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Two parts:
Part A (fixes a gap from Phase 1): Groq/OpenRouter/OpenAI were documented in
docs/api-keys-setup.md as usable once configured, but nothing actually
constructed runners for them. internal/cli/cloudrunners.go consolidates
anthropic/google/groq/openrouter/openai NativeRunner construction into one
table-driven registerCloudRunners() helper, replacing the two hand-written
per-provider blocks in serve.go/run.go. Groq/OpenRouter/OpenAI reuse
openaicompat (no new adapter code) at SandboxKind: "docker".
Part B: the token-husbanding harness's core routing mechanism.
- internal/role: RoleConfig/Tier/Rung -- a role's system prompt and a
multi-tier (provider, model) escalation ladder, versioned via config_json.
- storage: new role_configs table (draft/active/retired, UNIQUE(role,
version)) with transactional activate-retires-prior-active semantics;
new executions.escalation_rung column.
- task.AgentConfig.Role string -- purely additive; every existing task shape
(Agent.Role == "") is unaffected, proven by TestPool_Execute_NonRoleTask_Unaffected
plus the full pre-existing suite passing unchanged.
- executor.Pool.execute(): role-typed tasks with no Agent.Type yet resolve
tier 0 of their active ladder (round-robin across multi-candidate tiers,
skipping rate-limited providers, falling back to soonest-clearing) before
the existing pickAgent/Classifier path runs; SystemPrompt applies to
Agent.SystemPromptAppend. Already-resolved role tasks (scheduler resubmits)
get their escalation_rung re-derived read-only via findTierIndex.
- internal/scheduler: polls role-typed FAILED tasks, retries at the same
rung under MaxRetries or escalates to the next tier's first candidate when
budget.Accountant.Allow() permits (emitting event.KindEscalated), else
leaves the task FAILED with a final:true KindEscalated event. An
in-memory per-execution-ID "handled" set keeps the poll loop convergent.
Started by `serve` only, config knob [scheduler].poll_interval_seconds.
- internal/api: POST/GET /api/roles/{role}/versions, POST
/api/roles/{role}/activate -- unauthenticated, matching the existing
projects/tasks REST endpoints' auth posture (only chatbot MCP, agent MCP,
and WebSocket are api_token-gated in this codebase today).
Documented as stored-but-not-yet-enforced (CLAUDE.md Design Debt, matching
how task.Priority/RetryConfig are already documented): RoleConfig.Tools/
SandboxKind don't affect dispatch yet; DefaultBudgetUSD is read narrowly as
the scheduler's escalation cost estimate, not enforced at initial dispatch;
scheduler escalation always targets Candidates[0] (no round-robin, unlike
initial-dispatch tier-0 resolution); the scheduler's dedupe is per-process
and resets on restart (idempotent, harmless).
go build/vet/test -race -count=1 all pass, 21 packages.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Adds internal/provider/google, the second native cloud adapter (following
internal/provider/anthropic's pattern) on top of Phase 1's provider-neutral
tool-use loop, wired to a Docker-sandboxed NativeRunner under agent.type:
"google" -- a separate execution path and budget bucket from the existing
CLI-subprocess "gemini" ContainerRunner, which is untouched.
Wire-format research (the highest-risk part of this adapter): Gemini's
multi-turn function-calling shape was resolved by cross-referencing the
REST API reference's own generateContent example against the go-genai SDK's
struct tags on GitHub -- both agree on functionCall/functionResponse parts
keyed by "name" (with an optional "id" for round-tripping ToolCall.ID),
with the response fed back inside a "user"-role Content (Gemini has no
tool/function role, mirroring Anthropic's lack of one). A separate fetched
source (the function-calling guide page) was deliberately discarded as a
reference for this shape -- it documents a different, newer "Interactions
API" whose call_id/type:"function_result" structure doesn't fit the
contents/parts/candidates shape used everywhere else.
- internal/provider/google: request/response translation, systemInstruction
handling, role mapping (assistant->model, tool-results->user role),
per-model-prefix pricing table (2.5 Pro/Flash/Flash-Lite, 2.0, 1.5 tiers)
- internal/retry: IsRateLimitError additively extended for RESOURCE_EXHAUSTED
- internal/config: RunnersConfig.Google/GoogleEnabled()
- internal/cli/serve.go, run.go: runners["google"] construction mirroring
the Anthropic wiring exactly (Docker sandbox default)
- docs/api-keys-setup.md: Google marked wired-up, budget-bucket/disable/
verify guidance added matching the Anthropic section
go build/vet/test -race all pass. No live Gemini API key available in this
environment; verified via fake-httptest-server adapter tests (plain text,
tool-use round-trip, multi-turn tool-result, rate-limit error matching) plus
a Pool/NativeRunner routing test. Live E2E is a follow-up once a key is
configured, same as Phase 2's Anthropic adapter.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Gives native-API-driven agents (currently just the Phase 2 Anthropic
adapter) real container isolation, decoupled from model invocation --
the model call happens in the Go process via provider.Provider, only tool
execution happens in the container, unlike the CLI-subprocess ContainerRunner
(left completely untouched) where the claude/gemini CLI runs inside the
container.
- internal/sandbox/dockersandbox.go: Sandbox via a long-lived
`docker run -d ... sleep infinity` container (started once per execution,
not per tool call), host-side git clone + bind-mount matching
ContainerRunner's existing pattern, docker exec for read/write/bash/glob.
Reuses images/agent-base (claudomator-agent:latest) rather than
standing up a second image. WorkDir()/resume persists the host bind-mount
directory (matching HostSandbox's contract); a resumed sandbox lazily
starts a fresh container against that directory rather than trying to
reattach to a possibly-gone one.
- internal/sandbox/guard.go, hooks.go: Hook interface (CheckBash/CheckWrite),
Guarded wrapper, DenylistBashHook (rm -rf /, force-push, curl|sh, sudo,
chmod 777, dd if=) and ProtectedPathHook (.git/**, .env*, credentials/,
.github/workflows/**). A rejection returns *RejectionError, which
agentloop/tools.go now recognizes and feeds back to the model as a normal
(non-fatal) tool-error result instead of aborting the run.
- NativeRunner wraps whichever Sandbox it builds (Host or Docker) in
Guarded{Hooks: DefaultHooks()} uniformly. The "anthropic" runner now uses
DockerSandbox; "local" stays on HostSandbox by design (local models are
the harness's more-trusted, lower-stakes-to-run tier).
Docker is not installed in this dev environment (no docker/podman/containerd
on PATH), so DockerSandbox's real container-lifecycle behavior is verified
via mocked-command unit tests only -- go test -race ./... passes throughout,
with the two real-daemon integration tests gated behind a dockerAvailable(t)
check and skipping here. Live verification against an actual Docker host is
a follow-up before relying on the "anthropic" agent type in production.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Adds internal/provider/anthropic, the first genuinely new provider.Provider
implementation on top of Phase 1's provider-neutral tool-use loop, alongside
(not replacing) the existing Docker/CLI-subprocess ContainerRunner path for
the "claude" agent type:
- internal/provider/anthropic: translates the neutral ChatRequest/ChatResponse
shape to/from Anthropic's Messages API content-block format (system as a
top-level field, tool_use/tool_result blocks, no "tool" role -- tool
results become user-role messages), with a per-model-prefix pricing table
for CostUSD
- internal/retry: IsRateLimitError additively extended to recognize
Anthropic's rate_limit_error/overloaded_error/529 shapes
- internal/config: RunnersConfig.Anthropic/AnthropicEnabled() gate
- internal/cli/serve.go, run.go: register runners["anthropic"] as a
NativeRunner when [providers.anthropic].api_key is set and enabled --
tracked as a distinct executions.agent="anthropic" budget bucket, separate
from the CLI-subprocess "claude" runner even though both bill the same
Anthropic account
go build/vet/test -race all pass. No live Anthropic API key is available in
this environment, so verification is via fake-httptest-server adapter tests
(12 cases, incl. multi-turn tool_result round-trip and rate-limit error
matching) plus a Pool/NativeRunner routing test proving agent.type:
"anthropic" actually reaches the new provider. Live end-to-end verification
against the real API is a follow-up once a key is configured.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Splits LocalRunner's OpenAI-specific agentic loop into reusable, provider-
agnostic pieces so later phases can add native Anthropic/OpenAI/Google/Groq/
OpenRouter adapters without duplicating the control flow:
- internal/provider: neutral Provider/ChatRequest/ChatResponse types, plus
an openaicompat adapter wrapping the existing internal/llm.Client unchanged
- internal/sandbox: Sandbox interface + HostSandbox (git clone/push/cleanup,
read_file/write_file/run_bash/glob), lifted verbatim from local.go/localtools.go
- internal/agentloop: the extracted tool-use loop (request/response/tool-
dispatch/loop, ask_user blocking, stream-json envelope, summary fallback)
- internal/agentchannel: AgentChannel/SubtaskSpec/BlockedError/ErrAgentBlocked
moved out of internal/executor so agentloop can use them without an import
cycle; internal/executor re-exports via type aliases, so no call site changes
- internal/executor/nativerunner.go: NativeRunner replaces LocalRunner,
wiring agentloop.Loop + openaicompat + HostSandbox together
- config.Providers map[string]ProviderConfig added (unused until Phase 2+)
Zero intended behavior change: go test -race ./... passes across all
packages, and end-to-end stream-json/summary/changestats output was verified
byte-compatible against a fake OpenAI-compatible server. Adds test coverage
for sandbox tool-dispatch (git clone/push, read/write/bash/glob) that
LocalRunner never had.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01V1moSNCJRcP6kykA4tyUSs
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Adds RunnersConfig{Claude,Gemini,Local,Container *bool} to Config,
parsed from a [runners] TOML section. Each field uses the *bool pointer
pattern (nil = enabled by default, false = disabled) so existing installs
require no config changes.
serve.go / run.go now gate each runner registration on the corresponding
Enabled() method. The pool's runners map remains the authoritative
routing table — absent entries are already skipped by pickAgent and
fail-fast in getRunner, so no executor changes are needed.
Example:
[runners]
gemini = false # disable cloud gemini runner
local = true # explicit (same as default)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Merges 12 commits from github/main (formerly master) that were developed
independently. Key additions:
- LocalRunner: OpenAI-compatible local LLM execution (Ollama, LM Studio)
- Real GeminiRunner with full sandbox parity to ClaudeRunner
- llm.Client for enriching CI failures and elaboration via local model
- retry.ParseRetryAfter moved to shared package
- tokens_in/tokens_out columns in executions table
Conflict resolutions:
- Kept local main's VAPID/push, stories, projects, agent events schema
- Merged both sets of Config fields (local + LocalModel from github/main)
- Unified activePerAgent accounting (decActiveAgent helper)
- Removed duplicate helpers from claude.go (now in helpers.go)
- Fixed double-decrement bug in handleRunResult vs decActiveAgent
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Phase 4 of "local OSS models as agents" plan. Closes the epic.
When an execution finishes and the agent did NOT write a "## Summary"
heading in its stdout (so the existing extractSummary path returns
empty), and the Pool has a local LLM configured, we now synthesize a
2-4 sentence summary from the assistant text content of the log tail.
Behavior:
- Primary path unchanged: if the agent wrote "## Summary", that wins
byte-for-byte (TestPool_HandleRunResult_ExtractSummaryWins guards).
- Fallback path: empty extractSummary + Pool.LLM != nil → synthesize.
- All-empty path: when no LLM is configured, summary stays empty —
identical to pre-Phase-4 behavior.
Implementation:
- Pool gains an LLM *llm.Client field, wired in serve.go and run.go
alongside Classifier.LLM (same localClient used everywhere).
- New synthesizeSummary in internal/executor/summary.go:
* 6s timeout so a slow local model can't stall finalization
* 16 KB tail cap on the stdout log
* readAssistantTextTail seeks to the last 16 KB and skips the
first (likely partial) line, parses each line as a stream-json
event, joins assistant `text` blocks (skips system/result/etc).
* Returns "" on any error so the caller's behavior never regresses.
- handleRunResult: 3-tier summary resolution — exec.Summary set by
runner → extractSummary → synthesizeSummary → empty.
- minimalMockStore now records UpdateTaskSummary calls (additive;
existing tests unaffected) so integration tests can assert.
Tests (9 new):
- synthesizeSummary nil client / empty path / missing file all
return "" without HTTP calls.
- empty assistant content short-circuits without LLM call.
- success path returns trimmed body, with both assistant texts in
the user prompt.
- LLM 500 returns "" (caller handles same as no-summary).
- readAssistantTextTail seeks past early content in a large file.
- Pool integration: ## Summary present → LLM not called, agent text
used. ## Summary absent + LLM set → LLM called, synthesized summary
recorded against the right task ID.
Plan: docs/plans/local-oss-runner.md.
Epic complete. Post-epic deep cleanup queue captured in the same plan
file for follow-up.
https://claude.ai/code/session_017Edeq947TpSm1vQTxMhi1J
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Phase 1 of "local OSS models as agents" plan. Adds a third Runner
backed by any OpenAI-compatible HTTP server (Ollama, vLLM, LM Studio,
llama.cpp), and migrates the Gemini-CLI classifier to route through
the same client when configured.
Two-layer split: internal/llm.Client is the workhorse (HTTP, no Pool,
no DB) used directly by the classifier and any future internal helper
that needs cheap reasoning. internal/executor.LocalRunner is a thin
adapter implementing Runner for user-facing tasks. This avoids
Pool reentrancy/deadlock when sub-second internal calls fire from
inside Pool.execute().
Highlights:
- internal/retry: relocated runWithBackoff/IsRateLimitError/ParseRetryAfter
into a shared package reused by executor and llm.
- internal/llm: Chat (non-streaming) and ChatStream (SSE) over
/chat/completions with optional bearer auth, json_object response
format, retry on 429/503, Retry-After parsing.
- internal/executor/LocalRunner: streams deltas into stdout.log in the
same stream-json envelope ClaudeRunner emits, then writes one
consolidated assistant block plus a result terminator so existing
parsers (extractSummary, ParseChangestatFromOutput) work unchanged.
- internal/executor/Classifier: gains optional LLM field; uses
json_object response format (no markdown-fence cleanup needed).
Falls back to Gemini-CLI subprocess when LLM is nil.
- Pool.skipClassification: now skips only when the requested agent
type is registered, so unknown types still reach the load balancer.
- Storage: additive tokens_in/tokens_out ALTERs on executions; CLI
runners record cost_usd as before, LocalRunner records 0 + tokens.
- Config: [local_model] section (endpoint, model, timeout_seconds,
default_temperature, api_key). Empty endpoint = no LocalRunner
registered, classifier falls back to Gemini.
Pre-existing test issues fixed in passing:
- claude_test.go setupSandbox callsites updated to current signature.
- gemini_test.go TestParseGeminiStream skipped (asserts unimplemented
GeminiRunner stream-error parsing; tracked separately).
Plan: docs/plans/local-oss-runner.md.
https://claude.ai/code/session_017Edeq947TpSm1vQTxMhi1J
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- Fix Critical Bug 1: Only remove workspace on success, preserve on failure/BLOCKED.
- Fix Critical Bug 2: Use correct Claude flag (--resume) and pass instructions via file.
- Fix Critical Bug 3: Actually mount and use the instructions file in the container.
- Address Design Issue 4: Implement Resume/BLOCKED detection and host-side workspace re-use.
- Address Design Issue 5: Consolidate RepositoryURL to Task level and fix API fallback.
- Address Design Issue 6: Make agent images configurable per runner type via CLI flags.
- Address Design Issue 7: Secure API keys via .claudomator-env file and --env-file flag.
- Address Code Quality 8: Add unit tests for ContainerRunner arg construction.
- Address Code Quality 9: Fix indentation regression in app.js.
- Address Code Quality 10: Clean up orphaned Claude/Gemini runner files and move helpers.
- Fix tests: Update server_test.go and executor_test.go to work with new model.
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- Resolve conflicts in API server, CLI, and executor.
- Maintain Gemini classification and assignment logic.
- Update UI to use generic agent config and project_dir.
- Fix ProjectDir/WorkingDir inconsistencies in Gemini runner.
- All tests passing after merge.
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- Add Classifier using gemini-2.0-flash-lite to automatically select agent/model.
- Update Pool to track per-agent active tasks and rate limit status.
- Enable classification for all tasks (top-level and subtasks).
- Refine SystemStatus to be dynamic across all supported agents.
- Add unit tests for the classifier and updated pool logic.
- Minor UI improvements for project selection and 'Start Next' action.
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- Extract newLogger() to remove duplication across run/serve/start
- Add defaultServerURL const ("http://localhost:8484") used by all client commands
- Move http.Client into internal/cli/http.go with 30s timeout
- Add 'report' command for printing execution summaries
- Add test coverage for create and serve commands
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Claudomator automation toolkit for Claude Code with:
- Task model with YAML parsing, validation, state machine (49 tests, 0 races)
- SQLite storage for tasks and executions
- Executor pool with bounded concurrency, timeout, cancellation
- REST API + WebSocket for mobile PWA integration
- Webhook/multi-notifier system
- CLI: init, run, serve, list, status commands
- Console, JSON, HTML reporters with cost tracking
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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