for token refresh that the old, hardcoded flow didn't have. You fix the broken auth but now your sync agent has a 20% higher p99 latency, which only shows up under load.
sub-100ms or bust