langchain-ai/deepagents · error · RuntimeError

Context Hub cache failed to initialize

Error message

Context Hub cache failed to initialize

What it means

`ContextHubBackend._ensure_cache_locked` lazily loads the remote tree into an in-memory cache under the state lock; if after `_load_tree_locked()` the cache is still `None`, initialization failed silently and it raises `RuntimeError`. Any cache-reading or mutation-draining path depends on this cache being present.

Source

Thrown at libs/deepagents/deepagents/backends/context_hub.py:150

            if isinstance(entry, FileEntry):
                cache[path] = entry.content
            else:
                linked_entries[path] = entry.repo_handle
        return cache, linked_entries, context.commit_hash

    def _load_tree_locked(self) -> None:
        cache, linked_entries, commit_hash = self._fetch_tree()
        self._cache = cache
        self._linked_entries = linked_entries
        self._commit_hash = commit_hash

    def _ensure_cache_locked(self) -> dict[str, str]:
        if self._cache is None:
            # The state lock makes the cold remote pull single-flight.
            self._load_tree_locked()
        if self._cache is None:
            msg = "Context Hub cache failed to initialize"
            raise RuntimeError(msg)
        return self._cache

    @staticmethod
    def _overlay(cache: dict[str, str], changes: dict[str, str | None]) -> None:
        for path, content in changes.items():
            if content is None:
                cache.pop(path, None)
            else:
                cache[path] = content

    def _visible_cache_locked(self) -> dict[str, str]:
        visible = dict(self._ensure_cache_locked())
        for mutations in (self._mutations.in_flight, self._mutations.pending):
            for mutation in mutations:
                self._overlay(visible, mutation.changes)
        return visible

    def _ensure_cache(self) -> dict[str, str]:

View on GitHub (pinned to a1af029e6e)

Solutions

1. Check connectivity/auth to the Context Hub remote and retry the operation.
2. Verify the remote tree/project exists and is initialized before using the backend.
3. Inspect `_load_tree_locked` failure paths/logs to find why the pull produced no cache, and fix the underlying load error.

Example fix

// before: uninitialized remote
backend = ContextHubBackend(remote="https://hub.example.com/missing-project")
// after: ensure the project/tree is initialized first
create_remote_tree(remote="https://hub.example.com/my-project")
backend = ContextHubBackend(remote="https://hub.example.com/my-project")

# ensure the remote tree is reachable and initialized before constructing the backend
assert remote_tree_exists(hub_url, project), "Context Hub remote tree missing/uninitialized"

for attempt in range(3):
    try:
        entries = backend.get_linked_entries(key)
        break
    except RuntimeError as e:
        if "cache failed to initialize" not in str(e) or attempt == 2:
            raise
        time.sleep(0.5 * (attempt + 1))

When it happens

Trigger: Calling `get_linked_entries`, `has_prior_commits`, `_submit_changes`, `_complete_batch`, or reading the visible cache when the cold remote pull (`_load_tree_locked`) fails to populate `self._cache` — e.g. remote store unreachable or returns an empty/unexpected tree.

Common situations: Network/auth failures talking to the Context Hub remote on first use; misconfigured remote URL/credentials; remote project/tree deleted or never initialized.

Related errors

• Context Hub commit succeeded but its hash could not be resol
• Offload server returned an invalid cancellation acknowledgem
• Offload server completed without a typed result.
• Offload result has no status.
• Failed to initialize Codex model '{provider}:{model_name}':

AI-assisted analysis of langchain-ai/deepagents@a1af029e6e (2026-08-29). Data as JSON: /api/errors/e6f0461980a8933c. Report an issue: GitHub.