## Changes 🏗️
Adds Redis-based SSE reconnection support for long-running CoPilot
operations (like Agent Generator), enabling clients to reconnect and
resume receiving updates after disconnection.
### What this does:
- **Stream Registry** - Redis-backed task tracking with message
persistence via Redis Streams
- **SSE Reconnection** - Clients can reconnect to active tasks using
`task_id` and `last_message_id`
- **Duplicate Message Fix** - Filters out in-progress assistant messages
from session response when active stream exists
- **Completion Consumer** - Handles background task completion
notifications via Redis Streams
### Architecture:
```
1. User sends message → Backend creates task in Redis
2. SSE chunks written to Redis Stream for persistence
3. Client receives chunks via SSE subscription
4. If client disconnects → Task continues in background
5. Client reconnects → GET /sessions/{id} returns active_stream info
6. Client subscribes to /tasks/{task_id}/stream with last_message_id
7. Missed messages replayed from Redis Stream
```
### Key endpoints:
- `GET /sessions/{session_id}` - Returns `active_stream` info if task is
running
- `GET /tasks/{task_id}/stream?last_message_id=X` - SSE endpoint for
reconnection
- `GET /tasks/{task_id}` - Get task status
- `POST /operations/{op_id}/complete` - Webhook for external service
completion
### Duplicate message fix:
When `GET /sessions/{id}` detects an active stream:
1. Filters out the in-progress assistant message from response
2. Returns `last_message_id="0-0"` so client replays stream from
beginning
3. Client receives complete response only through SSE (single source of
truth)
### Frontend changes:
- Task persistence in localStorage for cross-tab reconnection
- Stream event dispatcher handles reconnection flow
- Deduplication logic prevents duplicate messages
### Testing:
- Manual testing of reconnection scenarios
- Verified duplicate message fix works correctly
## Related
- Resolves SSE timeout issues for Agent Generator
- Fixes duplicate message bug on reconnection
## Summary
Updates the Agent Generator client to enrich the description with
context before calling, instead of sending `user_instruction` as a
separate parameter.
## Context
Companion PR to Significant-Gravitas/AutoGPT-Agent-Generator#105 which
removes unused parameters from the decompose API.
## Changes
- Enrich `description` with `context` (e.g., clarifying question
answers) before sending
- Remove `user_instruction` from request payload
## How it works
Both input boxes and chat box work the same way - the frontend
constructs a formatted message with answers and sends it as a user
message. The backend then enriches the description with this context
before calling the external Agent Generator service.
## Problem
When marketplace agents are included in the `library_agents` payload
sent to the Agent Generator service, they were missing required fields
(`graph_id`, `graph_version`, `input_schema`, `output_schema`). This
caused Pydantic validation to fail with HTTP 422 Unprocessable Entity.
**Root cause:** The `MarketplaceAgentSummary` TypedDict had a different
shape than `LibraryAgentInfo` expected by the Agent Generator:
- Agent Generator expects: `graph_id`, `graph_version`, `name`,
`description`, `input_schema`, `output_schema`
- MarketplaceAgentSummary had: `name`, `description`, `sub_heading`,
`creator`, `is_marketplace_agent`
## Solution
1. **Add `agent_graph_id` to `StoreAgent` model** - The field was
already in the database view but not exposed
2. **Include `agentGraphId` in hybrid search SQL query** - Carry the
field through the search CTEs
3. **Update `search_marketplace_agents_for_generation()`** - Now fetches
full graph schemas using `get_graph()` and returns `LibraryAgentSummary`
(same type as library agents)
4. **Update deduplication logic** - Use `graph_id` instead of name for
more accurate deduplication
## Changes
- `backend/api/features/store/model.py`: Add optional `agent_graph_id`
field to `StoreAgent`
- `backend/api/features/store/hybrid_search.py`: Include `agentGraphId`
in SQL query columns
- `backend/api/features/store/db.py`: Map `agentGraphId` when creating
`StoreAgent` objects
- `backend/api/features/chat/tools/agent_generator/core.py`: Update
`search_marketplace_agents_for_generation()` to fetch and include full
graph schemas
## Testing
- [ ] Agent creation on dev with marketplace agents in context
- [ ] Verify no 422 errors from Agent Generator
- [ ] Verify marketplace agents can be used as sub-agents
Fixes: SECRT-1817
---------
Co-authored-by: majdyz <majdyz@users.noreply.github.com>
Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
## Context
When users ask the chat to create agents, they may want to compose
workflows that reuse their existing agents as sub-agents. For this to
work, the Agent Generator service needs to know what agents the user has
available.
**Challenge:** Users can have large libraries with many agents. Fetching
all of them would be slow and provide too much context to the LLM.
## Solution
This PR implements **search-based library agent fetching** with a
**two-phase search** strategy:
1. **Phase 1 (Initial Search):** When the user describes their goal, we
search for relevant library agents using the goal as the search query
2. **Phase 2 (Step-Based Enrichment):** After the goal is decomposed
into steps, we extract keywords from those steps and search for
additional relevant agents
This ensures we find agents that are relevant to both the high-level
goal AND the specific steps identified.
### Example Flow
```
User goal: "Create an agent that fetches weather and sends a summary email"
Phase 1: Search for "weather email summary" → finds "Weather Fetcher" agent
Phase 2: After decomposition identifies steps like "send email notification"
→ searches "send email notification" → finds "Gmail Sender" agent
```
### Changes
**Library Agent Fetching:**
- `get_library_agents_for_generation()` - Search-based fetching from
user's library
- `search_marketplace_agents_for_generation()` - Search public
marketplace
- `get_all_relevant_agents_for_generation()` - Combines both with
deduplication
**Two-Phase Search:**
- `extract_search_terms_from_steps()` - Extracts keywords from
decomposed steps
- `enrich_library_agents_from_steps()` - Searches for additional agents
based on steps
- Integrated into `create_agent.py` as "Step 1.5" after goal
decomposition
**Type Safety:**
- Added `TypedDict` definitions: `LibraryAgentSummary`,
`MarketplaceAgentSummary`, `DecompositionStep`, `DecompositionResult`
### Design Decisions
- **Search-based, not fetch-all:** Scalable for large libraries
- **Library agents prioritized:** They have full schemas; marketplace
agents have basic info only
- **Deduplication by name and graph_id:** Prevents duplicates across
searches
- **Graceful degradation:** Failures don't block agent generation
- **Limited to 3 search terms:** Avoids excessive API calls during
enrichment
## Related PR
- Agent Generator:
https://github.com/Significant-Gravitas/AutoGPT-Agent-Generator/pull/103
## Test plan
- [x] `test_library_agents.py` - 19 tests covering all new functions
- [x] `test_service.py` - 4 tests for library_agents passthrough
- [ ] Integration test: Create agent with library sub-agent composition
## Summary
- Add helper functions in `service.py` to create standardized error
responses with `error_type` classification
- Update service functions to return error dicts instead of `None`,
preserving error details from the Agent Generator microservice
- Update `core.py` to pass through error responses properly
- Update `create_agent.py` to handle error responses with user-friendly
messages based on error type
## Error Types Now Propagated
| Error Type | Description | User Message |
|------------|-------------|--------------|
| `llm_parse_error` | LLM returned unparseable response | "The AI had
trouble understanding this request" |
| `llm_timeout` / `timeout` | Request timed out | "The request took too
long" |
| `llm_rate_limit` / `rate_limit` | Rate limited | "The service is
currently busy" |
| `validation_error` | Agent validation failed | "The generated agent
failed validation" |
| `connection_error` | Could not connect to Agent Generator | Generic
error message |
| `http_error` | HTTP error from Agent Generator | Generic error message
|
| `unknown` | Unclassified error | Generic error message |
## Motivation
This enables better debugging for issues like SECRT-1817 where
decomposition failed due to transient LLM errors but the root cause was
unclear in the logs. Now:
1. Error details from the Agent Generator microservice are preserved
2. Users get more helpful error messages based on error type
3. Debugging is easier with `error_type` in response details
## Related PR
- Agent Generator side:
https://github.com/Significant-Gravitas/AutoGPT-Agent-Generator/pull/102
## Test Plan
- [ ] Test decomposition with various error scenarios (timeout, parse
error)
- [ ] Verify user-friendly messages are shown based on error type
- [ ] Check that error details are logged properly
## Summary
- Add support for delegating agent generation to an external
microservice when `AGENTGENERATOR_HOST` is configured
- Falls back to built-in LLM-based implementation when not configured
(default behavior)
- Add comprehensive tests for the service client and core integration
(34 tests)
## Changes
- Add `agentgenerator_host`, `agentgenerator_port`,
`agentgenerator_timeout` settings to `backend/util/settings.py`
- Add `service.py` client for external Agent Generator API endpoints:
- `/api/decompose-description` - Break down goals into steps
- `/api/generate-agent` - Generate agent from instructions
- `/api/update-agent` - Generate patches to update existing agents
- `/api/blocks` - Get available blocks
- `/health` - Health check
- Update `core.py` to delegate to external service when configured
- Export `is_external_service_configured` and
`check_external_service_health` from the module
## Related PRs
- Infrastructure repo:
https://github.com/Significant-Gravitas/AutoGPT-cloud-infrastructure/pull/273
## Test plan
- [x] All 34 new tests pass (`poetry run pytest test/agent_generator/
-v`)
- [ ] Deploy with `AGENTGENERATOR_HOST` configured and verify external
service is used
- [ ] Verify built-in implementation still works when
`AGENTGENERATOR_HOST` is empty
