# LangGraph RLM-Default Integration
This guide shows how to run LangGraph with Aleph MCP tools as the default path for context-heavy reasoning.
## What "RLM default" means
In this integration, the graph is configured so that context-dependent questions should use Aleph tools first:
1. `load_context` or `load_file`
2. `search_context` or `semantic_search`
3. `peek_context` or `exec_python`
4. `sub_query` or `sub_aleph` when decomposition is needed
5. `finalize`
If the first graph pass does not trigger tool activity for a data-heavy prompt, the helper retries with a stricter instruction.
## Graph topology (implemented)
The integration builds an explicit recursive LangGraph workflow:
1. `plan`
2. `call_model`
3. `decide_recurse`
4. `tool` (when tool calls exist and depth budget allows)
5. `aggregate`
6. loop back to `call_model`
7. `finalize`
Graph state includes:
- `messages`
- `recursion_depth`
- `plan`
- `subcalls`
- `intermediate_summaries`
- `final_answer`
`decide_recurse` enforces a recursion depth cap via `AlephRLMConfig.max_recursion_depth`.
## Install
```bash
pip install "aleph-rlm[mcp]"
pip install langchain langgraph langchain-mcp-adapters langchain-openai langsmith
```
## Start Aleph MCP server
Stdio transport (default) works directly with MCP adapters:
```bash
aleph
```
If you already expose Aleph over streamable HTTP in your environment, use that URL (for example `http://127.0.0.1:8765/mcp`).
## Quickstart (Python)
```python
import asyncio
from aleph.integrations.langgraph_rlm import (
AlephRLMConfig,
build_rlm_default_graph,
invoke_rlm,
)
async def main() -> None:
cfg = AlephRLMConfig(
transport="stdio",
command="aleph",
model="openai:gpt-4.1-mini",
)
graph = await build_rlm_default_graph(cfg)
await invoke_rlm(
graph,
"Load this content with context_id='doc':\\n```text\\n...big text...\\n```",
thread_id="demo",
config=cfg,
)
result = await invoke_rlm(
graph,
"Analyze recurring errors and cite evidence.",
thread_id="demo",
config=cfg,
)
print(result)
asyncio.run(main())
```
## Explicit stdio config
Use this when you want to set stdio settings explicitly:
```python
cfg = AlephRLMConfig(
transport="stdio",
command="aleph",
model="openai:gpt-4.1-mini",
)
```
## API Reference
- `AlephRLMConfig`: integration config
- `build_aleph_mcp_tools(config)`: create Aleph-backed LangChain tools
- `build_rlm_default_graph(config)`: build graph/agent wired to Aleph tools
- `invoke_rlm(graph, user_input, thread_id=None, config=None)`: invoke with retry policy
- `collect_tool_trace(result)`: extract tool activity names from graph output
## Checkpointing and resumability
- `build_rlm_default_graph` compiles with checkpointing when available.
- If `AlephRLMConfig.checkpointer` is provided, it is used directly.
- If not provided and `enable_checkpointing=True`, the integration attempts to use LangGraph `MemorySaver`.
- Pass `thread_id` to `invoke_rlm(...)` to resume the same thread state across calls and make runs debuggable.
## Example script
Run the included example:
```bash
python examples/langgraph_rlm_default.py --query "Find recurring auth failures"
```
It preloads sample context, runs a query, prints tool trace, and prints the final answer.
### Repo improver runner (LangSmith-ready)
Use the repo-focused runner to load selected files and ask for concrete improvement proposals:
```bash
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your_key>
export LANGSMITH_PROJECT=aleph-rlm
export OPENAI_API_KEY=<your_model_key>
python examples/langgraph_rlm_repo_improver.py \
--files README.md pyproject.toml aleph/integrations/langgraph_rlm.py \
--thread-id repo-improver-1
```
Dry-run setup validation (no model call):
```bash
python examples/langgraph_rlm_repo_improver.py --dry-run
```
## Failure behavior
- Missing integration dependencies raise actionable install errors.
- Tool failures are surfaced through graph output messages from the underlying agent/tool stack.
- If no tool activity is detected for context-heavy prompts, `invoke_rlm` performs configurable retry attempts (`tool_retry_attempts`).
